A method and system for managing and operating industrial and commercial photovoltaic power grids

Abstract

The present invention relates to the field of blockchain technology, and in particular to a method and system for the management and operation of industrial and commercial photovoltaic power storage grids. The method comprises the following steps: edge computing of terminal data distributed in various locations of photovoltaic power storage grids to obtain edge analysis data; blockchain construction based on edge analysis data to obtain photovoltaic blockchain data; execution processing based on photovoltaic blockchain data through preset operation and maintenance smart contract data to obtain operation and maintenance smart contract data to perform management and operation operations and obtain management and operation data; optimization of preset operation and maintenance smart contract data based on management and operation data to obtain operation and maintenance smart contract optimization data to perform management and operation operations of industrial and commercial photovoltaic power storage grids. The present invention improves operation and maintenance efficiency, reduces operating costs, and thereby enhances the stability and reliability of photovoltaic power storage grids.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a method and system for managing and operating industrial and commercial photovoltaic power grids. Background Art

[0002] In the field of industrial and commercial photovoltaic power grids, with the development and application of renewable energy, photovoltaic power grids as a new type of energy system are gradually receiving widespread attention and application. Photovoltaic power grids combine solar power generation and battery energy storage systems to store excess electricity when there is sufficient sunlight and release electricity when there is insufficient sunlight or peak demand, thereby improving energy utilization efficiency, reducing energy costs, and providing greater stability and reliability for the energy system.

[0003] However, the management and operation of photovoltaic power grids face some challenges. First, because photovoltaic power grids involve a large number of terminal devices and data, traditional management methods often cannot effectively process and utilize these data, resulting in low management efficiency. Second, the distributed nature of photovoltaic power grids makes data security and credibility a key issue, and traditional centralized management methods have the risk of data tampering and security vulnerabilities. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a method and system for managing and operating industrial and commercial photovoltaic power grids to solve at least one of the above technical problems.

[0005] The present application provides a method for managing and operating an industrial and commercial photovoltaic power grid, comprising the following steps:

[0006] Step S1: Perform edge computing on terminal data distributed throughout the photovoltaic power storage grid to obtain edge analysis data;

[0007] Step S2: constructing a blockchain based on edge analysis data to obtain photovoltaic blockchain data;

[0008] Step S3: Execute and process the preset operation and maintenance smart contract data according to the photovoltaic blockchain data to obtain the operation and maintenance smart contract data, so as to perform management and maintenance operations and obtain management and maintenance data;

[0009] Step S4: Optimize the preset operation and maintenance smart contract data according to the management and operation and maintenance data to obtain the operation and maintenance smart contract optimization data to carry out the management and operation of the industrial and commercial photovoltaic power grid.

[0010] In the present invention, edge computing is performed on the terminal data of each location of the photovoltaic storage power grid in real time to obtain edge analysis data, which reflects the operating status and performance of the photovoltaic storage power grid, provides real-time decision support for managers, and helps them to find problems and optimize operation strategies in a timely manner. The use of blockchain technology to construct photovoltaic blockchain data can ensure the security and traceability of management and operation data. The decentralized characteristics and tamper-proof recording method of blockchain can effectively prevent data tampering and loss, and improve the security and credibility of data. The preset operation and maintenance smart contract data is used to execute and optimize the management and operation data, which can improve the operation and maintenance efficiency. Smart contracts can automatically execute operations according to preset rules and conditions without manual intervention, thereby improving the efficiency and accuracy of management and operation. The optimization of management and operation data can continuously improve the operating performance and stability of the photovoltaic storage power grid. By analyzing the management and operation data and optimizing the smart contract, problems in operation can be discovered and improved, and the efficiency and performance of the photovoltaic storage power grid can be continuously improved.

[0011] Preferably, step S1 specifically includes:

[0012] Step S11: Acquire terminal data distributed throughout the photovoltaic power storage grid;

[0013] Step S12: preprocessing the terminal data to obtain terminal preprocessing data;

[0014] Step S13: extracting features from the terminal preprocessing data to obtain terminal parameter feature data;

[0015] Step S14: Use the preset distillation model to perform edge computing processing on the terminal parameter feature data to obtain edge analysis data.

[0016] The present invention can obtain terminal data distributed throughout the photovoltaic power storage grid, realize real-time monitoring and data collection of the power grid operation status, and help managers to timely understand the operation of the power grid, discover potential problems, and make corresponding adjustments and optimizations. Data preprocessing can be cleaned, denoised, normalized, etc., to improve the quality and availability of data, help reduce errors and errors in the data processing process, and ensure the accuracy and reliability of subsequent analysis. Feature extraction of preprocessed data can highlight important information and features in the data, help to better understand the characteristics and operating laws of photovoltaic power storage grids, and provide stronger support for management decisions. Using a preset distillation model to perform edge computing on terminal parameter feature data can quickly and efficiently process and analyze data on edge devices, reduce the cost and delay of data transmission and centralized processing, help to achieve real-time processing and rapid response to data, and improve the efficiency and effectiveness of management and operation.

[0017] Preferably, the steps of constructing the preset distillation model in step S14 are specifically:

[0018] Obtain terminal parameter characteristic data;

[0019] A deep learning model is constructed based on the terminal parameter feature data to obtain a teacher model;

[0020] Train the student model according to the teacher model to obtain the student model;

[0021] The terminal edge parameter data is obtained, and the student model is optimized and deployed according to the terminal edge parameter data to obtain a distillation model.

[0022] In the present invention, a teacher model is constructed by using terminal parameter feature data through deep learning model construction, and a model that can better understand and capture the complex characteristics and operating rules of photovoltaic power grids is constructed. Student model training and knowledge distillation improve the efficiency and generalization ability of the model by transferring the knowledge of the teacher model to the student model, which helps to reduce the calculation and storage costs of the model, while maintaining the prediction accuracy of the model and improving the efficiency and accuracy of management and operation. Optimization and deployment of student models, using terminal edge parameter data to optimize the model, and deploying the optimized model to the edge device for real-time processing. Model optimization and deployment can make full use of the computing resources of the terminal device, realize rapid processing and analysis of data, and improve the response speed and efficiency of management and operation. The obtained distillation model can be directly deployed on the edge device for edge computing processing, without the need to transmit data to a centralized server for processing, reducing the delay and cost of data transmission, and can realize real-time processing and response of data, improving the efficiency and real-time performance of management and operation.

[0023] Preferably, step S2 specifically includes:

[0024] Step S21: Deploy blockchain nodes according to the node distribution data in the edge analysis data to obtain blockchain stage deployment data;

[0025] Step S22: Verify the edge analysis data according to the blockchain stage deployment data to obtain edge verification data;

[0026] Step S23: Generate blocks based on edge verification data to obtain photovoltaic blockchain data.

[0027] In the present invention, blockchain nodes are deployed at each node of the photovoltaic power storage grid according to the node distribution in the edge analysis data, thereby constructing a distributed blockchain network to ensure the security and credibility of the data and prevent the data from being tampered with or deleted. The generation of edge verification data can verify the data on the blockchain to ensure the integrity and consistency of the data. Through the process of verifying the data, the behavior of malicious tampering or damage to the data can be effectively detected and prevented to ensure the reliability and credibility of the data. Block generation is the core step of blockchain technology, which can package the verified data into blocks and link them to the blockchain to achieve the data's non-tamperability and traceability, with a high degree of security and credibility, and ensure the integrity and reliability of the photovoltaic power storage grid management and operation data. Through the application of blockchain technology, the whole process of data traceability and auditing can be achieved to ensure that the source of the data and the operation records are traceable and verifiable, which helps to improve the transparency and credibility of management and operation, and enhance the reliability of data management and decision-making.

[0028] Preferably, step S21 is specifically:

[0029] Step S211: constructing a node network according to the node distribution data in the edge analysis data to obtain edge node network data;

[0030] Step S212: performing node quantity calculation, node density calculation and node load calculation on the edge node network data to obtain node quantity data, node density data and node load data;

[0031] Step S213: Perform node configuration planning according to the node quantity data, the node density data and the node load data to obtain node configuration planning data;

[0032] Step S214: Deploy blockchain nodes according to the node configuration planning data to obtain blockchain stage deployment data.

[0033] In the present invention, by constructing a node network according to the node distribution in the edge analysis data, the node network structure of the photovoltaic storage power grid can be effectively established, the layout of the nodes can be reasonably planned according to the actual situation, the distribution of the nodes can be ensured to be uniform and reasonable, and the stability and reliability of the network can be improved. The number of nodes, node density and node load of the edge node network data are calculated to understand the situation and performance of the nodes, which helps to determine the scale and capacity of the nodes, and reasonably plan the deployment and configuration of the nodes, so as to maximize the utilization and operation efficiency of the nodes. By planning the node configuration according to the number of nodes, node density and node load data, the location, function and resource configuration of each node can be reasonably determined, which helps to optimize the communication and collaboration between nodes, improve the performance and throughput of the network, and reduce the communication delay and energy consumption between nodes. Blockchain node deployment can deploy blockchain nodes at each node of the photovoltaic storage power grid according to the node configuration planning data, effectively guarantee the security and credibility of the data, prevent the data from being tampered with or deleted, and improve the security and reliability of management and operation data.

[0034] Preferably, the node density data includes first node density data and second node density data, and the node density calculation in step S212 is specifically as follows:

[0035] Performing global node density calculation on edge node network data to obtain first node density data;

[0036] Perform image mapping according to the edge node network data to obtain edge node network image data;

[0037] Generate matrix regions based on edge node network image data using preset regional parameter data to obtain matrix region data, and calculate node density based on the matrix region data and edge node network image data to obtain matrix region node density data;

[0038] Marking the edge node network image data according to the node distance data in the edge node network data to obtain edge node network image marking data;

[0039] Perform clustering calculation on edge node network image label data to obtain edge node network clustering data;

[0040] Select the center of the edge node network clustering data to obtain the edge node network clustering center data;

[0041] Calculate the node density based on the edge node network clustering data and the edge node network clustering center data to obtain the edge node distribution density data;

[0042] The edge node distribution density data and the matrix area node density data are merged to obtain the second node density data.

[0043] In the present invention, the edge node network can be divided into different areas by matrix area generation and node density calculation, and the node density of each area is calculated, which is helpful to evaluate the uniformity and density of node distribution and provide a reasonable reference basis for node deployment and configuration. Through edge node network image marking and cluster calculation, the cluster structure and cluster center in the node network are identified, which is helpful to find the correlation and similarity between nodes and provide effective guidance for the cluster deployment and management of nodes. By merging the first node density data and the second node density data, the global and local node density conditions are reasonably considered to evaluate the distribution and performance of nodes in more depth, which is helpful to formulate more accurate node deployment and configuration strategies and improve the utilization and operation efficiency of nodes. Conventional technical means only simply count the number of nodes, ignoring the distribution and density differences between nodes, ignoring some important information, and failing to truly reflect the distribution of nodes. The present invention combines global node density calculation with local node density calculation based on image processing to more comprehensively understand the distribution of nodes. Global node density calculation can help identify the overall dense area, while local node density calculation can more accurately analyze the specific node density.

[0044] Preferably, the node load calculation in step S212 is specifically as follows:

[0045] Obtain node resource usage data based on edge node network data;

[0046] Calculate the load of a single node based on the node resource usage data to obtain the load data of the single node;

[0047] Perform path load calculation on the node resource usage data to obtain path load data;

[0048] Calculate the load optimization range according to the single-node load data and the path load data to obtain the load optimization range data;

[0049] The single node load data, channel load data and load optimization amplitude data are merged to obtain the node load data.

[0050] In the present invention, node load calculation is used to reflect the resource usage and load status of each node, which helps to timely discover problems such as excessive or unbalanced loads of nodes. By calculating the load of a single node and a path, the load condition of each node and the communication load between nodes are deeply analyzed, which helps to determine the bottleneck and hot spots of the node and provide guidance for optimizing node deployment and communication networks. The load optimization amplitude is calculated based on the single-node load data, the path load data, and the load optimization amplitude data to quantify the effect and amplitude of the load optimization, which helps to evaluate the effectiveness and feasibility of the load optimization strategy. The single-node load data, the path load data, and the load optimization amplitude data are merged to understand the load conditions in various aspects, provide data support for management and operation decisions, and help to formulate more effective load management strategies and improve the operating efficiency and performance of photovoltaic power storage grids.

[0051] Preferably, step S3 specifically includes:

[0052] Call the smart contract according to the preset operation and maintenance smart contract address data to obtain the smart contract call data;

[0053] Extract photovoltaic block data from photovoltaic blockchain data according to smart contract call data to obtain photovoltaic block data;

[0054] Perform smart contract execution on photovoltaic block data according to smart contract call data to obtain operation and maintenance smart contract data;

[0055] Generate operation and maintenance operation log records for operation and maintenance smart contract data to obtain operation and maintenance operation log record data;

[0056] The job execution status is updated according to the operation and maintenance job log record data to obtain management operation and maintenance data.

[0057] In the present invention, the intelligent management of photovoltaic blockchain data is realized by calling and executing the smart contract in step S3. Smart contracts can automatically execute management and operation tasks according to preset conditions and logic, realize automated and intelligent management, and improve management efficiency and accuracy. Photovoltaic block data is extracted and recorded according to the smart contract call data, and effective management and tracking of photovoltaic blockchain data is realized, which is helpful to monitor the operation status and historical changes of photovoltaic storage and charging power grids. By recording the operation log and updating the status of the operation and maintenance smart contract data, the execution status and results of the management and operation tasks are recorded in time, which helps managers understand the execution status and progress of the operations, adjust and optimize the management strategies in time, and ensure the stable operation of the photovoltaic storage and charging power grid. This method uses smart contracts to execute management and operation tasks, which can realize the automation and optimization of the management and operation processes. Through the automatic execution of smart contracts, manual intervention and management costs are reduced, and the efficiency and quality of management and operation are improved, thereby ensuring the safe and stable operation of the photovoltaic storage and charging power grid.

[0058] Preferably, step S4 is specifically:

[0059] Step S41: performing node action analysis on the management and operation data to obtain node action data;

[0060] Step S42: Optimize the preset operation and maintenance smart contract data according to the node action data to obtain preliminary operation and maintenance smart contract optimization data;

[0061] Step S43: Verify the preliminary operation and maintenance smart contract optimization data to obtain completeness verification data;

[0062] Step S44: when it is determined that the integrity verification data is illegal integrity data, return to step S41; or, determine the preset operation and maintenance smart contract data as the operation and maintenance smart contract optimization data;

[0063] Step S45: When it is determined that the integrity verification data is legal integrity data, the parameters of the preset operation and maintenance smart contract data are updated and deployed according to the preliminary operation and maintenance smart contract optimization data to obtain the operation and maintenance smart contract optimization data.

[0064] In the present invention, the management and operation data are deeply analyzed and processed through node action parsing and optimization, and the key node action data are extracted, which is helpful to identify the key nodes and behaviors in the management and operation process. According to the node action data, the preset operation and maintenance smart contract data is optimized, and the parameters are updated and deployed to achieve the optimization and improvement of the smart contract, which is helpful to improve the execution efficiency and quality of the smart contract, further optimize the management and operation process, and improve the operation efficiency and performance of the photovoltaic storage power grid. By verifying the data of the preliminary operation and maintenance smart contract optimization data, the legality and validity of the optimization data can be ensured. If the verification result is illegal completeness data, it can be returned in time for correction and optimization; if the verification result is legal completeness data, the parameters can be directly updated and deployed to improve the efficiency and quality of management and operation. This method uses a cyclic iteration method to optimize and update the smart contract, which can continuously improve the management and operation process and gradually improve the performance and stability of the system. By continuously optimizing the smart contract, it can better adapt to the operating environment and demand changes of the photovoltaic storage power grid, and improve the adaptability and flexibility of the system.

[0065] Preferably, the present application also provides a management and operation system for industrial and commercial photovoltaic power storage grids, which is used to execute the management and operation method for industrial and commercial photovoltaic power storage grids as described above. The management and operation system for industrial and commercial photovoltaic power storage grids includes:

[0066] The photovoltaic power storage grid edge computing module is used to perform edge computing on terminal data distributed throughout the photovoltaic power storage grid to obtain edge analysis data;

[0067] Photovoltaic blockchain construction module, used to construct blockchain based on edge analysis data to obtain photovoltaic blockchain data;

[0068] The operation and maintenance smart contract execution module is used to execute and process the preset operation and maintenance smart contract data according to the photovoltaic blockchain data, obtain the operation and maintenance smart contract data, and perform management and maintenance operations to obtain management and maintenance data;

[0069] The operation and maintenance smart contract optimization module is used to optimize the preset operation and maintenance smart contract data according to the management and maintenance data, and obtain the operation and maintenance smart contract optimization data to carry out industrial and commercial photovoltaic power grid management and maintenance operations.

[0070] The beneficial effects of the present invention are: the present invention realizes real-time processing of terminal data, secure and reliable data storage and intelligent management and operation, which can effectively improve the efficiency and quality of management and operation. Blockchain construction ensures the security and immutability of photovoltaic blockchain data, so that important data generated in the management and operation process can be reliably stored and protected, which helps to prevent data leakage and tampering, and ensure the integrity and credibility of management and operation data. Smart contract optimization can optimize the preset smart contract according to real-time management and operation data, so as to achieve continuous improvement and optimization of the management and operation process, adapt to changes in the operating state of photovoltaic storage and power grid, and improve the flexibility and adaptability of the system. Smart contract execution can realize the automated execution of management and operation operations, reduce manual intervention and errors, improve the efficiency and reliability of management and operation, help reduce operation and maintenance costs, and improve the operating efficiency of the system. The large amount of data generated in the management and operation process can be effectively utilized, and through data analysis and intelligent optimization, the optimal decision of the photovoltaic storage and power grid can be realized, the operating efficiency and performance of the system can be improved, and energy consumption and operation and maintenance costs can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings:

[0072] Figure 1 A flowchart showing a method for managing and operating an industrial and commercial photovoltaic power grid according to an embodiment of the present invention is provided;

[0073] Figure 2 A flowchart of a photovoltaic power grid edge computing method according to an embodiment is shown;

[0074] Figure 3 A flowchart showing a method for constructing a photovoltaic blockchain according to an embodiment is shown;

[0075] Figure 4A flowchart of the steps of a photovoltaic blockchain node deployment method according to an embodiment is shown. DETAILED DESCRIPTION

[0076] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0077] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0078] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0079] See also Figures 1 to 4 The present application provides a method for managing and operating an industrial and commercial photovoltaic power grid, comprising the following steps:

[0080] Step S1: Perform edge computing on terminal data distributed throughout the photovoltaic power storage grid to obtain edge analysis data;

[0081] The present invention provides an embodiment, in which, in the actual application of a photovoltaic power grid, edge computing technology is used to process terminal data distributed in various places to obtain edge analysis data. For example, for real-time data monitoring of photovoltaic power stations, sensor equipment can be installed in each power station to collect data such as the power generation, temperature, and light of the solar panels. These data are processed and analyzed in real time through edge computing nodes to identify abnormal situations, optimize power generation efficiency, etc., thereby obtaining edge analysis data. For example, for a photovoltaic power station, the power generation data is monitored and processed in real time through edge computing nodes, and the power generation efficiency of a certain solar panel is analyzed to be low, indicating that cleaning and maintenance are required, thus obtaining a piece of edge analysis data.

[0082] Step S2: constructing a blockchain based on edge analysis data to obtain photovoltaic blockchain data;

[0083] The present invention provides an embodiment, based on the above-mentioned edge analysis data, a photovoltaic blockchain can be constructed to achieve secure storage and sharing of data. For example, through blockchain technology, the real-time data of each power station is encrypted, stored and shared to ensure the integrity and traceability of the data. The construction of the blockchain can use existing open source blockchain platforms such as Ethereum, Hyperledger, etc., and design smart contracts according to specific needs. In the embodiment, the edge analysis data is gradually stored in the blockchain in chronological order to form tamper-proof blockchain data, thereby obtaining photovoltaic blockchain data.

[0084] Deploy Ethereum nodes, deploy nodes on the Ethereum network, and build a photovoltaic blockchain network. Design smart contracts, write smart contract code, and implement the management and allocation functions of photovoltaic energy. For example, design a smart contract to monitor the power generation of photovoltaic cells and automatically allocate electricity according to real-time energy demand. / / Photovoltaic energy management smart contract pragmasolidity^0.8.0; contract SolarEnergyManagement{mapping(address=>uint)publicenergyBalance;function deposit()public payable{energyBalance[msg.sender]+=msg.value;}function withdraw(uint_amount)public{require(energyBa1ance[msg.sender]>=_amount,″Insufficient energy balance″);energyBalance[msg.sender]-=_amount;payable(msg.sender).transfer(_amount);}}, deploy smart contracts, deploy smart contracts on the Ethereum blockchain, and obtain the smart contract address. Call smart contracts, use client applications or other tools on the blockchain to interact with smart contracts, and call functions in the contract to perform corresponding operations.

[0085] Step S3: Execute and process the preset operation and maintenance smart contract data according to the photovoltaic blockchain data to obtain the operation and maintenance smart contract data, so as to perform management and maintenance operations and obtain management and maintenance data;

[0086] The present invention provides an embodiment, according to the photovoltaic blockchain data, a preset operation and maintenance smart contract can be executed to perform management and maintenance operations, and obtain management and maintenance data. For example, through smart contract programming, remote control and operation and maintenance of photovoltaic power stations can be realized. Smart contracts can automatically perform maintenance tasks based on information such as power generation efficiency and equipment status in photovoltaic blockchain data, such as remotely adjusting the angle of photovoltaic panels and sending maintenance alarms. In the embodiment, the operation and maintenance smart contract automatically schedules maintenance tasks according to photovoltaic blockchain data. For example, when the power generation efficiency of a solar panel is lower than a threshold, a cleaning maintenance task is automatically triggered, and a maintenance log is recorded to finally obtain management and maintenance data.

[0087] The present invention provides an embodiment, in which a smart contract needs to obtain relevant information in photovoltaic blockchain data, such as power generation efficiency, equipment status, etc. These data can be provided through the smart contract interface on the chain or an external data source. The smart contract makes conditional judgments based on the acquired data, such as checking whether the power generation efficiency of the solar panel is lower than a preset threshold, or checking whether the equipment status is abnormal. If the conditions are met, the smart contract will automatically schedule maintenance tasks. For example, when it is found that the power generation efficiency of the solar panel is lower than the threshold, the smart contract will trigger a cleaning maintenance task. The smart contract executes maintenance tasks, such as sending instructions to adjust the angle of the solar panel, sending maintenance alerts to maintenance personnel, etc. The smart contract will record relevant operation logs during the execution of the task, including information such as task execution status and results. Based on the results of the task execution and the recorded logs, the smart contract generates management and operation data. These data may include the execution status of maintenance tasks, changes in equipment status, maintenance logs, etc.

[0088] pragma solidity^0.8.0; contract SolarFarmMaintenanceContract{ / / Define PV panel status enum PanelStatus{Normal, Dirty, Damaged} / / Define PV panel data structure structSolarPanel{PanelStatus status;uint efficiency; / / PV panel efficiency} / / PV panel data mapping(uint=>SolarPanel) public solarPanels; / / Event: Maintenance task triggered eventMaintenanceTriggered(uint panelId, PanelStatus status); / / Function: Add PV panel data function addSolarPanel(uint_panelId, uint_efficiency) public{solarPanels[_panelId]=SolarPanel(PanelStatus.Normal, _efficiency);} / / Function: Trigger maintenance task function triggerMaintenance(uint_panelId) public{SolarPanel storage panel=solar Panels[_panelId]; / / If the efficiency of the PV panel is lower than the threshold, trigger the maintenance task if (panel.efficiency<90) { panel.status=PanelStatus.Dirty; emitMaintenanceTriggered(panelId, panel.status);}} / / Function: perform maintenance task function performMaintenance(uint _panelId) public { SolarPanel storage panel=solarPanels[_panelId]; / / Perform maintenance task if (panel.status==PanelStatus.Dirty) { / / Perform cleaning task... / / Update the status of the PV panel panel.stat us=PanelStatus.Normal;}}}

[0089] In this smart contract, a SolarFarmMaintenanceContract contract is predefined, including the status of the photovoltaic panel, data structure, function to add photovoltaic panel data, function to trigger maintenance tasks, and function to perform maintenance tasks. The specific process is as follows: users can add photovoltaic panel data, including photovoltaic panel ID and efficiency, through the addSolarPanel function. When the efficiency of the photovoltaic panel is less than 90%, the maintenance task is triggered, the photovoltaic panel status is set to dirty, and a maintenance task trigger event is issued. Maintenance personnel can call the performMaintenance function to perform maintenance tasks, such as cleaning the photovoltaic panel, and set the photovoltaic panel status back to normal.

[0090] Step S4: Optimize the preset operation and maintenance smart contract data according to the management and operation and maintenance data to obtain the operation and maintenance smart contract optimization data to carry out the management and operation of the industrial and commercial photovoltaic power grid.

[0091] The present invention provides an embodiment, which optimizes the preset operation and maintenance smart contract data according to the management and operation and maintenance data to obtain the operation and maintenance smart contract optimization data, so as to perform the management and operation and maintenance of the industrial and commercial photovoltaic power grid. For example, by analyzing and mining the management and operation and maintenance data, it is found that some smart contracts have low execution efficiency or shortcomings under specific conditions. By optimizing the logical structure, parameter settings and other aspects of the smart contract, its execution efficiency and reliability are improved. In the embodiment, the operation and maintenance personnel find that a smart contract has low execution efficiency under high load conditions based on the management and operation and maintenance data, optimize and adjust it, and update and deploy it through the blockchain network to obtain the operation and maintenance smart contract optimization data.

[0092] Suppose that the management and operation data shows that a smart contract has low execution efficiency under high load and needs to be optimized and adjusted. The following is the specific process of optimization: Collect management and operation data, including the execution status, load status, execution time and other information of the smart contract, and obtain it through monitoring tools, log records or transaction records in the blockchain network. Analyze the collected management and operation data. Pay special attention to those smart contracts that have low execution efficiency under high load conditions, and find out the reasons for the low efficiency, such as complex code logic and unreasonable parameter settings. Based on the analysis of management and operation data, determine the optimization plan for the smart contract, and adjust the logical structure, parameter settings, algorithm optimization and other aspects of the smart contract. For example, simplify the logic of the smart contract and reduce unnecessary calculations; optimize the algorithm to improve execution efficiency; adjust parameters to adapt to different load conditions, etc. According to the determined optimization plan, modify the code of the smart contract and optimize it, adjust the logic of the smart contract, reset the parameters, etc. Ensure that the optimized smart contract can be executed efficiently under various load conditions.

[0093] Collect the execution data of smart contracts from the blockchain network, including execution time, resource consumption, execution steps and other information, and obtain them through blockchain browsers or specific monitoring tools. Extract the execution data to obtain execution feature data, and perform repetitive calculation and resource competition processing on the execution feature data to obtain execution repetitive calculation data and resource competition data; generate execution efficiency reports based on execution repetitive calculation data and resource competition data to obtain execution efficiency report data for optimization operations.

[0094] Collect smart contract execution data: Use blockchain browsers or specific monitoring tools to obtain smart contract execution data from the blockchain network, including execution time, resource consumption (such as gas consumption), execution steps (such as function calls, state changes, etc.). Connect to the blockchain network through a node to obtain the execution transaction record of the smart contract. Perform feature extraction on the collected execution data to extract meaningful features, such as execution time, number of transactions, gas consumption, etc. These features can reflect the execution time, resource consumption, etc. of the smart contract. Perform repetitive calculation processing on the execution feature data to detect whether there are repeated execution patterns or operation sequences. By analyzing the execution records of the smart contract, find out whether there are similar or identical execution paths, and whether there are repeated transactions or function calls. Perform resource competition processing on the execution feature data to analyze whether there are resource competition situations during the execution of the smart contract, such as frequent access to shared resources, race conditions, etc. By checking the dependencies between transactions and the access to shared resources, determine whether there is a resource competition problem. Generate an execution efficiency report based on the execution repetitive calculation data and resource competition situation data. The report should include the main performance indicators of smart contract execution, such as execution time distribution, resource consumption, repeated execution mode, resource competition, etc. The report should also provide analysis and evaluation of execution efficiency issues and put forward optimization suggestions.

[0095] In the present invention, edge computing is performed on the terminal data of each location of the photovoltaic storage power grid in real time to obtain edge analysis data, which reflects the operating status and performance of the photovoltaic storage power grid, provides real-time decision support for managers, and helps them to find problems and optimize operation strategies in a timely manner. The use of blockchain technology to construct photovoltaic blockchain data can ensure the security and traceability of management and operation data. The decentralized characteristics and tamper-proof recording method of blockchain can effectively prevent data tampering and loss, and improve the security and credibility of data. The preset operation and maintenance smart contract data is used to execute and optimize the management and operation data, which can improve the operation and maintenance efficiency. Smart contracts can automatically execute operations according to preset rules and conditions without manual intervention, thereby improving the efficiency and accuracy of management and operation. The optimization of management and operation data can continuously improve the operating performance and stability of the photovoltaic storage power grid. By analyzing the management and operation data and optimizing the smart contract, problems in operation can be discovered and improved, and the efficiency and performance of the photovoltaic storage power grid can be continuously improved.

[0096] Preferably, step S1 specifically includes:

[0097] Step S11: Acquire terminal data distributed throughout the photovoltaic power storage grid;

[0098] The present invention provides an embodiment, in which the terminal data distributed in various places can be obtained through the monitoring equipment of each power station in the photovoltaic power storage grid. For example, the power generation, temperature, light and other data of the solar panel can be collected in real time through the sensor equipment installed on the solar panel. In addition, the operating status data of the energy storage system, inverter and other equipment can also be collected. By integrating these data, the terminal data distributed in various places of the photovoltaic power storage grid can be obtained.

[0099] Step S12: preprocessing the terminal data to obtain terminal preprocessing data;

[0100] The present invention provides an embodiment. After obtaining the original terminal data, it is necessary to pre-process it to clean and convert the data to obtain accurate and reliable terminal pre-processed data. For example, for the power generation data of solar panels, there are abnormal values ​​or missing values, and abnormal value processing and missing value filling are required. In addition, the data can be normalized or standardized for subsequent feature extraction and edge computing processing.

[0101] Step S13: extracting features from the terminal preprocessing data to obtain terminal parameter feature data;

[0102] The present invention provides an embodiment for performing feature extraction on the preprocessed terminal data to obtain terminal parameter feature data. Feature extraction is the process of converting raw data into a more representative and interpretable feature vector or feature set. For example, for the power generation data of solar panels, features such as average power generation, maximum power generation, and power generation fluctuation are extracted to reflect important information such as power generation efficiency and stability of solar panels.

[0103] Step S14: Use the preset distillation model to perform edge computing processing on the terminal parameter feature data to obtain edge analysis data.

[0104] The present invention provides an embodiment, which uses a preset distillation model to perform edge computing processing on terminal parameter feature data to obtain edge analysis data. The distillation model is a lightweight model, which is usually compressed by a deep learning model through knowledge distillation technology. It has a small model volume and fast reasoning speed. For example, a pre-trained distillation model is used to perform edge computing on feature data to identify potential abnormal situations, low power generation efficiency and other problems, thereby obtaining edge analysis data.

[0105] The input data is the terminal parameter feature data, including the current, voltage, temperature and other parameters of each terminal of the photovoltaic power station, as well as other features related to power generation efficiency. These data are usually time series data, recording the status and performance of each terminal over a period of time. A lightweight recurrent neural network (RNN) structure is adopted. The input layer accepts serialized terminal parameter feature data as input; the recurrent layer uses simple RNN units or more efficient gated recurrent units (such as LSTM or GRU) to capture the temporal dependency between sequence data; the output layer generates corresponding edge analysis data or prediction results based on the output of the recurrent layer. Model compression techniques such as deep separable convolution, channel pruning, quantization, knowledge distillation, etc. are applied to further reduce the model size and improve the inference speed of the lightweight recurrent neural network (RNN) structure.

[0106] The training process of the distillation model is divided into two stages: the training of the teacher model and the distillation of the student model. In the teacher model training, a large deep learning model is used to train the input data to capture the complex features and patterns in the data. The main purpose of the model is to provide high-quality labels and knowledge to guide the training of the student model. In the student model distillation, the output (soft label) of the teacher model is used as the supervision signal of the student model. The lightweight student model is trained through the distillation technology. During the training process, the student model tries to simulate the output of the teacher model in order to capture its knowledge and features. The distillation technology can be a knowledge-based distillation method, such as similarity-based distillation, adaptive distillation, etc. Output results: The output results are edge analysis data processed by the distillation model, including anomaly detection results of terminal parameter features, power generation efficiency prediction, equipment health status assessment, etc. Edge analysis data can assist power station managers to find problems in time and take corresponding measures. In the training process of the student model, the cross entropy loss function is used to measure the difference between the output of the student model and the output of the teacher model. The optimization process of the loss function aims to minimize the prediction error of the student model and make it as close as possible to the output of the teacher model. In addition to the traditional cross entropy loss function, other loss functions can also be combined, such as the mean square error (MSE) loss function.

[0107] The present invention can obtain terminal data distributed throughout the photovoltaic power storage grid, realize real-time monitoring and data collection of the power grid operation status, and help managers to timely understand the operation of the power grid, discover potential problems, and make corresponding adjustments and optimizations. Data preprocessing can be cleaned, denoised, normalized, etc., to improve the quality and availability of data, help reduce errors and errors in the data processing process, and ensure the accuracy and reliability of subsequent analysis. Feature extraction of preprocessed data can highlight important information and features in the data, help to better understand the characteristics and operating laws of photovoltaic power storage grids, and provide stronger support for management decisions. Using a preset distillation model to perform edge computing on terminal parameter feature data can quickly and efficiently process and analyze data on edge devices, reduce the cost and delay of data transmission and centralized processing, help to achieve real-time processing and rapid response to data, and improve the efficiency and effectiveness of management and operation.

[0108] Preferably, the steps of constructing the preset distillation model in step S14 are specifically:

[0109] Obtain terminal parameter characteristic data;

[0110] The present invention provides an embodiment, in a photovoltaic power storage grid, terminal parameter characteristic data can be obtained through various sensors and monitoring equipment, such as the power generation, temperature, light intensity and other data of the solar panel, the battery status, charge and discharge power and other data of the energy storage system, and the output power, voltage, current and other data of the inverter. These data are important descriptions of the operating status of the photovoltaic power storage grid and can be used as basic data for building a distillation model.

[0111] A deep learning model is constructed based on the terminal parameter feature data to obtain a teacher model;

[0112] The present invention provides an embodiment, using the acquired terminal parameter feature data, a deep learning model, such as a convolutional neural network, a recurrent neural network, etc., can be constructed as a teacher model. The model analyzes and predicts the terminal data by means of supervised learning, such as predicting the future power generation of solar panels, predicting the battery life of energy storage systems, etc.

[0113] Train the student model according to the teacher model to obtain the student model;

[0114] The present invention provides an embodiment, by performing knowledge distillation technology on the teacher model, the knowledge of the teacher model can be transferred to a student model that is lighter and more suitable for running on edge devices. The student model is a simplified neural network structure with a smaller model size and faster reasoning speed, while maintaining the predictive ability of the teacher model.

[0115] The terminal edge parameter data is obtained, and the student model is optimized and deployed according to the terminal edge parameter data to obtain a distillation model.

[0116] The present invention provides an embodiment, deploying a student model to an edge device of a photovoltaic power grid, and obtaining terminal edge parameter data in real time for optimization. For example, the student model is incrementally updated using online learning technology to adapt to the ever-changing environment and needs in the photovoltaic power grid. The optimized student model is a distillation model, which is used for edge computing processing to obtain edge analysis data.

[0117] In the present invention, a teacher model is constructed by using terminal parameter feature data through deep learning model construction, and a model that can better understand and capture the complex characteristics and operating rules of photovoltaic power grids is constructed. Student model training and knowledge distillation improve the efficiency and generalization ability of the model by transferring the knowledge of the teacher model to the student model, which helps to reduce the calculation and storage costs of the model, while maintaining the prediction accuracy of the model and improving the efficiency and accuracy of management and operation. Optimization and deployment of student models, using terminal edge parameter data to optimize the model, and deploying the optimized model to the edge device for real-time processing. Model optimization and deployment can make full use of the computing resources of the terminal device, realize rapid processing and analysis of data, and improve the response speed and efficiency of management and operation. The obtained distillation model can be directly deployed on the edge device for edge computing processing, without the need to transmit data to a centralized server for processing, reducing the delay and cost of data transmission, and can realize real-time processing and response of data, improving the efficiency and real-time performance of management and operation.

[0118] Preferably, step S2 specifically includes:

[0119] Step S21: Deploy blockchain nodes according to the node distribution data in the edge analysis data to obtain blockchain stage deployment data;

[0120] The present invention provides an embodiment, in which the number, location and configuration of blockchain nodes to be deployed in the photovoltaic power grid are first determined based on the node distribution in the edge analysis data. The optimal node deployment scheme is designed by considering factors such as the physical location of the node, the network topology, and the resource capacity. Then, according to this scheme, the blockchain nodes are deployed to the corresponding locations and the required software and hardware environment are configured. Finally, the blockchain stage deployment data is obtained, which records the deployment information and configuration parameters of each node.

[0121] Collect the physical location information of each location in the photovoltaic power storage grid, including longitude and latitude coordinates or other geographic location identifiers. According to the geographic location information of the nodes, combined with the layout and needs of the photovoltaic power station, classify the nodes, such as core nodes, edge nodes, edge gateways, etc. Network topology analysis, analyze the network topology of the photovoltaic power storage grid, understand the connection relationship and communication mode between nodes, including local area network, wide area network, cellular network, etc. Identify the key nodes in the network, such as nodes with important status or high load, and the communication path between nodes. Resource capacity evaluation, evaluate the resource capacity of each node, including computing resources, storage resources, communication bandwidth, etc. Determine the carrying capacity and maximum load of the node based on the resource capacity and performance characteristics of the node. Optimal deployment strategy design, combine the node location, network topology and resource capacity to design the optimal node deployment plan, including determining the number, location and deployment method of the node. Considering the characteristics of the photovoltaic power storage grid, a combination of distributed deployment and hierarchical deployment can be used to deploy nodes at different levels and locations to achieve flexibility and efficiency in data processing and management. Use network simulation tools or optimization algorithms to optimize and verify the designed deployment plan, including simulating communication delays, data transmission rates, load balancing, etc. between nodes, to verify whether the designed deployment plan can meet actual needs and achieve optimal performance. According to the results of optimization verification, adjust and optimize the deployment plan to ensure that the final deployment plan is optimal and can run effectively in the actual environment.

[0122] Step S22: Verify the edge analysis data according to the blockchain stage deployment data to obtain edge verification data;

[0123] The present invention provides an embodiment, in which the edge analysis data previously performed is verified using the blockchain stage deployment data, including verifying the validity of node deployment, confirming the communication connection between nodes, checking the operation status of nodes, etc. Through the verification process, the normal operation of the blockchain network and the reliability of the nodes can be ensured. The result of the verification process will obtain edge verification data, which records various information and results in the verification process.

[0124] Step S23: Generate blocks based on edge verification data to obtain photovoltaic blockchain data.

[0125] The present invention provides an embodiment, in which edge verification data is used to start the generation of blocks. According to the consensus mechanism and block generation algorithm of the blockchain, the edge verification data is packaged into blocks and added to the photovoltaic blockchain. Each block contains a certain amount of transaction data or verification information. The link structure of the blockchain ensures the immutability and transparency of the data. The generated photovoltaic blockchain data will be used for subsequent management and operation operations and decision analysis.

[0126] First, edge verification data is collected from each edge node or edge device, including transaction records, sensor data, device status, etc. Block packaging,

[0127] The collected edge verification data is packaged according to certain rules to form a block. The packaging rules are determined according to the blockchain system, including determining the block size, transaction order, transaction hash value, etc. Blockchain consensus mechanism, according to the consensus mechanism of the blockchain, determines the way to generate new blocks. The consensus mechanism includes proof of work (PoW), proof of stake (PoS), and Delegated Proof of Stake (DPoS). Blockchain node verification, sends the packaged blocks to the nodes in the blockchain network for verification. These nodes will verify whether the transactions in the block are legal and comply with the consensus rules, and ensure that the data in the block has not been tampered with. Blockchain network synchronization, the verified blocks will be added to the blockchain network and become new blockchain data, and the new blocks will be broadcast to the entire network, and each node will update its own blockchain data. Update the blockchain status, after the newly generated block is confirmed, the status of the blockchain will be updated, including the update of on-chain data, confirmation of transactions, etc.

[0128] Proof of Work (PoW) proves that a certain amount of work was done when creating a new block by solving a complex mathematical puzzle. Specifically, each block contains the hash value of the previous block, and miners need to find a random number (called "Nonce") that meets certain conditions so that the hash value of the block meets certain conditions (such as being less than a specific target value). Due to the irreversibility of the hash function, miners can only find a hash value that meets the conditions by constantly trying different random numbers, which consumes a lot of computing power and electricity. Therefore, PoW has a certain degree of security, because it takes a lot of computing resources to destroy the blockchain, but it also leads to high energy consumption.

[0129] Proof of Stake (PoS) does not require solving mathematical puzzles to create new blocks, but determines the generator of the next block based on the amount of cryptocurrency (stake) held by each node. Nodes with more cryptocurrency are more likely to be selected to generate new blocks because they contribute more to the network. PoS improves efficiency by reducing the waste of computing resources because it does not require a lot of computing power to compete to solve puzzles, but relies on the staking of currency in the network. However, PoS also has some disadvantages, such as the "rich get richer" situation, that is, nodes with more currency are more likely to get more rewards.

[0130] Delegated Proof of Stake (DPoS), DPoS is an improved version of PoS that introduces a proxy voting mechanism. In DPoS, coin holders can vote for representatives to participate in the network consensus on their behalf. These representatives are responsible for generating new blocks and verifying transactions, and are rewarded based on their contributions. DPoS improves the efficiency of the blockchain by reducing the number of participants and speeding up the decision-making process. However, compared with PoS, DPoS has a stronger tendency towards centralization because representatives can centrally control the network, leading to potential security risks.

[0131] In the present invention, blockchain nodes are deployed at each node of the photovoltaic power storage grid according to the node distribution in the edge analysis data, thereby constructing a distributed blockchain network to ensure the security and credibility of the data and prevent the data from being tampered with or deleted. The generation of edge verification data can verify the data on the blockchain to ensure the integrity and consistency of the data. Through the process of verifying the data, the behavior of malicious tampering or damage to the data can be effectively detected and prevented to ensure the reliability and credibility of the data. Block generation is the core step of blockchain technology, which can package the verified data into blocks and link them to the blockchain to achieve the data's non-tamperability and traceability, with a high degree of security and credibility, and ensure the integrity and reliability of the photovoltaic power storage grid management and operation data. Through the application of blockchain technology, the whole process of data traceability and auditing can be achieved to ensure that the source of the data and the operation records are traceable and verifiable, which helps to improve the transparency and credibility of management and operation, and enhance the reliability of data management and decision-making.

[0132] Preferably, step S21 is specifically:

[0133] Step S211: constructing a node network according to the node distribution data in the edge analysis data to obtain edge node network data;

[0134] The present invention provides an embodiment, firstly, the distribution data of each node in the photovoltaic power storage grid is collected, including the location of the node, the connection relationship and other information. Then, based on these data, an edge node network is constructed to determine the connection relationship between the nodes and the network topology structure, including network topology analysis, graph theory algorithm and other technologies. The edge node network data finally obtained records the connection status and network structure information between the nodes.

[0135] The collected node distribution data is subjected to network topology analysis, including determining the connection relationship between nodes, the network topology, etc. By analyzing the distance between nodes, connection strength and other information, a connection graph between nodes is constructed. The node distribution data is processed using graph theory algorithms to construct an edge node network, including the minimum spanning tree algorithm, the shortest path algorithm, the maximum flow minimum cut algorithm, etc., to determine the optimal connection method between nodes, as well as the stability and reliability of the network. The node connection relationship is determined based on the network topology and the results of the graph theory algorithm, including determining which nodes are directly connected, which nodes are connected through intermediate nodes, and information such as the strength and bandwidth of the connection. The edge node network data obtained records the connection status and network structure information between nodes, including parameters such as node identification, connection relationship, bandwidth, delay, and the network topology diagram.

[0136] Step S212: performing node quantity calculation, node density calculation and node load calculation on the edge node network data to obtain node quantity data, node density data and node load data;

[0137] The present invention provides an embodiment, which uses the edge node network data that has been constructed to calculate the number of nodes, node density and node load. By counting the number and location information of nodes, the node number data is obtained; by analyzing the connection between nodes, the node density data is calculated; by considering the resource utilization of the nodes, the node load is calculated. Thus, the specific data of the number of nodes, node density and node load are obtained.

[0138] Step S213: Perform node configuration planning according to the node quantity data, the node density data and the node load data to obtain node configuration planning data;

[0139] The present invention provides an embodiment, according to the node quantity, node density and node load data calculated above, a node configuration planning scheme is formulated, including node location selection, hardware configuration, network parameter setting and other aspects. According to the node configuration planning scheme, the specific deployment location and required resource configuration of each node are determined. The node configuration planning data finally obtained records the node deployment scheme and configuration parameters.

[0140] Analyze the node quantity data calculated previously to determine the number of nodes that need to be deployed in the photovoltaic power storage grid. The analysis is based on factors such as network coverage, service requirements, and load conditions. Generally, threshold-based scaling is used to determine the nodes. Node density data analysis: According to the node density data calculated previously, the distribution density of nodes in different areas is analyzed to determine the key areas and distribution strategies for node deployment. Key areas are divided into at least three levels, including high-level density / key areas, medium-level density / key areas, and low-level density / key areas. Level density / key areas can be divided into three equal parts according to the maximum value, or they can be divided based on thresholds. Node load data analysis: Analyze the node load data calculated previously to understand the current load conditions and resource utilization of each node, determine the important reference basis for node configuration, and avoid excessive node load or resource waste. Node location selection: Determine the deployment location of the node based on the node density data and load conditions. More nodes are often deployed in areas with high load or wide coverage to ensure network stability and service quality. Hardware configuration and network parameter setting: According to the deployment location and service requirements of the node, formulate the node hardware configuration and network parameter setting plan, select the appropriate device model, configure computing resources and storage resources, and set network bandwidth and latency parameters. Node configuration planning plan formulation: Combined with the data analysis results such as the number of nodes, density and load, formulate a node configuration planning plan, determine the specific deployment location, hardware configuration, network parameters and other information of each node, and record it in the node configuration planning data.

[0141] Step S214: Deploy blockchain nodes according to the node configuration planning data to obtain blockchain stage deployment data.

[0142] The present invention provides an embodiment, which performs specific deployment of blockchain nodes according to node configuration planning data. According to the planning scheme, the blockchain nodes are deployed to corresponding locations, and the required software and hardware environment are configured. The obtained blockchain stage deployment data records the deployment information and configuration parameters of each node.

[0143] In the present invention, by constructing a node network according to the node distribution in the edge analysis data, the node network structure of the photovoltaic storage power grid can be effectively established, the layout of the nodes can be reasonably planned according to the actual situation, the distribution of the nodes can be ensured to be uniform and reasonable, and the stability and reliability of the network can be improved. The number of nodes, node density and node load of the edge node network data are calculated to understand the situation and performance of the nodes, which helps to determine the scale and capacity of the nodes, and reasonably plan the deployment and configuration of the nodes, so as to maximize the utilization and operation efficiency of the nodes. By planning the node configuration according to the number of nodes, node density and node load data, the location, function and resource configuration of each node can be reasonably determined, which helps to optimize the communication and collaboration between nodes, improve the performance and throughput of the network, and reduce the communication delay and energy consumption between nodes. Blockchain node deployment can deploy blockchain nodes at each node of the photovoltaic storage power grid according to the node configuration planning data, effectively guarantee the security and credibility of the data, prevent the data from being tampered with or deleted, and improve the security and reliability of management and operation data.

[0144] Preferably, the node density data includes first node density data and second node density data, and the node density calculation in step S212 is specifically as follows:

[0145] Performing global node density calculation on edge node network data to obtain first node density data;

[0146] The present invention provides an embodiment for collecting the distribution data of each node in the photovoltaic power storage grid, including the location and connection relationship of the nodes. The global node density is calculated based on these data. The global node density refers to the average density of nodes in the entire network, which is calculated by counting the number of nodes and the total area of ​​the network.

[0147] Perform image mapping according to the edge node network data to obtain edge node network image data;

[0148] The present invention provides an embodiment, using the location information of nodes in a photovoltaic power storage grid to map node network data onto an image to form edge node network image data. In this image, the location and connection relationship of each node can be represented by pixels and line segments, and the relative position is represented in equal proportion by pixels, thereby forming a visual image.

[0149] Generate matrix regions based on edge node network image data using preset regional parameter data to obtain matrix region data, and calculate node density based on the matrix region data and edge node network image data to obtain matrix region node density data;

[0150] The present invention provides an embodiment, firstly, using preset regional parameter data, the edge node network image data is subjected to matrix region generation. The matrix region refers to dividing the image into multiple small regions, and recording the number of nodes in each region. Then, the node density is calculated based on the matrix region data and the edge node network image data. The calculation is performed for the node density in each matrix region, and can also be obtained by counting the number of nodes in each region.

[0151] Marking the edge node network image data according to the node distance data in the edge node network data to obtain edge node network image marking data;

[0152] The present invention provides an embodiment, using node distance data in edge node network data to mark edge node network image data. The degree of association or spatial position relationship between nodes is determined through the distance information between nodes. The marked data reflects the connection status or spatial distribution characteristics between nodes.

[0153] Perform clustering calculation on edge node network image label data to obtain edge node network clustering data;

[0154] The present invention provides an embodiment for performing clustering calculation on the marked edge node network image data. The clustering algorithm can classify nodes with similar characteristics into the same category, thereby forming different node clusters. Through clustering calculation, the similarity and group structure between nodes can be found.

[0155] Select the center of the edge node network clustering data to obtain the edge node network clustering center data;

[0156] The present invention provides an embodiment for selecting a representative central node from cluster data. The central node is usually the most representative node in each cluster or the node that can best reflect the characteristics of the cluster. By selecting the central node, the structure and characteristics of the entire network are better described, and the subsequent calculation process is simplified.

[0157] Calculate the node density based on the edge node network clustering data and the edge node network clustering center data to obtain the edge node distribution density data;

[0158] The present invention provides an embodiment, which uses edge node network clustering data and cluster center data to calculate node density. The distribution of each node cluster can be determined through clustering data, and the distribution density of the node can be better described in combination with the cluster center data. The obtained edge node distribution density data reflects the node density of each area in the network, which provides a basis for subsequent network analysis and optimization.

[0159] The present invention provides an embodiment, which uses the area data of the cluster of edge node network clustering data to map the cluster center data, so as to obtain the central node image data corresponding to the edge node network image data corresponding to the cluster center data; the node density is calculated according to the central node image data to obtain the edge node distribution density data.

[0160] The edge node distribution density data and the matrix area node density data are merged to obtain the second node density data.

[0161] The present invention provides an embodiment that combines edge node distribution density data and matrix region node density data into one data format to obtain second node density data.

[0162] In the present invention, the edge node network can be divided into different areas by matrix area generation and node density calculation, and the node density of each area is calculated, which is helpful to evaluate the uniformity and density of node distribution and provide a reasonable reference basis for node deployment and configuration. Through edge node network image marking and cluster calculation, the cluster structure and cluster center in the node network are identified, which is helpful to find the correlation and similarity between nodes and provide effective guidance for the cluster deployment and management of nodes. By merging the first node density data and the second node density data, the global and local node density conditions are reasonably considered to evaluate the distribution and performance of nodes in more depth, which is helpful to formulate more accurate node deployment and configuration strategies and improve the utilization and operation efficiency of nodes. Conventional technical means only simply count the number of nodes, ignoring the distribution and density differences between nodes, ignoring some important information, and failing to truly reflect the distribution of nodes. The present invention combines global node density calculation with local node density calculation based on image processing to more comprehensively understand the distribution of nodes. Global node density calculation can help identify the overall dense area, while local node density calculation can more accurately analyze the specific node density.

[0163] Preferably, the node load calculation in step S212 is specifically as follows:

[0164] Obtain node resource usage data based on edge node network data;

[0165] The present invention provides an embodiment, which first collects resource usage data of each node in a photovoltaic power storage grid, including information such as the node's CPU utilization, memory utilization, storage utilization, etc., and obtains it through a monitoring system, sensors or other data acquisition devices.

[0166] Calculate the load of a single node based on the node resource usage data to obtain the load data of the single node;

[0167] The present invention provides an embodiment, using the acquired node resource usage data to calculate the load of a single node. The load of a single node is evaluated based on factors such as the resource utilization rate of the node, the number and complexity of processing tasks, etc. Various load calculation algorithms, such as load balancing algorithms, weighted load algorithms, etc., are used to determine the load of a single node.

[0168] Perform path load calculation on the node resource usage data to obtain path load data;

[0169] The present invention provides an embodiment, which calculates the load of the path according to the communication conditions and data transmission volume between nodes. The load of the path can include indicators such as bandwidth utilization, transmission delay, packet loss rate, etc., and the indicators are weighted to obtain path load data. By monitoring network traffic and data transmission conditions, the path load condition is evaluated and calculated.

[0170] Calculate the load optimization range according to the single-node load data and the path load data to obtain the load optimization range data;

[0171] The present invention provides an embodiment for calculating the load optimization range based on the load data of a single node and the load data of a path. The load optimization range can be determined by comparing the gap between the current load state and the ideal load state. Various optimization algorithms and strategies, such as dynamic load balancing, task scheduling optimization, etc., are used to calculate the load optimization range.

[0172] The present invention provides an embodiment for obtaining the objectives and indicators of load optimization, such as maximizing system throughput, minimizing response time, or minimizing energy consumption. The load data of a single node and the load data of a path are compared with the ideal state, and the gap between the current load state and the ideal load state is calculated, which is achieved through some measurement methods, such as calculating the Euclidean distance, the Manhattan distance, etc. According to the analysis results of the gap, load optimization algorithms and strategies can be used to adjust and optimize the load, such as dynamic load balancing, task scheduling optimization, resource reallocation, etc. According to the adjustment results of the load optimization algorithm and strategy, the magnitude of load optimization is calculated, which is achieved by comparing the load states before and after optimization, or evaluated according to the optimized system performance indicators.

[0173] The single node load data, channel load data and load optimization amplitude data are merged to obtain the node load data.

[0174] The present invention provides an embodiment to merge the load data of a single node, the load data of a path, and the load optimization amplitude data, such as integrating them into one data format, to obtain the overall load data of the node.

[0175] In the present invention, node load calculation is used to reflect the resource usage and load status of each node, which helps to timely discover problems such as excessive or unbalanced loads of nodes. By calculating the load of a single node and a path, the load condition of each node and the communication load between nodes are deeply analyzed, which helps to determine the bottleneck and hot spots of the node and provide guidance for optimizing node deployment and communication networks. The load optimization amplitude is calculated based on the single-node load data, the path load data, and the load optimization amplitude data to quantify the effect and amplitude of the load optimization, which helps to evaluate the effectiveness and feasibility of the load optimization strategy. The single-node load data, the path load data, and the load optimization amplitude data are merged to understand the load conditions in various aspects, provide data support for management and operation decisions, and help to formulate more effective load management strategies and improve the operating efficiency and performance of photovoltaic power storage grids.

[0176] Preferably, step S3 specifically includes:

[0177] Call the smart contract according to the preset operation and maintenance smart contract address data to obtain the smart contract call data;

[0178] The present invention provides an embodiment, firstly, according to the preset operation and maintenance smart contract address data, the corresponding smart contract is called, and the program in the smart contract is executed through the blockchain network. Executing the smart contract includes querying the data on the blockchain, performing specific operations or triggering corresponding events. The smart contract call data includes the result of the call, the status of the execution and other information.

[0179] Extract photovoltaic block data from photovoltaic blockchain data according to smart contract call data to obtain photovoltaic block data;

[0180] The present invention provides an embodiment, according to the smart contract call data, the corresponding photovoltaic block data is extracted from the photovoltaic blockchain. The photovoltaic block data includes photovoltaic power generation information, energy storage information, electricity consumption information, etc. Through the call of the smart contract, the data related to the photovoltaic energy storage grid stored in the blockchain can be obtained.

[0181] Perform smart contract execution on photovoltaic block data according to smart contract call data to obtain operation and maintenance smart contract data;

[0182] The present invention provides an embodiment, which uses smart contract to call data and execute smart contracts on photovoltaic block data. The execution of smart contracts includes operations such as data processing, business logic execution, and security verification. The execution of smart contracts can be defined according to specific business needs, such as executing tasks such as energy scheduling, electricity price calculation, and equipment monitoring.

[0183] Generate operation and maintenance operation log records for operation and maintenance smart contract data to obtain operation and maintenance operation log record data;

[0184] The present invention provides an embodiment, which generates a corresponding operation and maintenance operation log record according to the result of executing a smart contract. The operation and maintenance operation log record data includes the process of smart contract execution, the result of execution, the record of abnormal conditions, etc., which is used for operation and maintenance management, troubleshooting, business analysis and other aspects.

[0185] The job execution status is updated according to the operation and maintenance job log record data to obtain management operation and maintenance data.

[0186] The present invention provides an embodiment, which updates the execution status of the job according to the operation and maintenance job log record data, and forms management and maintenance data. The management and maintenance data includes information such as the execution status of the job, the operation and maintenance status, and the system operation status, and is used to monitor the operation and maintenance status, optimize the operation and maintenance process, and improve the system stability.

[0187] In the present invention, the intelligent management of photovoltaic blockchain data is realized by calling and executing the smart contract in step S3. Smart contracts can automatically execute management and operation tasks according to preset conditions and logic, realize automated and intelligent management, and improve management efficiency and accuracy. Photovoltaic block data is extracted and recorded according to the smart contract call data, and effective management and tracking of photovoltaic blockchain data is realized, which is helpful to monitor the operation status and historical changes of photovoltaic storage and charging power grids. By recording the operation log and updating the status of the operation and maintenance smart contract data, the execution status and results of the management and operation tasks are recorded in time, which helps managers understand the execution status and progress of the operations, adjust and optimize the management strategies in time, and ensure the stable operation of the photovoltaic storage and charging power grid. This method uses smart contracts to execute management and operation tasks, which can realize the automation and optimization of the management and operation processes. Through the automatic execution of smart contracts, manual intervention and management costs are reduced, and the efficiency and quality of management and operation are improved, thereby ensuring the safe and stable operation of the photovoltaic storage and charging power grid.

[0188] Preferably, step S4 is specifically:

[0189] Step S41: performing node action analysis on the management and operation data to obtain node action data;

[0190] The present invention provides an embodiment, firstly analyzing and parsing the management and operation data, extracting the information related to the node operation, including the node state change, equipment failure information, abnormal events, etc. After parsing, the node action data is obtained, recording the specific operation performed by each node.

[0191] Step S42: Optimize the preset operation and maintenance smart contract data according to the node action data to obtain preliminary operation and maintenance smart contract optimization data;

[0192] The present invention provides an embodiment for optimizing a preset operation and maintenance smart contract using node action data. According to the analysis results of the node action data, the parameters, rules or logic in the smart contract are adjusted to improve the operation and maintenance efficiency, reduce costs or improve system performance. After optimization, preliminary operation and maintenance smart contract optimization data is obtained.

[0193] Step S43: Verify the preliminary operation and maintenance smart contract optimization data to obtain completeness verification data;

[0194] The present invention provides an embodiment for verifying the optimization data of the preliminary operation and maintenance smart contract. The purpose of the verification is to ensure that the optimized smart contract can meet the expected functional requirements and will not introduce new problems or risks. By verifying the data, completeness verification data is obtained, which records the verification results and relevant information during the verification process.

[0195] Step S44: when it is determined that the integrity verification data is illegal integrity data, return to step S41; or, determine the preset operation and maintenance smart contract data as the operation and maintenance smart contract optimization data;

[0196] The present invention provides an embodiment for judging and processing according to the result of the completeness verification data. If the verification result shows that there is illegal completeness data, that is, the optimized smart contract has problems or does not meet the requirements, then return to step S41, and re-parse and process the node action of the management and operation data. If the verification result is normal, the preset operation and maintenance smart contract data is determined as the operation and maintenance smart contract optimization data, and prepare to enter the next step of operation.

[0197] Step S45: When it is determined that the integrity verification data is legal integrity data, the parameters of the preset operation and maintenance smart contract data are updated and deployed according to the preliminary operation and maintenance smart contract optimization data to obtain the operation and maintenance smart contract optimization data.

[0198] The present invention provides an embodiment, when it is determined that the integrity verification data is legal integrity data, that is, the optimized smart contract has been verified and meets the expected functional requirements and security requirements. At this time, the preset operation and maintenance smart contract data is updated according to the preliminary operation and maintenance smart contract optimization data, and the updated smart contract is deployed to the system. Finally, the optimized operation and maintenance smart contract optimization data is obtained.

[0199] In the present invention, the management and operation data are deeply analyzed and processed through node action parsing and optimization, and the key node action data are extracted, which is helpful to identify the key nodes and behaviors in the management and operation process. According to the node action data, the preset operation and maintenance smart contract data is optimized, and the parameters are updated and deployed to achieve the optimization and improvement of the smart contract, which is helpful to improve the execution efficiency and quality of the smart contract, further optimize the management and operation process, and improve the operation efficiency and performance of the photovoltaic storage power grid. By verifying the data of the preliminary operation and maintenance smart contract optimization data, the legality and validity of the optimization data can be ensured. If the verification result is illegal completeness data, it can be returned in time for correction and optimization; if the verification result is legal completeness data, the parameters can be directly updated and deployed to improve the efficiency and quality of management and operation. This method uses a cyclic iteration method to optimize and update the smart contract, which can continuously improve the management and operation process and gradually improve the performance and stability of the system. By continuously optimizing the smart contract, it can better adapt to the operating environment and demand changes of the photovoltaic storage power grid, and improve the adaptability and flexibility of the system.

[0200] Preferably, the present application also provides a management and operation system for industrial and commercial photovoltaic power storage grids, which is used to execute the management and operation method for industrial and commercial photovoltaic power storage grids as described above. The management and operation system for industrial and commercial photovoltaic power storage grids includes:

[0201] The photovoltaic power storage grid edge computing module is used to perform edge computing on terminal data distributed throughout the photovoltaic power storage grid to obtain edge analysis data;

[0202] Photovoltaic blockchain construction module, used to construct blockchain based on edge analysis data to obtain photovoltaic blockchain data;

[0203] The operation and maintenance smart contract execution module is used to execute and process the preset operation and maintenance smart contract data according to the photovoltaic blockchain data, obtain the operation and maintenance smart contract data, and perform management and maintenance operations to obtain management and maintenance data;

[0204] The operation and maintenance smart contract optimization module is used to optimize the preset operation and maintenance smart contract data according to the management and maintenance data, and obtain the operation and maintenance smart contract optimization data to carry out industrial and commercial photovoltaic power grid management and maintenance operations.

[0205] The beneficial effects of the present invention are: the present invention realizes real-time processing of terminal data, secure and reliable data storage and intelligent management and operation, which can effectively improve the efficiency and quality of management and operation. Blockchain construction ensures the security and immutability of photovoltaic blockchain data, so that important data generated in the management and operation process can be reliably stored and protected, which helps to prevent data leakage and tampering, and ensure the integrity and credibility of management and operation data. Smart contract optimization can optimize the preset smart contract according to real-time management and operation data, so as to achieve continuous improvement and optimization of the management and operation process, adapt to changes in the operating state of photovoltaic storage and power grid, and improve the flexibility and adaptability of the system. Smart contract execution can realize the automated execution of management and operation operations, reduce manual intervention and errors, improve the efficiency and reliability of management and operation, help reduce operation and maintenance costs, and improve the operating efficiency of the system. The large amount of data generated in the management and operation process can be effectively utilized, and through data analysis and intelligent optimization, the optimal decision of the photovoltaic storage and power grid can be realized, the operating efficiency and performance of the system can be improved, and energy consumption and operation and maintenance costs can be reduced.

[0206] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0207] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for managing and operating an industrial and commercial photovoltaic power grid, characterized in that: The following steps are involved: Step S1: Perform edge computing on terminal data distributed throughout the photovoltaic power storage grid to obtain edge analysis data; Step S2: constructing a blockchain based on edge analysis data to obtain photovoltaic blockchain data; Step S3: Execute and process the preset operation and maintenance smart contract data according to the photovoltaic blockchain data to obtain the operation and maintenance smart contract data, so as to perform management and maintenance operations and obtain management and maintenance data; Step S4: Optimize the preset operation and maintenance smart contract data according to the management and operation and maintenance data to obtain the operation and maintenance smart contract optimization data to perform industrial and commercial photovoltaic power storage and power grid management and operation and maintenance operations; Wherein step S2 is specifically: Step S21: Deploy blockchain nodes according to the node distribution data in the edge analysis data to obtain blockchain stage deployment data; Step S22: Verify the edge analysis data according to the blockchain stage deployment data to obtain edge verification data; Step S23: Generate blocks according to edge verification data to obtain photovoltaic blockchain data; Wherein step S21 is specifically as follows: Step S211: constructing a node network according to the node distribution data in the edge analysis data to obtain edge node network data; Step S212: performing node quantity calculation, node density calculation and node load calculation on the edge node network data to obtain node quantity data, node density data and node load data; Step S213: Perform node configuration planning according to the node quantity data, the node density data and the node load data to obtain node configuration planning data; Step S214: Deploy blockchain nodes according to the node configuration planning data to obtain blockchain stage deployment data; The node density data includes first node density data and second node density data. The node density calculation in step S212 is specifically as follows: Performing global node density calculation on edge node network data to obtain first node density data; Perform image mapping according to the edge node network data to obtain edge node network image data; Generate matrix regions based on edge node network image data using preset regional parameter data to obtain matrix region data, and calculate node density based on the matrix region data and edge node network image data to obtain matrix region node density data; Marking the edge node network image data according to the node distance data in the edge node network data to obtain edge node network image marking data; Perform clustering calculation on edge node network image label data to obtain edge node network clustering data; Select the center of the edge node network clustering data to obtain the edge node network clustering center data; Calculate the node density based on the edge node network clustering data and the edge node network clustering center data to obtain the edge node distribution density data; The edge node distribution density data and the matrix area node density data are merged to obtain the second node density data.

2. The method according to claim 1, characterized in that Step S1 is specifically as follows: Step S11: Acquire terminal data distributed throughout the photovoltaic power storage grid; Step S12: preprocessing the terminal data to obtain terminal preprocessing data; Step S13: extracting features from the terminal preprocessing data to obtain terminal parameter feature data; Step S14: Use the preset distillation model to perform edge computing processing on the terminal parameter feature data to obtain edge analysis data.

3. The method according to claim 2, characterized in that The steps for constructing the preset distillation model in step S14 are specifically as follows: Obtain terminal parameter characteristic data; A deep learning model is constructed based on the terminal parameter feature data to obtain a teacher model; Train the student model according to the teacher model to obtain the student model; The terminal edge parameter data is obtained, and the student model is optimized and deployed according to the terminal edge parameter data to obtain a distillation model.

4. The method according to claim 1, characterized in that The node load calculation in step S212 is specifically as follows: Obtain node resource usage data based on edge node network data; Calculate the load of a single node based on the node resource usage data to obtain the load data of the single node; Perform path load calculation on the node resource usage data to obtain path load data; Calculate the load optimization range according to the single-node load data and the path load data to obtain the load optimization range data; The single node load data, channel load data and load optimization amplitude data are merged to obtain the node load data.

5. The method according to claim 1, characterized in that Step S3 is specifically as follows: Call the smart contract according to the preset operation and maintenance smart contract address data to obtain the smart contract call data; Extract photovoltaic block data from photovoltaic blockchain data according to smart contract call data to obtain photovoltaic block data; Perform smart contract execution on photovoltaic block data according to smart contract call data to obtain operation and maintenance smart contract data; Generate operation and maintenance operation log records for operation and maintenance smart contract data to obtain operation and maintenance operation log record data; The job execution status is updated according to the operation and maintenance job log record data to obtain management operation and maintenance data.

6. The method according to claim 1, characterized in that Step S4 is specifically as follows: Step S41: performing node action analysis on the management and operation data to obtain node action data; Step S42: Optimize the preset operation and maintenance smart contract data according to the node action data to obtain preliminary operation and maintenance smart contract optimization data; Step S43: Verify the preliminary operation and maintenance smart contract optimization data to obtain completeness verification data; Step S44: when it is determined that the integrity verification data is illegal integrity data, return to step S41; Alternatively, the preset operation and maintenance smart contract data is determined as the operation and maintenance smart contract optimization data; Step S45: When it is determined that the integrity verification data is legal integrity data, the parameters of the preset operation and maintenance smart contract data are updated and deployed according to the preliminary operation and maintenance smart contract optimization data to obtain the operation and maintenance smart contract optimization data.

7. A photovoltaic power grid management and operation system for industrial and commercial use, characterized in that: Used to execute the management and operation method for industrial and commercial photovoltaic power storage grid as claimed in claim 1, the management and operation system for industrial and commercial photovoltaic power storage grid includes: The photovoltaic power storage grid edge computing module is used to perform edge computing on terminal data distributed throughout the photovoltaic power storage grid to obtain edge analysis data; The photovoltaic blockchain construction module is used to construct a blockchain according to the edge analysis data to obtain photovoltaic blockchain data; wherein the blockchain construction is specifically: blockchain node deployment is performed according to the node distribution data in the edge analysis data to obtain blockchain stage deployment data; edge analysis data is verified and processed according to the blockchain stage deployment data to obtain edge verification data; block generation is performed according to the edge verification data to obtain photovoltaic blockchain data; wherein the blockchain node deployment is specifically: node network construction is performed according to the node distribution data in the edge analysis data to obtain edge node network data; node quantity calculation, node density calculation and node load calculation are performed on the edge node network data to obtain node quantity data, node density data and node load data; node configuration planning is performed according to the node quantity data, node density data and node load data to obtain node configuration planning data; blockchain node deployment is performed according to the node configuration planning data to obtain blockchain stage deployment data; wherein the node density data includes the first node density data and the second node density data, and the node density data includes the first node density data and the second node density data. The point density calculation is specifically as follows: performing global node density calculation on edge node network data to obtain first node density data; performing image mapping according to edge node network data to obtain edge node network image data; performing matrix region generation according to edge node network image data through preset regional parameter data to obtain matrix region data, and performing node density calculation according to matrix region data and edge node network image data to obtain matrix region node density data; marking edge node network image data according to node distance data in edge node network data to obtain edge node network image marking data; performing clustering calculation on edge node network image marking data to obtain edge node network clustering data; performing center selection on edge node network clustering data to obtain edge node network clustering center data; performing node density calculation according to edge node network clustering data and edge node network clustering center data to obtain edge node distribution density data; merging edge node distribution density data and matrix region node density data to obtain second node density data; The operation and maintenance smart contract execution module is used to execute and process the preset operation and maintenance smart contract data according to the photovoltaic blockchain data, obtain the operation and maintenance smart contract data, and perform management and maintenance operations to obtain management and maintenance data; The operation and maintenance smart contract optimization module is used to optimize the preset operation and maintenance smart contract data according to the management and maintenance data, and obtain the operation and maintenance smart contract optimization data to carry out industrial and commercial photovoltaic power grid management and maintenance operations.

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