Power grid data collection system

Abstract

The invention discloses a power grid data collection system. The system comprises a sensing layer module, an edge calculation module, a network transmission module and a cloud platform processing module, the sensing layer module is used for collecting key data of a power grid, performing data processing on the collected key data, and sending preliminary data obtained after data processing to the edge calculation module; the edge calculation module is used for processing the received preliminary data based on edge calculation operation to obtain target data and sending the target data to the network transmission module; the network transmission module is used for adjusting a data transmission path and bandwidth allocation to send the target data to the cloud platform processing module after receiving the target data through a software defined network technology; and the cloud platform processing module is used for storing the received target data. Through the technical scheme of the invention, power grid data collection can be realized, the efficiency of power grid data collection work is improved, and the redundancy of data is reduced.

Description

Technical Field

[0001] The invention relates to the field of data collection, and in particular to a power grid data collection system. Background Art

[0002] With the rapid development of smart grids, the number of sensing devices deployed in the power grid continues to increase, and the massive data generated by them places increasingly higher requirements on data transmission and processing.

[0003] However, the existing traditional grid sensing device access system has serious problems. On the one hand, the data transmission delay is large and the processing efficiency is extremely low, which greatly reduces the efficiency of data collection. On the other hand, due to the existence of many sensing devices, multiple devices will collect similar or identical data, which leads to great data redundancy and serious waste of data storage and processing resources. This situation of low data collection efficiency and large data redundancy has a great negative impact on the stable operation and intelligent analysis capabilities of the power grid. In addition, the inflexible network configuration further hinders the efficient transmission and processing of data.

[0004] In summary, when completing data collection work through the existing power grid data collection system, there are problems such as low work efficiency and large data redundancy. Summary of the invention

[0005] The present invention provides a power grid data collection system, which can solve the problems of low working efficiency and large data redundancy when completing data collection work through the existing power grid data collection system.

[0006] According to one aspect of the present invention, a power grid data collection system is provided, the system comprising: a perception layer module, an edge computing module, a network transmission module and a cloud platform processing module, the perception layer module is communicatively connected to the power grid, the perception layer module and the network transmission module are communicatively connected to the edge computing module respectively, and the network transmission module is communicatively connected to the cloud platform processing module;

[0007] The perception layer module is used to collect key data of the power grid, process the collected key data, and send the preliminary data obtained after data processing to the edge computing module;

[0008] The edge computing module is used to process the received preliminary data based on the edge computing operation to obtain target data, and send the target data to the network transmission module;

[0009] The network transmission module is used to adjust the data transmission path and bandwidth allocation after receiving the target data through the software defined network technology to send the target data to the cloud platform processing module;

[0010] The cloud platform processing module is used to store the received target data.

[0011] Optionally, the perception layer module includes: a frequency calculation unit, which is used to analyze historical power grid data through a convolutional neural network to obtain the accuracy and frequency of current data collection operations; intelligent sensors, which are deployed at key points of the power grid and connected to the power grid, and are used to continuously monitor and collect key data of the power grid according to the accuracy and frequency of current data collection operations; a data cleaning unit, which is used to clean the collected key data to eliminate obvious outliers and noise contained in the key data to obtain preliminary data.

[0012] Furthermore, the frequency calculation unit is specifically used to: when it is detected that the grid load increases, obtain grid data that matches the current grid load in the historical grid data; input the grid data into the convolutional neural network to obtain the accuracy and frequency of the current data acquisition operation.

[0013] Optionally, the edge computing module includes: a point calculation unit, used to calculate the location of the deployed edge node according to the geographical layout and equipment distribution of the power grid; a preprocessing unit, used to receive preliminary data, and process the preliminary data based on a data processing algorithm to obtain preprocessed data, and send it to a deep processing unit; a deep processing unit, used to input the preprocessed data into a pre-trained deep learning model after receiving the preprocessed data, to obtain target data.

[0014] Optionally, the network transmission module includes: a switch unit, which establishes a communication connection with the edge computing module, and is used to receive the target data sent by the edge computing module and send it to the controller unit; a controller unit, which is used to monitor the received target data in real time, and at the same time, perform real-time analysis on the current network traffic and target data through a pre-configured real-time data analysis model to obtain a data transmission strategy that matches the current network traffic, wherein the data transmission strategy includes: data transmission commands and target transmission traffic; the switch unit is also used to send the target data to the cloud platform processing module based on the data transmission strategy.

[0015] Furthermore, the controller unit is specifically used to: determine whether the target data is legal based on preset rules; obtain network traffic information of the current network, and input the network traffic information into a real-time data analysis model to obtain target transmission traffic; after determining that the target data is legal and the target transmission traffic is a non-zero positive number, generate a data transmission strategy, and send the data transmission strategy to the switch unit, so that the switch unit sends the target data to the cloud platform processing module based on the data transmission strategy.

[0016] Optionally, the cloud platform processing module includes: an encryption unit, used to encrypt the target data using the TLS encryption protocol after receiving the target data; a data storage unit, used to store the encrypted target data through a distributed file system and a NoSQL database; a data analysis unit, used to analyze the received target data through a preset big data processing framework, obtain a power grid load forecast result matching the target data and send it to the data storage unit for storage; an intrusion detection unit, used to monitor and analyze network traffic in real time to identify and respond to potential malicious activities or attacks.

[0017] Optionally, the perception layer module further includes: a first safety monitoring unit, configured to track the operating status of the data cleaning unit through a real-time monitoring system to ensure safe operation of the perception layer module.

[0018] Optionally, the edge computing module also includes: a second safety monitoring unit, used to track the operating status of the data processing algorithm in the edge computing module through a real-time monitoring system to ensure the safe operation of the edge computing module.

[0019] The technical solution of the embodiment of the present invention collects key data of the power grid through the perception layer module, processes the collected key data, and sends the preliminary data obtained after the data processing to the edge computing module, which processes the received preliminary data based on the edge computing operation to obtain target data, and sends the target data to the network transmission module. The network transmission module is used to adjust the data transmission path and bandwidth allocation after receiving the target data through software-defined network technology to send the target data to the cloud platform processing module, and the cloud platform processing module is used to store the received target data, which solves the problems of low work efficiency and large data redundancy when completing data collection work through the existing power grid data collection system, realizes the collection of power grid data, improves the efficiency of power grid data collection work, and reduces data redundancy.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a structural diagram of a power grid data collection system provided according to Embodiment 1 of the present invention;

[0023] Figure 2 It is a structural diagram of an edge computing module provided according to Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, any variation of the terms "including" and "having" is intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Embodiment 1

[0027] Figure 1 This is a schematic diagram of the structure of a power grid data collection system provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of collecting power grid data.

[0028] like Figure 1 As shown, the system includes: a perception layer module 110, an edge computing module 120, a network transmission module 130 and a cloud platform processing module 140.

[0029] Among them, the perception layer module 110 is communicatively connected to the power grid, the perception layer module 110 and the network transmission module 130 are communicatively connected to the edge computing module 120 respectively, and the network transmission module 130 is communicatively connected to the cloud platform processing module 140.

[0030] The perception layer module 110 is used to collect key data of the power grid, process the collected key data, and send the preliminary data obtained after the data processing to the edge computing module 120;

[0031] The key data include the voltage, current and frequency of the target power grid.

[0032] Specifically, the perception layer module 110 is mainly responsible for the important task of collecting key data of the power grid; in this embodiment, the perception layer module 110 is a hardware or software component that can perceive and obtain information from the physical world, such as a temperature sensor, a humidity sensor, etc. It needs to process the collected key data; further, the data processing is to perform various operations on the data to make it usable, such as data cleaning and data conversion; then, the preliminary data obtained after data processing is sent to the edge computing module 120. Among them, the key data covers the voltage, current and frequency of the target power grid; further, the key data can be data that is of great significance to a specific system or scenario. For example, in the medical field, the patient's heart rate, blood pressure, etc. are key data, and in the transportation field, the speed and position of the vehicle are also key data. For this embodiment, the key data includes voltage, current and frequency. It should be noted that the specific content of the key data can be flexibly adjusted according to the actual application scenario, and the specific data types included in the key data are not specifically limited in this embodiment; for example, in certain specific scenarios, the key data may also include the power factor and harmonic content of the power grid; and in other scenarios, there may be other key data types, such as the operating status of the equipment, the light intensity of the environment, etc.

[0033] Specifically, in this embodiment, the perception layer module 110 includes: a frequency calculation unit, which is used to analyze historical power grid data through a convolutional neural network to obtain the accuracy and frequency of current data collection operations; intelligent sensors, which are deployed at key points of the power grid and connected to the power grid, and are used to continuously monitor and collect key data of the power grid according to the accuracy and frequency of current data collection operations; a data cleaning unit, which is used to clean the collected key data to eliminate obvious outliers and noise contained in the key data to obtain preliminary data.

[0034] Furthermore, the frequency calculation unit is specifically used to: when it is detected that the grid load increases, obtain grid data that matches the current grid load in the historical grid data; input the grid data into the convolutional neural network to obtain the accuracy and frequency of the current data acquisition operation.

[0035] In a specific implementation of this embodiment, the convolutional neural network is a feedforward neural network, which can perform operations such as feature extraction and classification on input data, and is widely used in the fields of image recognition, speech recognition, etc. The frequency calculation unit uses a convolutional neural network to conduct an in-depth analysis of a large amount of historical power grid data, thereby obtaining the precise accuracy and frequency of the current data acquisition operation; further, the intelligent sensor is a sensor with information processing function, which can transform the sensed information into electrical signals or other required forms of information output according to a certain rule. These intelligent sensors are carefully deployed at various key nodes of the power grid and are closely connected to the power grid. Then, according to the accuracy and frequency of the current data acquisition operation, the key data of the power grid are continuously and stably monitored and collected; further, the data cleaning is the last procedure for discovering and correcting identifiable errors in the data file, including checking data consistency, processing invalid values ​​and missing values, etc. The data cleaning unit is responsible for carefully processing the collected key data, eliminating the obvious abnormal values ​​and noise contained therein, so as to obtain pure and reliable preliminary data.

[0036] Furthermore, when the frequency calculation unit is in operation, it will take immediate action when it detects that the grid load increases; specifically, the grid load is the sum of the electric power consumed by the electrical equipment connected to the grid. At this time, the frequency calculation unit will obtain the part of the grid data in the historical grid data that matches the current grid load, and then input these grid data into the convolutional neural network. Through the calculation and analysis capabilities of the convolutional neural network, the accuracy and frequency of the current data acquisition operation can be accurately obtained. In this way, it can ensure that the subsequent data collection work is more efficient and accurate, and provide a solid data foundation for the stable operation and intelligent analysis of the entire system. In summary, through the perception layer module 110, the key data of the grid operation can be collected in real time and accurately, effectively improving the efficiency and accuracy of data collection. At the same time, the convolutional neural network is used to perform preliminary processing on the data, and the data collection frequency and accuracy are automatically adjusted, which not only greatly improves the intelligence of data processing, but also significantly reduces the interference of abnormal values ​​and noise, and ensures data quality.

[0037] Optionally, the perception layer module 110 also includes: a first safety monitoring unit, which is used to track the operating status of the data cleaning unit through a real-time monitoring system to ensure the safe operation of the perception layer module 110; specifically, the first safety monitoring unit can use the real-time monitoring system to continuously track the operating status of the data cleaning unit; wherein, the real-time monitoring system is a system that can instantly obtain and feedback information; in this embodiment, through such close tracking, any situation that may endanger the safe operation of the perception layer module 110 can be discovered in a timely and effective manner, and responses can be made quickly, thereby effectively ensuring that the perception layer module 110 can operate safely, stably and reliably at any time.

[0038] The edge computing module 120 is used to process the received preliminary data based on the edge computing operation to obtain the target data, and send the target data to the network transmission module; optionally, the edge computing module 120 includes: a second safety monitoring unit, which is used to track the operating status of the data processing algorithm in the edge computing module through a real-time monitoring system to ensure the safe operation of the edge computing module.

[0039] The network transmission module 130 is used to adjust the data transmission path and bandwidth allocation after receiving the target data through the software defined network technology to send the target data to the cloud platform processing module;

[0040] Among them, the network transmission module 130 specifically includes: a switch unit, which establishes a communication connection with the edge computing module, and is used to receive the target data sent by the edge computing module and send it to the controller unit; a controller unit, which is used to monitor the received target data in real time, and at the same time, perform real-time analysis on the current network traffic and target data through a pre-configured real-time data analysis model to obtain a data transmission strategy that matches the current network traffic, wherein the data transmission strategy includes: data transmission commands and target transmission traffic; the switch unit is also used to send the target data to the cloud platform processing module based on the data transmission strategy.

[0041] Among them, the software-defined network technology is a new type of network innovation architecture, which realizes flexible control of network traffic by separating the control plane and data plane of network devices. Using software-defined network technology, the network transmission module 130 can more intelligently adjust the data transmission path and bandwidth allocation to adapt to different needs; further, the switch unit is a device that completes the information exchange function in the communication system, which plays a key role in data transfer and transmission here. It establishes a communication connection with the edge computing module, receives the target data sent from the edge computing module, and passes it to the controller unit; further, the controller unit is responsible for real-time monitoring of the received target data. It also uses a pre-configured real-time data analysis model to perform in-depth real-time analysis of the current network traffic and target data. Through this analysis, it can derive a data transmission strategy that matches the current network traffic.

[0042] Optionally, in this embodiment, the controller unit may be a SND (Software Defined Network Controller), and the switch may be a switch that implements SDN compatibility.

[0043] Furthermore, the controller unit is specifically used to: determine whether the target data is legal based on preset rules; obtain network traffic information of the current network, and input the network traffic information into a real-time data analysis model to obtain target transmission traffic; after determining that the target data is legal and the target transmission traffic is a non-zero positive number, generate a data transmission strategy, and send the data transmission strategy to the switch unit, so that the switch unit sends the target data to the cloud platform processing module based on the data transmission strategy.

[0044] Among them, the preset rules are a series of standards and conditions set in advance for determining the legality of target data. These rules are usually formulated based on security policies, business needs and other factors, and can be modified and set according to actual implementation scenarios. This embodiment does not limit this; further, the controller unit determines whether the target data is legal based on these preset rules; further, the controller unit obtains network traffic information of the current network. The "network traffic information" here reflects the real-time status of data transmission in the network, including key information such as data flow rate and direction. Then, the network traffic information is input into the real-time data analysis model, and the target transmission traffic is obtained through calculation and analysis of the model; wherein, the "real-time data analysis model" is a model that can quickly process and analyze real-time data. It can derive the most appropriate transmission traffic parameters according to the current network status and needs. After judging that the target data is legal and the target transmission traffic is a non-zero positive number, the controller unit will generate a data transmission strategy. The "data transmission strategy" is like an accurate action guide, which contains specific transmission commands and related traffic parameters and other information; based on the above steps, the data transmission strategy is sent to the switch unit, and the switch unit can accurately send the target data to the cloud platform processing module based on this strategy, thereby realizing an efficient and secure data transmission process.

[0045] In summary, the network transmission module 130 realizes dynamic adjustment of data transmission path and bandwidth by adopting software-defined network technology, effectively improves the flexibility of network management, and ensures efficient and stable operation of the network. At the same time, the control method through SDN also strengthens the implementation of network security strategies and enhances the security of the entire system.

[0046] The cloud platform processing module 140 is used to store the received target data.

[0047] Among them, the cloud platform processing module 140 includes: an encryption unit, which is used to encrypt the target data using the TLS encryption protocol after receiving the target data; a data storage unit, which is used to store the encrypted target data through a distributed file system and a NoSQL database; a data analysis unit, which is used to analyze the received target data through a preset big data processing framework, obtain the power grid load forecast result matching the target data and send it to the data storage unit for storage; an intrusion detection unit, which is used to monitor and analyze network traffic in real time to identify and respond to potential malicious activities or attacks.

[0048] Specifically, the encryption unit is responsible for using specific encryption technology to ensure the security of the target data after receiving it; wherein, the TLS (Transport Layer Security) encryption protocol, by establishing an encrypted channel between the client and the server, ensures that the data is not stolen or tampered with during the transmission process. For example, when we conduct online banking transactions, we rely on this encryption protocol to protect the security of our account information; further, the data storage unit stores the encrypted target data through a distributed file system and a NoSQL database; wherein, the distributed file system stores data in a dispersed manner on multiple nodes, improving the reliability and scalability of storage, such as Hadoop's HDFS is a typical distributed file system; further, the NoSQL database, such as MongoDB, Cassandra, etc., has a flexible data model and high scalability, and is suitable for processing large-scale unstructured data; further, the data analysis unit performs in-depth analysis on the received target data through a preset big data processing framework; exemplary, the big data processing framework, such as Spark, Flink, etc., can efficiently process massive data. After analyzing the target data, it obtains the power grid load forecast result that matches the target data. It should be noted that in actual applications, the type and function of the big data processing framework can be determined based on factors such as the specific implementation scenario and computing power requirements, and this embodiment does not limit this.

[0049] In summary, the cloud platform processing module 140 realizes rapid storage and in-depth analysis of data through the application of distributed file systems and big data processing frameworks, and ensures the security and reliability of data transmission and storage processes through message authentication, network intrusion detection and encryption technologies.

[0050] The technical solution of the embodiment of the present invention collects key data of the power grid through the perception layer module, processes the collected key data, and sends the preliminary data obtained after the data processing to the edge computing module, which processes the received preliminary data based on the edge computing operation to obtain target data, and sends the target data to the network transmission module. The network transmission module is used to adjust the data transmission path and bandwidth allocation after receiving the target data through software-defined network technology to send the target data to the cloud platform processing module, and the cloud platform processing module is used to store the received target data, thereby realizing the collection of power grid data, improving the efficiency of power grid data collection, and reducing data redundancy.

[0051] Embodiment 2

[0052] Figure 2 This is a structural diagram of an edge computing module provided in Embodiment 2 of the present invention. This embodiment can be applied to the situation of collecting power grid data.

[0053] like Figure 2 As shown, the edge computing module includes: a point calculation unit 210, a preprocessing unit 220 and a depth processing unit 230.

[0054] The point calculation unit 210 is used to calculate the location of the deployed edge node according to the geographical layout and equipment distribution of the power grid;

[0055] The geographical layout of the power grid is the distribution of various power equipment, lines, etc. in the actual geographical space, which is a complex and huge system; further, the equipment distribution specifically describes the location and relationship of various power equipment, such as substations, transformers, etc.; in this embodiment, the point calculation unit 210 can accurately calculate the location of the deployed edge node through comprehensive analysis and calculation of these information. For example, in a city's power grid, by considering factors such as power consumption and equipment density in each area, it is determined at which key locations the edge nodes should be set to achieve optimal network coverage and data processing efficiency.

[0056] Further, the pre-processing unit 220 is used to receive preliminary data, and process the preliminary data based on a data processing algorithm to obtain pre-processed data, and send the pre-processed data to the deep processing unit;

[0057] Specifically, the preprocessing unit 220 is responsible for receiving preliminary data, and then the preprocessing unit 220 uses a specific data processing algorithm to process the preliminary data to obtain preprocessed data; in this embodiment, there are many types of data processing algorithms, such as a data cleaning algorithm that can remove noise and outliers in the data, a data normalization algorithm that can convert the data into a specific range, etc. It should be noted that in actual applications, the type and function of the data processing algorithm can be determined based on factors such as the specific implementation scenario and computing power requirements, and this embodiment does not limit this.

[0058] Furthermore, the deep processing unit 230 is used to input the preprocessed data into a pre-trained deep learning model after receiving the preprocessed data to obtain target data.

[0059] Specifically, after receiving the pre-processed data, the deep processing unit 230 can input the data into a pre-trained deep learning model; wherein the deep learning model is a powerful data processing tool based on a neural network, which can automatically learn complex patterns and rules from a large amount of data. For example, convolutional neural networks (CNN) are often used for tasks such as image recognition, and recurrent neural networks (RNN) are suitable for processing sequence data. By inputting the pre-processed data into the deep learning model for processing, the target data is finally obtained. These target data often have higher accuracy and value, and can provide strong support for subsequent decision-making and analysis.

[0060] The technical solution of the embodiment of the present invention is to calculate the location of the deployed edge node according to the geographical layout and equipment distribution of the power grid through a point calculation unit, then receive preliminary data through a preprocessing unit, and process the preliminary data based on a data processing algorithm to obtain preprocessed data, and send it to a deep processing unit. Finally, after receiving the preprocessed data, the deep processing unit inputs the preprocessed data into a pre-trained deep learning model to obtain target data, thereby realizing the collection of power grid data, improving the efficiency of power grid data collection, and reducing data redundancy.

[0061] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A power grid data collection system, It is characterized in that It includes a perception layer module, an edge computing module, a network transmission module and a cloud platform processing module. The perception layer module is connected to the power grid in communication. The perception layer module and the network transmission module are connected to the edge computing module in communication. The network transmission module is connected to the cloud platform processing module in communication. The perception layer module is used to collect key data of the power grid, process the collected key data, and send the preliminary data obtained after data processing to the edge computing module; The edge computing module is used to process the received preliminary data based on the edge computing operation to obtain target data, and send the target data to the network transmission module; The network transmission module is used to adjust the data transmission path and bandwidth allocation after receiving the target data through the software defined network technology to send the target data to the cloud platform processing module; The cloud platform processing module is used to store the received target data.

2. The system according to claim 1, It is characterized in that The key data include voltage, current and frequency of the target power grid.

3. The system according to claim 1, It is characterized in that The perception layer module includes: A frequency calculation unit is used to analyze historical power grid data through a convolutional neural network to obtain the accuracy and frequency of the current data collection operation; Smart sensors are deployed at key points of the power grid and connected to the grid to continuously monitor and collect key data of the grid according to the accuracy and frequency of current data collection operations; The data cleaning unit is used to clean the collected key data to remove obvious outliers and noise contained in the key data to obtain preliminary data.

4. The system according to claim 3, It is characterized in that The frequency calculation unit is specifically used for: When an increase in the grid load is detected, grid data matching the current grid load in historical grid data is obtained; The power grid data is input into a convolutional neural network to obtain the accuracy and frequency of the current data acquisition operation.

5. The system according to claim 1, It is characterized in that The edge computing module includes: A point calculation unit is used to calculate the location of the deployed edge node according to the geographical layout and equipment distribution of the power grid; A preprocessing unit, configured to receive preliminary data, process the preliminary data based on a data processing algorithm, obtain preprocessed data, and send the preprocessed data to a deep processing unit; The deep processing unit is used to input the pre-processed data into a pre-trained deep learning model after receiving the pre-processed data to obtain target data.

6. The system according to claim 1, It is characterized in that The network transmission module comprises: A switch unit, establishing a communication connection with the edge computing module, for receiving target data sent by the edge computing module and sending the target data to the controller unit; The controller unit is used to monitor the received target data in real time, and to analyze the current network traffic and the target data in real time through a pre-configured real-time data analysis model to obtain a data transmission strategy that matches the current network traffic, wherein the data transmission strategy includes: a data transmission command and a target transmission traffic; The switch unit is also used to send the target data to the cloud platform processing module based on the data transmission strategy.

7. The system according to claim 6, It is characterized in that The controller unit is specifically used for: Determining whether the target data is legal based on preset rules; Obtaining network traffic information of the current network, and inputting the network traffic information into a real-time data analysis model to obtain target transmission traffic; After determining that the target data is legal and the target transmission flow is a non-zero positive number, a data transmission strategy is generated and sent to the switch unit, so that the switch unit sends the target data to the cloud platform processing module based on the data transmission strategy.

8. The system according to claim 1, It is characterized in that The cloud platform processing module includes: An encryption unit, used to encrypt the target data using the TLS encryption protocol after receiving the target data; A data storage unit, used for storing the encrypted target data through a distributed file system and a NoSQL database; A data analysis unit is used to analyze the received target data through a preset big data processing framework, obtain a power grid load prediction result matching the target data, and send the result to the data storage unit for storage; Intrusion detection units that monitor and analyze network traffic in real time to identify and respond to potential malicious activity or attacks.

9. The system according to claim 3, It is characterized in that The perception layer module further includes: The first safety monitoring unit is used to track the operating status of the data cleaning unit through a real-time monitoring system to ensure the safe operation of the perception layer module.

10. The system according to claim 5, It is characterized in that The edge computing module also includes: The second safety monitoring unit is used to track the operating status of the data processing algorithm in the edge computing module through a real-time monitoring system to ensure the safe operation of the edge computing module.

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