A web-based graphical power grid metadata online analysis system

Abstract

The application discloses a web graphical power grid metadata online analysis system, wherein a client receives user login information, a server determines a data range allowed to be accessed by a user according to the user login information and displays the data range through a client web interface; the client receives user access request data, the server obtains corresponding data information according to the user access request data and stores the data information into a user personal data space; the client receives user data selection, model selection, model parameter setting and result display mode selection information, the server obtains a target analysis model according to the model selection and model parameter setting information, takes the data selected by the user as model input, compiles a code file of the target analysis model, obtains a data analysis processing result, and the client web interface displays the graphical data analysis processing result. The application can enable the user to conveniently and quickly access metadata in a database within an allowed permission range and efficiently obtain a data analysis processing result of a specified analysis type.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology and cloud computing technology, in particular to a web graphical power grid metadata online analysis system. BACKGROUND

[0002] With the rise of machine learning and big data, python language has become a mainstream tool for data mining analysis and machine learning. Any machine learning and data analysis cannot be separated from data, the size of the data and the quality of the data determine the result of data analysis, and the way of obtaining data is particularly important. At present, the data is mainly obtained through computer system database data, and for the purpose of security and privacy, users cannot directly access the database data, and it is a feasible method to obtain the metadata with permission through the system level for the user.

[0003] Flexible data analysis cannot be separated from the support of programming language, and python language provides a good development environment, but before use, it still needs to build a development environment and development tools, and the installation process is complicated and time-consuming, and cannot realize out-of-box use. SUMMARY

[0004] The purpose of the present application is to provide a web graphical power grid metadata online analysis system, so that users can conveniently and quickly access the metadata in the database within the scope of the allowed permission, and can efficiently obtain the data analysis processing result of the specified analysis type after data acquisition.

[0005] To achieve the above purpose, the technical scheme adopted by the present application is: a web graphical power grid metadata online analysis system, comprising a client and a server;

[0006] The client receives user login information and transmits it to the server;

[0007] The server determines the data range allowed to be accessed by the corresponding user according to the user login information, and displays it to the user through the client web interface;

[0008] The client receives user access request data, transmits it to the server, and the user access request data includes the access target data information selected by the user from the data range displayed by the web interface;

[0009] The server acquires corresponding data information according to the user access request data, stores it in the user's personal data space, and displays the corresponding information through the client web interface;

[0010] The client receives the data selection information, model selection information and result display mode selection information input by the user, sends the data selection information to the server, and sends the corresponding model template code request to the server according to the model selection information.

[0011] The server determines a data set to be analyzed according to the data selection information, and determines a corresponding analysis model according to the model template code request;

[0012] The client obtains user model parameter setting information, and the server obtains a code file of a target data analysis model according to the user model parameter setting information;

[0013] The server takes the data set to be analyzed as an input of the data analysis model, compiles and runs the code file corresponding to the data analysis model, performs corresponding data analysis processing, and obtains a data analysis processing result;

[0014] The server or the client performs display mode processing on the data analysis processing result according to the result display mode selection information, and displays a graphical data analysis processing result to the user through a web interface.

[0015] Optionally, the server respectively configures corresponding user access levels for each registered user, different user access levels are configured with different data access allowed ranges, the data access allowed range includes metadata tables allowed to be accessed and database fields allowed to be accessed, and the higher the user access level is, the more metadata allowed to be accessed.

[0016] Optionally, the user access request data further includes an unauthorized access request information, and the server outputs corresponding user information and the unauthorized access request information in response to receiving the unauthorized access request information, and requests manual review of the corresponding unauthorized access request. For different unauthorized access target data, the server can set a user with an unauthorized access approval right according to a data source or a data management party in advance, and send a corresponding unauthorized access review request to the corresponding user, and if the unauthorized access request is approved, the server subsequently allows the corresponding user to access the corresponding data target.

[0017] Optionally, the server respectively configures corresponding user personal data spaces for each registered user;

[0018] The server obtains corresponding data information according to the user access request data and stores it in the user personal data space, including:

[0019] According to the metadata table information to be accessed in the access target information, the corresponding metadata table is located;

[0020] According to the database field information to be accessed in the access target information, the query parameter is determined;

[0021] The query parameter is substituted into a preset SQL query template to obtain an SQL statement for accessing the corresponding data;

[0022] The data obtained by the data query is written into a file, and the file is stored in the personal data space of the user.

[0023] Optionally, the server is provided with a model template database for storing model codes of preset analysis models, and the server is further provided with a model management module, and the user interface of the corresponding client of the user with the preset model management permission is provided with a model management interface.

[0024] The client is invoked in response to the model management interface, acquires model management data, and transmits the model management data to the server, and the model management module of the server performs addition, deletion or modification processing on the analysis models in the model template database according to the model management data. That is, the application supports the user to manage the analysis models that can be provided by the server through the client according to the analysis requirements of the user, and the permission of the model management can be configured to enable the ordinary user to only have the model addition permission and mark the creation user information of the added model in the analysis model database.

[0025] Optionally, the analysis model includes one or more of a regression analysis model, a classification model, a prediction model and an evaluation model. The user can select the corresponding model through the web interface to build a complete data analysis model under different data analysis requirements, and then the server runs the data analysis model code to obtain the analysis result.

[0026] Optionally, the client web interface includes a data display area, a model display area and a result display area, the data display area is used to display the selectable files in the personal data space of the user, the model display area is used to display the names of a plurality of selectable analysis models in a tree form, and the result display area is used to display the data analysis processing result obtained after the data analysis model is compiled and run in a chart form according to the display mode selected by the user.

[0027] Optionally, for the selected analysis model, the client web interface provides a model parameter setting interface for the user; and according to the user model parameter setting information, the server acquires the code file of the target data analysis model, including:

[0028] The client receives the model parameter setting data input by the user through the model parameter setting interface and forwards the model parameter setting data to the server, and the server modifies the template code parameters of the selected analysis model according to the model parameter setting data to obtain the target analysis model code.

[0029] As another way of synchronizing the configuration parameters of the analysis model by the server, optionally, for the selected analysis model, the client web interface provides a model parameter setting interface for the user; and according to the user model parameter setting information, the server acquires the code file of the target data analysis model, including:

[0030] The server returns the template code of the determined corresponding analysis model to the client according to the model template code request, the client receives the model parameter setting data input by the user through the model parameter setting interface, performs parameter setting on the analysis model code, obtains target analysis model code, and sends the target analysis model code to the server.

[0031] Optionally, the model display area is further provided with a model code editing interface and a code editing area, the client is invoked in response to the model code editing interface, obtains the template code of the selected analysis model from the server, and outputs the template code to the code editing area.

[0032] After the user completes code editing, the client sends the edited target analysis model code to the server.

[0033] Advantages

[0034] Compared with the prior art, the present application has the following advantages and progress:

[0035] (1) The present application can enable the user to conveniently and quickly access the metadata in the server database within the permission range, ensuring the validity of the data set; meanwhile, the compilation and running of the model code are performed on the server, and the computing capacity requirement of the client is not high, which can improve the overall efficiency of data analysis.

[0036] (2) The present application can make the user more easily construct the required data analysis model through graphical model construction, greatly reducing the difficulty of data analysis model construction on the user side.

[0037] (3) The online compilation and running environment provided by the present application does not require the user to perform tedious environment installation, is convenient for accessing the target data set, and can achieve the effect of out-of-box use.

[0038] (4) The present application can realize graphical display of the analysis result according to the form required by the user, so that the user can intuitively see the analysis result, improving the analysis efficiency and user experience. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 Fig. 1 shows an architecture schematic diagram of a web end graphical power grid metadata online analysis system in an embodiment of the present application;

[0040] Figure 2 Fig. 2 shows a flowchart of user application of a metadata set;

[0041] Figure 3 Fig. 3 shows a generation flowchart of a metadata set. DETAILED DESCRIPTION

[0042] Further description is made below in combination with the drawings and specific embodiments.

[0043] The technical concept of the present application is that, considering the security and effectiveness of data, the user is authenticated to realize the secure access of the user to the metadata in the database, and the data is positioned and the data set for data analysis is generated according to the selection of the user;

[0044] Considering the model construction ability of the user, the user is facilitated to construct the mathematical model for data analysis through the graphical interface without processing the related code, and the code compilation and running are executed by the server according to the operation ability of the client, so that the overall data analysis efficiency is improved.

[0045] The web graphical power grid metadata online analysis system of the present application includes a client and a server, and the process of executing the power grid metadata online analysis mainly includes:

[0046] The client receives the user login information and transmits it to the server;

[0047] The server determines the data range allowed to be accessed by the corresponding user according to the user login information, and shows it to the user through the client web interface;

[0048] The client receives the user access request data, transmits it to the server, and the user access request data includes the access target data information selected by the user from the data range shown by the web interface;

[0049] The server acquires the corresponding data information according to the user access request data, stores it in the user's personal data space, and shows the corresponding information through the client web interface;

[0050] The client receives the data selection information, model selection information and result display mode selection information input by the user, sends the data selection information to the server, and sends the corresponding model template code request to the server according to the model selection information;

[0051] The server determines the data set to be analyzed according to the data selection information, and determines the corresponding analysis model according to the model template code request;

[0052] The client acquires the user model parameter setting information, and the server acquires the code file of the target data analysis model according to the user model parameter setting information;

[0053] The server takes the data set to be analyzed as the input of the data analysis model, compiles and runs the code file corresponding to the data analysis model, performs corresponding data analysis processing, and obtains the data analysis processing result;

[0054] The server or client selects the appropriate display method for the data analysis and processing results, and then presents the graphical data analysis and processing results to the user through a web interface. Example

[0055] like Figure 1 As shown, the analysis system in this embodiment includes a client web interface and a server. The web interface directly faces the user and is used to display and respond to user operations. It includes a personal space display and management module, a metadata application module, a model graphical construction and display module, and a model code execution control module. The server provides a network interface for the web interface to call. The main services include file services, dataset generation services, permission authentication services, code compilation and execution services, and database management services. The database management services include metadata database management and model template database management.

[0056] The web-based personal space display and management module retrieves data by calling the server's file service, displays files in the user's personal space, and provides file management functionality for users to manage their personal space files. The metadata request module allows users to request metadata. The model graphical construction and display module enables the graphical construction of analysis models, while the server-side compilation and execution service enables online code compilation and execution. The server creates an independent code compilation and execution service for each user, and uses a code execution management service to manage all execution services, allowing multiple users to use the service simultaneously without interference. The code execution control module provides users with the ability to control code execution.

[0057] Specifically, this embodiment mainly involves the following aspects.

[0058] I. User Authentication

[0059] When it is necessary to acquire and analyze power grid metadata, users must first log in to the system through the client interface. The client receives the user's login information and transmits it to the server.

[0060] To ensure secure user access to power grid metadata in the database, user authentication is essential. This embodiment designs user access levels and database field permissions: for each registered user, the server configures a corresponding user access level. Different user access levels have different allowed data access ranges, allowing access to different metadata tables and database fields. The lowest-level user cannot access database metadata, while higher-level users have access to more metadata. The server determines the data range allowed to a given user based on their login information and displays it to the user through the client's web interface.

[0061] When a user submits a data access application through a web interface, the user can only select authorized data tables and data fields. If the user wants to access unauthorized data, the user access request data also includes unauthorized access request information. In this case, the access request needs to be submitted for manual review. The specific implementation can be as follows: the server responds to the receipt of the unauthorized access request information, and outputs the corresponding user information and unauthorized access request information, and requests manual review of the corresponding unauthorized access request.

[0062] For different unauthorized access target data, the server can set users with unauthorized access approval authority according to the data source or the data management party in advance, and send the corresponding unauthorized access review request to the corresponding user. If the unauthorized access request is approved, the server will subsequently allow the corresponding user to access the corresponding data target.

[0063] Figure 2 A flowchart of a user applying for a metadata set is shown. The user first logs in to the system through permission authentication, and enters the metadata application process after clicking the metadata application button on the page. At this time, the system displays the data tables that the user can access according to the user's permissions. The user selects the data table. Only one data table can be selected each time. After selection is completed, click Next. The system displays the data table fields that the user can access. The user checks the data table fields. The user can check multiple table fields. After checking, click Submit Application. At this time, the user completes the metadata application.

[0064] II. Target data acquisition

[0065] After the user selects the target data through the web interface and submits the access request, the client transmits the user access request data to the server. The user access request data includes the access target data information selected by the user from the data range displayed on the web interface, including the metadata table and the data field.

[0066] The server configures a corresponding user personal data space for each registered user. After receiving the user access request, the server acquires the corresponding data information according to the user access request data, stores it in the user personal data space, and displays the corresponding information through the client web interface.

[0067] Figure 3 A flowchart of a system generating a user request target data set is shown. First, the server acquires the data table and field selected by the user according to the user access request data, and then dynamically generates a query SQL according to the user selection and the SQL template. The SQL is used to query the database to obtain the metadata. The system writes the queried metadata into a file and stores it in the user personal space. The specific steps are as follows:

[0068] Locate the corresponding metadata table according to the metadata table information to be accessed in the access target information;

[0069] Determine the query parameter according to the database field information to be accessed in the access target information;

[0070] Substitute the query parameter into the preset SQL query template to obtain an SQL statement for accessing the corresponding data;

[0071] Perform data query by using the SQL statement, write the data obtained by the query into a file, and store the file in the user personal data space.

[0072] The data display area of the client web interface can display the data set files available for selection in the user personal data space for the user to select, or can default to the data set file generated for the latest request of the analysis target.

[0073] To facilitate the user to use the data set, the system encapsulates a data set reading method for the user to use. The metadata set file applied by the user is stored in the user personal space for the analysis model to use.

[0074] III. Graphical construction of data analysis model

[0075] The core of data analysis is the analysis model, and the construction of the analysis model is often implemented by programming language. This process requires the analyst to be familiar with both the mathematical model and the programming language. The embodiment uses a graphical model construction method to reduce the difficulty of the analyst in analyzing data using the analysis model. The graphical model construction method does not require direct use of programming language when constructing the analysis model. Each analysis model is implemented in advance by programming language, tested and verified to form a code template and stored in the database. The user only needs to select the corresponding model button on the browser page to use the model, and then can adjust the model through parameters. The system generates the python code of the model according to the user parameters. The graphical model construction method only requires the analyst to have a basic understanding of the analysis model used.

[0076] Specifically, in the embodiment, the server is provided with a model template database for storing model codes of preset analysis models. The analysis model can include regression analysis model, classification model, prediction model, evaluation model and other models required for power grid data analysis. The user can select the corresponding model through the web interface to construct a complete data analysis model under different data analysis requirements.

[0077] In the system of the embodiment, the client web interface includes a data display area, a model display area and a result display area, the data display area is used to display the selectable files in the user's personal data space, the model display area is used to display the names of various selectable analysis models in a tree form, and the result display area is used to display the data analysis processing results obtained after the data analysis model is compiled and run in a chart form according to the display mode selected by the user.

[0078] The implementation steps of the graphical model construction are as follows.

[0079] The user selects the analysis model to be used through the model display area of the web interface, and the selection mode can be point selection or drag and drop, etc. The selected analysis model is added to the newly constructed model area in a graphical form. Similarly, the data set selected by the user from the file display area of the user's personal data space can also be added to the newly constructed model area in a graphical form, so that the user can understand the construction process of the analysis model more intuitively.

[0080] The user sets the parameters required by the model: for the selected analysis model, the client web interface provides a model parameter setting interface for the user, through which the model parameter setting data input by the user is received.

[0081] The server obtains the template code of the corresponding analysis model from the database according to the user's model selection information, sets the parameters in the analysis model template code according to the model parameter setting data, obtains the updated target analysis model code, and generates the final python code implementation.

[0082] In addition to the above model generation mode, according to the user's model parameter setting information, the server can also obtain the code file of the target data analysis model in the following ways:

[0083] After the client sends a model template code request, the server returns the determined template code of the corresponding analysis model to the client according to the model template code request, and the client receives the model parameter setting data input by the user through the model parameter setting interface, sets the parameters of the analysis model code, obtains the target analysis model code, and sends it to the server.

[0084] For the second target analysis model generation mode described above, considering the user's code editing ability and needs, the model display area of the embodiment can also set a model code editing interface and a code editing area. The client obtains the template code of the selected analysis model from the server in response to the model code editing interface being called, and outputs it to the code editing area.

[0085] After the user completes the code editing, the client generates the data analysis model according to the edited analysis model code and the data selection information. As mentioned earlier, the model code in the present application can be selected as python language.

[0086] In order to realize the expansion of the system analysis function, in the embodiment, the server can also set a model management module, and a model management interface is set on the user interface of the client corresponding to a user with preset model management permission; the client acquires model management data in response to the model management interface being invoked and transmits the model management data to the server, and the model management module of the server performs addition, deletion or modification processing on the analysis models in the model template database according to the model management data. Thus, the application supports users to manage the analysis models that can be provided by the server through the client according to their own analysis needs, and the permission for model management can be configured to allow ordinary users to only have the model addition permission and mark the creation user information of the added model in the analysis model database.

[0087] IV. Code compilation and result display

[0088] The system in the embodiment realizes a python compilation and running environment on the server, and after the target data set and the target analysis model code are determined, the server takes the data set to be analyzed as the input of the data analysis model, compiles and runs the code file corresponding to the data analysis model, performs corresponding data analysis processing, and obtains the data analysis processing result.

[0089] After the data analysis processing result is obtained, the server can process the data analysis processing result according to the user result display mode selection information to obtain graphical analysis result data and transmit the graphical analysis result data to the client for display through the web interface; or the server can also directly transmit the data analysis processing result to the client, and the client generates corresponding graphical analysis result according to the user-selected result display and displays the graphical analysis result to the user through the web interface.

[0090] In the result display mode selection, the client interface can provide line charts, column charts, pie charts and other chart forms for the user to select, and even if the result display mode processing is realized by the client, it will not have a significant impact on the operation processing efficiency of the client.

[0091] In summary of the above embodiments, the application can not only guarantee the access security of the meta database, but also reduce the difficulty of user-side data analysis model construction and improve the efficiency of client data analysis processing.

[0092] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system or a computer program product. Therefore, the application can be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0093] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps of the flowchart block or blocks.

[0094] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps of the flowchart block or blocks.

[0095] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps of the flowchart block or blocks.

[0096] The embodiments of the present application described above are merely intended to illustrate the present application, but not to limit the present application. The ordinary skilled person in the art can make many modifications and improvements without departing from the spirit and scope of the present application. Therefore, the scope of the present application should be defined by the appended claims.

Claims

1. A web-based graphical power grid metadata online analysis system, comprising a client and a server; characterized in that: the client receives user login information and transmits it to the server; the server determines the data range that the corresponding user is allowed to access according to the user login information and displays it to the user through the client web interface; the client receives user access request data, which includes the access target data information selected by the user from the data range displayed on the web interface, and transmits it to the server; the server obtains the corresponding data information according to the user access request data, stores it in the user's personal data space, and displays the corresponding information through the client web interface; the client receives data selection information, model selection information, and result display mode selection information input by the user, sends the data selection information to the server, and sends the corresponding model template code request to the server according to the model selection information; the server determines the data set to be analyzed according to the data selection information and determines the corresponding analysis model according to the model template code request; the client obtains user model parameter setting information, and the server obtains the code file of the target data analysis model according to the user model parameter setting information; the server takes the data set to be analyzed as the input of the data analysis model, compiles and runs the code file corresponding to the data analysis model, performs corresponding data analysis processing, and obtains the data analysis processing result; the server or the client processes the data analysis processing result according to the result display mode selection information and displays the graphical data analysis processing result to the user through the web interface.

2. The analytical system of claim 1, wherein, For each registered user, the server configures a corresponding user access level, different user access levels are configured with different data access allowed ranges, and the data access allowed range includes metadata tables allowed to access and database fields allowed to access. The higher the user access level, the more metadata allowed to access.

3. The analytical system of claim 2, wherein, The user access request data also includes over-reach access request information. In response to receiving the over-reach access request information, the server outputs the corresponding user information and over-reach access request information and requests manual review of the corresponding over-reach access request.

4. The analytical system of claim 1, wherein, For each registered user, the server configures a corresponding user personal data space; The server obtains the corresponding data information according to the user access request data and stores it in the user's personal data space, including: locating the corresponding metadata table according to the metadata table information in the access target information; determining the query parameter according to the database field information in the access target information; substituting the query parameter into the preset SQL query template to obtain an SQL statement for accessing the corresponding data; using the SQL statement to query the data and writing the queried data into a file and storing the file in the user's personal data space.

5. The analytical system of claim 1, wherein, The server is provided with a model template database for storing model codes of preset analysis models; the server is also provided with a model management module, and for a user with preset model management permission, a model management interface is arranged on the corresponding client-side user interface; The client acquires model management data in response to the model management interface being invoked and transmits the model management data to the server, and the server model management module performs addition, deletion or modification processing on the analysis models in the model template database according to the model management data.

6. The analytical system of claim 1 or 5, characterized in that The analysis models include one or more of a regression analysis model, a classification model, a prediction model and an evaluation model.

7. The analytical system of claim 1, wherein, The client-side web interface includes a data display area, a model display area and a result display area, the data display area is used to display selectable files in a user personal data space, the model display area is used to display a plurality of selectable analysis model names in a tree form, and the result display area is used to display data analysis processing results obtained after a data analysis model is compiled and run in a chart form according to a user-selected display mode.

8. The analytical system of claim 1, wherein, The client-side web interface provides a model parameter setting interface for the user for the selected analysis model; The server acquires a code file of a target data analysis model according to the user model parameter setting information, including: The client receives user input model parameter setting data through the model parameter setting interface and forwards the model parameter setting data to the server, and the server modifies template code parameters of the selected analysis model according to the model parameter setting data to obtain target analysis model code.

9. The analytical system of claim 1, wherein, The client-side web interface provides a model parameter setting interface for the user for the selected analysis model; The server acquires a code file of a target data analysis model according to the user model parameter setting information, including: The server returns the determined template code of the corresponding analysis model to the client according to the model template code request, the client receives user input model parameter setting data through the model parameter setting interface, performs parameter setting on the analysis model code to obtain target analysis model code, and sends the target analysis model code to the server.

10. The analytical system of claim 7, wherein, The model display area is also provided with a model code editing interface and a code editing area, and the client acquires the template code of the selected analysis model from the server in response to the model code editing interface being invoked and outputs the template code to the code editing area; After the user completes code editing, the client sends the edited target analysis model code to the server.

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