Intelligent monitoring and scheduling method for rural energy

Abstract

The invention relates to the technical field of rural energy scheduling, in particular to an intelligent monitoring and scheduling method for rural energy. The method comprises the following steps: performing rural energy real-time monitoring processing through a rural energy digital twinborn platform, and constructing a rural energy distribution network according to real-time rural energy data; performing rural energy consumption anomaly analysis according to the historical rural energy consumption data, and performing energy distribution network optimization reconstruction processing on the rural energy distribution network based on the rural energy consumption anomaly data; performing rural energy correction processing on the rural energy storage abnormal transmission data based on the reconstructed rural energy distribution network, designing a rural energy scheduling strategy based on the rural energy supply demand data and the corrected rural energy data, executing rural energy scheduling operation, and performing rural energy scheduling data monitoring processing in real time; and performing dynamic optimization operation on the rural energy scheduling strategy based on the real-time rural energy scheduling data. According to the invention, intelligent monitoring and scheduling of rural energy are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of rural energy dispatching, and in particular to an intelligent monitoring and dispatching method for rural energy. Background Art

[0002] Rural energy is the key to meeting the needs of rural daily life, agricultural production and rural development. However, due to its wide distribution and diverse forms, such as solar energy, wind energy and biomass energy, and the lack of centralized intelligent monitoring and scheduling, rural energy faces problems such as unbalanced rural energy scheduling, energy waste and untimely energy supply. Traditional rural energy management and scheduling methods mainly rely on manual monitoring and the execution of fixed-plan rural energy scheduling, which cannot monitor rural energy conditions in real time and perform intelligent rural energy scheduling according to real-time changes in rural energy demand, resulting in rural energy scheduling strategies being difficult to accurately match actual needs, which in turn results in the inability to timely and accurately execute rural energy scheduling. Summary of the invention

[0003] Based on this, the present invention provides an intelligent monitoring and scheduling method for rural energy to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a rural energy intelligent monitoring and dispatching method includes the following steps:

[0005] Step S1: Real-time rural energy monitoring and processing is performed through the rural energy digital twin platform to generate real-time rural energy data; energy distribution analysis is performed in rural areas based on the real-time rural energy data to build a rural energy distribution network;

[0006] Step S2: Acquire historical rural energy consumption data; perform rural energy consumption anomaly analysis based on the historical rural energy consumption data to generate rural energy consumption anomaly data; perform energy distribution network optimization and reconstruction processing on the rural energy distribution network based on the rural energy consumption anomaly data to generate a reconstructed rural energy distribution network;

[0007] Step S3: Performing rural energy storage abnormal transmission monitoring and processing according to real-time rural energy data to generate rural energy storage abnormal transmission data; performing rural energy correction processing on the rural energy storage abnormal transmission data based on the reconstructed rural energy distribution network to generate corrected rural energy data;

[0008] Step S4: Acquire rural energy supply demand data, design a rural energy dispatching strategy based on the rural energy supply demand data and the revised rural energy data; execute rural energy dispatching operations according to the rural energy dispatching strategy, and perform real-time rural energy dispatching data monitoring and processing on the rural energy dispatching operations; perform rural energy dispatching strategy dynamic optimization operations on the rural energy dispatching strategy based on the real-time rural energy dispatching data.

[0009] Preferably, step S1 comprises the following steps:

[0010] Step S11: Performing real-time rural energy monitoring and processing through the rural energy digital twin platform to generate real-time rural energy data;

[0011] Step S12: performing rural energy data standardization processing on the real-time rural energy data to generate standard rural energy data;

[0012] Step S13: extracting rural energy monitoring nodes according to standard rural energy data to generate rural energy monitoring node data;

[0013] Step S14: dividing the standard rural energy data into rural energy regional ranges according to the rural energy monitoring node data to generate regional rural energy data;

[0014] Step S15: Perform energy distribution analysis in rural areas based on regional rural energy data, generate rural energy distribution data, and construct a rural energy distribution network based on the rural energy distribution data.

[0015] The present invention performs real-time monitoring and processing of rural energy through a rural energy digital twin platform, monitors the real-time rural energy situation, and provides timely and accurate data for subsequent rural energy analysis and scheduling. Standardization of rural energy data is performed on real-time rural energy data to eliminate differences in the format and unit of rural energy data collected by the rural energy digital twin platform due to different devices, regions and time periods, thereby ensuring the consistency of real-time rural energy data. Rural energy monitoring nodes are extracted according to standard rural energy data, and the regions and devices of rural energy are accurately identified to achieve refined monitoring and management of rural energy, while providing a targeted data basis for subsequent rural energy distribution analysis and division. Rural energy regional ranges are divided for standard rural energy data according to rural energy monitoring node data to refine rural energy monitoring and management. Energy distribution analysis of rural areas is performed based on regional rural energy data to generate rural energy distribution data, which can fully understand the distribution of rural energy, and build a rural energy distribution network based on rural energy distribution data. The rural energy distribution network can fully monitor the rural energy status and transmission path, realize comprehensive real-time monitoring of rural energy, and flexibly perform rural energy scheduling.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: Obtain historical rural energy consumption data;

[0018] Step S22: Performing historical rural energy consumption peak analysis based on historical rural energy consumption data to generate historical rural energy consumption peak data;

[0019] Step S23: dividing the historical rural energy consumption peak data into energy consumption peak values ​​based on the preset energy consumption peak range data, and generating classified rural energy consumption peak data;

[0020] Step S24: performing rural energy consumption fluctuation frequency analysis on the classified rural energy consumption peak data to generate rural energy consumption fluctuation frequency data;

[0021] Step S25: performing rural energy consumption anomaly analysis on historical rural energy consumption data according to rural energy consumption fluctuation frequency data to generate rural energy consumption anomaly data;

[0022] Step S26: Based on the rural energy consumption abnormality data, the rural energy distribution network is optimized and reconstructed to generate a reconstructed rural energy distribution network.

[0023] The present invention performs a historical rural energy consumption peak analysis based on historical rural energy consumption data, analyzes the energy consumption change trend in different time periods in rural areas, helps to accurately predict future rural energy demand, and takes load adjustment measures in advance during the peak energy consumption period to avoid rural energy supply shortages or overloads, and improves rural energy supply stability and the timeliness of energy scheduling. Based on the preset energy consumption peak range data, the historical rural energy consumption peak data is divided into energy consumption peaks to achieve refined monitoring of rural energy consumption, while quickly identifying rural energy consumption anomalies and making timely adjustments. The rural energy consumption fluctuation frequency analysis is performed on the classified rural energy consumption peak data to identify and quantify the fluctuation laws and frequencies of rural energy consumption peaks of different classifications, and monitor the fluctuation of rural energy consumption. According to the rural energy consumption fluctuation frequency data, the rural energy consumption anomaly analysis is performed on the historical rural energy consumption data, which can accurately identify and locate abnormal fluctuations and irregular changes in rural energy consumption, improve the ability to respond to potential rural energy problems, and ensure the reliability and stability of rural energy scheduling. Based on the abnormal rural energy consumption data, the rural energy distribution network is optimized and reconstructed, the key nodes and transmission paths in the rural energy distribution network are optimized and adjusted, and potential rural energy scheduling problems are repaired, thereby achieving more efficient rural energy scheduling.

[0024] Preferably, step S3 comprises the following steps:

[0025] Step S31: extracting rural energy storage data of real-time rural energy data;

[0026] Step S32: Analyze rural energy storage transmission characteristics based on rural energy storage data to generate rural energy storage transmission characteristic data;

[0027] Step S33: performing rural energy storage abnormal transmission detection processing based on the rural energy storage transmission characteristic data to generate rural energy storage abnormal transmission data;

[0028] Step S34: Perform rural energy correction processing on the rural energy storage abnormal transmission data based on the reconstruction of the rural energy distribution network to generate corrected rural energy data.

[0029] The present invention extracts rural energy storage data from real-time rural energy data, monitors the status and performance of rural energy storage in real time, optimizes the dispatching management of rural energy storage in real time, and ensures the stability and timeliness of rural energy dispatching. According to the rural energy storage data, the rural energy storage transmission characteristics are analyzed, the dynamic characteristics of rural energy storage in the energy transmission process are analyzed, the potential problems in the rural energy transmission process are monitored and optimized, and the overall stability of rural energy dispatching is ensured. Based on the rural energy storage transmission characteristic data, the rural energy storage abnormal transmission detection and processing are performed to accurately identify the abnormal transmission existing in the rural energy storage transmission, and the potential problems are discovered and processed in time, thereby ensuring the reliability and stability of rural energy storage transmission and energy dispatching. Based on the reconstruction of the rural energy distribution network, the rural energy storage abnormal transmission data is subjected to rural energy correction processing, and the rural energy data is corrected by using the reconstruction of the rural energy distribution network to ensure the stability of rural energy transmission and dispatching.

[0030] Preferably, step S33 includes the following steps:

[0031] Analyze and process rural energy storage transmission load according to rural energy storage transmission characteristic data to generate rural energy storage transmission load data;

[0032] Perform rural energy supply load path analysis based on rural energy storage and transmission load data to generate rural energy supply load path data;

[0033] Perform rural energy storage and transmission node anomaly detection based on rural energy supply load path data to generate rural energy storage and transmission node anomaly data;

[0034] Abnormal data of rural energy storage transmission nodes are detected and processed to generate abnormal transmission data of rural energy storage.

[0035] The present invention performs rural energy storage transmission load analysis and processing based on rural energy storage transmission characteristic data, monitors the load distribution and pressure conditions in the rural energy storage transmission process, identifies potential load bottlenecks and energy configuration problems, thereby optimizing the distribution of rural energy storage and ensuring the efficiency and stability of rural energy scheduling and transmission. Perform rural energy supply load path analysis based on rural energy storage transmission load data, monitor the energy flow path and efficiency of rural energy storage transmission to the load, and optimize the configuration of rural energy storage transmission paths. Perform rural energy storage transmission node anomaly detection based on rural energy supply load path data, accurately identify and locate the anomalies existing in the nodes in the rural energy transmission path, take targeted repair measures, and ensure the continuity and stability of rural energy scheduling. Perform energy storage transmission abnormality detection and processing on the abnormal data of rural energy storage transmission nodes, deeply analyze and confirm the abnormalities in rural energy storage transmission nodes, promptly solve rural energy scheduling and transmission problems, and ensure the overall stability of rural energy scheduling.

[0036] Preferably, S34 includes the following steps:

[0037] Perform rural energy storage transmission abnormal time series analysis based on rural energy storage abnormal transmission data to generate rural energy storage transmission abnormal time series data;

[0038] According to the abnormal time series data of rural energy storage transmission, the abnormal correlation characteristics of rural energy storage transmission are analyzed to generate abnormal correlation characteristic data of rural energy storage transmission;

[0039] Based on the abnormal correlation characteristic data of rural energy storage and transmission and the abnormal time series data of rural energy storage and transmission, rural energy anomaly positioning is performed to generate rural energy anomaly positioning data;

[0040] Based on the reconstruction of the rural energy distribution network, the rural energy anomaly location data is processed for rural energy correction to generate corrected rural energy data.

[0041] The present invention performs abnormal timing analysis of rural energy storage transmission according to abnormal transmission data of rural energy storage, monitors the time distribution and persistence of abnormal energy transmission, identifies the frequency, periodicity and changing trend of abnormal phenomena, monitors and predicts potential problems in energy storage, takes timely measures to reduce abnormal phenomena in rural energy storage transmission, and improves the overall stability of rural energy dispatching. According to the abnormal timing data of rural energy storage transmission, the abnormal correlation characteristics of rural energy storage transmission are analyzed, the mutual correlation and common characteristics of abnormal transmission of rural energy storage are analyzed, and the source of the problem is accurately located. Based on the abnormal correlation characteristic data of rural energy storage transmission and the abnormal timing data of rural energy storage transmission, rural energy abnormal positioning is performed, and the abnormal areas and nodes of rural energy are accurately identified and located. Combined with the abnormal timing pattern and correlation characteristics, the specific location and root cause of abnormal energy are identified, and targeted maintenance and repair are taken. Finally, based on the reconstruction of the rural energy distribution network, the rural energy abnormal positioning data is corrected for rural energy, the rural energy distribution is optimized, the abnormal energy is repaired, the stability of rural energy transmission and dispatching is improved, and the timeliness of rural energy supply is ensured.

[0042] Preferably, step S4 comprises the following steps:

[0043] Step S41: Acquire rural energy supply demand data;

[0044] Step S42: performing rural energy supply priority sorting processing according to the rural energy supply demand data to generate priority rural energy supply data;

[0045] Step S43: designing a rural energy dispatch strategy based on the priority rural energy supply data and the revised rural energy data;

[0046] Step S44: executing the rural energy dispatching operation according to the rural energy dispatching strategy, and performing real-time rural energy dispatching monitoring and processing on the rural energy dispatching operation to generate real-time rural energy dispatching data;

[0047] Step S45: Performing a rural energy scheduling strategy dynamic optimization operation on the rural energy scheduling strategy based on the real-time rural energy scheduling data.

[0048] The present invention obtains rural energy supply demand data, performs rural energy supply priority sorting processing according to the rural energy supply demand data, accurately sorts the energy supply priorities of different rural areas according to the actual energy demand and importance of each rural area, and optimizes the order of rural energy scheduling. Design a rural energy scheduling strategy based on the priority rural energy supply data and the revised rural energy data, comprehensively consider the energy supply priorities of each rural area and the actual rural energy situation, and formulate a scientific and reasonable rural energy scheduling strategy. Execute rural energy scheduling operations according to the rural energy scheduling strategy, and perform real-time rural energy scheduling monitoring and processing on the rural energy scheduling operations, monitor the actual rural energy change data in the rural energy scheduling process in real time, and immediately feedback the actual situation of rural energy scheduling to ensure the stability of rural energy scheduling. Perform rural energy scheduling strategy dynamic optimization operations on the rural energy scheduling strategy based on real-time rural energy scheduling data, timely adjust and optimize the rural energy scheduling strategy according to the real-time energy scheduling data, ensure that the rural energy scheduling strategy can be adjusted according to the changes in the actual rural energy scheduling, improve the flexibility of rural energy scheduling, and improve the efficiency and stability of the overall rural energy scheduling.

[0049] Preferably, step S42 includes the following steps:

[0050] Analyze the impact range of rural energy supply targets based on rural energy supply demand data and generate data on the impact range of rural energy supply targets;

[0051] Perform rural energy supply impact range intersection processing according to rural energy supply target impact range data to generate rural energy supply impact range intersection data;

[0052] Based on the intersection data of rural energy supply impact range, rural energy supply demand data is weighted to generate rural energy supply weighted data;

[0053] Rural energy supply priorities are sorted according to the rural energy supply weighted data to generate priority rural energy supply data.

[0054] The present invention analyzes the impact range of rural energy supply targets based on rural energy supply demand data, analyzes the impact range of each energy supply target in different areas, formulates an accurate rural energy scheduling strategy, ensures that key areas and high-demand points are given priority in rural energy supply, and ensures the efficiency and effectiveness of rural energy scheduling. According to the rural energy supply target impact range data, the rural energy supply impact range intersection processing is performed to determine the overlapping impact ranges between different rural energy supply target areas to avoid rural energy scheduling conflicts. Based on the rural energy supply impact range intersection data, rural energy supply demand data is subjected to rural energy supply weighted processing to generate rural energy supply weighted data, and rural energy supply priority sorting processing is performed based on the rural energy supply weighted data, and each rural energy supply demand is accurately evaluated, so that rural energy can be reasonably scheduled according to the urgency and impact range of the demand, and the rationality and efficiency of the overall rural energy scheduling are optimized.

[0055] Preferably, step S43 includes the following steps:

[0056] Step S431: performing actual rural energy dispatch analysis on the priority rural energy supply data to generate actual rural energy dispatch data;

[0057] Step S432: performing rural energy dispatch association processing according to the actual rural energy dispatch data and the revised rural energy data to generate rural energy dispatch association data;

[0058] Step S433: using the reconstructed rural energy distribution network to perform rural energy dispatch path simulation analysis on the rural energy dispatch related data, and generating simulated rural energy dispatch path data;

[0059] Step S434: Design a rural energy scheduling strategy based on the rural energy scheduling related data and the simulated rural energy scheduling path data.

[0060] The present invention performs actual rural energy dispatch analysis on priority rural energy supply data, analyzes the actual rural energy dispatch demand of each region and demand point, and the actual rural energy dispatch data accurately divides the distribution and dispatch of rural energy. According to the actual rural energy dispatch data and the revised rural energy data, rural energy dispatch association processing is performed, and the corresponding rural energy is retrieved according to the actual energy dispatch data to ensure that rural energy is reasonably distributed according to actual demand and priority, and the flexibility and accuracy of rural energy dispatch are achieved. By reconstructing the rural energy distribution network, the rural energy dispatch path simulation analysis is performed on the rural energy dispatch association data, which can evaluate and optimize the effect and timeliness of different energy dispatch paths, and ensure that rural energy dispatch is more efficient and reliable. Rural energy dispatch strategies are designed based on rural energy dispatch association data and simulated rural energy dispatch path data to ensure that rural energy dispatch decisions fully consider the actual needs and optimization paths of rural energy distribution, and achieve efficient dispatch of rural energy.

[0061] Preferably, step S45 includes the following steps:

[0062] Monitor rural energy dispatch turnover based on real-time rural energy dispatch data to generate real-time rural energy dispatch turnover data;

[0063] Conduct rural energy dispatch time difference analysis based on real-time rural energy dispatch turnover data to generate real-time rural energy dispatch time difference data;

[0064] Based on the real-time rural energy scheduling time difference data, the actual rural energy scheduling data is dynamically optimized, and the optimized actual rural energy scheduling data is fed back to step S432 to execute the rural energy scheduling strategy dynamic optimization operation.

[0065] The present invention monitors rural energy dispatch turnover on real-time rural energy dispatch data, detects the execution of rural energy dispatch and rural energy turnover in real time and makes adjustments in advance to ensure the stability and timeliness of rural energy dispatch. According to the real-time rural energy dispatch turnover data, rural energy dispatch time difference analysis is performed, the time delay or difference in the rural energy dispatch process is monitored and analyzed, and timely adjustments are made to ensure the timeliness of rural energy dispatch. Based on the real-time rural energy dispatch time difference data, the actual rural energy dispatch data is dynamically optimized for rural energy dispatch, and the optimized actual rural energy dispatch data is fed back to step S432 to perform the dynamic optimization operation of the rural energy dispatch strategy, dynamically optimize the rural energy dispatch strategy, adjust the actual rural energy dispatch data to adapt to the real-time rural energy dispatch demand, optimize rural energy distribution, and achieve the accuracy and flexibility of rural energy dispatch.

[0066] The beneficial effect of the present application is that the rural energy intelligent monitoring and dispatching method of the present invention performs real-time monitoring and processing of rural energy through a rural energy digital twin platform, monitors rural energy data in real time and constructs an energy distribution network that conforms to the local rural areas, and then analyzes historical rural energy consumption data to identify abnormal rural energy consumption patterns and abnormal rural energy transmission lines and energy nodes, and optimizes and reconstructs the rural energy distribution network based on abnormal rural energy consumption data, thereby improving the monitoring and processing speed of rural energy anomalies and optimizing rural energy transmission paths. According to real-time rural energy data, rural energy storage abnormal transmission monitoring and processing are performed to generate rural energy storage abnormal transmission data; rural energy correction processing is performed on rural energy storage abnormal transmission data based on the reconstructed rural energy distribution network to ensure the stability of rural energy dispatching. By obtaining rural energy supply demand data, and designing rural energy dispatching strategies based on rural energy supply demand data and correcting rural energy data, we can ensure that rural energy dispatching can meet the actual rural energy supply demand, avoid uneven rural energy distribution leading to uncoordinated energy dispatching, execute rural energy dispatching operations according to rural energy dispatching strategies, and conduct real-time rural energy dispatching data monitoring and processing on rural energy dispatching operations, execute rural energy dispatching strategy dynamic optimization operations based on real-time rural energy dispatching data, dynamically monitor the execution of rural energy dispatching, ensure the stability of rural energy dispatching, and flexibly adjust the rural energy dispatching strategy, realize dynamic optimization of rural energy dispatching strategy, improve the flexibility and accuracy of rural energy dispatching, ensure the efficiency and stability of rural energy dispatching, and realize intelligent monitoring and dispatching of rural energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of the steps of a method for intelligent monitoring and dispatching of rural energy according to the present invention;

[0068] Figure 2 for Figure 1 Detailed implementation steps of step S1 in FIG.

[0069] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0070] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0071] Figure 5 for Figure 1 Detailed implementation steps of step S43;

[0072] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0073] 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.

[0074] 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.

[0075] 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.

[0076] To achieve this, please refer to Figures 1 to 5 The present invention provides a method for intelligent monitoring and dispatching of rural energy, comprising the following steps:

[0077] Step S1: Real-time rural energy monitoring and processing is performed through the rural energy digital twin platform to generate real-time rural energy data; energy distribution analysis is performed in rural areas based on the real-time rural energy data to build a rural energy distribution network;

[0078] Step S2: Acquire historical rural energy consumption data; perform rural energy consumption anomaly analysis based on the historical rural energy consumption data to generate rural energy consumption anomaly data; perform energy distribution network optimization and reconstruction processing on the rural energy distribution network based on the rural energy consumption anomaly data to generate a reconstructed rural energy distribution network;

[0079] Step S3: Performing rural energy storage abnormal transmission monitoring and processing according to real-time rural energy data to generate rural energy storage abnormal transmission data; performing rural energy correction processing on the rural energy storage abnormal transmission data based on the reconstructed rural energy distribution network to generate corrected rural energy data;

[0080] Step S4: Acquire rural energy supply demand data, design a rural energy dispatching strategy based on the rural energy supply demand data and the revised rural energy data; execute rural energy dispatching operations according to the rural energy dispatching strategy, and perform real-time rural energy dispatching data monitoring and processing on the rural energy dispatching operations; perform rural energy dispatching strategy dynamic optimization operations on the rural energy dispatching strategy based on the real-time rural energy dispatching data.

[0081] The present invention uses a rural energy digital twin platform to monitor and process rural energy in real time, monitors and collects real-time rural energy data efficiently and accurately, and ensures the timeliness and accuracy of subsequent rural energy scheduling. According to real-time rural energy data, energy distribution analysis of rural areas is carried out. By analyzing the geographical distribution, time characteristics and consumption patterns of real-time rural energy data, an accurate rural energy distribution network is constructed to monitor the distribution of rural energy in different regions, which is conducive to the subsequent efficient scheduling of rural energy. Rural energy consumption anomaly analysis is carried out through historical rural energy consumption data, abnormal patterns of rural energy use and scheduling are analyzed, and energy distribution network optimization and reconstruction processing is performed on the rural energy distribution network based on rural energy consumption anomaly data, anomalies are identified and the existing rural energy distribution network is optimized, rural energy distribution and energy transmission paths are adjusted, rural energy scheduling is made more accurate and timely, the stability and efficiency of rural energy scheduling are guaranteed, and the rural energy distribution network is reconstructed to meet the actual needs of different rural areas, and accurate supply and efficient scheduling management of rural energy are realized. According to the real-time rural energy data, abnormal transmission of rural energy storage is monitored and processed, and unstable factors in abnormal transmission of rural energy storage are accurately detected to prevent the reduction of rural energy storage transmission efficiency and other causes of failure. Based on the reconstruction of the rural energy distribution network, rural energy storage abnormal transmission data is corrected for rural energy to ensure that rural energy transmission and dispatching remain normal at all times, and the stability of rural energy dispatching is ensured. At the same time, the transmission path of rural energy dispatching is optimized to improve the reliability and timeliness of rural energy dispatching. Rural energy supply demand data is obtained, and rural energy dispatching strategies are designed based on rural energy supply demand data and corrected rural energy data to ensure that rural energy dispatching can meet the actual rural energy supply demand and avoid uneven rural energy distribution leading to uncoordinated rural energy dispatching. Execute rural energy dispatching operations according to rural energy dispatching strategies, and conduct real-time rural energy dispatching data monitoring and processing for rural energy dispatching operations, dynamically monitor the real-time situation of rural energy dispatching, ensure the stability of rural energy dispatching while being able to flexibly respond to emergencies of rural energy dispatching, and dynamically optimize rural energy dispatching strategies based on real-time rural energy dispatching data to achieve dynamic optimization of rural energy dispatching strategies, improve the flexibility and accuracy of rural energy dispatching, ensure the efficiency and stability of rural energy dispatching, and realize intelligent monitoring and dispatching of rural energy.

[0082] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a method for intelligent monitoring and dispatching of rural energy in the present invention. In this embodiment, the method for intelligent monitoring and dispatching of rural energy includes the following steps:

[0083] Step S1: Real-time rural energy monitoring and processing is performed through the rural energy digital twin platform to generate real-time rural energy data; energy distribution analysis is performed in rural areas based on the real-time rural energy data to build a rural energy distribution network;

[0084] In the embodiment of the present invention, rural energy real-time monitoring and processing is performed through the rural energy digital twin platform. The rural area collects various energy forms such as solar energy, wind energy and biomass energy in rural areas in real time through the deployed IoT sensor nodes. Each sensor node collects rural energy data in real time, including parameters such as power generation, consumption, and energy storage level, and transmits them to the data storage module of the rural energy digital twin platform, so as to monitor the real-time rural energy data in the data storage module of the rural energy digital twin platform in real time. Energy distribution analysis of rural areas is performed based on real-time rural energy data. For example, data is transmitted to the cloud through the LoRa communication protocol, and the rural energy distribution is analyzed using an algorithm based on big data analysis. A rural energy distribution network is constructed based on the analysis results, and rural energy intelligent monitoring and scheduling are performed through the rural energy distribution network.

[0085] Step S2: Acquire historical rural energy consumption data; perform rural energy consumption anomaly analysis based on the historical rural energy consumption data to generate rural energy consumption anomaly data; perform energy distribution network optimization and reconstruction processing on the rural energy distribution network based on the rural energy consumption anomaly data to generate a reconstructed rural energy distribution network;

[0086] In an embodiment of the present invention, by acquiring historical rural energy consumption data, an analysis of rural energy consumption anomalies is performed based on the historical rural energy consumption data. For example, in the peak energy consumption in the past winter, the energy consumption of solar energy and wind energy fluctuated greatly, and the average daily consumption in a certain week increased from the usual 30kWh to 60kWh. An anomaly detection algorithm, such as an anomaly detection method based on Z-score, is used to detect that the energy consumption anomaly is due to a failure of wind energy equipment in a certain agricultural facility. Based on the rural energy consumption anomaly data, the existing rural energy distribution network is optimized and reconstructed. The reconstruction of the rural energy distribution network can better monitor sudden rural energy consumption fluctuations and achieve more stable rural energy distribution and scheduling. For example, by optimizing the compensation of wind energy equipment and increasing the energy storage capacity of solar energy, the rural energy supply is more balanced, and the energy storage unit capacity in the abnormal area is increased from 200kWh to 300kWh. A load reduction strategy is introduced to give priority to ensuring the power supply of key equipment and alleviate the pressure of energy supply in abnormal areas.

[0087] Step S3: Performing rural energy storage abnormal transmission monitoring and processing according to real-time rural energy data to generate rural energy storage abnormal transmission data; performing rural energy correction processing on the rural energy storage abnormal transmission data based on the reconstructed rural energy distribution network to generate corrected rural energy data;

[0088] In an embodiment of the present invention, rural energy storage abnormal transmission monitoring and processing is performed based on real-time rural energy data. For example, the energy storage level of solar power generation is normally 80% full every day. Data monitoring on a certain day shows that the energy storage level is only 50%, and the transmission power drops from the normal 5kW to 2kW. Through the analysis of the transmission anomaly detection algorithm (such as an anomaly detection method based on time series), the energy storage transmission line or a node in the area is abnormal. Rural energy correction processing is performed on the rural energy storage abnormal transmission data based on the reconstruction of the rural energy distribution network. For example, the energy in the area is redistributed, and the output of wind energy storage is increased from the original 3kW to 4kW. At the same time, the energy storage transmission path is adjusted to bypass the faulty node and transmit power from the backup energy storage line.

[0089] Step S4: Acquire rural energy supply demand data, design a rural energy dispatching strategy based on the rural energy supply demand data and the revised rural energy data; execute rural energy dispatching operations according to the rural energy dispatching strategy, and perform real-time rural energy dispatching data monitoring and processing on the rural energy dispatching operations; perform rural energy dispatching strategy dynamic optimization operations on the rural energy dispatching strategy based on the real-time rural energy dispatching data.

[0090] In an embodiment of the present invention, rural energy supply demand data is obtained, and a rural energy dispatching strategy is designed based on the rural energy supply demand data and the revised rural energy data. For example, the daily energy supply demand of household users and agricultural facilities is 250kWh, and the daily energy supply demand of agricultural facilities during the busy farming season is increased by an additional 50kWh. Based on this rural energy supply demand data and the previously revised rural energy data (including the distribution and energy storage status of solar energy, wind energy and biomass energy), a rural energy dispatching strategy is designed. The solar energy storage level has recovered to 90%, while the wind energy storage is only 60%, and solar energy is used to power agricultural facilities first. The rural energy dispatching operation is performed according to the rural energy dispatching strategy, and the rural energy dispatching operation is monitored and processed in real time for the rural energy dispatching data. The rural energy dispatching strategy is dynamically optimized based on the real-time rural energy dispatching data. For example, in the process of executing rural energy dispatching operations, the rural energy dispatching operations are monitored in real time through the rural energy digital twin platform. The actual demand of agricultural facilities is higher than estimated, and the energy supply demand increases by 10kWh. The energy dispatching strategy is dynamically adjusted to increase the wind power output to 6kW and reduce the energy supply of some low-priority energy consumption targets to ensure that the power supply needs of agricultural facilities are met.

[0091] Preferably, step S1 comprises the following steps:

[0092] Step S11: Performing real-time rural energy monitoring and processing through the rural energy digital twin platform to generate real-time rural energy data;

[0093] Step S12: performing rural energy data standardization processing on the real-time rural energy data to generate standard rural energy data;

[0094] Step S13: extracting rural energy monitoring nodes according to standard rural energy data to generate rural energy monitoring node data;

[0095] Step S14: dividing the standard rural energy data into rural energy regional ranges according to the rural energy monitoring node data to generate regional rural energy data;

[0096] Step S15: Perform energy distribution analysis in rural areas based on regional rural energy data, generate rural energy distribution data, and construct a rural energy distribution network based on the rural energy distribution data.

[0097] The present invention performs real-time monitoring and processing of rural energy through a rural energy digital twin platform, monitors the real-time rural energy situation, and provides timely and accurate data for subsequent rural energy analysis and scheduling. Standardization of rural energy data is performed on real-time rural energy data to eliminate differences in the format and unit of rural energy data collected by the rural energy digital twin platform between different devices, regions and time periods, thereby ensuring the consistency of real-time rural energy data. Rural energy monitoring nodes are extracted according to standard rural energy data, and the regions and devices of rural energy are accurately identified to achieve refined monitoring and management of rural energy, while providing a targeted data basis for subsequent rural energy distribution analysis and division. Rural energy regional ranges are divided for standard rural energy data according to rural energy monitoring node data to refine rural energy monitoring and management. Energy distribution analysis of rural areas is performed based on regional rural energy data to generate rural energy distribution data, so that the distribution of rural energy can be fully understood, and a rural energy distribution network is constructed based on rural energy distribution data. The rural energy distribution network can fully monitor the rural energy status and transmission path, realize comprehensive real-time monitoring of rural energy, and flexibly perform rural energy scheduling.

[0098] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S1 in the flowchart, in this example, step S1 includes:

[0099] Step S11: Performing real-time rural energy monitoring and processing through the rural energy digital twin platform to generate real-time rural energy data;

[0100] In the embodiment of the present invention, through the deployment of a variety of IoT devices, such as smart meters, sensors and data collectors, connected to the data cloud of the rural energy digital twin platform through a low-power wide area network (LPWAN) such as LoRa or NB-IoT, the rural energy digital twin platform collects rural energy terrain features through GIS technology, establishes a rural energy distribution architecture, and maps the collected rural energy data corresponding to the rural energy terrain distribution architecture, and collects a variety of rural energy data including electricity, solar energy, wind energy, etc. in real time. In addition, the collected rural energy data can be initially processed by edge computing equipment and transmitted to the cloud in real time through the 5G network, and the collected rural energy data can be monitored and processed in real time to obtain real-time rural energy data.

[0101] Step S12: performing rural energy data standardization processing on the real-time rural energy data to generate standard rural energy data;

[0102] In the embodiment of the present invention, the rural energy data collected in real time comes from different types of equipment and energy sources, such as photovoltaic power generation, wind power generation, and grid power supply. Since the rural energy data formats and units of different equipment are different, for example, the power data output by solar photovoltaic power generation equipment is expressed in kilowatt-hours (kWh), while the power data of grid power supply is expressed in watts (W), by using a data normalization algorithm, such as the Min-Max normalization method, the real-time rural energy data is converted to the same unit and range, for example, all power data are unified into watts (W).

[0103] Step S13: extracting rural energy monitoring nodes according to standard rural energy data to generate rural energy monitoring node data;

[0104] In an embodiment of the present invention, rural energy monitoring nodes are extracted based on standard rural energy data, and the real-time rural energy data after standardization processing comes from multiple monitoring points, such as the main power supply station, wind power tower, photovoltaic panel array and energy storage device of the village. In order to efficiently monitor and dispatch different rural energy sources, a clustering algorithm (such as K-means clustering algorithm) is used to analyze rural energy data at different locations, and key rural energy monitoring nodes are extracted, for example, 10 wind power towers and 5 photovoltaic panel arrays. By analyzing the power generation frequency, stability and abnormal fluctuations of each device, 3 wind power towers with the highest power generation efficiency and less fluctuation are selected as monitoring nodes, and 2 photovoltaic panels are selected as photovoltaic monitoring nodes. At the same time, the selection of rural energy monitoring nodes can be optimized according to parameters such as historical energy consumption data of energy equipment operation, power generation stability, geographical location distribution, and network signal transmission quality.

[0105] Step S14: dividing the standard rural energy data into rural energy regional ranges according to the rural energy monitoring node data to generate regional rural energy data;

[0106] In an embodiment of the present invention, the rural energy area range is divided for the standard rural energy data according to the rural energy monitoring node data, and the extracted rural energy monitoring nodes cover the main rural energy generation and consumption areas, and the entire rural area is divided into multiple energy consumption areas for more refined rural energy monitoring and scheduling. For example, two photovoltaic power generation areas, three wind power generation areas and one energy storage station, based on the monitoring node data, use the geographic information system (GIS) to calibrate the geographical location of the energy equipment, and divide the entire village into three main rural energy monitoring and management areas according to the power generation capacity and load of each energy equipment: photovoltaic power supply area, wind power supply area and mixed power supply area. At the same time, according to parameters such as geographical distance, power supply radius, and energy transmission efficiency, the power supply range of adjacent areas is optimized and adjusted.

[0107] Step S15: Perform energy distribution analysis in rural areas based on regional rural energy data, generate rural energy distribution data, and construct a rural energy distribution network based on the rural energy distribution data.

[0108] In an embodiment of the present invention, an energy distribution analysis of rural areas is performed based on regional rural energy data. For example, an energy flow analysis algorithm (such as a network flow analysis algorithm) is used to analyze the rural energy supply and demand of each region to determine the energy flow direction of each region. 70% of the energy in the photovoltaic power generation area is directly supplied to nearby residential areas, 30% is transmitted to the energy storage area, 50% of the wind power generation area is directly supplied, and the remaining 50% is transmitted to distant villages. A rural energy distribution network is constructed based on rural energy distribution data. For example, the minimum spanning tree algorithm in graph theory is used to optimize the rural energy transmission path by reconfiguring the connection path between the photovoltaic power generation area and the energy storage area. The rural energy distribution network can not only comprehensively monitor rural energy in real time, but also flexibly dispatch rural energy.

[0109] Preferably, step S2 comprises the following steps:

[0110] Step S21: Obtain historical rural energy consumption data;

[0111] Step S22: Performing historical rural energy consumption peak analysis based on historical rural energy consumption data to generate historical rural energy consumption peak data;

[0112] Step S23: dividing the historical rural energy consumption peak data into energy consumption peak values ​​based on the preset energy consumption peak range data, and generating classified rural energy consumption peak data;

[0113] Step S24: performing rural energy consumption fluctuation frequency analysis on the classified rural energy consumption peak data to generate rural energy consumption fluctuation frequency data;

[0114] Step S25: performing rural energy consumption anomaly analysis on historical rural energy consumption data according to rural energy consumption fluctuation frequency data to generate rural energy consumption anomaly data;

[0115] Step S26: Based on the rural energy consumption abnormality data, the rural energy distribution network is optimized and reconstructed to generate a reconstructed rural energy distribution network.

[0116] The present invention performs a historical rural energy consumption peak analysis based on historical rural energy consumption data, analyzes the energy consumption change trend in different time periods in rural areas, helps to accurately predict future rural energy demand, and takes load adjustment measures in advance during the peak energy consumption period to avoid rural energy supply shortages or overloads, and improves rural energy supply stability and the timeliness of energy scheduling. Based on the preset energy consumption peak range data, the historical rural energy consumption peak data is divided into energy consumption peaks to achieve refined monitoring of rural energy consumption, while quickly identifying rural energy consumption anomalies and making timely adjustments. The rural energy consumption fluctuation frequency analysis is performed on the classified rural energy consumption peak data to identify and quantify the fluctuation laws and frequencies of rural energy consumption peaks of different classifications, and monitor the fluctuation of rural energy consumption. According to the rural energy consumption fluctuation frequency data, the rural energy consumption anomaly analysis is performed on the historical rural energy consumption data, which can accurately identify and locate abnormal fluctuations and irregular changes in rural energy consumption, improve the ability to respond to potential rural energy problems, and ensure the reliability and stability of rural energy scheduling. Based on the abnormal rural energy consumption data, the rural energy distribution network is optimized and reconstructed, the key nodes and transmission paths in the rural energy distribution network are optimized and adjusted, and potential rural energy scheduling problems are repaired, thereby achieving more efficient rural energy scheduling.

[0117] In an embodiment of the present invention, historical rural energy consumption data is obtained from a database, including daily energy consumption peaks, average power consumption, and the use of various energy sources (such as solar energy, wind energy, and biomass energy). According to the historical rural energy consumption data, historical rural energy consumption peak analysis is performed. For example, through analysis, it is found that in the past two years, the average daily power consumption in summer is 150kWh, and the peak power consumption can reach 200kWh. In winter, due to heating demand, the peak power consumption in a certain week reaches 230kWh, while the daily power consumption is only 180kWh. The rural energy consumption peak data is marked using an ARIMA model based on time series analysis, and some peak power consumption time periods are mainly concentrated in the morning from 6:00 to 9:00, and in the evening from 18:00 to 22:00. The marked rural energy consumption peak data provides a basis for subsequent rural energy consumption abnormality monitoring and energy scheduling and optimization. The historical rural energy consumption peak data is divided into energy consumption peak values ​​based on the preset energy consumption peak range data. For example, three energy consumption peak ranges are preset: low peak 0-150kWh, medium peak 150-200kWh, and high peak above 200kWh. The average daily peak electricity consumption in summer is 180kWh, which belongs to the medium peak range, while the daily peak electricity consumption in a certain week in winter is 230kWh, which is classified as the high peak range. The classified rural energy consumption peak data were used to analyze the frequency of rural energy consumption fluctuations. For example, within the peak value range of 150-200kWh in the rural area, the energy consumption fluctuation frequency was higher in summer, with three energy consumption fluctuations occurring per week (with a fluctuation range of ±20kWh), while the energy consumption fluctuations in winter were more severe, with the frequency reaching ±50kWh during peak hours. Frequency analysis algorithms such as Fourier Transform were used to identify the periodic pattern of energy consumption fluctuations. The rural energy consumption fluctuation frequency was most significant in the morning from 6:00 to 9:00 and in the evening from 18:00 to 22:00, with fluctuation ranges of ±15kWh and ±30kWh, respectively. The rural energy consumption fluctuation frequency data can show the characteristics of energy consumption fluctuations in different seasons and different electricity consumption periods. Based on the rural energy consumption fluctuation frequency data, the rural energy consumption anomaly analysis is performed on the historical rural energy consumption data. For example, the normal energy consumption fluctuation frequency is set to ±20kWh, but the electricity consumption data of a certain winter suddenly expands to ±70kWh during the morning and evening peak hours, far exceeding the normal range. To further determine the cause of the anomaly, an anomaly detection algorithm based on Z-score can be used for in-depth analysis. The heating equipment of a certain agricultural facility has problems with the energy transmission path or node due to a malfunction, resulting in an abnormal increase in energy consumption. The energy consumption anomaly data is marked and related data is recorded, including the abnormal time period, fluctuation amplitude, and affected energy transmission path.Based on the abnormal rural energy consumption data, the rural energy distribution network is optimized and reconstructed, and the existing rural energy distribution network is redesigned using optimization algorithms (such as genetic algorithms or linear programming). For example, additional solar power generation equipment is added, the output power of the original wind power generators is increased, and the energy transmission lines are rearranged to share the overloaded load.

[0118] Preferably, step S3 comprises the following steps:

[0119] Step S31: extracting rural energy storage data of real-time rural energy data;

[0120] Step S32: Analyze rural energy storage transmission characteristics based on rural energy storage data to generate rural energy storage transmission characteristic data;

[0121] Step S33: performing rural energy storage abnormal transmission detection processing based on the rural energy storage transmission characteristic data to generate rural energy storage abnormal transmission data;

[0122] Step S34: Perform rural energy correction processing on the rural energy storage abnormal transmission data based on the reconstruction of the rural energy distribution network to generate corrected rural energy data.

[0123] The present invention extracts rural energy storage data from real-time rural energy data, monitors the status and performance of rural energy storage in real time, optimizes the dispatching management of rural energy storage in real time, and ensures the stability and timeliness of rural energy dispatching. According to the rural energy storage data, the rural energy storage transmission characteristics are analyzed, the dynamic characteristics of rural energy storage in the energy transmission process are analyzed, the potential problems in the rural energy transmission process are monitored and optimized, and the overall stability of rural energy dispatching is ensured. Based on the rural energy storage transmission characteristic data, the rural energy storage abnormal transmission detection and processing are performed to accurately identify the abnormal transmission existing in the rural energy storage transmission, and the potential problems are discovered and processed in time, thereby ensuring the reliability and stability of rural energy storage transmission and energy dispatching. Based on the reconstruction of the rural energy distribution network, the rural energy storage abnormal transmission data is subjected to rural energy correction processing, and the rural energy data is corrected by using the reconstruction of the rural energy distribution network to ensure the stability of rural energy transmission and dispatching.

[0124] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S3 in the flowchart, in this example, step S3 includes:

[0125] Step S31: extracting rural energy storage data of real-time rural energy data;

[0126] In an embodiment of the present invention, rural energy storage data of real-time rural energy data is extracted, and rural energy storage data is extracted from the Internet of Things sensor and the data acquisition module, and summarized, including specific energy conditions such as solar energy storage and wind energy storage, and transmitted to the cloud in real time through high-frequency data acquisition and transmission technology (such as LoRa communication protocol). The rural energy storage data is summarized and analyzed to ensure that rural energy storage can meet the current rural energy demand and effectively dispatch rural energy. At the same time, real-time monitoring of rural energy storage data can timely understand the changes in rural energy storage and make necessary adjustments and optimizations.

[0127] Step S32: Analyze rural energy storage transmission characteristics based on rural energy storage data to generate rural energy storage transmission characteristic data;

[0128] In an embodiment of the present invention, rural energy storage transmission characteristics are analyzed based on rural energy storage data, and data analysis tools (such as regression analysis and spectrum analysis) are used, including charging efficiency curves, transmission power fluctuation amplitudes, and load cycle characteristics, to monitor the status of rural energy storage and transmission efficiency issues.

[0129] Step S33: performing rural energy storage abnormal transmission detection processing based on the rural energy storage transmission characteristic data to generate rural energy storage abnormal transmission data;

[0130] In an embodiment of the present invention, abnormal transmission detection and processing of rural energy storage is performed based on rural energy storage transmission characteristic data, and an abnormal detection algorithm, such as a threshold-based detection method or a machine learning model, is used to identify that the transmission power fluctuation of the energy storage exceeds the normal range by comparing the historical normal transmission characteristic data, and mark the transmission abnormal data of this time period, including the time of abnormal occurrence, abnormal amplitude, abnormal transmission path, possible causes and impact range, which helps to quickly locate and solve energy storage transmission problems and ensure the stability and reliability of rural energy supply and scheduling.

[0131] Step S34: Perform rural energy correction processing on the rural energy storage abnormal transmission data based on the reconstruction of the rural energy distribution network to generate corrected rural energy data.

[0132] In an embodiment of the present invention, rural energy correction processing is performed on abnormal transmission data of rural energy storage based on reconstruction of the rural energy distribution network. Through correction algorithms, such as optimized scheduling algorithms and data smoothing technology, rural energy storage data and transmission data affected by the abnormalities are corrected to a normal state, thereby ensuring the stability of rural energy scheduling.

[0133] Preferably, step S33 includes the following steps:

[0134] Analyze and process rural energy storage transmission load according to rural energy storage transmission characteristic data to generate rural energy storage transmission load data;

[0135] Perform rural energy supply load path analysis based on rural energy storage and transmission load data to generate rural energy supply load path data;

[0136] Perform rural energy storage and transmission node anomaly detection based on rural energy supply load path data to generate rural energy storage and transmission node anomaly data;

[0137] Abnormal data of rural energy storage transmission nodes are detected and processed to generate abnormal transmission data of rural energy storage.

[0138] The present invention performs rural energy storage transmission load analysis and processing based on rural energy storage transmission characteristic data, monitors the load distribution and pressure conditions in the rural energy storage transmission process, identifies potential load bottlenecks and energy configuration problems, thereby optimizing the distribution of rural energy storage and ensuring the efficiency and stability of rural energy scheduling and transmission. Perform rural energy supply load path analysis based on rural energy storage transmission load data, monitor the energy flow path and efficiency of rural energy storage transmission to the load, and optimize the configuration of rural energy storage transmission paths. Perform rural energy storage transmission node anomaly detection based on rural energy supply load path data, accurately identify and locate the anomalies existing in the nodes in the rural energy transmission path, take targeted repair measures, and ensure the continuity and stability of rural energy scheduling. Perform energy storage transmission abnormality detection and processing on the abnormal data of rural energy storage transmission nodes, deeply analyze and confirm the abnormalities in rural energy storage transmission nodes, promptly solve rural energy scheduling and transmission problems, and ensure the overall stability of rural energy scheduling.

[0139] In an embodiment of the present invention, rural energy storage transmission load analysis and processing are performed according to rural energy storage transmission characteristic data, for example, monitoring the transmission power of solar energy storage for abnormal fluctuations within a certain period of time, and the load exceeds 20% of the normal range. Rural energy supply load path analysis is performed according to rural energy storage transmission load data to determine the load paths of different nodes, including identifying the connection path of each node with the main energy grid and its load distribution. Rural energy storage transmission node anomaly detection is performed according to rural energy supply load path data, and anomaly detection algorithms (such as anomaly detection based on machine learning or statistical threshold methods) are applied to identify significant anomalies in the transmission power of two energy storage nodes, and nodes whose transmission power deviates from the normal value by more than 30% of the preset value are marked as abnormal nodes. Abnormal transmission detection and processing of rural energy storage transmission node abnormal data is performed, and the abnormal state and influencing factors of the node (such as equipment failure or external interference) are further analyzed, including the time of abnormal occurrence, abnormal amplitude, abnormal transmission path, possible causes and impact range, to locate and repair rural energy storage and transmission problems, and ensure the stability of rural energy scheduling.

[0140] Preferably, S34 includes the following steps:

[0141] Perform rural energy storage transmission abnormal time series analysis based on rural energy storage abnormal transmission data to generate rural energy storage transmission abnormal time series data;

[0142] According to the abnormal time series data of rural energy storage transmission, the abnormal correlation characteristics of rural energy storage transmission are analyzed to generate abnormal correlation characteristic data of rural energy storage transmission;

[0143] Based on the abnormal correlation characteristic data of rural energy storage and transmission and the abnormal time series data of rural energy storage and transmission, rural energy anomaly positioning is performed to generate rural energy anomaly positioning data;

[0144] Based on the reconstruction of the rural energy distribution network, the rural energy anomaly location data is processed for rural energy correction to generate corrected rural energy data.

[0145] The present invention performs abnormal timing analysis of rural energy storage transmission according to abnormal transmission data of rural energy storage, monitors the time distribution and persistence of abnormal energy transmission, identifies the frequency, periodicity and changing trend of abnormal phenomena, monitors and predicts potential problems in energy storage, takes timely measures to reduce abnormal phenomena in rural energy storage transmission, and improves the overall stability of rural energy dispatching. According to the abnormal timing data of rural energy storage transmission, the abnormal correlation characteristics of rural energy storage transmission are analyzed, the mutual correlation and common characteristics of abnormal transmission of rural energy storage are analyzed, and the source of the problem is accurately located. Based on the abnormal correlation characteristic data of rural energy storage transmission and the abnormal timing data of rural energy storage transmission, rural energy abnormal positioning is performed, and the abnormal areas and nodes of rural energy are accurately identified and located. Combined with the abnormal timing pattern and correlation characteristics, the specific location and root cause of abnormal energy are identified, and targeted maintenance and repair are taken. Finally, based on the reconstruction of the rural energy distribution network, the rural energy abnormal positioning data is corrected for rural energy, the rural energy distribution is optimized, the abnormal energy is repaired, the stability of rural energy transmission and dispatching is improved, and the timeliness of rural energy supply is ensured.

[0146] In an embodiment of the present invention, an abnormal time series analysis of rural energy storage transmission is performed based on abnormal transmission data of rural energy storage. For example, by using time series analysis technology (such as autoregressive integrated moving average model ARIMA), the transmission power of a certain node has an obvious abnormal pattern in the time series data of the past month. Between 3 pm and 5 pm every day, the transmission power continues to decline and the power fluctuates frequently. An abnormal correlation feature analysis of rural energy storage transmission is performed based on the abnormal time series data of rural energy storage transmission. The abnormal time series data is used to perform transmission abnormality correlation feature analysis. For example, the abnormal fluctuation of the node is related to specific weather conditions (such as strong wind or high temperature) and equipment status (such as battery aging). Rural energy anomaly positioning is performed based on the abnormal correlation feature data of rural energy storage transmission and the abnormal time series data of rural energy storage transmission. By applying an abnormal positioning algorithm (such as a positioning method based on data fusion or multi-dimensional anomaly detection), the root cause of the anomaly is determined, including the abnormal source, the scope of influence and repair suggestions. Rural energy correction processing is performed on the rural energy abnormal positioning data based on the reconstruction of the rural energy distribution network, the energy storage transmission path and load distribution are optimized, and the load of the abnormal node is redistributed to the nearby healthy nodes.

[0147] Preferably, step S4 comprises the following steps:

[0148] Step S41: Acquire rural energy supply demand data;

[0149] Step S42: performing rural energy supply priority sorting processing according to the rural energy supply demand data to generate priority rural energy supply data;

[0150] Step S43: designing a rural energy dispatch strategy based on the priority rural energy supply data and the revised rural energy data;

[0151] Step S44: executing the rural energy dispatching operation according to the rural energy dispatching strategy, and performing real-time rural energy dispatching monitoring and processing on the rural energy dispatching operation to generate real-time rural energy dispatching data;

[0152] Step S45: Performing a rural energy scheduling strategy dynamic optimization operation on the rural energy scheduling strategy based on the real-time rural energy scheduling data.

[0153] The present invention obtains rural energy supply demand data, performs rural energy supply priority sorting processing according to the rural energy supply demand data, accurately sorts the energy supply priorities of different rural areas according to the actual energy demand and importance of each rural area, and optimizes the order of rural energy scheduling. Design a rural energy scheduling strategy based on the priority rural energy supply data and the revised rural energy data, comprehensively consider the energy supply priorities of each rural area and the actual rural energy situation, and formulate a scientific and reasonable rural energy scheduling strategy. Execute rural energy scheduling operations according to the rural energy scheduling strategy, and perform real-time rural energy scheduling monitoring and processing on the rural energy scheduling operations, monitor the actual rural energy change data in the rural energy scheduling process in real time, and immediately feedback the actual situation of rural energy scheduling to ensure the stability of rural energy scheduling. Perform rural energy scheduling strategy dynamic optimization operations on the rural energy scheduling strategy based on real-time rural energy scheduling data, timely adjust and optimize the rural energy scheduling strategy according to the real-time energy scheduling data, ensure that the rural energy scheduling strategy can be adjusted according to the changes in the actual rural energy scheduling, improve the flexibility of rural energy scheduling, and improve the efficiency and stability of the overall rural energy scheduling.

[0154] As an example of the present invention, refer to Figure 4 As shown, Figure 1 Detailed implementation steps of step S4 in the embodiment are shown in the flowchart. In this embodiment, step S4 includes:

[0155] Step S41: Acquire rural energy supply demand data;

[0156] In the embodiment of the present invention, to obtain rural energy demand data, first obtain the actual energy demand data of the rural area. For example, a village needs 500kW of electricity in summer, while the demands of several neighboring small villages are 300kW and 200kW respectively, which serves as the basis for subsequent energy supply priority sorting and rural energy scheduling strategy formulation.

[0157] Step S42: performing rural energy supply priority sorting processing according to the rural energy supply demand data to generate priority rural energy supply data;

[0158] In an embodiment of the present invention, rural energy supply priorities are sorted based on rural energy supply demand data. According to the setting standards of rural energy supply priorities (such as residential electricity use takes precedence over agricultural electricity use), for example, the priority weight of village A is 0.65, village B is 0.35, and village C is 0.1. The demand of village A is ranked with the highest priority, followed by village B, and village C has the lowest priority.

[0159] Step S43: designing a rural energy dispatch strategy based on the priority rural energy supply data and the revised rural energy data;

[0160] In an embodiment of the present invention, a rural energy scheduling strategy is designed based on priority rural energy supply data and revised rural energy data. For example, during periods of high demand (such as peak hours in summer), priority is given to ensuring the needs of village A, and when the rural energy supply is insufficient, load adjustment is performed to reduce the power supply of village B to 250kW, while the power supply of village C is temporarily reduced to 100kW.

[0161] Step S44: executing the rural energy dispatching operation according to the rural energy dispatching strategy, and performing real-time rural energy dispatching monitoring and processing on the rural energy dispatching operation to generate real-time rural energy dispatching data;

[0162] In an embodiment of the present invention, rural energy scheduling operations are performed according to the rural energy scheduling strategy, and real-time rural energy scheduling monitoring and processing are performed on the rural energy scheduling operations. For example, in the actual rural energy scheduling process, it is monitored that the subsequent power demand of village A is 500kW, the demand of village B is 300kW, and the demand of village C is 150kW. The rural energy scheduling strategy is dynamically adjusted according to the real-time monitored rural energy scheduling data.

[0163] Step S45: Performing a rural energy scheduling strategy dynamic optimization operation on the rural energy scheduling strategy based on the real-time rural energy scheduling data.

[0164] In the embodiment of the present invention, a rural energy scheduling strategy is dynamically optimized based on real-time rural energy scheduling data, and a dynamic optimization operation of the strategy is performed. For example, if there is a power supply deviation in the real-time rural energy scheduling data, the actual demand of village B is 50kW higher than expected. The rural energy scheduling strategy is adjusted through an optimization algorithm (such as linear programming) to allocate more energy to village B, while appropriately reducing the power supply to village A to ensure that the power supply needs of all villages are met.

[0165] Preferably, step S42 includes the following steps:

[0166] Analyze the impact range of rural energy supply targets based on rural energy supply demand data and generate data on the impact range of rural energy supply targets;

[0167] Perform rural energy supply impact range intersection processing according to rural energy supply target impact range data to generate rural energy supply impact range intersection data;

[0168] Based on the intersection data of rural energy supply impact range, rural energy supply demand data is weighted to generate rural energy supply weighted data;

[0169] Rural energy supply priorities are sorted according to the weighted data of rural energy supply to generate priority rural energy supply data.

[0170] The present invention analyzes the impact range of rural energy supply targets based on rural energy supply demand data, analyzes the impact range of each energy supply target in different areas, formulates an accurate rural energy scheduling strategy, ensures that key areas and high-demand points are given priority in rural energy supply, and ensures the efficiency and effectiveness of rural energy scheduling. According to the rural energy supply target impact range data, the rural energy supply impact range intersection processing is performed to determine the overlapping impact ranges between different rural energy supply target areas to avoid rural energy scheduling conflicts. Based on the rural energy supply impact range intersection data, rural energy supply demand data is subjected to rural energy supply weighted processing to generate rural energy supply weighted data, and rural energy supply priority sorting processing is performed based on the rural energy supply weighted data, and each rural energy supply demand is accurately evaluated, so that rural energy can be reasonably scheduled according to the urgency and impact range of the demand, and the rationality and efficiency of the overall rural energy scheduling are optimized.

[0171] In an embodiment of the present invention, the rural energy supply target impact range analysis is performed based on the rural energy supply demand data. For example, the power supply demand impact range of village A includes major residential areas and key facilities, the impact range of village B includes some agricultural areas and a small number of residential areas, and the impact range of village C is limited to remote areas and some agricultural land. The generated rural energy supply target impact range data shows that the impact range score of village A is 0.7, village B is 0.5, and village C is 0.3. The rural energy supply impact range intersection processing is performed based on the rural energy supply target impact range data. For example, through analysis, the impact range intersection of village A and village B on energy supply is large in certain time periods, while the impact range intersection of village C is small. The intersection data shows that the intersection impact degree of village A and village B during the summer peak period is 0.6, while that of village C is 0.2. Based on the rural energy supply impact range intersection data, the rural energy supply demand data is subjected to rural energy supply weighted processing, and the demand weight of village A is increased to 0.65, the weight of village B is 0.35, and the weight of village C remains unchanged. Rural energy supply priorities are sorted according to the weighted data of rural energy supply. The weighted energy supply data indicate that the demand priority of village A is 0.65, village B is 0.35, and village C is 0.1. In actual rural energy scheduling, the high-priority demand of village A can be met first, while the demands of villages B and C can be moderately met when resources are limited, so as to achieve more reasonable rural energy scheduling.

[0172] Preferably, step S43 includes the following steps:

[0173] Step S431: performing actual rural energy dispatch analysis on the priority rural energy supply data to generate actual rural energy dispatch data;

[0174] Step S432: performing rural energy dispatch association processing according to the actual rural energy dispatch data and the revised rural energy data to generate rural energy dispatch association data;

[0175] Step S433: using the reconstructed rural energy distribution network to perform rural energy dispatch path simulation analysis on the rural energy dispatch related data, and generating simulated rural energy dispatch path data;

[0176] Step S434: Design a rural energy scheduling strategy based on the rural energy scheduling related data and the simulated rural energy scheduling path data.

[0177] The present invention performs actual rural energy dispatch analysis on priority rural energy supply data, analyzes the actual rural energy dispatch demand of each region and demand point, and the actual rural energy dispatch data accurately divides the distribution and dispatch of rural energy. According to the actual rural energy dispatch data and the revised rural energy data, rural energy dispatch association processing is performed, and the corresponding rural energy is retrieved according to the actual energy dispatch data to ensure that rural energy is reasonably distributed according to actual demand and priority, and the flexibility and accuracy of rural energy dispatch are achieved. By reconstructing the rural energy distribution network, the rural energy dispatch path simulation analysis is performed on the rural energy dispatch association data, which can evaluate and optimize the effect and timeliness of different energy dispatch paths, and ensure that rural energy dispatch is more efficient and reliable. Rural energy dispatch strategies are designed based on rural energy dispatch association data and simulated rural energy dispatch path data to ensure that rural energy dispatch decisions fully consider the actual needs and optimization paths of rural energy distribution, and achieve efficient dispatch of rural energy.

[0178] As an example of the present invention, refer to Figure 5 As shown, Figure 1 FIG. 4 is a flow chart of detailed implementation steps of step S43. In this example, step S43 includes:

[0179] Step S431: performing actual rural energy dispatch analysis on the priority rural energy supply data to generate actual rural energy dispatch data;

[0180] In an embodiment of the present invention, an actual rural energy scheduling analysis is performed on the priority rural energy supply data. The demand priority of village A is 0.65, village B is 0.35, and village C is 0.1. By analyzing the actual rural energy supply situation, for example, the total energy of the energy scheduling is 800kW, the actual energy supply of village A is 500kW, village B is 250kW, and village C is 50kW.

[0181] Step S432: performing rural energy dispatch association processing according to the actual rural energy dispatch data and the revised rural energy data to generate rural energy dispatch association data;

[0182] In an embodiment of the present invention, rural energy scheduling association processing is performed based on actual rural energy scheduling data and revised rural energy data. For example, the revised rural energy data includes load adjustment for certain nodes. By combining the actual rural energy scheduling data with the revised rural energy data, it is detected that the actual load of a certain node is higher than expected. The rural energy scheduling association processing needs to redistribute the load for optimization.

[0183] Step S433: using the reconstructed rural energy distribution network to perform rural energy dispatch path simulation analysis on the rural energy dispatch related data, and generating simulated rural energy dispatch path data;

[0184] In an embodiment of the present invention, a rural energy dispatching path simulation analysis is performed on rural energy dispatching related data by reconstructing the rural energy distribution network, such as simulating the dispatching path based on the network optimization of the shortest path algorithm, simulating different rural energy dispatching paths, and simulating and analyzing that the needs of village A can be met first through the newly added efficient transmission lines, and the load can be transferred from 200kW in village B to village C to balance the load and ensure the stability and timeliness of rural energy dispatching.

[0185] Step S434: Design a rural energy scheduling strategy based on the rural energy scheduling related data and the simulated rural energy scheduling path data.

[0186] In an embodiment of the present invention, a rural energy scheduling strategy is designed based on rural energy scheduling related data and simulated rural energy scheduling path data. For example, the demand of village A is prioritized, the load distribution between villages B and C is adjusted, and the energy transmission path is optimized. It is detected that the actual load demand of village B is higher than the prediction, and there is a certain loss in the energy transmission line. By reconstructing the rural energy distribution network to perform scheduling path simulation analysis, the shortest path algorithm is applied to optimize the energy scheduling path, reduce energy transmission losses, and by adding efficient transmission lines, 200kW of load is transferred from village B to village C to ensure the balance and stability of rural energy scheduling.

[0187] Preferably, step S45 includes the following steps:

[0188] Monitor rural energy dispatch turnover based on real-time rural energy dispatch data to generate real-time rural energy dispatch turnover data;

[0189] Conduct rural energy dispatch time difference analysis based on real-time rural energy dispatch turnover data to generate real-time rural energy dispatch time difference data;

[0190] Based on the real-time rural energy scheduling time difference data, the actual rural energy scheduling data is dynamically optimized, and the optimized actual rural energy scheduling data is fed back to step S432 to execute the rural energy scheduling strategy dynamic optimization operation.

[0191] The present invention monitors rural energy dispatch turnover on real-time rural energy dispatch data, detects the execution of rural energy dispatch and rural energy turnover in real time and makes adjustments in advance to ensure the stability and timeliness of rural energy dispatch. According to the real-time rural energy dispatch turnover data, rural energy dispatch time difference analysis is performed, the time delay or difference in the rural energy dispatch process is monitored and analyzed, and timely adjustments are made to ensure the timeliness of rural energy dispatch. Based on the real-time rural energy dispatch time difference data, the actual rural energy dispatch data is dynamically optimized for rural energy dispatch, and the optimized actual rural energy dispatch data is fed back to step S432 to perform the dynamic optimization operation of the rural energy dispatch strategy, dynamically optimize the rural energy dispatch strategy, adjust the actual rural energy dispatch data to adapt to the real-time rural energy dispatch demand, optimize rural energy distribution, and achieve the accuracy and flexibility of rural energy dispatch.

[0192] In an embodiment of the present invention, rural energy scheduling turnover monitoring is performed on real-time rural energy scheduling data. For example, a monitoring window is set to 30 minutes, and the actual energy supply and consumption data of villages A, B, and C are collected. The energy scheduling turnover time of village A is 25 minutes, village B is 30 minutes, and village C is 20 minutes. Rural energy scheduling time difference analysis is performed based on real-time rural energy scheduling turnover data. For example, the actual scheduling time of village B is 5 minutes longer than the expected time, the time difference of village A is -2 minutes (completed ahead of schedule), village B is +5 minutes (delayed), and village C is -3 minutes (completed ahead of schedule). Based on the real-time rural energy scheduling time difference data, the actual rural energy scheduling data is dynamically optimized, and the optimized actual rural energy scheduling data is fed back to step S432 to execute the dynamic optimization operation of the rural energy scheduling strategy. For example, the scheduling time of village B is adjusted through an optimization algorithm (such as linear programming or dynamic programming), its energy supply strategy is improved, and the energy scheduling sequence and energy supply ratio are optimized and adjusted, so that the scheduling time difference of village B is reduced to 1 minute, and the overall energy scheduling turnaround time is shortened to 24 minutes. The optimized actual rural energy scheduling data is fed back to step S432, and the rural energy scheduling strategy is dynamically optimized.

[0193] The beneficial effect of the present application is that the rural energy intelligent monitoring and dispatching method of the present invention performs real-time monitoring and processing of rural energy through a rural energy digital twin platform, monitors rural energy data in real time and constructs an energy distribution network that conforms to the local rural areas, and then analyzes historical rural energy consumption data to identify abnormal rural energy consumption patterns and abnormal rural energy transmission lines and energy nodes, and optimizes and reconstructs the rural energy distribution network based on abnormal rural energy consumption data, thereby improving the monitoring and processing speed of rural energy anomalies and optimizing rural energy transmission paths. According to real-time rural energy data, rural energy storage abnormal transmission monitoring and processing are performed to generate rural energy storage abnormal transmission data; rural energy correction processing is performed on rural energy storage abnormal transmission data based on the reconstructed rural energy distribution network to ensure the stability of rural energy dispatching. By obtaining rural energy supply demand data, and designing rural energy dispatching strategies based on rural energy supply demand data and correcting rural energy data, we can ensure that rural energy dispatching can meet the actual rural energy supply demand, avoid uneven rural energy distribution leading to uncoordinated energy dispatching, execute rural energy dispatching operations according to rural energy dispatching strategies, and conduct real-time rural energy dispatching data monitoring and processing on rural energy dispatching operations, execute rural energy dispatching strategy dynamic optimization operations based on real-time rural energy dispatching data, dynamically monitor the execution of rural energy dispatching, ensure the stability of rural energy dispatching, and flexibly adjust the rural energy dispatching strategy, realize dynamic optimization of rural energy dispatching strategy, improve the flexibility and accuracy of rural energy dispatching, ensure the efficiency and stability of rural energy dispatching, and realize intelligent monitoring and dispatching of rural energy.

[0194] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0195] 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 intelligent monitoring and dispatching of rural energy, characterized in that: The following steps are involved: Step S1: Real-time rural energy monitoring and processing is performed through the rural energy digital twin platform to generate real-time rural energy data; energy distribution analysis is performed in rural areas based on the real-time rural energy data to build a rural energy distribution network; Step S2: Acquire historical rural energy consumption data; perform rural energy consumption anomaly analysis based on the historical rural energy consumption data to generate rural energy consumption anomaly data; perform energy distribution network optimization and reconstruction processing on the rural energy distribution network based on the rural energy consumption anomaly data to generate a reconstructed rural energy distribution network; Step S3: Performing rural energy storage abnormal transmission monitoring and processing according to real-time rural energy data to generate rural energy storage abnormal transmission data; performing rural energy correction processing on the rural energy storage abnormal transmission data based on the reconstructed rural energy distribution network to generate corrected rural energy data; Step S4: Acquire rural energy supply demand data, and design a rural energy dispatch strategy based on the rural energy supply demand data and the revised rural energy data; Execute rural energy dispatching operations according to rural energy dispatching strategies, and monitor and process rural energy dispatching data in real time; The rural energy dispatching strategy is dynamically optimized based on the real-time rural energy dispatching data.

2. The intelligent monitoring and dispatching method for rural energy according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Performing real-time rural energy monitoring and processing through the rural energy digital twin platform to generate real-time rural energy data; Step S12: performing rural energy data standardization processing on the real-time rural energy data to generate standard rural energy data; Step S13: extracting rural energy monitoring nodes according to standard rural energy data to generate rural energy monitoring node data; Step S14: dividing the standard rural energy data into rural energy regional ranges according to the rural energy monitoring node data to generate regional rural energy data; Step S15: Perform energy distribution analysis in rural areas based on regional rural energy data, generate rural energy distribution data, and construct a rural energy distribution network based on the rural energy distribution data.

3. The intelligent monitoring and dispatching method for rural energy according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Obtain historical rural energy consumption data; Step S22: Performing historical rural energy consumption peak analysis based on historical rural energy consumption data to generate historical rural energy consumption peak data; Step S23: dividing the historical rural energy consumption peak data into energy consumption peak values ​​based on the preset energy consumption peak range data, and generating classified rural energy consumption peak data; Step S24: performing rural energy consumption fluctuation frequency analysis on the classified rural energy consumption peak data to generate rural energy consumption fluctuation frequency data; Step S25: performing rural energy consumption anomaly analysis on historical rural energy consumption data according to rural energy consumption fluctuation frequency data to generate rural energy consumption anomaly data; Step S26: Based on the rural energy consumption abnormality data, the rural energy distribution network is optimized and reconstructed to generate a reconstructed rural energy distribution network.

4. The intelligent monitoring and dispatching method for rural energy according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: extracting rural energy storage data of real-time rural energy data; Step S32: Analyze rural energy storage transmission characteristics based on rural energy storage data to generate rural energy storage transmission characteristic data; Step S33: performing rural energy storage abnormal transmission detection processing based on the rural energy storage transmission characteristic data to generate rural energy storage abnormal transmission data; Step S34: Perform rural energy correction processing on the rural energy storage abnormal transmission data based on the reconstruction of the rural energy distribution network to generate corrected rural energy data.

5. The intelligent monitoring and dispatching method for rural energy according to claim 1 is characterized in that: Step S33 includes the following steps: Analyze and process rural energy storage transmission load according to rural energy storage transmission characteristic data to generate rural energy storage transmission load data; Perform rural energy supply load path analysis based on rural energy storage and transmission load data to generate rural energy supply load path data; Perform rural energy storage and transmission node anomaly detection based on rural energy supply load path data to generate rural energy storage and transmission node anomaly data; Abnormal data of rural energy storage transmission nodes are detected and processed to generate abnormal transmission data of rural energy storage.

6. The intelligent monitoring and dispatching method for rural energy according to claim 1 is characterized in that: Step S34 includes the following steps: Perform rural energy storage transmission abnormal time series analysis based on rural energy storage abnormal transmission data to generate rural energy storage transmission abnormal time series data; According to the abnormal time series data of rural energy storage transmission, the abnormal correlation characteristics of rural energy storage transmission are analyzed to generate abnormal correlation characteristic data of rural energy storage transmission; Based on the abnormal correlation characteristic data of rural energy storage and transmission and the abnormal time series data of rural energy storage and transmission, rural energy anomaly positioning is performed to generate rural energy anomaly positioning data; Based on the reconstruction of the rural energy distribution network, the rural energy anomaly location data is processed for rural energy correction to generate corrected rural energy data.

7. The intelligent monitoring and dispatching method for rural energy according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Acquire rural energy supply demand data; Step S42: performing rural energy supply priority sorting processing according to the rural energy supply demand data to generate priority rural energy supply data; Step S43: designing a rural energy dispatch strategy based on the priority rural energy supply data and the revised rural energy data; Step S44: executing the rural energy dispatching operation according to the rural energy dispatching strategy, and performing real-time rural energy dispatching monitoring and processing on the rural energy dispatching operation to generate real-time rural energy dispatching data; Step S45: Performing a rural energy scheduling strategy dynamic optimization operation on the rural energy scheduling strategy based on the real-time rural energy scheduling data.

8. The intelligent monitoring and dispatching method for rural energy according to claim 1 is characterized in that: Step S42 includes the following steps: Analyze the impact range of rural energy supply targets based on rural energy supply demand data and generate data on the impact range of rural energy supply targets; Perform rural energy supply impact range intersection processing according to rural energy supply target impact range data to generate rural energy supply impact range intersection data; Based on the intersection data of rural energy supply impact range, rural energy supply demand data is weighted to generate rural energy supply weighted data; Rural energy supply priorities are sorted according to the weighted data of rural energy supply to generate priority rural energy supply data.

9. The intelligent monitoring and dispatching method for rural energy according to claim 1 is characterized in that: Step S43 includes the following steps: Step S431: performing actual rural energy dispatch analysis on the priority rural energy supply data to generate actual rural energy dispatch data; Step S432: performing rural energy dispatch association processing according to the actual rural energy dispatch data and the revised rural energy data to generate rural energy dispatch association data; Step S433: using the reconstructed rural energy distribution network to perform rural energy dispatch path simulation analysis on the rural energy dispatch related data, and generating simulated rural energy dispatch path data; Step S434: Design a rural energy scheduling strategy based on the rural energy scheduling related data and the simulated rural energy scheduling path data.

10. The intelligent monitoring and dispatching method for rural energy according to claim 1 is characterized in that: Step S45 includes the following steps: Monitor rural energy dispatch turnover based on real-time rural energy dispatch data to generate real-time rural energy dispatch turnover data; Conduct rural energy dispatch time difference analysis based on real-time rural energy dispatch turnover data to generate real-time rural energy dispatch time difference data; Based on the real-time rural energy scheduling time difference data, the actual rural energy scheduling data is dynamically optimized, and the optimized actual rural energy scheduling data is fed back to step S432 to execute the rural energy scheduling strategy dynamic optimization operation.

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