Distributed photovoltaic operation management system

Abstract

The invention provides a distributed photovoltaic operation management system, and belongs to the technical field of photovoltaic power generation. The system comprises an equipment control module which comprises an intelligent electric meter arranged in a photovoltaic power station, a data collector arranged on an inverter, video monitoring equipment and intelligent wearable equipment, and the equipment control module is used for collecting data and environment information of photovoltaic power station equipment and regulating and controlling the photovoltaic power station equipment; the data transmission module is used for providing an encrypted data transmission channel and supporting a breakpoint resume mechanism; the data processing module is used for receiving the data transmitted by the data transmission module and processing the received data; wherein data acquired by the equipment control module is encrypted and transmitted to the data processing module through the data transmission module. According to the invention, multiple technologies are combined, multiple intelligent services are provided for photovoltaic operation, and the efficiency of operation management is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a distributed photovoltaic operation and management system. Background Art

[0002] With the development of society and the advancement of science and technology, the position of renewable energy in the energy structure is becoming increasingly prominent. As a clean and efficient energy source, photovoltaic power generation is gradually becoming an important force in the global energy transformation. Under the background of "dual carbon", the development momentum of industrial and commercial photovoltaics is strong, and its advantages and profit mechanism have also attracted much attention.

[0003] At present, most distributed industrial and commercial photovoltaic projects adopt the operation mode of "self-generation and self-use, surplus power to the grid", and the electricity charges for self-use need to be settled between investors and users. At present, the information management of industrial and commercial photovoltaic projects is usually still based on the cloud platform of inverter manufacturers, which can only realize the working status and data collection monitoring of the inverter equipment itself, and it is difficult to support the daily business operations of the power station investor (or operator) after taking over the power station. The project unit currently adopts the method of manual on-site meter reading to complete the data reading and accounting work, which consumes a lot of manpower, material and financial resources, and offline manual calculations are prone to errors, resulting in delayed electricity bill settlement, which has a negative impact on the long-term cooperation between the two parties. In addition, the problem of electricity fee recovery is even more difficult in the industry. Due to factors such as power confirmation, invoice delivery, and long funding approval processes, the photovoltaic construction party has slow or even unrecoverable funds, resulting in funding gaps and operating risks. Summary of the invention

[0004] The present invention provides a distributed photovoltaic operation and management system, which is used to solve the problems of single function and low efficiency existing in the existing photovoltaic operation and management system.

[0005] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a distributed photovoltaic operation and management system on the one hand, which includes: an equipment control module, including a data collector, a video monitoring device and an intelligent wearable device deployed on a smart meter and an inverter in a photovoltaic power station, wherein the equipment control module is used to collect data and environmental information of photovoltaic power station equipment, and to regulate the photovoltaic power station equipment; a data transmission module is used to provide an encrypted data transmission channel and support a breakpoint resume mechanism; a data processing module is used to receive data transmitted by the data transmission module and process the received data; wherein the data collected by the equipment control module is encrypted and transmitted to the data processing module by the data transmission module.

[0006] Optionally, the data processing module includes: a real-time monitoring sub-module for cleaning and anomaly detection of the power data collected by the smart meter; a electricity bill settlement sub-module for generating bills and calculating PV subsidies according to the power data and time-of-use tariff rules; a safety analysis sub-module for identifying potential safety hazards based on the environmental information collected by the video monitoring device and the smart wearable device.

[0007] Optionally, the electricity bill settlement sub-module includes: a blockchain evidence storage unit for recording the hash value of the electricity bill transaction through Hyperledger Fabric; an automatic invoicing unit for connecting to the tax system and generating e-invoices; an electricity bill collection unit integrating multiple payment interfaces and supporting automatic deduction and manual payment.

[0008] Optionally, a preset intelligent mapping model is built into the data processing module, and the intelligent mapping model is used to simulate the equipment operation risks under extreme weather conditions.

[0009] Optionally, the method for building the intelligent mapping model includes: obtaining the 3D point cloud data of the PV power station; entering the models and locations of the equipment in the PV power station into the 3D point cloud data to generate a 3D grid model with equipment attributes; marking the electrical connection topological relationships in the 3D grid model; connecting to the meteorological data interface to obtain the future light intensity and temperature predictions.

[0010] Optionally, the operation process of the intelligent mapping model includes: obtaining the data uploaded by the equipment control module and the meteorological information within a preset future time period; predicting the equipment failure probability within the preset future time period through a preset failure prediction model according to the obtained data and meteorological information; highlighting the risk equipment in the 3D grid model according to the predicted failure probability; optimizing the parameters of the algorithm in the failure prediction model according to the difference between the actual maintenance records and the prediction results of the failure prediction model; wherein the failure prediction model is built based on a generative adversarial network and an attention mechanism.

[0011] Optionally, the step of predicting the equipment failure probability within a preset future time period through a preset failure prediction model according to the obtained data and meteorological information includes: simulating and generating equipment operation data through a generator according to the historical data of the PV power station equipment; analyzing the obtained data and meteorological information through an attention mechanism to screen out the features related to failures; predicting the equipment failure probability within the preset future time period according to the equipment operation data simulated by the generator, the features, and the meteorological information within the preset future time period.

[0012] Optionally, the data processing module is configured to: during data transmission, use the AES encryption algorithm to encrypt various types of data transmitted by the device control module; according to a preset data retransmission and recovery algorithm, when a network anomaly occurs, identify the breakpoint position of data transmission and initiate a data transmission request again.

[0013] Optionally, the distributed photovoltaic operation management system further includes a control terminal configured with a display unit. The control terminal is used to receive the processing result of the data processing module and provide an interaction interface; the control terminal issues a control instruction according to user operations. The control instruction is verified by the data processing module, and the verified control instruction is transmitted to the device control module through the data transmission module. The device control module performs specific operations according to the received control instruction.

[0014] Optionally, the breakpoint continuation mechanism is configured to: when transmission is interrupted, preferentially retransmit data blocks associated with high-risk devices marked by the fault prediction model, and combine meteorological prediction data during the transmission interruption to skip the transmission of inverter data blocks that have been predicted to be normal.

[0015] A distributed photovoltaic operation management system provided by the present invention integrates devices such as smart meters and inverters to achieve multi-source data collection and edge preprocessing, and ensures transmission security through AES encryption and breakpoint continuation; the data processing module integrates three-dimensional dynamic modeling and generative adversarial network (GAN) to accurately predict device failures and self-optimize, and combines blockchain to achieve transparent electricity bill settlement; the control terminal provides visual interaction and closed-loop instruction verification, forming a distributed photovoltaic full-link management system integrating data intelligent analysis, risk warning, and efficient operation and maintenance, significantly reducing operation and maintenance costs and enhancing the anti-risk ability of the power station. At the same time, it provides various services such as power collection, electricity settlement, invoice issuance, and user services. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings: Figure 1 is a schematic structural diagram of a distributed photovoltaic operation management system provided by an embodiment of the present invention; Figure 2 is a schematic structural diagram of a data processing module provided by an embodiment of the present invention; Figure 3 is a flowchart of the encryption and breakpoint continuation mechanism of the data transmission module provided by an embodiment of the present invention; Figure 4 It is a flowchart for constructing an intelligent mapping model provided by an embodiment of the present invention; Figure 5 It is a flowchart for fault prediction and self-optimization provided by an embodiment of the present invention; Figure 6 It is a functional diagram of a distributed photovoltaic operation management system provided by an embodiment of the present invention; Figure 7 It is a schematic diagram of a system electricity meter query interface provided by an embodiment of the present invention; Figure 8 It is a schematic diagram of a system report statistics interface provided by an embodiment of the present invention; Figure 9 It is a schematic diagram of a system operation and maintenance management interface provided by an embodiment of the present invention; Figure 10 It is a schematic diagram of a system settlement interface provided by an embodiment of the present invention. Detailed implementation manners

[0017] The following will detail the specific implementation manners of the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific implementation manners described herein are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

[0018] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solution of this application all comply with the relevant regulations of national laws and regulations. In the embodiments of this application, some industry-existing solutions such as certain software, components, models, etc. may be mentioned. They should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0019] Under the background of the global active promotion of the clean energy transformation, distributed photovoltaic power generation has become an important direction for the development of the energy field due to its advantages such as being close to the load center, reducing transmission losses, and flexible installation. In recent years, the construction scale of distributed photovoltaic power stations has shown an explosive growth, widely distributed in various places such as industrial factories, commercial buildings, and residential houses, making significant contributions to the optimization of the energy supply structure and sustainable development. However, with the continuous expansion of the scale and the increasing number of photovoltaic power stations, the disadvantages of the traditional management mode have gradually emerged, making it difficult to meet the needs of modern power station operation management.

[0020] The present invention is dedicated to solving many problems of traditional photovoltaic operation management systems. The present invention integrates devices such as smart meters and inverters to achieve multi-source data collection and edge preprocessing, and ensures transmission security through AES encryption and resume breakpoint transmission; the data processing module integrates 3D dynamic modeling and generative adversarial network (GAN) to accurately predict equipment failures and self-optimize, and combines blockchain to achieve transparent electricity bill settlement; the control terminal provides visual interaction and closed-loop instruction verification, and can realize functions such as power station asset equipment management, fault detection and SMS push, automatic settlement of electricity consumption and electricity bills, online issuance of electronic invoices, bill notification, and electricity bill recovery management. At the same time, it provides self-service query and download services for electricity customers.

[0021] The following will Figures 1-10 describe the present invention specifically.

[0022] As Figure 1 shown, an embodiment of the present invention provides a distributed photovoltaic operation management system, and the distributed photovoltaic operation management system includes: a device control module, including data collectors, video monitoring devices, and intelligent wearable devices deployed on smart meters and inverters in a photovoltaic power station, and the device control module is used to collect data and environmental information of photovoltaic power station equipment, and regulate photovoltaic power station equipment; a data transmission module, used to provide an encrypted data transmission channel and support a resume breakpoint transmission mechanism; a data processing module, used to receive the data transmitted by the data transmission module and process the received data; wherein, the data collected by the device control module is encrypted and transmitted to the data processing module through the data transmission module.

[0023] For the distributed photovoltaic operation management system provided by the present invention, the device control module integrates smart meters, inverters, video monitoring, and intelligent wearable devices, overcomes the limitations of traditional single collection, and realizes comprehensive multi-dimensional data collection, providing a rich and accurate data basis for system operation. The data transmission module uses encryption technology to ensure data transmission security, prevent leakage and tampering, and at the same time supports resume breakpoint transmission to avoid data loss in case of unstable network, ensuring reliable transmission. The data processing module has strong data processing capabilities, breaks through the weak function dilemma of existing systems, can deeply clean power data, accurately identify abnormalities, adapt to complex electricity bill settlement rules, and accurately identify potential safety hazards, greatly improving the accuracy of judging the operation status of power stations, and reducing operation costs and risks. In terms of fault prediction and potential hazard identification, the system can effectively handle complex working conditions and environments, accurately predict equipment failures and identify potential safety hazards, providing strong support for equipment maintenance and stable operation of power stations.

[0024] Preferably, as Figure 2As shown, the data processing module includes: a real-time monitoring sub-module for cleaning and anomaly detection of the power data collected by the smart meter; a electricity bill settlement sub-module for generating bills and calculating PV subsidies according to the power data and time-of-use tariff rules; a safety analysis sub-module for identifying potential safety hazards based on the environmental information collected by the video surveillance device and the smart wearable device.

[0025] Further preferably, the electricity bill settlement sub-module includes: a blockchain evidence storage unit for recording the hash value of the electricity bill transaction through Hyperledger Fabric; an automatic invoice issuing unit for interfacing with the tax system and generating electronic invoices; an electricity bill collection unit integrating multiple payment interfaces and supporting automatic deduction and manual payment.

[0026] In the preferred embodiment of the present invention, the data processing module serves as the core processing center of the entire distributed PV operation management system. The real-time monitoring, electricity bill settlement, and safety analysis sub-modules it contains provide strong support for the efficient and stable operation of the system from different dimensions. The real-time monitoring sub-module cleans and detects anomalies in the power data collected by the smart meter. By applying advanced data cleaning algorithms, it can quickly remove noise, error values, and outliers in the data, ensuring the accuracy and reliability of the data. The electricity bill settlement sub-module generates bills and calculates PV subsidies based on the power data and time-of-use tariff rules, with high accuracy and comprehensiveness. This module uses an accurate electricity bill calculation model, combined with complex time-of-use tariff rules, to automatically and accurately calculate the electricity bill amount for users and generate standardized bills. At the same time, according to the national and local PV subsidy policies and regulations, as well as the actual power generation situation of the power station, through a professional subsidy calculation model, it ensures that users can enjoy the due subsidies in a timely and full amount. Moreover, this module further integrates a blockchain evidence storage unit, an automatic invoice issuing unit, and an electricity bill collection unit to realize the automated, intelligent, and standardized management of the entire electricity bill settlement process, effectively improving the efficiency and fairness of electricity bill settlement. The safety analysis sub-module identifies potential safety hazards based on the environmental information collected by the video surveillance device and the smart wearable device, greatly enhancing the safety of the power station. It uses cutting-edge image recognition technology and advanced environmental parameter analysis algorithms to comprehensively and deeply analyze and evaluate the overall safety status of the power station. It can real-time and accurately identify various potential safety hazards such as personnel intrusion, fire and smoke, equipment overheating, and abnormal vibration, and generate early warning information in a timely manner, providing strong technical support for the safe operation of the power station and effectively preventing the occurrence of safety accidents.

[0027] For example, in the electricity bill settlement sub-module, taking a certain distributed photovoltaic power station as an example, a large number of distributed photovoltaic panels are connected to this power station, and the smart meter collects the power generation data of each period in real time. Assume that the local implementation of peak-valley time-of-use electricity price, the electricity price during peak hours is 1.2 yuan per degree, and the electricity price during off-peak hours is 0.5 yuan per degree. During a certain settlement period, the smart meter statistics show that the power generation during peak hours is 5000 degrees, and the power generation during off-peak hours is 8000 degrees. The electricity bill settlement sub-module accurately calculates the electricity bill for this period according to the time-of-use electricity price rule: (5000×1.2)+(8000×0.5)=6000+4000=10000 yuan. At the same time, according to the local photovoltaic subsidy policy, a subsidy of 0.2 yuan per degree of electricity is provided. This module calculates the subsidy amount for this period through the subsidy calculation model as (5000+8000)×0.2=2600 yuan. Subsequently, the blockchain evidence storage unit records the key information such as the amount, time, and calculation basis of this electricity bill transaction in the blockchain distributed ledger, the automatic invoicing unit connects to the tax system to generate an electronic invoice, and the electricity bill collection unit supports users to pay through various payment interfaces such as WeChat payment and Alipay payment, completing the full process operation of electricity bill settlement.

[0028] Preferably, the data processing module is built-in with an intelligent mapping model, and the intelligent mapping model is used to simulate the equipment operation risk under extreme weather.

[0029] Further preferably, as Figure 4 shown, the construction method of the intelligent mapping model includes: obtaining the three-dimensional point cloud data of the photovoltaic power station; inputting the models and positions of each device in the photovoltaic power station into the three-dimensional point cloud data to generate a three-dimensional grid model with device attributes; marking the electrical connection topology relationship in the three-dimensional grid model; connecting the meteorological data interface to obtain the future light intensity and temperature prediction.

[0030] Further preferably, as Figure 5 shown, the operation process of the intelligent mapping model includes: obtaining the data uploaded by the device control module and the meteorological information within a future preset time period; according to the obtained data and meteorological information, through a preset fault prediction model, predicting the device failure probability within a future preset time period; according to the predicted failure probability, highlighting the risk devices in the three-dimensional grid model; optimizing the parameters of the algorithm in the fault prediction model according to the difference between the actual maintenance records and the prediction results of the fault prediction model; wherein, the fault prediction model is constructed based on a generative adversarial network and an attention mechanism.

[0031] In the preferred embodiment of the present invention, the intelligent mapping model built in the data processing module provides strong support for coping with the operation risks of equipment under extreme weather. This model can accurately simulate the impact of extreme weather on equipment operation and transmit the simulation results to the control terminal to assist users in making scientific decisions, effectively reducing the damage of extreme weather to the power station and ensuring the stability and reliability of power generation. In terms of the construction of the intelligent mapping model, first, accurate three-dimensional point cloud data of the photovoltaic power station is obtained, which provides basic spatial information for subsequent modeling. Then, the models and locations of each device are detailedly entered into it to generate a three-dimensional grid model with device attributes, making the model more in line with the actual power station situation. Next, the electrical connection topological relationship is marked to clearly present the association between devices. Finally, the meteorological data interface is connected to obtain future light intensity and temperature predictions, enabling the model to conduct simulations in combination with meteorological factors. In terms of the operation of the intelligent mapping model, first, the data uploaded by the device control module and the meteorological information in the future preset time period are collected to provide a rich data basis for prediction. The fault prediction model constructed based on the generative adversarial network and the attention mechanism can fully explore the potential features and complex relationships in the data and accurately predict the future device fault probability. According to the prediction results, the risk devices are highlighted in the three-dimensional grid model, allowing users to intuitively understand the risk distribution. At the same time, by comparing the actual maintenance records and the prediction results, the algorithm parameters of the fault prediction model are continuously optimized, so that the prediction accuracy and reliability of the model are continuously improved to better adapt to the actual operation of the power station. In summary, the intelligent mapping model ensures the stable operation of the distributed photovoltaic operation management system with its scientific construction method and efficient operation process.

[0032] In the preferred embodiment of the present invention, when the network interruption is restored, the data blocks associated with the high-risk devices marked by the fault prediction model are preferentially transmitted to ensure that the system can quickly obtain the real-time status of key devices. Combining meteorological prediction data (such as stable future light intensity and no extreme weather), the system can skip the transmission of inverter data blocks determined to be "abnormal-free". For example, if the meteorological prediction shows that there is no thunderstorm in the next 3 hours, the relevant inverter data can be postponed for transmission, saving bandwidth for high-priority tasks, reducing network congestion, and improving the overall transmission efficiency. Skipping the transmission of low-risk data blocks not only saves network resources but also reduces the storage pressure on the cloud or local server.

[0033] For example, the fault prediction model predicts that in the upcoming extreme weather, a group of photovoltaic panels located at the wind outlet of the valley may be hit by strong winds, with a failure probability of 30%, and an inverter near the foot of the mountain, which is prone to water accumulation, has a failure probability of 25% due to moisture risk. Based on these prediction results, the corresponding photovoltaic panels and inverters are highlighted in red in the three-dimensional grid model. After the power station operation and maintenance personnel saw these risky devices through the control terminal, they immediately organized personnel to reinforce the photovoltaic panels at the wind outlet and take waterproof and moisture-proof measures for the inverters in the water-prone areas in advance.

[0034] Preferably, the method of predicting the probability of equipment failure within a preset time period in the future based on the acquired data and meteorological information through a preset fault prediction model includes: simulating and generating equipment operation data through a generator based on historical data of photovoltaic power station equipment; analyzing the acquired data and meteorological information through an attention mechanism to screen out features related to the fault; predicting the probability of equipment failure within a preset time period in the future based on the equipment operation data simulated by the generator and the features, combined with the meteorological information within a preset time period in the future.

[0035] In a preferred embodiment of the present invention, in the fault prediction link, first, the generator in the fault prediction model simulates and generates the equipment operation data based on the historical data of the photovoltaic power station equipment. The historical data covers the power generation power, temperature, light intensity of the photovoltaic panel, the input and output voltage, current, conversion efficiency and other information of the inverter. The generator refers to the operating characteristics of the equipment under different working conditions and environments in the historical data, such as specific meteorological conditions (high temperature, high humidity, etc.) or operating time periods (peak, valley), and simulates the possible operation data of the equipment in the future preset time period. Then, the attention mechanism intervenes to analyze the acquired equipment operation data and meteorological information, and screen out the features related to the fault. For photovoltaic panels, features that may cause faults, such as excessive temperature and drastic fluctuations in light intensity, will be given high weights and screened out; for inverters, key features such as unstable input and output voltages, abnormal conversion efficiency, and excessive internal temperature will be focused on. In terms of meteorological information, factors that have a significant impact on equipment failures, such as wind speed and direction, rainfall, humidity and other features of strong winds, will be identified and screened. Finally, the fault prediction model uses an internal algorithm to calculate the equipment operation data simulated by the comprehensive generator, the features screened by the attention mechanism, and the meteorological information in the future preset time period, so as to predict the failure probability of the equipment in the future preset time period and provide data support for subsequent operation and maintenance decisions.

[0036] For example, based on the data during historical heavy rain weather, the generator simulates the operating data such as the decrease in the power generation power of the photovoltaic panels and the possible parameter fluctuations of the inverter due to moisture during the next week if heavy rain occurs. The attention mechanism analyzes the data and meteorological information, and selects the features related to faults, such as indicators that are greatly affected by heavy rain, such as the sealing degree of the photovoltaic panels and the moisture-proof performance of the inverter. Combining the simulated data, the selected features, and the meteorological information of heavy rain in the next week, the model predicts that the probability of water accumulation failure of the photovoltaic panels is 5%, and the probability of moisture-related failure of the inverter is 20%.

[0037] Preferably, as Figure 3 shown, the data transmission module is configured to: during the data transmission process, use the AES encryption algorithm to encrypt various types of data transmitted by the device control module; according to the preset data retransmission and recovery algorithm, when an abnormal situation occurs in the network, identify the breakpoint position of the data transmission, and re-initiate a data transmission request.

[0038] In a preferred embodiment of the present invention, when various types of data such as the electrical energy data, inverter status information, and environmental information collected by the device control module are ready to be transmitted, the data transmission module immediately enables the AES encryption algorithm. Taking the electrical energy data collected by the smart meter as an example, before transmission, the key information such as the power generation amount, power, current, and voltage in the data is scrambled and reorganized into a string of ciphertext to prevent the operation risks of the power station caused by data leakage. When an abnormality occurs in the network, such as data transmission interruption caused by signal interference or line failure, the data transmission module acts quickly according to the preset data retransmission and recovery algorithm. For example, if the network is interrupted during the transmission of a set of inverter operating status data, the data transmission module can determine the data segments that have been successfully transmitted before the interruption and the parts that have not been transmitted. Subsequently, it automatically re-initiates a data transmission request and continues to transmit the remaining data from the breakpoint, ensuring that the data arrives at the data processing module intact. This mechanism effectively avoids data loss caused by network abnormalities, ensures the stability of data interaction in the entire system, provides a reliable basis for the subsequent processing and analysis of data by the data processing module, and ensures that the operation management decisions of the power station are always based on accurate and complete data.

[0039] Preferably, the distributed photovoltaic operation management system further includes a control terminal configured with a display unit. The control terminal is used to receive the processing results of the data processing module and provide an interaction interface; according to user operations, issue corresponding control instructions, display the data collected by the device control module, and generate a warning notice according to the potential safety hazards identified by the security analysis sub-module.

[0040] In a preferred embodiment of the present invention, when the user operates in the control terminal interface, the control terminal will respond quickly and issue corresponding control instructions. For example, the operation and maintenance personnel find that the photovoltaic panels in a certain area have decreased power generation efficiency due to dust accumulation. After selecting the equipment in the area in the control terminal interface, click the "Start Cleaning Equipment" operation button, and the control terminal will immediately convert this control instruction into a specific electrical signal instruction format, and finally transmit it to the equipment control module through the data transmission module and the data processing module, drive the corresponding cleaning equipment to start working, and clean the photovoltaic panels, thereby improving the power generation efficiency. In terms of data display, the control terminal clearly displays the electric energy data collected by the smart meter, such as real-time power generation, power curve, etc., in the form of charts, so that users can grasp the power generation status of the power station at a glance. For the inverter status data, the conversion efficiency, fault code and other information are presented in a detailed list, which is convenient for users to understand the operating status of the equipment at any time. The environmental image data collected by the video monitoring equipment is displayed by the control terminal in the form of a real-time video stream to ensure that users can view the power station environment in real time. Once the safety analysis submodule identifies a safety hazard, the control terminal quickly generates an early warning notification. Assume that the safety analysis submodule determines that a person has entered a certain area and is close to high-voltage equipment through video surveillance image analysis and smart wearable device data, posing a risk of electric shock. The control terminal immediately sends an early warning notification to the power station management personnel and related operation and maintenance personnel in various ways such as pop-up windows and text messages. The notification content details the location, type and possible harm of the hidden danger, reminding relevant personnel to take timely measures to eliminate safety hazards and ensure the safe operation of the power station.

[0041] Preferably, the device control module also includes a data processing unit for pre-processing the data collected by the device control module using edge computing technology.

[0042] In a preferred embodiment of the present invention, the data processing unit uses edge computing technology. Taking the electric energy data collected by the smart meter as an example, the data processing unit will use the data filtering algorithm in the edge computing technology to preliminarily screen the collected electric energy data and remove the noise data and abnormal values. For example, when the current value collected by the smart meter at a certain moment shows an obvious jump, and the difference with the previous and subsequent data is too large, the data processing unit will judge the data as an abnormal value and remove it to ensure that the data transmitted to the data processing module is accurate and reliable. The data processing unit uses edge computing technology to pre-process the data collected by the device control module, which not only reduces the pressure of network transmission and improves the efficiency of data transmission, but also can quickly and accurately provide valuable data to the data processing module, providing strong support for the stable operation and efficient decision-making of the entire distributed photovoltaic operation and management system.

[0043] Preferably, the resume - interrupted - transfer mechanism is configured as follows: when the transmission is interrupted, it preferentially re - transmits the data blocks associated with the high - risk devices marked by the fault prediction model, and combines the meteorological prediction data during the transmission interruption to skip the transmission of the inverter data blocks that have been predicted to be normal.

[0044] For example, a photovoltaic power station encounters strong wind and heavy rain weather, resulting in a 2 - hour interruption of the network signal. Inverter A (high - risk device): The previous fault prediction model marked its abnormal heat dissipation, and the fault probability reached 85%; Inverter B (low - risk device): operates normally, and the predicted fault probability is <5%; Data block 1: current and temperature data of Inverter A; Data block 2: normal status data of Inverter B; The data is transmitted to Data block 1 (completed) and Data block 2 (interrupted when 50% is transmitted), and the data processing module records the breakpoint position. When the network signal is restored, Data block 1 is preferentially transmitted, and Data block 2 is skipped.

[0045] In summary, as Figure 6 shown, a distributed photovoltaic operation and management system provided by the present invention integrates intelligent electricity meters, inverters, video monitoring, and intelligent wearable devices through an equipment control module to achieve real - time multi - dimensional data collection and edge - computing pre - processing. Combining the AES encryption and resume - interrupted - transfer technology of the data transmission module ensures the safe and reliable data transmission; The data processing module constructs an intelligent mapping model based on three - dimensional point cloud dynamic modeling, generative adversarial network (GAN), and attention mechanism to accurately predict the equipment failure risk under extreme weather and self - optimize the algorithm. At the same time, relying on blockchain evidence storage and automated electricity bill settlement, it realizes the transparency and efficient recovery of photovoltaic bills; The control terminal provides a user - friendly decision - making support and safe operation environment through a visual interaction interface and a closed - loop control instruction verification mechanism. The overall system integrates edge computing and artificial intelligence technologies with a modular hierarchical architecture, significantly reducing the operation and maintenance cost, improving the power generation efficiency, and enhancing the risk resistance ability of the power station, providing a full - link solution for the intelligent and digital operation of distributed photovoltaics, and integrating functions such as electric energy collection, electricity bill settlement, invoice issuance, and user services, which is convenient for users. At the same time, through the integration of resume - interrupted - transfer and fusion prediction models, it realizes dynamic hierarchical classification of data priorities and intelligent trimming of transmission content, and can more accurately identify key data blocks, avoiding the efficiency bottleneck caused by "indiscriminate resume - interrupted - transfer".

[0046] The above describes a distributed photovoltaic operation and management system in the embodiments of the present application. Based on the same inventive concept, the operation process of this system is as Figures 7-10 shown: As Figure 7As shown, in the electricity meter query interface, when clicking on a certain station in the left list, the electricity meter readings and some electricity meter parameters (such as electricity meter ID, collector ID, multiplication factor, type, online status) can be viewed on the right. By default, the electricity meter base numbers at 0, 4, 8, 12, 16, and 20 o'clock every day are displayed. Clicking on "Switch" can view the base numbers at each whole hour of 24 hours. And the query date can be selected.

[0047] Such as Figure 8 As shown, in the report statistics interface, four types of reports can be displayed: multi-day report, multi-month report, cabinet report, and time-sharing report. The types of data for statistics are the same. The differences are as follows: The multi-day and multi-month reports are for statistics of the electricity consumption data of the station. The cabinet report is for statistics of the electricity consumption data of each cabinet of the station. The time-sharing report is for statistics of the electricity consumption data of each hour of the station. All support exporting the excel report file.

[0048] Such as Figure 9 As shown, in the operation and maintenance management interface, it is mainly used for adding records and files, uploading and viewing various types of files, mainly including learning records, inspection records, work files, and hidden danger investigation lists.

[0049] Such as Figure 10 As shown, in the settlement interface, the invoicing information can be maintained, which is convenient for exporting the invoice template in the bill and invoice interface. The differences between the marketing perspective and the user perspective lie in the display. The marketing perspective shows according to company - project - settlement unit - site, and the user perspective shows according to company - settlement unit - project - site. The yellow stations displayed on the left represent merged stations. Clicking on the tree on the left, the corresponding information appears on the right, and the settlement unit can be queried, added, edited, and deleted on the interface.

[0050] It should be understood that in various embodiments of the present invention, the magnitudes of the serial numbers of the above processes do not mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0051] In addition, the terms "system" and "network" are often used interchangeably in this article. The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0052] It should be understood that in the embodiments of the present invention, "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0053] Those of ordinary skill in the art will realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described in terms of function in the above description. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0054] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0055] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can also be electrical, mechanical, or other forms of connection.

[0056] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.

[0057] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0058] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by firmware, or by a combination thereof. When implemented in software, the above functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. The computer-readable medium includes computer storage media and communication media, where the communication media includes any medium that facilitates the transfer of a computer program from one place to another. The storage media can be any available medium that can be accessed by a computer. By way of example but not limitation: the computer-readable medium can include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer. In addition, any connection can suitably be a computer-readable medium. For example, if the software is transmitted using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technologies such as infrared, radio and microwave from a website, server or other remote source, then the coaxial cable, fiber optic cable, twisted pair, DSL or wireless technologies such as infrared, wireless and microwave are included in the definition of the medium. As used in the present invention, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks generally reproduce data magnetically, while discs reproduce data optically with a laser. The above combinations should also be included within the scope of protection of the computer-readable medium.

[0059] In summary, the above are only the preferred embodiments of the technical solution of the present invention, and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A distributed photovoltaic operation and management system, characterized in that: The distributed photovoltaic operation and management system comprises: The equipment control module includes a data collector, a video monitoring device and a smart wearable device deployed on a smart meter and an inverter in a photovoltaic power station. The equipment control module is used to collect data and environmental information of photovoltaic power station equipment and to regulate photovoltaic power station equipment; Data transmission module, used to provide encrypted data transmission channel and support breakpoint resume mechanism; A data processing module, used for receiving data transmitted by the data transmission module and processing the received data; The data collected by the device control module is encrypted and transmitted to the data processing module by the data transmission module.

2. The distributed photovoltaic operation and management system according to claim 1, characterized in that: The data processing module comprises: A real-time monitoring submodule, used for cleaning and detecting anomalies of the electric energy data collected by the smart meter; The electricity fee settlement submodule generates a bill and calculates the photovoltaic subsidy according to the electricity data and the time-of-use electricity price rules; The safety analysis submodule is used to identify safety hazards based on the environmental information collected by the video surveillance device and the smart wearable device.

3. The distributed photovoltaic operation and management system according to claim 2 is characterized in that: The electricity fee settlement submodule includes: Blockchain evidence storage unit, used to record the hash value of electricity fee transactions through Hyperledger Fabric; Automatic invoicing unit, used to connect to the tax system and generate electronic invoices; The electricity fee recovery unit integrates multiple payment interfaces and supports automatic deduction and manual payment.

4. The distributed photovoltaic operation and management system according to claim 1, characterized in that: The data processing module has a built-in preset intelligent mapping model, which is used to simulate the equipment operation risks under extreme weather conditions.

5. The distributed photovoltaic operation and management system according to claim 4, characterized in that: The method for constructing the intelligent mapping model includes: Obtain 3D point cloud data of photovoltaic power stations; Entering the model and location of each device in the photovoltaic power station into the three-dimensional point cloud data to generate a three-dimensional grid model with device attributes; marking electrical connection topological relationships in the three-dimensional grid model; Connect to the meteorological data interface to obtain future light intensity and temperature forecasts.

6. The distributed photovoltaic operation and management system according to claim 5, characterized in that: The intelligent mapping model operation process includes: Obtaining data uploaded by the device control module and weather information within a preset time period in the future; Based on the acquired data and meteorological information, the probability of equipment failure within a preset time period in the future is predicted through a preset fault prediction model; According to the predicted failure probability, highlighting risk equipment in the three-dimensional grid model; Optimizing the parameters of the algorithm in the fault prediction model according to the difference between the actual maintenance record and the prediction result of the fault prediction model; Among them, the fault prediction model is built based on generative adversarial network and attention mechanism.

7. The distributed photovoltaic operation and management system according to claim 6, characterized in that: The method of predicting the probability of equipment failure within a preset time period in the future based on the acquired data and meteorological information by using a preset fault prediction model includes: Based on the historical data of photovoltaic power station equipment, the generator is used to simulate and generate equipment operation data; Through the attention mechanism, the acquired data and meteorological information are analyzed to filter out the features related to the fault; According to the equipment operation data simulated by the generator and the characteristics, combined with the meteorological information in the future preset time period, the probability of equipment failure in the future preset time period is predicted.

8. The distributed photovoltaic operation and management system according to claim 6, characterized in that: The data transmission module is configured as follows: During the data transmission process, the AES encryption algorithm is used to encrypt various types of data transmitted by the device control module; According to the preset data retransmission and recovery algorithm, when an abnormal situation occurs in the network, the breakpoint location of data transmission is identified and the data transmission request is re-initiated.

9. The distributed photovoltaic operation and management system according to claim 1, characterized in that: The distributed photovoltaic operation and management system further comprises a control terminal configured with a display unit, wherein the control terminal is used to receive the processing result of the data processing module and provide an interactive interface; The control terminal issues a control instruction according to the user operation, the control instruction is verified by the data processing module, the verified control instruction is transmitted to the device control module through the data transmission module, and the device control module performs a specific operation according to the received control instruction.

10. The distributed photovoltaic operation and management system according to claim 6, characterized in that: The breakpoint resume mechanism is configured as follows: When transmission is interrupted, data blocks associated with high-risk devices marked by the fault prediction model are retransmitted preferentially. Combined with the meteorological forecast data during the transmission interruption period, the transmission of inverter data blocks that are predicted to have no abnormalities is skipped.

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