Intelligent power data monitoring management system based on React and Django

Abstract

The invention relates to an electric power data monitoring management system based on React and Django, in particular to the technical field of industrial automation and control systems.An SCADA system is used for collecting electric power data of key parameters of a power grid in real time and transmitting the electric power data to a rear end in real time, a front-end user interface constructed by the React is used for sending a data request instruction to the rear end, and the data request instruction is sent to the rear end; the method comprises the following steps: acquiring power data acquired by an SCADA system in real time through a rear end constructed by a Django framework, constructing a recurrent neural network prediction model in cooperation with an asynchronous task processing and caching strategy, taking the power data after data processing as model input, and predicting future power data; after the rear end receives a request instruction of the front end, the rear end and the MySQL database start to interact, process data storage and retrieval, generate a processing result, package a prediction result and the processing result into a JSON format and feed back the JSON format to a front-end user interface, and then the front-end user interface is dynamically updated based on new data fed back by the rear end so as to reflect the state of the power grid in real time and give a fault alarm.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation and control systems, and more specifically, to an electric power data monitoring and management system based on React and Django. Background Art

[0002] In modern power system management, with the expansion of power grid scale and the increase in the types of power equipment, power data monitoring systems face huge technical challenges. First, the surge in data volume requires the system to have efficient processing and real-time analysis capabilities to cope with increasingly complex data flows and rapidly changing power grid conditions. Second, the stable operation of the power system depends on accurate data monitoring and rapid fault response, which requires the system to have extremely high reliability and real-time response capabilities. In addition, the system should also support a high degree of user interactivity, allowing operators to receive key information and make decisions instantly. However, traditional power monitoring systems often rely on outdated technical architectures, which not only show inefficiency in processing large-scale data, but also have many deficiencies in system integration, scalability, and user interface design. These systems often fail to achieve real-time and accurate analysis of power data, and it is difficult to provide sufficiently flexible user configuration options, thus affecting the optimized operation and fault recovery of the power grid. Summary of the invention

[0003] In view of the technical problems existing in the prior art, the present invention provides a power data monitoring and management system based on React and Django, which solves the problems raised in the above background technology through modules and modules.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: specifically comprising: a SCADA monitoring module, a front-end user interface, a back-end and a database module;

[0005] SCADA monitoring module: By deploying sensors for measuring key parameters of the power grid in the power grid, the SCADA system is used to collect power data of key parameters in the power grid operation process in real time, and the collected power data is encrypted and transmitted to the back end in real time via Ethernet;

[0006] Front-end user interface: Users send data request instructions to the back-end in the front-end user interface built by React, including querying real-time or historical power data, or setting monitoring parameters. After receiving the data from the back-end, the React component of the front-end user interface dynamically updates the front-end user interface according to the new data received, including refreshing charts and displaying warnings to reflect the power grid status in real time, helping users to monitor and analyze the power grid status in real time and optimize resource allocation in a timely manner;

[0007] Backend: The backend built by the Django framework is used to receive the power data sent by the SCADA monitoring module, and with the help of asynchronous task processing and caching strategies, the received power data is processed and transmitted to the database module. A recursive neural network prediction model is built, and the processed power data is used as the model input. Based on the historical power data and the current input, the future power data is predicted to generate the prediction results.

[0008] After the backend receives the frontend request instruction, it starts to interact with the MySQL database, processes data storage and retrieval, and generates processing results;

[0009] Encapsulate the prediction results and processing results into JSON format and feed them back to the front-end user interface for updating the user interface;

[0010] Database module: After receiving the power data sent by the back end, it is stored and managed using the MySQL database.

[0011] In a preferred embodiment, the key parameters of the power grid measured in the SCADA monitoring module specifically include voltage, current, power, temperature, humidity, operating status of power equipment, and fault signals;

[0012] The SCADA system adopts the SCADA system developed by Qt5 technology, and the sensors include voltage sensors, current transformers, power sensors, temperature sensors and humidity sensors, and are connected to the SCADA system through field bus technology.

[0013] In a preferred embodiment, the SCADA system is integrated with the power equipment and communicates with the power equipment through an API interface. Through the API interface, the SCADA system sends a request to the power equipment to obtain the real-time operating status and fault signal power data of the equipment.

[0014] In a preferred embodiment, the front-end user interface includes a form for querying real-time or historical power data. When the user issues a query request instruction, the React component processes the request instruction and sends the request instruction to the back end through Web technology to realize dynamic query of power data.

[0015] In a preferred embodiment, in the front-end user interface, a form is created for the user to set monitoring parameters, and the form includes an input box, a slider, a drop-down menu, and modification and confirmation components so that the user can input and select parameters. When the user modifies the parameter value, the onChange event processing mechanism of React is used to capture the parameter change. When the user clicks to confirm, the parameter value is sent to the back end via Ajax of Web technology. After the back end receives the parameter setting request instruction sent by the front-end user interface, the received parameters are saved in the database module for subsequent use. After the setting is successful, a message or notification is displayed on the front-end user interface, or the front-end user interface is refreshed to display the latest parameter settings.

[0016] In a preferred embodiment, the back-end data processing of power data includes data decryption, data cleaning, data conversion and data verification.

[0017] In a preferred embodiment, the specific steps of constructing the recursive neural network prediction model are:

[0018] S1. Sort the voltage, current, power, temperature, humidity, power equipment operating status, and fault signal power data in the order of collection time and use them as a time step sequence. The data of each time step is used as the input of the model. The input sequence is represented as X = [x1, x2, ..., x t ], where x t represents the input of the tth time step;

[0019] S2. Use the RNN model to maintain a hidden state h t , and predict future values ​​based on past time step information;

[0020] S3. Calculate the hidden state of the current time step based on the current input and the hidden state of the previous time step. The specific calculation formula is:

[0021] h t =f(W1x t +W2h t-1 +b)

[0022] Among them, W1 and W2 represent weight matrices, b represents bias vector, and f represents activation function.

[0023] S4. Calculate the output prediction result according to the hidden state of the current time step. The specific calculation formula is:

[0024] output=activation_function(W output *h t +b output )

[0025] output represents the output prediction result, W output represents the weight matrix of the output layer, h t represents the hidden state of the current time step, b output It represents the bias term of the output layer, and activation_function represents the activation function.

[0026] In a preferred embodiment, the backend establishes a connection with a MySQL database, and according to the type and parameters of the request instruction, the backend performs corresponding database operations:

[0027] S1. Data storage: If the request instruction is to store data, the backend inserts the data into the corresponding table in the MySQL database, or updates the existing data.

[0028] S2. Data retrieval: If the request instruction is to retrieve data, the backend executes the SQL query statement according to the specified conditions to retrieve the required data from the MySQL database.

[0029] In a preferred embodiment, after receiving the result returned by the MySQL database, the backend processes it as needed and generates a final processing result.

[0030] In a preferred embodiment, the backend processes the result returned by the MySQL database in the following steps:

[0031] S1. For data storage operations, check whether the operation is successful and return corresponding status information;

[0032] S2. For data retrieval operations, the backend further processes, filters or formats the returned data.

[0033] In a preferred embodiment, the method specifically includes the following steps:

[0034] S101, the SCADA system collects power data of key parameters of the power grid in real time and transmits it to the back end in real time. The back end processes the data and stores it in batches in the MySQL database in an optimized manner. For the power equipment fault signal obtained by the SCADA monitoring module, the back end feeds back the front end user interface in real time to issue a real-time fault alarm;

[0035] S102, using the React framework to build a front-end user interface, and sending data request instructions to the back-end, including querying real-time or historical power data and setting monitoring parameters, and then dynamically updating the front-end user interface based on new data fed back by the back-end, including refreshing charts and displaying warnings, so as to reflect the power grid status in real time, help users monitor and analyze the power grid status in real time, and timely optimize resource allocation to improve operational responsiveness and user interaction experience;

[0036] S103. Use the backend built by the Django framework to obtain the power data collected by the SCADA system in real time, and cooperate with asynchronous task processing and caching strategies. Through the constructed recursive neural network prediction model, the processed power data is used as the model input to predict future power data. After the backend receives the front-end request instruction, the backend starts to interact with the MySQL database, processes data storage and retrieval, generates processing results, and encapsulates the prediction results and processing results into JSON format to feed back to the front-end user interface.

[0037] The beneficial effects of the present invention are:

[0038] 1. Improved data processing efficiency: By utilizing the powerful data processing capabilities of the Django backend, combined with asynchronous task processing and caching strategies, the system can quickly respond to and process large-scale concurrent data requests, ensuring the stability and response speed of the system under high data traffic conditions.

[0039] 2. Enhanced user interaction experience: The front-end uses React technology to provide a dynamic and responsive user interface, allowing users to view changes in power data in real time, conduct real-time monitoring and operations, greatly improving the convenience and intuitiveness of operations.

[0040] 3. Achieved high modularity and integration: The modular design of the system allows for rapid adaptation to changes in the grid structure and technological upgrades. It integrates the SCADA system and MySQL database, achieves seamless integration of multiple technologies, and improves the flexibility and scalability of the overall system.

[0041] 4. Optimized the operation and management of the power grid: SCADA system integration allows real-time monitoring of key parameters of the power grid, timely detection and handling of abnormal situations. The system supports complex data analysis and prediction functions to help operators optimize power grid operation and resource allocation, and enhance the stability and efficiency of the power grid.

[0042] 5. Enhanced system security and integrity: Advanced security measures such as data encryption and access control are used to ensure the security and integrity of data during transmission and processing, which is especially important for sensitive power data, ensuring the confidentiality of information and preventing data tampering.

[0043] Through the above advantages, this application not only improves the processing capability and monitoring efficiency of power data, but also ensures the safe, stable and efficient operation of the power network, and has important practical application value for modern power system management. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the method of the present invention;

[0045] Figure 2 This is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0047] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0048] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0049] Example 1

[0050] This embodiment provides Figure 1 The power data monitoring and management system based on React and Django specifically includes the following steps:

[0051] S101, the SCADA system collects power data of key parameters of the power grid in real time and transmits it to the back end in real time. The back end processes the data and stores it in batches in the MySQL database in an optimized manner. For the power equipment fault signal obtained by the SCADA monitoring module, the back end feeds back the front end user interface in real time to issue a real-time fault alarm;

[0052] S102, using the React framework to build a front-end user interface, and sending data request instructions to the back-end, including querying real-time or historical power data and setting monitoring parameters;

[0053] S103, using the backend built by the Django framework to obtain the power data collected by the SCADA system in real time, and in conjunction with asynchronous task processing and caching strategies, using the constructed recursive neural network prediction model, using the processed power data as model input to predict future power data; after the backend receives the front-end request instruction, the backend starts to interact with the MySQL database, processes data storage and retrieval, generates processing results, and encapsulates the prediction results and processing results into JSON format to feed back to the front-end user interface;

[0054] S104. Dynamically update the front-end user interface based on new data fed back by the back-end, including refreshing charts and displaying warnings, to reflect the power grid status in real time, help users monitor and analyze the power grid status in real time, and optimize resource allocation in a timely manner to improve operational responsiveness and user interaction experience.

[0055] Example 2

[0056] This embodiment provides Figure 2 The power data monitoring and management system shown is based on React and Django, which specifically includes: a SCADA monitoring module, a front-end user interface, a back-end and a database module;

[0057] SCADA monitoring module: By deploying sensors for measuring key parameters of the power grid in the power grid, the SCADA system is used to collect power data of key parameters in the power grid operation process in real time, and the collected power data is encrypted and transmitted to the back end in real time via Ethernet. After the power data is collected, it is encrypted and transmitted to prevent the data from being obtained or tampered with by unauthorized personnel, ensuring the integrity and confidentiality of the data;

[0058] Front-end user interface: Users send data request instructions to the back-end in the front-end user interface built by React, including querying real-time or historical power data, or setting monitoring parameters. After receiving the data from the back-end, the React component of the front-end user interface dynamically updates the front-end user interface according to the new data received, including refreshing charts and displaying warnings to reflect the power grid status in real time, helping users to monitor and analyze the power grid status in real time and optimize resource allocation in a timely manner. This dynamic update not only improves the responsiveness and real-time performance of the operation, but also enhances the user's interactive experience;

[0059] Backend: The backend built by the Django framework is used to receive the power data sent by the SCADA monitoring module, and with the help of asynchronous task processing and caching strategies, the received power data is processed and transmitted to the database module. A recursive neural network prediction model is built, and the processed power data is used as the model input. Based on the historical power data and the current input, the future power data is predicted to generate the prediction results.

[0060] After the backend receives the frontend request instruction, it starts to interact with the MySQL database, processes data storage and retrieval, and generates processing results;

[0061] Encapsulate the prediction results and processing results into JSON format and feed them back to the front-end user interface for updating the user interface;

[0062] Database module: After receiving the power data sent by the back end, the MySQL database is used to store and manage it, supporting fast data insertion, query and report generation. The database optimization design ensures data integrity and consistency, and has high concurrent processing capabilities.

[0063] In this embodiment, it is specifically necessary to explain the SCADA monitoring module. The key parameters of the power grid measured in the SCADA monitoring module specifically include voltage, current, power, temperature, humidity, power equipment operating status, and fault signals. The above parameters can provide important information about the power grid operating status and equipment health status, which is crucial for monitoring and controlling the power system;

[0064] The SCADA system adopts the SCADA system developed by Qt5 technology, which can realize accurate real-time monitoring of power facilities, enhance the accuracy and response speed of monitoring, and the sensors include voltage sensors, current transformers, power sensors, temperature sensors and humidity sensors, and are connected to the SCADA system through field bus technology. Specifically, when using field bus, such as Profibus, Modbus, CAN, Ethernet, by installing a PLC programmable logic controller device on site, the above sensors are connected to the PLC, and the PLC is responsible for reading data from these sensors and sending it to the SCADA system. This method can realize high-speed data transmission and real-time monitoring, and can improve the reliability and safety of power grid operation;

[0065] The SCADA system is integrated with the power equipment and communicates with the power equipment through the API interface. Through the API interface, the SCADA system sends a request to the power equipment to obtain the real-time operating status of the equipment and the power data of the fault signal. When using the API interface, the communication method preferably adopted in this application is an Ethernet-based communication protocol, such as HTTP(S) or WebSocket. These protocols allow the SCADA system to communicate with the equipment through the network and send a request to obtain the operating status and power data of the equipment. In addition, in order to ensure the data security during the communication process, it is preferred to use an encryption protocol, such as HTTPS, to protect the security of data transmission. At the same time, through the cooperation of the above-mentioned field bus and API interface technology, the SCADA system can realize the comprehensive collection and monitoring of the power grid and power equipment, improve the reliability, safety and stability of the power grid, and also reduce the cost and risk of maintenance and operation.

[0066] In this embodiment, the front-end user interface specifically needs to be explained. The front-end user interface includes a form for querying real-time or historical power data. When a user issues a query request instruction, the React component processes the request instruction and sends the request instruction to the back-end through Web technology such as Ajax to realize dynamic query of power data. The componentized characteristics of the React framework make the front-end user interface design flexible and responsive. It can visualize power data in real time and query dynamically according to user input, helping operators to efficiently monitor and analyze the power grid status and optimize resource allocation in a timely manner.

[0067] In the front-end user interface, a form is created for users to set monitoring parameters. The form includes an input box, a slider, a drop-down menu, and modification and confirmation components so that users can input and select parameters. When the user modifies the parameter value, the onChange event processing mechanism of React is used to capture the parameter change. When the user clicks to confirm, the parameter value is sent to the back end through Ajax of Web technology. After the back end receives the parameter setting request instruction sent by the front-end user interface, the received parameters are saved in the database module for subsequent use. After the setting is successful, a message or notification is displayed on the front-end user interface, or the front-end user interface is refreshed to display the latest parameter settings. Through the above steps, the function of setting monitoring parameters in the front-end user interface can be realized, and the user can easily modify and set parameters on the front-end user interface, and then send the parameters to the back end for processing through the data request instruction, so as to optimize resource allocation according to the set monitoring parameters and reflect the power grid status in real time.

[0068] In this embodiment, the back end is specifically required to be explained. The data processing of the power data by the back end includes data decryption, data cleaning, data conversion and data verification. The power equipment fault signal obtained by the SCADA monitoring module is fed back to the front end user interface in real time by the back end to perform real-time fault alarm;

[0069] The specific steps to build a recursive neural network prediction model are:

[0070] S1. Sort the voltage, current, power, temperature, humidity, power equipment operating status, and fault signal power data in the order of collection time and use them as a time step sequence. The data of each time step (such as voltage, current, etc.) is used as the input of the model. The input sequence is represented as X = [x1, x2, ..., x t ], where x t represents the input of the tth time step;

[0071] S2. Use the RNN model to maintain a hidden state h t , and predict future values ​​based on past time step information;

[0072] S3. Calculate the hidden state of the current time step based on the current input and the hidden state of the previous time step. The specific calculation formula is:

[0073] h t =f(W1x t +W2h t-1 +b)

[0074] Among them, W1 and W2 represent weight matrices, b represents bias vector, and f represents activation function, which can be any one of tanh or ReLU.

[0075] S4. According to the hidden state of the current time step, the output prediction result is calculated. The output result is a prediction value or a set of prediction values ​​or a warning, depending on the problem requirements. The specific calculation formula is:

[0076] output=activation_function(W output *h t +b output )

[0077] output represents the output prediction result, W output represents the weight matrix of the output layer, h t represents the hidden state of the current time step, b output Represents the bias term of the output layer, and activation_function represents the activation function, such as sigmoid, tanh, or ReLU;

[0078] The backend establishes a connection with the MySQL database and uses the MySQL database connection pool to manage the connection to improve efficiency and performance. According to the type and parameters of the request instruction, the backend performs the corresponding database operation:

[0079] S1. Data storage (insert or update): If the request instruction is to store data, the backend inserts the data into the corresponding table in the MySQL database, or updates the existing data.

[0080] S2. Data retrieval (query): If the request instruction is to retrieve data, such as querying real-time or historical power data, the backend executes the SQL query statement according to the specified conditions to retrieve the required data from the MySQL database;

[0081] After receiving the results returned by the MySQL database, the backend processes them as needed and generates the final processing results, which include formatting the data into a specific data structure, generating reports or statistical information, or returning the processing results to the frontend;

[0082] The backend processes the results returned by the MySQL database in the following steps:

[0083] S1. For data storage operations, check whether the operation is successful and return corresponding status information;

[0084] S2. For data retrieval operations, the backend further processes, filters or formats the returned data.

[0085] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0086] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take 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.

[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0091] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A power data monitoring and management system based on React and Django, characterized in that: Specifically include: SCADA monitoring module, front-end user interface, back-end and database modules; SCADA monitoring module: By deploying sensors for measuring key parameters of the power grid in the power grid, the SCADA system is used to collect power data of key parameters in the power grid operation process in real time, and the collected power data is encrypted and transmitted to the back end in real time via Ethernet; Front-end user interface: Users send data request instructions to the back-end in the front-end user interface built by React, including querying real-time or historical power data, or setting monitoring parameters. After receiving the data from the back-end, the React component of the front-end user interface dynamically updates the front-end user interface according to the new data received, including refreshing charts and displaying warnings to reflect the power grid status in real time, helping users to monitor and analyze the power grid status in real time and optimize resource allocation in a timely manner; Backend: The backend built by the Django framework is used to receive the power data sent by the SCADA monitoring module, and with the help of asynchronous task processing and caching strategies, the received power data is processed and transmitted to the database module. A recursive neural network prediction model is built, and the processed power data is used as the model input. Based on the historical power data and the current input, the future power data is predicted to generate the prediction results. After the backend receives the frontend request instruction, it starts to interact with the MySQL database, processes data storage and retrieval, and generates processing results; Encapsulate the prediction results and processing results into JSON format and feed them back to the front-end user interface for updating the user interface; Database module: After receiving the power data sent by the back end, it is stored and managed using the MySQL database.

2. According to claim 1, a power data monitoring and management system based on React and Django, characterized in that: The key parameters of the power grid measured in the SCADA monitoring module specifically include voltage, current, power, temperature, humidity, operating status of power equipment, and fault signals; The SCADA system adopts the SCADA system developed by Qt5 technology. The sensors include voltage sensors, current transformers, power sensors, temperature sensors and humidity sensors, and are connected to the SCADA system through field bus technology.

3. According to claim 2, a power data monitoring and management system based on React and Django, characterized in that: The SCADA system is integrated with the power equipment and communicates with the power equipment through an API interface. Through the API interface, the SCADA system sends a request to the power equipment to obtain the real-time operating status and fault signal power data of the equipment.

4. According to claim 1, a power data monitoring and management system based on React and Django, characterized in that: The front-end user interface includes a form for querying real-time or historical power data. When a user issues a query request instruction, the React component processes the request instruction and sends the request instruction to the back end through Web technology, thereby realizing dynamic query of power data.

5. According to claim 1, a power data monitoring and management system based on React and Django, characterized in that: In the front-end user interface, a form is created for users to set monitoring parameters. The form includes an input box, a slider, a drop-down menu, and modification and confirmation components so that users can input and select parameters. When the user modifies the parameter value, the onChange event processing mechanism of React is used to capture the parameter change. When the user clicks to confirm, the parameter value is sent to the back end through Ajax of Web technology. After the back end receives the parameter setting request instruction sent by the front-end user interface, the received parameters are saved in the database module for subsequent use. After the setting is successful, a message or notification is displayed on the front-end user interface, or the front-end user interface is refreshed to display the latest parameter settings.

6. According to claim 1, a power data monitoring and management system based on React and Django, characterized in that: The back-end data processing of power data includes data decryption, data cleaning, data conversion and data verification.

7. The power data monitoring and management system based on React and Django according to claim 2, characterized in that: The specific steps of constructing the recursive neural network prediction model are: S1. Sort the voltage, current, power, temperature, humidity, power equipment operating status, and fault signal power data in the order of collection time and use them as a time step sequence. The data of each time step is used as the input of the model. The input sequence is represented as X = [x1, x2, ..., x t ], where x t represents the input of the tth time step; S2. Use the RNN model to maintain a hidden state h t , and predict future values ​​based on past time step information; S3. Calculate the hidden state of the current time step based on the current input and the hidden state of the previous time step. The specific calculation formula is: h t =f(W1x t +W2h t-1 +b) Among them, W1 and W2 represent weight matrices, b represents bias vector, and f represents activation function; S4. Calculate the output prediction result according to the hidden state of the current time step. The specific calculation formula is: output=activation_function(W output *h t +b output ) output represents the output prediction result, W output represents the weight matrix of the output layer, h t represents the hidden state of the current time step, b output Represents the bias term of the output layer, and activation_function represents the activation function.

8. The power data monitoring and management system based on React and Django according to claim 1, characterized in that: The backend establishes a connection with the MySQL database and performs corresponding database operations according to the type and parameters of the request instruction: S1. Data storage: If the request instruction is to store data, the backend inserts the data into the corresponding table in the MySQL database, or updates the existing data. S2. Data retrieval: If the request instruction is to retrieve data, the backend executes the SQL query statement according to the specified conditions to retrieve the required data from the MySQL database.

9. The power data monitoring and management system based on React and Django according to claim 8, characterized in that: After receiving the result returned by the MySQL database, the backend processes it as needed and generates a final processing result.

10. The power data monitoring and management system based on React and Django according to claim 1, characterized in that: The backend processes the results returned by the MySQL database in the following steps: S1. For data storage operations, check whether the operation is successful and return corresponding status information; S2. For data retrieval operations, the backend further processes, filters or formats the returned data.

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