KR20220002814A - Method, electronic device, and storage medium for processing visual data of deep model - Google Patents

Abstract

The present invention provides a method, a device, and an electronic device for processing visualization data of a deep model and relates to a field of data processing, and in particular, to a field of an artificial intelligence (AI) technology such as cloud computing, natural language processing, big data, deep learning, and the like. The method for processing visualization data of a deep model according to the present invention comprises the following steps of: determining a user identifier in a display request in response when the display request for individual work space is obtained; obtaining a set of history visualization data related to the user identifier; and displaying the set of the history visualization data on a page for the individual work space, which corresponds to the user identifier. Accordingly, the history visualization data can be rapidly displayed and seen and there is no need for re-establishing a model or reproducing a history model to enhance efficiency of training as much as possible and save a time.

Description

DEEP MODEL VISUAL DATA OF DEEP MODEL PROCESSING METHOD, APPARATUS AND ELECTRONIC DEVICE, AND STORAGE MEDIUM FOR PROCESSING VISUAL DATA OF DEEP MODEL

This application relates to the field of data processing, specifically, to the field of artificial intelligence such as cloud computing, natural language processing, big data, and deep learning, and in particular, to a processing method, apparatus, and electronic device of deep model visualization data.

With the active development of computer technology, the field of machine learning has also developed rapidly, and the effect of deep learning in each field is gradually becoming clearer. Deep learning is an indispensable part of technologies such as image identification, voice identification, and recommendation system, and it is very important to quickly and easily process deep model visualization data.

The present application provides a method, an apparatus, and an electronic device for processing deep model visualization data.

According to an aspect of the present application,

determining a user identifier in the display request in response to the personal workspace display request being obtained;

obtaining a historical visualization data set related to the user identifier; and,

and displaying the historical visualization data set on a user's personal workspace page corresponding to the user identifier.

According to another aspect of the present application,

a first determining module for determining a user identifier in the display request in response to the personal workspace display request being obtained;

an acquiring module for acquiring a historical visualization data set related to the user identifier; and,

and a display module for displaying the historical visualization data set on a user's personal workspace page corresponding to the user identifier.

According to another aspect of the present application,

at least one processor; and,

a memory communicatively coupled to the at least one processor;

An instruction executable by the at least one processor is stored in the memory, and the instruction is executed by the at least one processor to cause the at least one processor of the deep model visualization data according to the embodiment of the one aspect. An electronic device for performing a processing method is provided.

According to another aspect of the present application, there is provided a non-transitory computer readable storage medium having computer instructions stored therein, wherein the computer instructions cause the computer to process a deep model visualization data according to an embodiment of the one aspect. to do

According to another aspect of the present application, a computer program product including a computer program is provided, and when the computer program is executed by a processor, the deep model visualization data processing method according to the embodiment of the one aspect is implemented.

According to another aspect of the present application, there is further provided a computer program stored in a computer-readable medium, wherein when an instruction in the computer program is executed, the deep model visualization data processing method according to the embodiment of the one aspect this is executed

The deep model visualization data processing method, apparatus, electronic device and storage medium provided in the present application have the following beneficial effects.

In response to obtaining the personal workspace display request, determine a user identifier in the display request, and then obtain a historical visualization dataset related to the user identifier, and in the user's personal workspace page corresponding to the user identifier, the historical visualization data set to exhibit This allows for rapid display and determination of historical visualization data and eliminates the need to rebuild or reproduce historical models, maximizing training efficiency and saving time.

It should be understood that the content described herein is not intended to indicate key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will be more readily understood from the following specification.

The accompanying drawings are for better understanding of the present method, and do not limit the present application. here,
1A is a flowchart of a method for processing deep model visualization data provided in an embodiment of the present application;
1B is a page display diagram of a method for processing deep model visualization data provided in an embodiment of the present application;
2A is a flowchart of a processing method of deep model visualization data provided in another embodiment of the present application;
2B is a page display diagram of a method for processing deep model visualization data provided in another embodiment of the present application;
2C is a page display diagram of another deep model visualization data processing method provided in another embodiment of the present application;
3A is a flowchart of a processing method of deep model visualization data provided in another embodiment of the present application;
3B is a page display diagram of a method for processing deep model visualization data provided in another embodiment of the present application;
3C is a page display diagram of another deep model visualization data processing method provided in another embodiment of the present application;
4A is a page display diagram of a method for processing deep model visualization data provided in an embodiment of the present application;
4B is a page display diagram of another deep model visualization data processing method provided in an embodiment of the present application;
5A is a page display diagram of another deep model visualization data processing method provided in an embodiment of the present application;
5B is a page display diagram of another deep model visualization data processing method provided in an embodiment of the present application;
5C is a page display diagram of another deep model visualization data processing method provided in an embodiment of the present application;
5D is a page display diagram of another deep model visualization data processing method provided in an embodiment of the present application;
6 is a schematic diagram of a deep model visualization data processing apparatus provided in an embodiment of the present application;
7 is a schematic diagram of a deep model visualization data processing apparatus provided in another embodiment of the present application;
8 is a block diagram of an electronic device implementing a method of processing deep model visualization data according to an embodiment of the present application.

Hereinafter, exemplary embodiments of the present application will be described in conjunction with the accompanying drawings, and various details of the embodiments of the present application are included to aid understanding, which should be regarded as exemplary only. Accordingly, it should be understood by those of ordinary skill in the art of the present application that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. In addition, for clarity and conciseness, descriptions of well-known functions and structures are omitted from the description below.

Data processing refers to the collection, storage, investigation, processing, transformation and transmission of data. Different methods exist for data processing as the structure method of the processing device, the operation method, and the space-time distribution method of the data are different. Different processing methods require different hardware and software support, and each processing method has unique characteristics, so the appropriate processing method should be selected according to the actual environment of the application problem.

Artificial intelligence is a discipline that studies computer to simulate some human thinking processes and intelligent actions (eg, learning, reasoning, thinking, planning, etc.). There are technology at the hardware level as well as technology at the software level. Artificial intelligence hardware technology generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing. Artificial intelligence software technology mainly includes several big directions: computer vision technology, speech identification technology, natural language processing technology and machine learning, deep learning, big data processing technology, knowledge graph technology.

Cloud computing refers to a shared, elastic and scalable pool of physical or virtual resources through network access, which can include servers, operating systems, network software, applications and storage devices, etc., and deploys resources in an on-demand, self-service manner. It is a technology system that can be used and managed, and through cloud computing technology, it can provide high-efficiency and powerful data processing capabilities for application of technologies such as artificial intelligence and blockchain, and model training.

Natural language processing is the processing, understanding, and operation of human language (eg, Chinese, English, etc.) with a computer. It is a cross discipline between computer science and linguistics, and is often referred to as computer linguistics. Natural language processing embodies the highest tasks and boundaries of artificial intelligence, since natural language is a fundamental marker that distinguishes humans from other animals, and without language there is nothing to say about human thinking. In other words, a computer realizes true intelligence only when it has the ability to process natural language.

Big data technology implements the collection of large amounts of data through various routes, and implements in-depth mining and analysis of data using cloud computing technology to ensure that rules and characteristics between data can be found in a timely manner. It refers to aggregating and summarizing the value of data. Big data technology has very important significance in understanding the characteristics of data and predicting development trends.

Deep learning refers to a multi-layered artificial neural network and a method for training it. A neural network of one layer takes a large number of matrix numbers as input, takes weights through a nonlinear activation method, and then generates another data set as output. Through an appropriate matrix quantity, multiple layers of tissue are connected together to form a neural network “cerebrum” that performs precise and complex processing just like humans recognize objects and mark pictures.

Hereinafter, with reference to the accompanying drawings, a processing method, apparatus, and electronic device of the deep model visualization data of an embodiment of the present application will be described.

1A is a flowchart of a processing method of deep model visualization data provided in an embodiment of the present application.

The deep model visualization data processing method of the embodiment of the present application may be performed by the deep model visualization data processing apparatus provided in the embodiment of the present application, and the apparatus may be configured in an electronic device.

As shown in Figure 1A, the deep model visualization data processing method includes the following steps.

Step 101, a user identifier in the display request is determined in response to the personal workspace display request being obtained.

The personal workspace may be a shared space used by an individual when using the deep model visualization application.

The user identifier is the only identifier that can represent the user identity when using the deep model visualization application.

It may be understood that the personal workspace display request may include a user identifier or other content, and the present application is not limited thereto.

In an embodiment of the present application, when a personal workspace display request is obtained, the user identifier included therein is determined by processing the personal workspace display request.

Step 102, obtain a historical visualization data set related to the user identifier.

The historical visualization data set may include one or more historical visualization data, may include a creation time corresponding to each historical visualization data, or may include information such as a project name to which each historical visualization data belongs. , the present application is not limited thereto.

In an embodiment of the present application, after the user identifier is determined, a historical visualization data set related to the user identifier may be obtained.

Step 103, displaying the historical visualization data set on the user's personal workspace page corresponding to the user identifier.

When displaying the historical visualization data set on the user's personal workspace page corresponding to the user identifier, there may be various arrangement methods, for example, according to the generation time of each historical visualization data, or each history It can also be arranged according to the project name to which the visualization data belongs.

For example, a plurality of historical visualization data may be displayed one by one on the user's personal workspace page according to the generation time of each historical visualization data.

You can set the “Time Alignment” control on your personal workspace page, and click on the control to adjust how the time is aligned. For example, if each historical visualization data is displayed one by one in the order of the generation time of each historical visualization data from front to back, you can click the control to adjust the time alignment method so that the generation time of each historical visualization data is from back to front. have. Alternatively, by arranging each historical visualization data in the order that each historical visualization data generation time is from back to front, and clicking the control, each historical visualization data can be adjusted so that the generation time is arranged in the front-to-back order.

Alternatively, a plurality of historical visualization data may be displayed one by one on the user's personal workspace page according to the project name to which each historical visualization data belongs.

You can install a “Project Name” sort control on your personal workspace page, and click the control to adjust how project names are sorted. For example, if the existing project names are displayed one by one according to the order of A to Z, it is possible to adjust the order from Z to A by clicking the control. Alternatively, if the existing sorting method is to display one by one according to the order from Z to A, you can click the control to adjust the order from A to Z.

What should be described is that the above example is for illustrative purposes only, and is not a limitation on the visualization data set arrangement in the embodiment of the present application.

It is understandable that different electronic devices may correspond to display interfaces of different sizes, so when determining history visualization data using different electronic devices, the quantity of historical visualization data that can be displayed on each page depends on the size of the electronic device used. It may be slightly different depending on the size of the display interface.

For example, when determining using a mobile phone, the quantity of historical visualization data that can be displayed on the user's personal workspace page may be relatively small, for example, 5, 10, or the like.

Alternatively, when determining using a computer, the quantity of historical visualization data that can be displayed on the user's personal workspace page may be relatively large, for example, 20, 30, or the like.

It should be explained that the quantity of historical visualization data displayed on the user's personal workspace page and the electronic device used are merely examples, and the historical visualization data set displayed on the personal workspace page of the embodiment of the present application does not limit

It is understandable that only a certain amount of historical visualization data may be displayed on each page of the personal workspace page, and other historical visualization data exceeding the quantity may be displayed on the next or subsequent pages, and the user Pages” or the corresponding number of pages such as “2” or “3” may be clicked, or pages may be turned in other ways, but the present application is not limited thereto.

In a possible implementation situation, operations such as page turning can be performed by adding controls such as “previous page” and “next page” to the page as needed. For example, the page may display three historical visualization data including a project name, a storage address, and a creation time of each historical visualization data on the current page, as shown in FIG. 1B . The storage address corresponding to the project name “AAA” is “WAA”, the creation time is “2020.10.01”, the storage address corresponding to the project name “BBB” is “WBB”, and the creation time is “2020.10.10” , the storage address corresponding to the project name “CCC” is “WCC”, and the creation time is “2020.11.11”. The page may further include “previous page”, “next page” controls, “1”, “2”, “3”, and it can be seen that the current page is the second page, and the user can see the “previous page”; You can turn pages by clicking the “next page” control, click “1” to go to page 1, or click “3” to go to page 3.

It should be noted that each name under the project name, each address under the storage address, and each time under the creation time, each control of the page are merely exemplary descriptions, and the present application is not limited to displaying historical visualization data .

From this, when the user needs to use the historical visualization data later, the user can directly determine the historical visualization data on the personal workspace page, and there is no need to rebuild the model or reproduce the historical model again, thereby maximizing the efficiency of training and improving the training efficiency. It saves time and reduces the burden on users.

An embodiment of the present application determines a user identifier in a display request in response to a personal workspace display request being obtained, and then acquires a historical visualization dataset related to the user identifier, and a personal task of the user corresponding to the user identifier Display the historical visualization data set on the Spatial page. This allows for rapid display and determination of historical visualization data and eliminates the need to rebuild or reproduce historical models, maximizing training efficiency and saving time.

The embodiment can display the historical visualization data set on the user's personal workspace page according to the user identifier determined by the obtained personal workspace display request and the historical visualization data set related to the user identifier, thereby maximizing the efficiency improve In addition, corresponding processing operations such as deletion and sharing may be further performed for each visualization data, and the process of processing the visualization data will be described in detail below in conjunction with FIG. 2A.

Step 201, determining a user identifier in the display request in response to the personal workspace display request being obtained.

Step 202, obtain a historical visualization data set related to the user identifier.

A plurality of historical visualization data may be included in the historical visualization data set, and other contents such as a storage address related to each historical visualization data may be included, but the present application is not limited thereto.

Step 203, displaying the historical visualization data set on the user's personal workspace page corresponding to the user identifier.

The personal workspace page may include a search input box or other content, and the present application is not limited thereto.

In step 204, in response to a selection operation on any one of the obtained visualization data, any one visualization data is enlarged and displayed in the preview layer, but the preview layer is located above the layer in which the personal workspace page is located.

The visualization data may be existing historical visualization data or may be newly added visualization data, but the present application is not limited thereto.

When any one visualization data is selected, there may be various display methods.

For example, the visualization data selected in the preview layer may be enlarged and displayed. Alternatively, when visualization data is selected, the corresponding background color is changed to gray and the font is brightened. Alternatively, when the visualization data is selected, a “check” symbol appears in the corresponding identification box.

It should be noted that the method of characterizing that the visualization data is in the selected state is merely an example, and the visualization data of the embodiment of the present application is not limited to the selected state.

It can be understood that the user may allow one visualization data to be in a selected state, or a plurality of visualization data to be selected at the same time, and the present application is not limited thereto.

In step 205, in response to a click operation on a shared control related to any one of the obtained visualization data, a storage address related to any one of the visualization data is copied.

A user's personal workspace page may further include a "share" control.

When the user clicks the “share” control related to any one visualization data, by copying the storage address related to the visualization data, the storage address can be directly shared with others or the address can be saved in a related file, Since the visualization data can be used directly, the process of exchange and sharing between users and others is made as simple as possible.

In a possible implementation situation, for example, the page may be as shown in 2B, in which the project name to which the visualization data belongs is “AAA”, the storage address is “WAA” and the corresponding copy control, and create The time is “2020.10.01”, and “delete” and “share” controls correspond to manipulation. When the user clicks the “share” control, a suggestion message such as “link copy success” appears, which explains that the user can successfully copy and share the address link corresponding to the visualization data already.

Alternatively, historical visualization data may be shared using other methods.

For example, in the page shown in Fig. 2B, the user can click the "Copy" control in the storage address part to copy the link and use it.

It should be noted that each control, project name, storage address, and creation time in the above page are merely exemplary descriptions, and are not limited to the page, each control, project name, storage address and creation time of the embodiment of the present application. .

What should be described is that the sharing method is only described as an example, and is not limited to copying the storage address related to any one visualization data in the embodiments of the present application.

In step 206, a deletion decision message is displayed in a pop-up window in response to a click operation on a deletion control related to any one of the obtained visualization data.

In step 207, any one visualization data is processed according to a processing method corresponding to any one control in response to a click operation on any one control of the obtained pop-up window.

The user's personal workspace page may further include a “Delete” control, and the user can click the “Delete” control to delete the corresponding historical visualization data.

In a possible implementation situation, for example, the page may be as shown in Fig. 2B, and when the user clicks on the "Delete" control, a corresponding pop-up window will appear, the page of the pop-up window may be as shown in Fig. 2C and , “Are you sure you want to delete this visualization? After deletion, the current visualization result will be cleared” and so on, there are “Decision” control and “Cancel” control. When the user clicks the “decision” control in the pop-up window, the historical visualization data can be deleted, and when the user clicks the “cancel” control, the deletion of the historical visualization data is canceled.

Alternatively, the pop-up window page that appears after clicking the “Delete” control may display prompt contents such as “Decision to delete the current content”, “Decision” control, “Cancel” control, and “Close” control, and when the user clicks “Close” Clicking on the control will exit the current pop-up window page so that the visualization data may not be processed.

It should be explained that the presentation text of the pop-up window page may be different content, and the above content is merely an example and is not limited to the pop-up window content, and the pop-up window page may include other content, The above presentation text and each control are merely illustrative and not limited to the pop-up window page of the present application.

Step 208, a current search title in the search input window is determined in response to the obtained search request.

The user's personal workspace page may include a search input window, and the user may input the content to be inquired into the search input window, for example, a project name, creation time, etc., and the present application is limited thereto. I never do that.

In step 209, the historical visualization data set is searched according to the search title to obtain target historical visualization data.

The target visualization data obtained according to the search title may be one or a plurality of pieces, and the present application is not limited thereto.

Step 210, displaying the target historical visualization data.

A method of displaying the target history visualization data may be various. For example, the target history visualization data acquired in the preview layer may be enlarged and displayed, or a plurality of target history visualization data lists may be directly displayed. Also, the present application is not limited thereto.

It should be explained that in the embodiment of the present application, performing a selection operation on the visualization data, performing a delete operation on the visualization data, performing a storage address copy on the visualization data, and depending on the search title The order before and after the step of displaying the target historical visualization data is not a limitation on the time when the deep model visualization application performs these operations, it is merely an exemplary description, and in actual use, the deep model visualization application provides a corresponding command according to the acquired operation. can be done

An embodiment of the present application determines the user identifier in the display request in response to the personal workspace display request being obtained, and then acquires a historical visualization data set related to the user identifier, and the user's personal task corresponding to the user identifier Display the historical visualization data set on the spatial page, enlarge and display any one visualization data in the preview layer, copy the storage address related to any one visualization data, delete any visualization data in the pop-up window, enter By searching the target history visualization data in the window, operations such as selection, sharing, deletion, and search for visualization data can be implemented, and the visualization data can be processed quickly, so the user is easy to operate.

In addition, when historical visualization data is included in the database related to the user identifier, corresponding historical visualization data may be added, and the addition of historical visualization data will be described in detail below in conjunction with FIG. 3A.

Step 301, determining that a personal workspace display request is obtained in response to a click operation on the acquired personal workspace control.

Users can open their personal workspaces by clicking on the Personal Workspaces control.

For example, in the page shown in Figure 3B, the page has the prompt word “workspace page” and the “show full function”, “Workspace” controls, and when you click “show full function”, the relevant function of the deep model visualization application and demonstration instructions, click on the “Workspace” control to open the personal work control.

It should be noted that the prompt words on the page and each control are merely exemplary descriptions, and may be appropriately modified as needed in actual use, and the present application is not limited thereto.

In a possible implementation, the user may also open a personal workspace using the command line.

What should be described is that the above example is merely an example, and is not a limitation on a method of opening a personal workspace in an embodiment of the present application.

Step 302, determining a user identifier in the display request in response to the personal workspace display request being obtained.

In step 303, if the user corresponding to the user identifier does not log in to the deep model visualization application, the login interface of the deep model visualization application is displayed.

For example, if the user is not logged in to the deep model visualization application, the page may be as shown in Figure 3C, and the page may contain "Not logged in yet. There are prompts such as “You can view the saved history experiment results after logging in”, “If you are a new user, you can use your personal workspace after registration”.

If the user has already used the deep model visualization application, click “Login” to go to the login interface of the deep model visualization application, and the user can log in by selecting QR code login, identifier password login, or other methods and the present application is not limited thereto.

Alternatively, if the current user does not use the deep model visualization application, he may click “Register” to log in after completing the registration to use the deep model visualization application.

It should be noted that the interface of the above example is merely an example and is not limited to prompting the user login page and login interface of the present application.

In step 304, in response to the historical visualization data not being included in the database related to the user identifier, a visualization data addition interface is displayed.

For example, if historical visualization data is not included in the database related to the user identifier, the corresponding display page may be as shown in FIG. 4A , and the page displays “There are no historical experimental results stored. This may include a prompt saying "You can view after adding a new experiment" and a "Add new experiment" control.

Alternatively, the Add Visualization Data interface may be as shown in FIG. 4B , and the page will include a “Workspace is your personal workspace that helps you save and share your visualization results” and a “Add New Experiment” control. and the user sends a request to add visualization data by clicking the “Add New Experiment” control.

It should be noted that the presentation text and controls of the page are merely exemplary descriptions, and are not limited to the visualization data addition interface of the embodiment of the present application.

In step 305, in response to the request to add the visualization data obtained in the data addition interface, the visualization data is added to the database related to the user identifier.

When the visualization data is added to the database related to the user identifier, it may include adding the name of the visualization data, and the name of the visualization data may be the name of the model, the name of the log, or the name of the model and the log name and the like, and the present application is not limited thereto.

In a possible implementation situation, corresponding contents and controls may be added to the data addition interface, for example, the initial state of the visualization data addition page is “add new experiment”, “experiment name”, as shown in FIG. 5A , , “log” and related “file upload” controls, “model” and related “file upload” controls, and “decision” controls, “cancel” controls. If the user clicks the “file upload” control corresponding to “log”, one or more logs can be uploaded, and when the user clicks the “file upload” control corresponding to “model”, the model is uploaded.

It should be explained that the contents and each control such as “add new experiment”, “experiment name”, “log”, “model”, etc. of the initial state of the data addition page are merely exemplary descriptions, and the data of the embodiments of the present application There is no limitation on the content and controls of the initial state of the additional page.

It may be understood that, after obtaining the request to add visualization data in the visualization data addition interface, the additional standby visualization data may be verified first.

Next, when the additional atmospheric visualization data has passed the verification, the name of the additional atmospheric visualization data is displayed in the data addition interface.

In a possible implementation situation, if the additional atmospheric visualization data passes the verification, the visualization data addition interface may be as shown in FIG. 5B . The interface includes “add new experiment”, “experiment name” and corresponding input window, “log” and related “continue upload” control, “file name 01.txt”, “file name 01.txt”, “file name” 01.txt”, and “model” and related “file upload” controls, “filename01.txt”. If you select a file that has already been uploaded, a “Delete” control may appear, and if you click on the control, you can delete the currently added file. The visualization data addition interface may include other contents and controls, but the present application is not limited thereto. Only one model may be related to one historical data, but since one model may be trained multiple times, there may be a plurality of historical visualization data. Additional waiting visualization data has already passed validation, the “file upload” controller related to “log” in the interface is changed to “continue upload”, “model upload” is grayed out and if you click “cancel” nothing is saved The pop-up window is canceled without is not limited thereto.

It should be noted that the contents of the data addition interface, the input window, each control, the pop-up window page, and the prompt contents are only exemplary descriptions, and are not limited to data addition in the embodiments of the present application.

Alternatively, if the additional atmospheric visualization data does not pass the verification, the data anomaly cause is displayed in the data addition interface.

In a possible implementation situation, if the additional standby visualization data does not pass the verification, the visualization data addition interface may be as shown in FIG. 5C , and the page corresponds to “Add new experiment”, “Experiment name”, “Log” Controls such as “re-upload”, “file format error, only support uploading log files generated by VisualDL”, “re-upload”, a control related to “model”, “supports main stream model structure visualization, Please refer to the usage guide for specific model support formats”, “decide” controls, “cancel” controls, and may include other content and controls. It can be seen that the cause of the current data error is “file format error, only support log file upload generated by VisualDL”, and the cause of data error in actual use may be other causes, and this application is not limited thereto.

It should be noted that the contents, controls, presentation phrases, etc. of the data addition interface are only exemplary descriptions, and are not limited to the data addition interface of the embodiment of the present application.

It is understandable, when the user enters the experiment name to add visualization data, the suggestion phrase “The name length must not exceed 16 character codes and must not include the character code %” may appear, and other suggestion phrases may appear. may or may not appear, and the present application is not limited thereto.

In a possible implementation situation, when an input window corresponding to “experiment name” is clicked, an input window interactive state page may appear, for example, in the page shown in FIG. 5D, the page is “default state” and corresponding An input window corresponding to the “input state” in which “this is” is displayed, an input window corresponding to the “completed state” in which “this is a new name” is displayed, and “this is a new %” An input window corresponding to the displayed “error state” may be included, and at the lower side of the input window there is a suggestion “Name does not support character code %”, and at the lower side of “error state” there is a “character code” There is a suggestion saying “Do not display beyond the limit”.

It should be noted that each content of the input window interactive state page, each input window, the content displayed in each input window, and the presented text are merely exemplary descriptions, and the contents of the input window interactive state page of the present application, input There is no limitation on the window and the presentation text.

In an embodiment of the present application, when the additional atmospheric visualization data passes verification, the visualization data is added to the database related to the user identifier, and when the additional atmospheric visualization data does not pass the verification, the cause of data abnormality in the data addition interface can be displayed.

An embodiment of the present application provides a method for determining that a personal workspace display request is obtained in response to obtaining a click operation on a personal workspace control, or in response to obtaining a command line command to open a personal workspace. Determines that a workspace display request is obtained. Determine the user identifier in the display request in response to the personal workspace display request being obtained, and if the user corresponding to the user identifier does not log in to the deep model visualization application, display a login interface of the deep model visualization application, the user A visualization data addition interface is displayed in response to the fact that the historical visualization data is not included in the database related to the identifier, and the visualization data is added to the database related to the user identifier in response to a request to add visualization data obtained from the visualization data addition interface. From this, users can open their personal workspace via personal workspace control or command line commands, log in from the login interface of the deep model visualization application, and then add visualization data to the database, allowing users to make visualization data as simple as possible. By storing, adding, and recording, the efficiency is improved, so that a good feeling of use can be given to the user.

An embodiment of the present application further provides a deep model visualization data-based processing device. 6 is a structural schematic diagram of a deep model visualization data-based processing device provided in an embodiment of the present application.

6 , the deep model visualization data-based processing apparatus 600 includes a first determination module 610 , an acquisition module 620 , and a display module 630 .

The first determining module 610 determines a user identifier in the display request in response to the personal workspace display request being obtained.

The acquiring module 620 acquires a historical visualization data set related to the user identifier.

The display module 630 displays the historical visualization data set on the user's personal workspace page corresponding to the user identifier.

It should be noted that the functions and specific implementation principles of each module in the embodiments of the present application may refer to the respective method embodiments, and thus will not be described further herein.

The deep model visualization data processing apparatus of the embodiment of the present application determines the user identifier in the display request in response to the personal workspace display request being obtained, and then acquires a historical visualization dataset related to the user identifier, Display the historical visualization data set on the corresponding user's personal workspace page. This allows you to quickly display and view historical visualization data, and saves time and maximizes training efficiency by eliminating the need to rebuild or reproduce historical models.

7 is a structural schematic diagram of a deep model visualization data processing apparatus provided in another embodiment of the present application.

7 , the deep model visualization data-based processing device 700 includes a first determination module 710 , an acquisition module 720 , a display module 730 , a preview module 740 , and a sharing module 750 , a deletion module 760 , a search module 770 , an addition module 780 , and a second determination module 790 .

The first determining module 710 determines a user identifier in the display request in response to the personal workspace display request being obtained.

It can be understood that the first determining module 710 of the present embodiment and the first determining module 610 of the above embodiment may have the same function and structure.

The acquiring module 720 acquires a historical visualization data set related to the user identifier.

It can be understood that the acquiring module 720 of this embodiment and the acquiring module 620 of the above embodiment may have the same function and structure.

The display module 730 displays the historical visualization data set on the user's personal workspace page corresponding to the user identifier.

In a possible implementation, the display module 730 may specifically display the plurality of historical visualization data one by one in the user's personal workspace page according to the generation time of each of the historical visualization data.

In a possible implementation, the display module 730 may also specifically display the plurality of historical visualization data one by one on the user's personal workspace page according to the project name to which each of the historical visualization data belongs.

It can be understood that the display module 730 of this embodiment and the display module 630 of the above embodiment may have the same function and structure.

The preview module 740 enlarges and displays the one visualization data in a preview layer in response to a selection operation for the obtained one visualization data, wherein the preview layer is where the personal workspace page is located. It is located on top of the layer.

The sharing module 750 copies the storage address related to the one visualization data in response to a click operation on the shared control related to the obtained one visualization data.

The deletion module 760 displays a deletion decision message in a pop-up window in response to a click operation on a deletion control related to the obtained one of the visualization data, and also clicks on any one control of the obtained pop-up window. In response to the manipulation, the one visualization data is processed according to a processing method corresponding to the one control.

The search module 770 determines a current search title in the search input window in response to the obtained search request, and obtains target historical visualization data by inquiring the historical visualization data set according to the search title, In addition, the target history visualization data is displayed.

The adding module 780 displays a visualization data addition interface in response to the fact that historical visualization data is not included in the database related to the user identifier, and responds to a request to add visualization data obtained from the data addition interface to the user identifier. Add visualization data to the relevant database.

In a possible implementation, the adding module 780 specifically verifies the additional atmospheric visualization data, and if the additional atmospheric visualization data passes the verification, displays the name of the additional atmospheric visualization data in the data adding interface, , when the additional atmospheric visualization data does not pass the verification, the data addition interface displays the cause of data abnormality.

A second determining module 790 is configured to determine that the personal workspace display request is obtained in response to a click operation on the personal workspace control being obtained, or a command line command to open the personal workspace is obtained. determining that the workspace display request is obtained in response to

In a possible implementation, the second determining module 790 may also specifically display a login interface of the deep model visualization application when the user corresponding to the user identifier does not log in to the deep model visualization application.

It should be noted that the functions and specific implementation principles of each module in the embodiments of the present application may refer to the respective method embodiments, and thus will not be described further herein.

The deep model visualization data processing apparatus of the embodiment of the present application determines the user identifier in the display request in response to the personal workspace display request being obtained, and then obtains a historical visualization data set related to the user identifier, and corresponds to the user identifier Displays the historical visualization data set on the user's personal workspace page, enlarges any one visualization data in the preview layer, copies the storage address related to any one visualization data, and displays any one visualization data in the pop-up window By deleting, searching target history visualization data in the input window and adding visualization data in the visualization data addition interface, you can implement operations such as selection, sharing, deletion, search, and addition of any one visualization data. It can perform rapid display, decision and corresponding selection, sharing, deletion, search, addition, etc. processing, and there is no need to rebuild the model or reproduce the historical model, so it is easy for users to operate and maximizes training efficiency. improve and save time.

According to an embodiment of the present application, the present application further provides an electronic device, a readable storage medium, and a computer program product. According to an embodiment of the present application, there is further provided a computer program stored in a computer-readable medium. When the instructions in the computer program are executed, the deep model visualization data processing method is executed.

8 is an exemplary block diagram of an exemplary electronic device 800 that may implement an embodiment of the present application. Electronic equipment refers to various types of digital computers such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers and other suitable computers. Electronic devices may also refer to various types of mobile devices such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are illustrative only and are not intended to limit the implementations described and/or claimed herein.

As shown in FIG. 8 , the electronic device 800 may include a computing unit 801 , which may include a computer program stored in a read-only memory (ROM) 802 or random access memory (RAM) from a storage unit 808 . ) 803 may perform various appropriate operations and processing based on the loaded computer program. Various programs and data necessary for the operation of the device 800 may be further stored in the RAM 803 . The computing unit 801 , the ROM 802 , and the RAM 803 are connected to each other via a bus 804 . An input/output (I/O) interface 805 is also coupled to the bus 804 .

A number of components in the device 800 include an input unit 806 such as a keyboard, mouse, and the like; output units 807, such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, CD-ROM, or the like; and a communication unit 809 such as a network card, modem, or wireless communication transceiver. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the Internet and/or various telegraph networks.

The computing unit 801 may be any number of general-purpose and/or dedicated processing assemblies having processing and computing capabilities. Some examples of computing unit 801 include a central processing unit (CPU), a graphics processing unit (GPU), several dedicated artificial intelligence (AI) computing chips, a computing unit running several machine learning model algorithms, a digital signal processor ( DSP) and any suitable processor, controller, microcontroller, and the like. The computing unit 801 performs each method and processing described above, for example, a processing method of deep model visualization data. For example, in some embodiments, the method of processing deep model visualization data may be implemented as a computer software program, which may be tangibly included in a machine-readable medium such as the storage unit 808 . In some embodiments, some or all of the computer program may be loaded and/or loaded into the device 800 via the ROM 802 and/or the communication unit 809 . When the computer program is loaded into the RAM 803 and executed by the computing unit 801 , one or more steps in the deep model visualization data processing method described above may be performed. Alternatively, in other embodiments, computing unit 801 may be arranged to perform a method of processing deep model visualization data by any other suitable manner (eg, firmware).

Various embodiments of the systems and technical subject matter described herein include digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), dedicated integrated circuits (ASICs), application specific standard products (ASSPs), system on It may be implemented in a chip system (SOC), complex programmable logic element (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments are embodied in one or more computer programs, wherein the one or more computer programs may be executed and/or interpreted in a programmable system of at least one programmable processor, the programmable processor comprising: A dedicated or general-purpose programmable processor, the processor capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and capable of receiving data and instructions from the storage system, at least one input device, and at least one output device. It may include a method of transmitting to an output device.

The program code for implementing the method of the present application may be programmed using one or any combination of a plurality of programming languages. Such program code may be provided to a processor or controller of a general-purpose computer, dedicated computer, or other programmable data processing device to cause the program code, when executed by the processor or controller, to carry out the functions/operations specified in the flowcharts and/or block diagrams. can The program code may run entirely on the machine or partly on the machine, and as independent software packages, partly running on the machine and partly running on the remote machine, or all running on the remote machine or server.

In the preceding and following sentences of the present invention, a machine-readable medium may be a tangible medium, which may include a program for use by being provided in an instruction execution system, apparatus, or device, or for use in combination with an instruction execution system, apparatus, or device. may store, the machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may be an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, apparatus or appliance, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include one or more line electrical connections, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

To provide interactivity with a user, the systems and techniques described herein may be implemented on a computer, which computer may include a display device (eg, a cathode ray tube (CRT) or liquid crystal display (LCD)) for presenting information to a user. ) a monitor), a keyboard, and a pointing device (eg, a mouse or a track ball), and the user can input information through the keyboard and the pointing device and provide it to the computer. Other types of devices may be used to provide interaction with the user, for example, the feedback provided to the user may be any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback). and may receive information received by the user in any form (including voice input, speech input, or tactile input).

The systems and techniques described herein may include a computing system including a back-end component (eg, a data server) or a computing system including a middleware component (eg, an application server) or a front-end component (eg, an application server). For example, it can be a user computer having a graphical user interface or web browser, and the user can interact with embodiments of the systems and techniques described herein via a graphical user interface or web browser). It may be implemented by a computing system or a computing system including any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by digital data communication (eg, a communication network) in any form or medium. Examples of communication networks may include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

A computer system may include a client and a server. Clients and servers are typically remote from each other, and typically interact via a communications network. The relationship between client and server is created by computer programs running on that computer and having a client-server relationship to each other. The server may be a cloud server, and is also called a cloud computing server or a cloud host, which is a host product in a cloud computing service system and is difficult to manage in a traditional physical host and a VPS service (“Virtual Private Server” or abbreviation “VPS”). It solves the disadvantage of large size and weak service scalability. A server may be a server in a distributed system or a server combined with a blockchain.

The technical solution of the present application is to determine the user identifier in the display request in response to the personal workspace display request being obtained, and then obtain a historical visualization dataset related to the user identifier, and the user's individual corresponding to the user identifier Display the historical visualization data set on the workspace page. This allows you to quickly display and view historical visualization data, and saves time and maximizes training efficiency by eliminating the need to rebuild or reproduce historical models.

It should be understood that the various types of processes described above can be used to rearrange and increment or delete steps. For example, each step described in this application may be performed simultaneously, sequentially, or may be performed in a different order. It is not limited to this.

The above specific embodiments do not limit the protection scope of the present application. Those of ordinary skill in the art may understand that various modifications, combinations, sub-combinations and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application are all included within the protection scope of the present application.

Claims (15)

In the deep model visualization data processing method,
determining a user identifier in the display request in response to the personal workspace display request being obtained;
obtaining a historical visualization data set related to the user identifier; and,
displaying the historical visualization data set on a user's personal workspace page corresponding to the user identifier;
A method of processing deep model visualization data, characterized in that. According to claim 1,
The historical visualization data set includes a plurality of historical visualization data and a corresponding creation time,
The step of displaying the historical visualization data set on a user's personal workspace page corresponding to the user identifier comprises:
Displaying the plurality of historical visualization data one by one on the user's personal workspace page according to the generation time of each of the historical visualization data
A method of processing deep model visualization data, characterized in that. According to claim 1,
The historical visualization data set includes a plurality of historical visualization data and a project name to which each historical visualization data belongs,
The step of displaying the historical visualization data set on a user's personal workspace page corresponding to the user identifier comprises:
Displaying the plurality of historical visualization data one by one on the user's personal workspace page according to the project name to which each of the historical visualization data belongs
A method of processing deep model visualization data, characterized in that. According to claim 1,
In response to a selection operation on any one of the obtained visualization data, the one visualization data is enlarged and displayed in a preview layer, wherein the preview layer is located above the layer in which the personal workspace page is located. more containing
A method of processing deep model visualization data, characterized in that. According to claim 1,
The historical visualization data set includes a storage address related to each historical visualization data,
The method is
In response to a click operation on a shared control related to the obtained any one of the visualization data, further comprising the step of copying the storage address related to the any one visualization data
A method of processing deep model visualization data, characterized in that. According to claim 1,
The historical visualization data set includes a storage address related to each historical visualization data,
The method is
displaying a deletion decision message in a pop-up window in response to a click operation on a deletion control related to the obtained any one visualization data; and,
In response to a click operation on any one control of the obtained pop-up window, further comprising processing the one visualization data according to a processing method corresponding to the one control
A method of processing deep model visualization data, characterized in that. According to claim 1,
The personal workspace page includes a search input box,
The method is
determining a current search title in the search input window in response to the obtained search request;
obtaining target historical visualization data by inquiring the historical visualization data set according to the search title; and,
Further comprising the step of displaying the target history visualization data
A method of processing deep model visualization data, characterized in that. According to claim 1,
After determining the user identifier in the login request,
displaying a visualization data addition interface in response to the fact that historical visualization data is not included in the database related to the user identifier; and,
In response to a request to add visualization data obtained from the data addition interface, further comprising the step of adding visualization data to a database related to the user identifier
A method of processing deep model visualization data, characterized in that. 9. The method of claim 8,
After the request to add the visualization data obtained from the data addition interface,
validating additional atmospheric visualization data;
If the additional atmospheric visualization data has passed verification, displaying the name of the additional atmospheric visualization data in the data addition interface; and,
If the additional atmospheric visualization data does not pass verification, further comprising the step of displaying a data abnormality cause in the data addition interface
A method of processing deep model visualization data, characterized in that. According to claim 1,
determining that the personal workspace display request is obtained in response to a click operation on a personal workspace control being obtained;
or,
determining that the workspace display request is obtained in response to obtaining a command line command to open the personal workspace
A method of processing deep model visualization data, characterized in that. 11. The method of claim 10,
After the personal workspace display request is obtained,
When the user corresponding to the user identifier does not log in to the deep model visualization application, further comprising the step of displaying a login interface of the deep model visualization application
A method of processing deep model visualization data, characterized in that. In the deep model visualization data processing apparatus,
a first determining module for determining a user identifier in the display request in response to the personal workspace display request being obtained;
an acquiring module for acquiring a historical visualization data set related to the user identifier; and
and a display module for displaying the historical visualization data set on a user's personal workspace page corresponding to the user identifier.
Deep model visualization data processing device, characterized in that. In electronic devices,
at least one processor; and
a memory communicatively coupled to the at least one processor;
An instruction executable by the at least one processor is stored in the memory, and the instruction is executed by the at least one processor to cause the at least one processor according to any one of claims 1 to 11 way to do
Electronic device, characterized in that. A non-transitory computer-readable storage medium having computer instructions stored thereon,
The computer instructions cause the computer to perform the method according to any one of claims 1 to 11.
Non-transitory computer-readable storage medium, characterized in that. In the computer program stored in a computer-readable storage medium,
12. The method according to any one of claims 1 to 11 is executed when the instructions in the computer program are executed.
A computer program stored in a computer-readable storage medium, characterized in that.

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