SCADA (Supervisory Control And Data Acquisition) system optimization method for smart power grid

Abstract

The invention discloses an SCADA (supervisory control and data acquisition) system optimization method for an intelligent power grid, which improves data processing efficiency and system response speed by performing data grading processing on edge computing nodes, and reduces data transmission load by adopting a differential transmission strategy and an intelligent compression technology. A deep learning algorithm is utilized to construct a self-learning fault prediction model to enhance fault prediction and diagnosis capability, a multi-layer data security and privacy protection mechanism is introduced to improve data security and privacy protection, and an intelligent scheduling module is introduced to enhance system collaboration and dynamic adaptability. According to the comprehensive optimization methods, the real-time performance, reliability and safety of the SCADA system are remarkably improved, so that the SCADA system is more suitable for the complex environment of the smart power grid, and the efficient, stable and safe operation requirements of the smart power grid for the SCADA system are met.

Description

Technical Field

[0001] The present application relates to the technical field of SCADA system optimization, and in particular to a SCADA system optimization method for smart grids. Background Art

[0002] With the rapid development of power systems, smart grid technology has gradually become popular. Smart grids use advanced information, communication and control technologies to achieve observability, automated regulation and optimal resource allocation of power grids, greatly improving the operation efficiency and power supply quality of power grids. During the operation of smart grids, the SCADA (Supervisory Control and Data Acquisition) system, as a core monitoring tool, is mainly used to collect and monitor various data of the power system in real time, including voltage, current, load status and other information, and provides real-time monitoring means and operation guidance for operators.

[0003] Although traditional SCADA systems play an important role in the application of smart grids, they still face some technical bottlenecks when facing complex grid environments, high data volumes, and high security requirements:

[0004] Heavy data transmission load: With the continuous expansion of smart grids, the amount of data in the power system is growing exponentially. Traditional SCADA systems often need to process a large amount of data during the transmission process, resulting in heavy network load, increased data transmission delays, and even data loss or errors. In addition, the demand for bandwidth for frequent data transmission is also increasing, which makes it difficult to ensure the real-time and stability of transmission when the system is overloaded.

[0005] Low data processing efficiency: When processing large amounts of data, existing SCADA systems usually rely on centralized processing by central servers. As the types and amounts of monitoring data increase, the processing pressure on central servers increases significantly, which may lead to delays in data processing and fail to meet the needs of real-time monitoring of smart grids. In addition, the traditional centralized processing model also increases the difficulty of system maintenance, especially when data transmission barriers occur between edge nodes and central servers, the system may find it difficult to respond in a timely manner.

[0006] Limited fault prediction capabilities: Existing SCADA systems are limited in predicting power grid faults, usually relying on traditional rules and empirical judgments, and are difficult to accurately predict potential faults in a complex power grid environment. Some common fault modes may be able to be judged by existing rules, but for smart grids with huge amounts of data and high real-time requirements, relying solely on rules is difficult to meet the accuracy and timeliness requirements.

[0007] Data security and privacy risks: Due to the openness and interconnectedness of smart grids, SCADA systems face certain security and privacy risks when transmitting and processing large amounts of sensitive power data. At present, the identity authentication and data encryption methods of traditional SCADA systems are relatively simple, making it difficult to fully respond to potential cyber attacks. Especially during data transmission, if data encryption and transmission security measures are insufficient, sensitive information may be maliciously tampered with or leaked.

[0008] Poor system coordination and adaptability: Traditional SCADA system modules are usually designed independently, lacking cross-module coordination and scheduling mechanisms. With the diversification of smart grid monitoring needs, the coordination between different modules is becoming more and more important. However, the traditional system lacks the ability in data sharing, scheduling optimization and dynamic adaptability, which easily leads to waste of resources and delayed system response.

[0009] In summary, the existing SCADA system still faces many challenges in the complex environment of smart grid, especially in terms of data transmission efficiency, processing speed, security, prediction ability and system coordination. Therefore, the optimization method of SCADA system in smart grid environment has become a hot topic in the industry, in order to improve the applicability and reliability of SCADA system in smart grid through technical means. Summary of the invention

[0010] The present application provides a SCADA system optimization method for smart grids, aiming to solve the problems of heavy data transmission load, low data processing efficiency, limited fault prediction capability, data security and privacy risks, and poor system coordination and adaptability in the prior art.

[0011] A SCADA system optimization method for a smart grid, the method comprising:

[0012] Data optimization and hierarchical processing: Based on data priority and importance, high-priority data is processed first at the edge computing node, the cache duration is dynamically adjusted using an adaptive cache strategy, and a dynamic load balancing mechanism is used to reduce the load on the central server, so that the system can adapt to dynamic environment requirements;

[0013] Intelligent data transmission and recovery: Use differential transmission strategies to intelligently compress high-priority and abnormal data, and achieve rapid recovery of data transmission based on two-way verification;

[0014] Fault prediction and self-learning diagnosis: Deep learning algorithms are used to extract key features of historical and real-time data, and model parameters are dynamically adjusted based on real-time feedback to achieve self-learning diagnosis of faults.

[0015] Multi-layer data security and privacy protection: Adopting adaptive encryption strategies based on data types, using blockchain distributed authentication mechanisms to achieve decentralized authentication of node and user identities, ensuring system anti-attack and high availability;

[0016] Multi-module collaborative optimization: The intelligent scheduling module optimizes the parameters of each module in real time to achieve collaborative work among data processing, transmission and fault diagnosis.

[0017] In this embodiment, the edge computing node has dynamic load balancing and adaptive cache adjustment functions. When the data collection load increases, high-priority data is processed first, cache space is automatically released, and high-priority data is retained first.

[0018] In this embodiment, the intelligent data transmission and recovery module adopts a differential transmission strategy, only transmits incremental change data and compresses abnormal data packets, which is suitable for high-frequency change data and abnormal fluctuation data to reduce transmission volume and bandwidth occupancy.

[0019] In this embodiment, the fault prediction and self-learning diagnosis module uses a deep learning algorithm to establish a fault prediction model. By collecting real-time data during power grid operation, the model parameters are automatically adjusted based on real-time feedback to adapt to the dynamic operating environment of the smart grid and improve the accuracy of fault prediction.

[0020] In this embodiment, the adaptive encryption strategy is based on the AES encryption algorithm, applies encryption of different strengths to data of different priorities, and dynamically adjusts the encryption strength based on the priority and sensitivity of the data to ensure data security and efficiency during transmission.

[0021] In this embodiment, the blockchain distributed authentication mechanism is used to record the identity information of each node and user, ensure the transparency and non-tamperability of the identity authentication information through the decentralized storage of the blockchain, and control the access rights of the nodes through smart contracts.

[0022] In this embodiment, the intelligent scheduling module can automatically adjust the task priority and data processing frequency of the edge computing node according to the real-time load status of the system, and enter low power consumption or sleep mode when the load is low to reduce the energy consumption of the system.

[0023] In this embodiment, the multi-module collaborative optimization includes a data sharing mechanism between modules, and data reuse between modules is achieved through a distributed data sharing framework. It is suitable for high-frequency data access and repeated data processing scenarios, avoids repeated collection and calculation, and achieves efficient data processing.

[0024] In this embodiment, the data recovery mechanism includes data packet verification and retransmission functions. If a data packet is intercepted or damaged during the transmission process, retransmission and verification are performed through an adaptive transmission protocol and redundant verification technology to ensure data integrity.

[0025] In this embodiment, the self-learning diagnosis module stores fault types, fault characteristics and warning rules by constructing a knowledge base and a rule base, and adaptively updates them based on the operating frequency during the operation of the power grid, continuously improving the knowledge base to enhance the system's ability to identify new fault types.

[0026] Compared with the prior art, this application has at least the following beneficial effects:

[0027] This application is based on further analysis and research on the problems of the prior art, and recognizes that the prior art has the problems of large data transmission load, low data processing efficiency, limited fault prediction capability, data security and privacy risks, and poor system coordination and adaptability. By performing data hierarchical processing on edge computing nodes to improve data processing efficiency and system response speed, differential transmission strategy and intelligent compression technology are adopted to reduce data transmission load, and deep learning algorithms are used to build a self-learning fault prediction model to enhance fault prediction and diagnosis capabilities. Multi-layer data security and privacy protection mechanisms are introduced to improve data security and privacy protection, and the introduction of intelligent scheduling modules is used to enhance system coordination and dynamic adaptability. These comprehensive optimization methods significantly improve the real-time, reliability and security of the SCADA system, making it more suitable for the complex environment of smart grids and meeting the efficient, stable and safe operation requirements of smart grids for SCADA systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flowchart of a SCADA system optimization method for a smart grid provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0030] In one embodiment, Figure 1 As shown, a SCADA system optimization method for smart grid is provided, comprising the following steps:

[0031] Data optimization and hierarchical processing: Based on data priority and importance, high-priority data is processed first at the edge computing node, the cache duration is dynamically adjusted using an adaptive cache strategy, and a dynamic load balancing mechanism is used to reduce the load on the central server, so that the system can adapt to dynamic environment requirements;

[0032] Intelligent data transmission and recovery: Use differential transmission strategies to intelligently compress high-priority and abnormal data, and achieve rapid recovery of data transmission based on two-way verification;

[0033] Fault prediction and self-learning diagnosis: Deep learning algorithms are used to extract key features of historical and real-time data, and model parameters are dynamically adjusted based on real-time feedback to achieve self-learning diagnosis of faults.

[0034] Multi-layer data security and privacy protection: Adopting adaptive encryption strategies based on data types, using blockchain distributed authentication mechanisms to achieve decentralized authentication of node and user identities, ensuring system anti-attack and high availability;

[0035] Multi-module collaborative optimization: The intelligent scheduling module optimizes the parameters of each module in real time to achieve collaborative work among data processing, transmission and fault diagnosis.

[0036] This embodiment performs data hierarchical processing on edge computing nodes and prioritizes high-priority data. This method effectively reduces the load on the central server and reduces the latency problem caused by centralized data processing. The adaptive caching strategy and dynamic load balancing mechanism of the edge node enable the system to automatically adjust the processing priority according to the real-time load conditions, thereby ensuring real-time processing of high-priority data. This edge computing and hierarchical processing mechanism significantly improves the response speed of the system and meets the needs of smart grids for efficient data processing.

[0037] This application adopts a differential transmission strategy, only transmitting incremental changes in data, and intelligently compressing abnormal data, thereby reducing the overall transmission volume of data packets and effectively reducing network load. Rapid recovery is achieved through a two-way verification mechanism, which can efficiently restore data when transmission is interrupted and avoid retransmitting all data, thereby improving the reliability and efficiency of data transmission. This solution significantly reduces the bandwidth demand of the SCADA system and improves the stability of the system under high data load.

[0038] The fault prediction module of this application builds a self-learning fault prediction model through a deep learning algorithm. The model can self-adjust and optimize based on the collected historical data and real-time data to adapt to the complex operating environment of the smart grid. Through the dynamic update mechanism of the knowledge base and rule base, the model can identify previously unseen fault types, achieve more accurate predictions and timely diagnosis, improve the system's fault response capabilities, and meet the high reliability and security requirements of the smart grid.

[0039] In order to solve the security risks of the existing SCADA system in data transmission, this application proposes a multi-layer data security and privacy protection mechanism. Through the adaptive encryption strategy, the encryption strength is dynamically adjusted according to the data type and priority, and flexible protection of the transmitted data is achieved. Combined with the blockchain distributed authentication mechanism, the system can realize decentralized authentication of node and user identity, effectively preventing data tampering and leakage during transmission. This technical solution significantly improves the security of data transmission and the system's anti-attack capability.

[0040] Through the introduction of the intelligent scheduling module, this application can dynamically optimize the parameters of each module according to the real-time status of the system, and realize efficient collaboration among edge computing, data transmission and fault prediction modules. The intelligent scheduling module also has low power consumption and sleep mode management functions, which enables the system to automatically enter energy-saving mode when the load is low, improving the overall resource utilization efficiency. In addition, the data sharing mechanism between multiple modules avoids repeated data collection and processing, enabling the system to flexibly adapt to the diverse monitoring needs of the smart grid, significantly improving the system's operating efficiency.

[0041] The SCADA system optimization method described in this embodiment effectively solves the problems of low data processing efficiency, large transmission load, insufficient fault prediction ability, poor data security and weak system coordination of the SCADA system in the smart grid environment in the prior art. The method significantly improves the real-time, reliability and security of the SCADA system, making it more suitable for the complex environment of the smart grid and meeting the efficient, stable and safe operation requirements of the smart grid for the SCADA system.

[0042] In this embodiment, the edge computing node has dynamic load balancing and adaptive cache adjustment functions. When the data collection load increases, high-priority data is processed first, cache space is automatically released, and high-priority data is retained first.

[0043] This embodiment relates to a SCADA system optimization method with dynamic load balancing and adaptive cache adjustment functions on edge computing nodes. In a smart grid environment, the amount of data collected changes dynamically over time, especially at peak loads, and traditional centralized processing methods may cause data processing delays. In this application, through dynamic load balancing and adaptive cache adjustment functions, edge computing nodes can achieve intelligent resource allocation and cache optimization, improving the processing efficiency and response speed of the system.

[0044] First, when the amount of data increases or the load on a node is too high, the dynamic load balancing function automatically allocates other idle nodes to share the data processing tasks by detecting the load status of the node, avoiding the processing bottleneck of a single node. This distributed load management method makes the system more stable and scalable when processing large-scale data. Compared with the traditional centralized processing method, dynamic load balancing can significantly improve the fault tolerance of the system, allowing it to maintain efficient operation during the peak period of data collection, reducing delays and packet loss during data processing.

[0045] Secondly, the adaptive cache adjustment function further enhances the dynamic response capability of the system. The edge computing node will adjust the cache strategy in real time according to the priority and access frequency of the data. High-priority data will be retained in the cache first, and low-priority data will be automatically released when the cache space is tight. This adaptive cache strategy not only effectively improves the access speed of high-priority data, but also optimizes the response time of the system under limited cache resources. In the process of collecting large-scale data, the system can make more reasonable use of cache space and avoid system bottlenecks caused by insufficient cache resources.

[0046] In addition, the edge computing nodes can optimize the performance of the SCADA system without increasing hardware resources by combining dynamic load balancing with cache adjustment. This functional integration solution reduces the dependence on the central server, making the system's processing capabilities in the smart grid more flexible and efficient. Dynamic load balancing and adaptive cache adjustment not only improve the system's resource utilization, but also reduce system delays caused by data congestion, making it more suitable for smart grid environments with large data fluctuations. Through this optimization solution, the stability and processing efficiency of the SCADA system in the face of large-scale data collection and high load conditions have been significantly improved.

[0047] In this embodiment, the intelligent data transmission and recovery module adopts a differential transmission strategy, only transmits incremental change data and compresses abnormal data packets, which is suitable for high-frequency change data and abnormal fluctuation data to reduce transmission volume and bandwidth occupancy.

[0048] This embodiment adopts differential transmission strategy and abnormal data intelligent compression technology to reduce network load and improve the stability and real-time performance of data transmission. In the smart grid environment, the SCADA system needs to transmit massive amounts of real-time data, and the traditional full-volume transmission method is prone to bandwidth overload and transmission delay. Through the differential transmission strategy, this application only transmits incremental changes in data, that is, only transmits data that has changed in time or is different in state, thereby reducing the amount of redundant data transmission and reducing network load.

[0049] The application of differential transmission strategy enables high-frequency changing data to be updated quickly without full transmission, thus significantly reducing the amount of data in the transmission process. This approach not only optimizes the use of bandwidth resources, but also improves the response speed and stability of the system under high-load transmission. Especially when transmitting critical data, the differential transmission strategy can ensure that the system does not affect real-time performance while updating data, thereby meeting the high-efficiency requirements of smart grids for data transmission.

[0050] In addition, in order to further reduce the transmission load, the present application also performs intelligent compression processing on abnormal data packets. Abnormal data usually occupies more bandwidth during transmission, but its frequency of occurrence is low. Through intelligent compression technology, the bandwidth occupancy required for its transmission can be effectively reduced, ensuring the reasonable allocation of bandwidth resources. At the same time, the compression and transmission of abnormal data combined with the differential transmission strategy realizes the dual optimization of bandwidth resources, so that the transmission efficiency of the SCADA system can be improved when facing abnormal data or sudden peak loads.

[0051] The fast recovery mechanism ensures data integrity through two-way verification in the event of transmission interruption. There is no need to retransmit all data, but only to restore the differential data of the interrupted part. This recovery mechanism effectively solves the problems of packet loss and errors in the data transmission process, and ensures the reliability and real-time performance of data transmission. By combining differential transmission, intelligent compression and fast recovery, the transmission optimization scheme of this application can significantly reduce the amount of data transmission, reduce the network bandwidth load, and improve the data transmission efficiency and fault tolerance of the SCADA system in the smart grid environment.

[0052] In this embodiment, the fault prediction and self-learning diagnosis module uses a deep learning algorithm to establish a fault prediction model. By collecting real-time data during power grid operation, the model parameters are automatically adjusted based on real-time feedback to adapt to the dynamic operating environment of the smart grid and improve the accuracy of fault prediction.

[0053] This embodiment automatically adjusts the prediction model through historical and real-time data to achieve the self-learning fault diagnosis capability of the SCADA system. In the smart grid environment, traditional fault diagnosis methods mainly rely on fixed rules and static models, which cannot meet the requirements of high real-time performance and diversified fault types of smart grids. The self-learning fault prediction model constructed by the deep learning algorithm in this application can automatically adapt to the operating status and environmental changes of the system, thereby achieving accurate prediction and timely diagnosis of faults.

[0054] First, the self-learning fault diagnosis module can extract key features from a large amount of historical data and real-time data through deep learning algorithms to build a fault prediction model. In the complex environment of the smart grid, the characteristics of various faults may change continuously in time and space. The adaptive adjustment function of the self-learning model enables the system to dynamically update the model parameters according to the changes in real-time data, thereby ensuring the accuracy of fault prediction. Compared with the traditional rule-based diagnosis method, the self-learning model can identify fault types that have never occurred before, significantly improving the fault prediction ability of the SCADA system.

[0055] Secondly, the self-learning diagnosis module uses the dynamic update mechanism of the knowledge base and rule base to enable the system to automatically record relevant data and update the knowledge base and rule base content after each fault or abnormality occurs. This dynamic update mechanism ensures that the system can continuously accumulate new fault characteristics and operating data, so that the prediction model can continuously improve its fault identification accuracy over time. At the same time, the dynamic update of the knowledge base also enables the system to respond faster when dealing with similar faults, thereby improving the system's fault response efficiency.

[0056] In addition, the self-learning diagnosis module can issue real-time alarms based on the fault characteristics formed by historical data, especially when the system detects that key operating data exceeds the normal range, it automatically triggers an early warning signal and provides suggestions for countermeasures. Such a real-time early warning mechanism can not only improve the security of the system, but also reduce power outages or other major losses caused by faults. By combining self-learning diagnosis with dynamic updates, the fault diagnosis module of this application enables the SCADA system to have a higher level of intelligence and fault identification capabilities, meeting the operation requirements of the smart grid in a high-security and high-real-time environment.

[0057] In summary, the self-learning fault diagnosis module based on deep learning realizes the intelligent and efficient fault prediction and diagnosis capabilities of the SCADA system through the automated analysis of historical and real-time data. This self-learning and self-updating fault diagnosis method significantly improves the system's fault prediction accuracy, reduces the risk of power outages, and provides reliable protection for the stable operation of the smart grid.

[0058] In this embodiment, the adaptive encryption strategy is based on the AES encryption algorithm, applies encryption of different strengths to data of different priorities, and dynamically adjusts the encryption strength based on the priority and sensitivity of the data to ensure data security and efficiency during transmission.

[0059] The present embodiment adopts different encryption strengths for data of different priorities to ensure the security and efficiency of data transmission. The application of SCADA system in smart grid needs to process a large amount of sensitive data, which may be subject to external malicious attacks during transmission. Therefore, it is particularly important to ensure the security and integrity of data. However, the traditional unified encryption method may affect the transmission efficiency under high security requirements, and is difficult to adapt to the diversified data needs of smart grid. In view of this problem, the adaptive encryption strategy proposed in this application has achieved a good balance between data security and transmission efficiency.

[0060] First, the adaptive encryption strategy dynamically adjusts the encryption strength of different data according to the priority and sensitivity of the data. For high-priority and sensitive data, such as the real-time status of the power grid and load conditions, high-strength encryption such as AES-256 is used to ensure the high security of these data during transmission. In contrast, for low-priority or low-sensitivity data, such as historical data or ordinary status data, lower-strength encryption methods such as AES-128 can be used to reduce computing resource usage and transmission delays. This strategy of dynamically adjusting encryption strength greatly improves the transmission efficiency of the system while ensuring the security of important data.

[0061] Secondly, the adaptive encryption strategy based on data priority can optimize the resource allocation of the system, so that it can reduce resource consumption in the encryption and decryption process while meeting security requirements. In traditional encryption methods, all data uses the same high-intensity encryption, which not only increases the burden on the CPU and memory, but also prolongs the time required for decryption during transmission. The adaptive encryption strategy can intelligently identify the security requirements of different data types, realize the reasonable allocation of encryption computing resources, and reduce the impact on system performance. This method is particularly suitable for high-frequency and real-time data transmission scenarios in smart grids.

[0062] In addition, the adaptive encryption strategy not only improves the security of data transmission, but also enhances the system's ability to protect against potential security threats. Through encryption settings of different strengths, the system can prioritize the security of high-priority data under abnormal circumstances and prevent important data from being leaked due to external attacks. Combined with the efficient encryption performance of the AES algorithm, the adaptive encryption strategy can further improve the operating efficiency and stability of the SCADA system on the basis of ensuring security. This dynamic encryption management method is suitable for the complex and changeable application environment of smart grids, and significantly improves the overall performance of the SCADA system.

[0063] In this embodiment, the blockchain distributed authentication mechanism is used to record the identity information of each node and user, ensure the transparency and non-tamperability of the identity authentication information through the decentralized storage of the blockchain, and control the access rights of the nodes through smart contracts.

[0064] This embodiment ensures the transparency and non-tamperability of identity authentication by putting the identity authentication information of each node and user in the SCADA system on the chain. In the smart grid, the SCADA system needs to interact with multiple remote nodes and users, and the traditional centralized authentication method is vulnerable to single point failures and malicious attacks. In addition, as the scale of smart grids continues to expand, the security of user identity management and authentication information becomes increasingly important. In order to solve these problems, the distributed authentication mechanism based on blockchain proposed in this application effectively improves the security and reliability of the SCADA system.

[0065] First, the decentralized nature of blockchain means that authentication information no longer relies on a single central server, but is distributed and stored on multiple nodes, which enhances the system's anti-attack capability. Through the distributed ledger technology of blockchain, the identity authentication information of each node and user is recorded on the chain and is not affected by central node failure or tampering. This decentralized authentication method avoids the risk of single point failure and improves the stability and data reliability of the system under high load or malicious attacks.

[0066] Secondly, through the application of smart contracts, this application realizes rapid identity authentication and access control based on blockchain. Smart contracts can automatically execute preset identity authentication rules to ensure that only legitimate users or nodes can access relevant data of the SCADA system. Compared with traditional authentication methods, smart contracts do not require human intervention and can be automatically executed when the trigger conditions are met, thereby simplifying the authentication process, shortening the identity authentication time, and improving the authentication efficiency of the system. In addition, the immutability of smart contracts ensures the transparency of the authentication process and helps system administrators conduct security audits.

[0067] In addition, the immutability of blockchain provides additional security for the identity authentication process of the SCADA system. After the authentication information is recorded on the chain, it can not only prevent external malicious tampering, but also provide traceability. In the event of a security incident, the system can trace the identity authentication process through the on-chain records and quickly find the problematic node or user. This distributed authentication mechanism ensures the security and efficiency of identity management of the SCADA system in the smart grid, and is suitable for power system application scenarios with high distribution and high security requirements.

[0068] In summary, the distributed authentication mechanism based on blockchain significantly improves the security, transparency and efficiency of identity authentication in the SCADA system. Through decentralized distributed storage and automated execution of smart contracts, the system has stronger anti-attack capabilities and authentication efficiency, meeting the smart grid's requirements for high security and stability of the SCADA system.

[0069] In this embodiment, the intelligent scheduling module can automatically adjust the task priority and data processing frequency of the edge computing node according to the real-time load status of the system, and enter low power consumption or sleep mode when the load is low to reduce the energy consumption of the system.

[0070] The intelligent scheduling module of this embodiment solves the deficiencies of traditional SCADA systems in resource allocation and energy consumption management through dynamic priority adjustment and low power consumption management. This module not only improves the real-time responsiveness and task processing efficiency of the system, but also realizes energy-saving optimization of the system through low power consumption mode, meeting the requirements of efficient, stable and energy-saving operation of SCADA systems in smart grids.

[0071] In this embodiment, the multi-module collaborative optimization includes a data sharing mechanism between modules, and data reuse between modules is achieved through a distributed data sharing framework. It is suitable for high-frequency data access and repeated data processing scenarios, avoids repeated collection and calculation, and achieves efficient data processing.

[0072] The multi-module data sharing mechanism of this embodiment achieves the collaborative optimization of the SCADA system by eliminating repeated collection, reducing redundant calculations and improving data utilization. This mechanism greatly improves the operating efficiency and data consistency of the system, and meets the smart grid's demand for efficient and stable data processing of the SCADA system.

[0073] In this embodiment, the data recovery mechanism includes data packet verification and retransmission functions. If a data packet is intercepted or damaged during the transmission process, retransmission and verification are performed through an adaptive transmission protocol and redundant verification technology to ensure data integrity.

[0074] The data recovery mechanism of this embodiment solves the problems of data loss, error and delay in the transmission process of the SCADA system in the smart grid by combining the adaptive transmission protocol and the redundant verification technology, greatly improves the reliability and real-time performance of data transmission, and ensures the stable operation of the system.

[0075] In this embodiment, the self-learning diagnosis module stores fault types, fault characteristics and warning rules by constructing a knowledge base and a rule base, and adaptively updates them based on the operating frequency during the operation of the power grid, continuously improving the knowledge base to enhance the system's ability to identify new fault types.

[0076] The self-learning diagnosis module of this embodiment realizes the efficient identification and early warning of faults by the SCADA system through the dynamic update of the knowledge base and the rule base. This module not only enhances the adaptability of the system to new faults, but also effectively improves the self-learning and fault prediction capabilities in the smart grid, providing important support for the safe and stable operation of the power grid.

[0077] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A SCADA system optimization method for smart grid, characterized in that: The method comprises: Data optimization and hierarchical processing: Based on data priority and importance, high-priority data is processed first at the edge computing node, the cache duration is dynamically adjusted using an adaptive cache strategy, and a dynamic load balancing mechanism is used to reduce the load on the central server, so that the system can adapt to dynamic environment requirements; Intelligent data transmission and recovery: Use differential transmission strategies to intelligently compress high-priority and abnormal data, and achieve rapid recovery of data transmission based on two-way verification; Fault prediction and self-learning diagnosis: Deep learning algorithms are used to extract key features of historical and real-time data, and model parameters are dynamically adjusted based on real-time feedback to achieve self-learning diagnosis of faults. Multi-layer data security and privacy protection: Adopting adaptive encryption strategies based on data types, using blockchain distributed authentication mechanisms to achieve decentralized authentication of node and user identities, ensuring system anti-attack and high availability; Multi-module collaborative optimization: The intelligent scheduling module optimizes the parameters of each module in real time to achieve collaborative work among data processing, transmission and fault diagnosis.

2. The SCADA system optimization method according to claim 1, characterized in that: The edge computing node has dynamic load balancing and adaptive cache adjustment functions. When the data collection load increases, high-priority data is processed first, cache space is automatically released, and high-priority data is retained first.

3. The SCADA system optimization method according to claim 1, characterized in that: The intelligent data transmission and recovery module adopts a differential transmission strategy, only transmits incremental change data and compresses abnormal data packets, and is suitable for high-frequency change data and abnormal fluctuation data to reduce transmission volume and bandwidth occupancy.

4. The SCADA system optimization method according to claim 1, characterized in that: The fault prediction and self-learning diagnosis module uses a deep learning algorithm to establish a fault prediction model. By collecting real-time data from the power grid operation, the model parameters are automatically adjusted based on real-time feedback to adapt to the dynamic operating environment of the smart grid and improve the accuracy of fault prediction.

5. The SCADA system optimization method according to claim 1, characterized in that: The adaptive encryption strategy is based on the AES encryption algorithm, applies different encryption strengths to data of different priorities, and dynamically adjusts the encryption strength based on the priority and sensitivity of the data to ensure data security and efficiency during transmission.

6. The SCADA system optimization method according to claim 1, characterized in that: The blockchain distributed authentication mechanism is used to record the identity information of each node and user, ensure the transparency and non-tamperability of identity authentication information through the decentralized storage of the blockchain, and control the access rights of the nodes through smart contracts.

7. The SCADA system optimization method according to claim 1, characterized in that: The intelligent scheduling module can automatically adjust the task priority and data processing frequency of the edge computing node according to the real-time load status of the system, and enter low power consumption or sleep mode when the load is low to reduce the energy consumption of the system.

8. The SCADA system optimization method according to claim 1, characterized in that: The multi-module collaborative optimization includes a data sharing mechanism between modules, and realizes data reuse between modules through a distributed data sharing framework. It is suitable for high-frequency data access and repeated data processing scenarios, avoids repeated collection and calculation, and realizes efficient data processing.

9. The SCADA system optimization method according to claim 1, characterized in that: The data recovery mechanism includes data packet verification and retransmission functions. If a data packet is intercepted or damaged during transmission, retransmission and verification are performed through adaptive transmission protocol and redundancy verification technology to ensure data integrity.

10. The SCADA system optimization method according to claim 1, characterized in that: The self-learning diagnosis module stores fault types, fault characteristics and warning rules by building a knowledge base and a rule base, and adaptively updates based on the operating frequency during the operation of the power grid, continuously improving the knowledge base to enhance the system's ability to identify new fault types.

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