Rural energy-oriented digital twinborn analysis method and system

Abstract

The invention relates to the technical field of digital twinborn analysis, in particular to a rural energy-oriented digital twinborn analysis method and system. The method comprises the following steps: constructing a rural energy geographic model; collecting rural energy characteristic data; mapping the rural energy feature data to a rural energy geographic model to perform rural energy feature mapping processing, and generating a rural energy feature model; performing rural energy physical characteristic integrated analysis based on the rural energy characteristic model to generate rural energy physical characteristic integrated data; performing rural energy digital twinborn analysis based on the rural energy physical characteristic integrated data and the rural energy characteristic model to generate a rural energy digital twinborn model; and performing rural energy supply scheduling intelligent optimization operation based on the rural energy digital twinborn model. According to the invention, through accurate and efficient rural energy digital twinborn analysis, detailed digital information feedback and energy supply scheduling optimization of rural energy are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twin analysis, and in particular to a digital twin analysis method and system for rural energy. Background Art

[0002] With the continuous growth of energy demand and the enhancement of environmental protection awareness, the development and utilization of renewable energy have received extensive attention in various regions. As an important part of energy consumption, the energy structure and management mode of rural areas have an important impact on the overall energy strategy. Digital twin analysis for rural energy can provide a new management and optimization method for complex rural energy systems. By creating a virtual digital twin model, it can reflect the operating status of the rural energy system in real time and analyze the interaction and overall efficiency of different energy sources. It can also support remote monitoring and intelligent decision-making of rural energy operating status, provide more accurate and sustainable solutions for energy management in rural areas, and thus promote the modernization and green development of rural energy systems. However, the traditional digital twin analysis method for rural energy lacks effective integration and analysis of multi-source heterogeneous data, making it difficult to achieve comprehensive collection and integration of these data, resulting in insufficient timeliness and integrity of the data. In addition, it relies on fixed models and empirical rules for digital rural energy analysis, lacks intelligent rural energy digital twin analysis methods, and cannot fully reflect the dynamic characteristics of rural energy systems. Summary of the invention

[0003] Based on this, the present invention provides a digital twin analysis method and system for rural energy to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a digital twin analysis method for rural energy includes the following steps:

[0005] Step S1: Acquire rural remote sensing data of rural management areas; perform rural energy geographic structure modeling based on the rural remote sensing data to generate a rural energy geographic model;

[0006] Step S2: collecting energy data in the rural management area according to the sensor integration device to generate rural energy data; performing feature analysis on the rural energy data to obtain rural energy feature data; mapping the rural energy feature data to a rural energy geographic model to perform rural energy feature mapping processing to generate a rural energy feature model;

[0007] Step S3: Analyze the multidimensional physical characteristics of rural energy based on the rural energy characteristic model to generate multidimensional physical characteristics data of rural energy; perform rural energy physical characteristics integration processing on the rural energy multidimensional physical characteristics data to generate rural energy physical characteristics integrated data;

[0008] Step S4: Based on the integrated data of rural energy physical characteristics, nonlinear driving characteristics of rural energy are modeled and processed to generate a rural energy driving model; based on the rural energy driving model, a rural energy driving engine is constructed, and the rural energy characteristic model is analyzed by the rural energy driving engine to generate a rural energy digital twin model;

[0009] Step S5: Perform digital energy supply scheduling analysis and processing based on the rural energy digital twin model to generate digital energy supply scheduling data; design an intelligent optimization model for rural energy supply scheduling based on the digital energy supply scheduling data to obtain a rural energy supply scheduling optimization model; and perform rural energy supply scheduling intelligent optimization operations based on the rural energy supply scheduling optimization model.

[0010] By acquiring remote sensing data, the present invention can accurately capture the geographical features within the rural management area, including land use, terrain changes, and infrastructure distribution, etc., providing a comprehensive and accurate geographical background for subsequent energy analysis, creating a rural energy geographic model, and providing basic data support for the subsequent mapping and analysis of energy features, reflecting the spatial distribution characteristics of energy equipment in rural areas, and helping to understand and optimize the spatial distribution and utilization efficiency of different energy sources. Rural energy data (such as photovoltaic power generation, wind power generation, biomass energy production, transmission, and use, etc.) are collected in real time through sensor integrated equipment to ensure the real-time and diversity of data, reflecting the dynamic characteristics of the rural energy system, and by mapping energy feature data to a geographical model, the distribution and use of energy in different regions can be better understood, and the accuracy of energy management and scheduling can be improved. The rural energy feature model can be customized and analyzed according to the specific conditions of different regions, which is convenient for tailoring optimization strategies for each rural area. By analyzing the multidimensional physical characteristics of energy (such as production characteristics, energy storage characteristics, transmission behavior, etc.), we can obtain a more comprehensive and in-depth understanding of the rural energy system, identify the physical characteristics of each type of energy, and provide comprehensive energy physical characteristics data through integrated processing of multidimensional physical characteristics. Therefore, the rural energy digital twin analysis can more accurately reflect the actual operation status of rural energy, which is of great help to optimize the production, storage and transmission management of energy and improve the overall efficiency of energy management. The nonlinear driving characteristic modeling of rural energy based on the integrated data of rural energy physical characteristics can capture the complex dynamic characteristics of the rural energy system, so that the model can more accurately predict future energy demand and supply, and support smarter energy management decisions. By building a rural energy driving engine, real-time digital twin analysis can be carried out, the effects of different decisions can be simulated in a virtual environment, and the optimization strategy can be quickly adjusted to improve the response speed and accuracy of the decision. The generation and use of digital twin models have introduced a high level of intelligent management means for the entire rural energy system, making energy management more refined and personalized. Supply scheduling analysis based on digital twin models can more accurately reflect real-time energy demand and supply conditions, thereby greatly improving the accuracy of energy supply. The design of the intelligent optimization model for rural energy supply scheduling can intelligently optimize energy scheduling according to actual conditions, and maintain energy scheduling on the original energy supply scheduling requirements to the greatest extent, thereby reducing energy waste, reducing costs, and improving energy utilization efficiency. When responding to the volatility and diversity of rural energy demand, the intelligent optimization operation of rural energy supply scheduling can quickly adapt to changes, thereby improving the adaptability and flexibility of the energy management system.

[0011] This specification provides a digital twin analysis system for rural energy, which is used to execute the digital twin analysis method for rural energy described above. The digital twin analysis system for rural energy includes:

[0012] The rural energy geographic structure modeling module is used to obtain rural remote sensing data of rural management areas; based on rural remote sensing data, rural energy geographic structure modeling is performed to generate a rural energy geographic model;

[0013] The rural energy mapping module is used to collect energy data in the rural management area according to the sensor integrated device to generate rural energy data; perform feature analysis on the rural energy data to obtain rural energy feature data; map the rural energy feature data to the rural energy geographic model to perform rural energy feature mapping processing to generate a rural energy feature model;

[0014] A rural energy physical characteristics analysis module is used to analyze the multi-dimensional physical characteristics of rural energy based on a rural energy characteristic model to generate rural energy multi-dimensional physical characteristics data; to perform rural energy physical characteristics integration processing on the rural energy multi-dimensional physical characteristics data to generate rural energy physical characteristics integration data;

[0015] The rural energy driving characteristic analysis module is used to model and process the nonlinear driving characteristics of rural energy based on the integrated data of rural energy physical characteristics to generate a rural energy driving model; a rural energy driving engine is constructed based on the rural energy driving model, and a rural energy digital twin analysis is performed on the rural energy characteristic model based on the rural energy driving engine to generate a rural energy digital twin model;

[0016] The rural energy supply scheduling optimization module is used to perform digital energy supply scheduling analysis and processing based on the rural energy digital twin model to generate digital energy supply scheduling data; design an intelligent optimization model for rural energy supply scheduling based on the digital energy supply scheduling data to obtain a rural energy supply scheduling optimization model; and perform rural energy supply scheduling intelligent optimization operations based on the rural energy supply scheduling optimization model.

[0017] The beneficial effect of the present application is that, by designing a data integration interface and optimizing the data collection process, the present invention can efficiently collect and integrate multi-source heterogeneous data from remote sensing, sensors, historical energy consumption, etc., ensure the comprehensiveness and diversity of the data, improve the timeliness and integrity of the data, and provide more accurate basic data support for digital twin analysis. And the rural energy characteristic data is mapped to the corresponding geographic model, which fully reflects the digital information of the overall rural energy system and realizes the comprehensive collection and integration of rural energy-related information. By constructing a digital twin model based on multi-dimensional physical properties and multi-scale driving factors, the dynamic change characteristics of the rural energy system are reflected in real time. Unlike the traditional analysis method that relies on fixed models and empirical rules, the digital twin model can capture and simulate the complex dynamic behavior of the rural energy system in real time, improving the accuracy and adaptability of the analysis. Advanced artificial intelligence algorithms, including decision trees, long short-term memory networks (LSTM), reinforcement learning, etc., are used to model nonlinear driving characteristics and optimize supply scheduling. These intelligent means automatically learn and adapt to different energy usage scenarios, improve the intelligence level of rural energy management, and achieve more accurate and effective analysis and decision support. Taking into account the multi-dimensional physical characteristics of production, storage, and transmission in rural energy systems, the intelligent optimization and dynamic adjustment of energy supply scheduling are realized, which improves the overall efficiency and reliability of rural energy systems and ensures the optimal allocation and utilization of resources. Through modular design and digital data-driven analysis methods, the rural energy digital twin model has high flexibility and scalability. It can flexibly adjust and expand its functions according to different regions and demand changes, and support the management needs of multiple energy types and complex energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the steps of a digital twin analysis method for rural energy of the present invention;

[0019] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0020] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0021] 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

[0022] The technical method of the present invention is described clearly and completely below 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.

[0023] 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.

[0024] 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.

[0025] To achieve this, please refer to Figures 1 to 3 The present invention provides a digital twin analysis method for rural energy, comprising the following steps:

[0026] Step S1: Acquire rural remote sensing data of rural management areas; perform rural energy geographic structure modeling based on the rural remote sensing data to generate a rural energy geographic model;

[0027] Step S2: collecting energy data in the rural management area according to the sensor integration device to generate rural energy data; performing feature analysis on the rural energy data to obtain rural energy feature data; mapping the rural energy feature data to a rural energy geographic model to perform rural energy feature mapping processing to generate a rural energy feature model;

[0028] Step S3: Analyze the multidimensional physical characteristics of rural energy based on the rural energy characteristic model to generate multidimensional physical characteristics data of rural energy; perform rural energy physical characteristics integration processing on the rural energy multidimensional physical characteristics data to generate rural energy physical characteristics integrated data;

[0029] Step S4: Based on the integrated data of rural energy physical characteristics, nonlinear driving characteristics of rural energy are modeled and processed to generate a rural energy driving model; based on the rural energy driving model, a rural energy driving engine is constructed, and the rural energy characteristic model is analyzed by the rural energy driving engine to generate a rural energy digital twin model;

[0030] Step S5: Perform digital energy supply scheduling analysis and processing based on the rural energy digital twin model to generate digital energy supply scheduling data; design an intelligent optimization model for rural energy supply scheduling based on the digital energy supply scheduling data to obtain a rural energy supply scheduling optimization model; and perform rural energy supply scheduling intelligent optimization operations based on the rural energy supply scheduling optimization model.

[0031] By acquiring remote sensing data, the present invention can accurately capture the geographical features within the rural management area, including land use, terrain changes, and infrastructure distribution, etc., providing a comprehensive and accurate geographical background for subsequent energy analysis, creating a rural energy geographic model, and providing basic data support for the subsequent mapping and analysis of energy features, reflecting the spatial distribution characteristics of energy equipment in rural areas, and helping to understand and optimize the spatial distribution and utilization efficiency of different energy sources. Rural energy data (such as photovoltaic power generation, wind power generation, biomass energy production, transmission, and use, etc.) are collected in real time through sensor integrated equipment to ensure the real-time and diversity of data, reflecting the dynamic characteristics of the rural energy system, and by mapping energy feature data to a geographical model, the distribution and use of energy in different regions can be better understood, and the accuracy of energy management and scheduling can be improved. The rural energy feature model can be customized and analyzed according to the specific conditions of different regions, which is convenient for tailoring optimization strategies for each rural area. By analyzing the multidimensional physical characteristics of energy (such as production characteristics, energy storage characteristics, transmission behavior, etc.), we can obtain a more comprehensive and in-depth understanding of the rural energy system, identify the physical characteristics of each type of energy, and provide comprehensive energy physical characteristics data through integrated processing of multidimensional physical characteristics. Therefore, the rural energy digital twin analysis can more accurately reflect the actual operation status of rural energy, which is of great help to optimize the production, storage and transmission management of energy and improve the overall efficiency of energy management. The nonlinear driving characteristic modeling of rural energy based on the integrated data of rural energy physical characteristics can capture the complex dynamic characteristics of the rural energy system, so that the model can more accurately predict future energy demand and supply, and support smarter energy management decisions. By building a rural energy driving engine, real-time digital twin analysis can be carried out, the effects of different decisions can be simulated in a virtual environment, and the optimization strategy can be quickly adjusted to improve the response speed and accuracy of the decision. The generation and use of digital twin models have introduced a high level of intelligent management means for the entire rural energy system, making energy management more refined and personalized. Supply scheduling analysis based on digital twin models can more accurately reflect real-time energy demand and supply conditions, thereby greatly improving the accuracy of energy supply. The design of the intelligent optimization model for rural energy supply scheduling can intelligently optimize energy scheduling according to actual conditions, and maintain energy scheduling on the original energy supply scheduling requirements to the greatest extent, thereby reducing energy waste, reducing costs, and improving energy utilization efficiency. When responding to the volatility and diversity of rural energy demand, the intelligent optimization operation of rural energy supply scheduling can quickly adapt to changes, thereby improving the adaptability and flexibility of the energy management system.

[0032] As an embodiment of the present invention, refer to Figure 1The above is a schematic diagram of the steps of a digital twin analysis method for rural energy in the present invention. In this embodiment, the digital twin analysis method for rural energy includes the following steps:

[0033] Step S1: Acquire rural remote sensing data of rural management areas; perform rural energy geographic structure modeling based on the rural remote sensing data to generate a rural energy geographic model;

[0034] In an embodiment of the present invention, the rural remote sensing data of the rural management area, including topography, land use type, vegetation coverage, water resource distribution and other information, are obtained through satellite remote sensing images and drone monitoring images pre-stored in remote sensing software. And multi-spectral imaging equipment is used to obtain remote sensing images of multiple bands of rural remote sensing data, which can reflect the spectral characteristics of different objects. The obtained remote sensing data is used for data preprocessing, such as removing noise, correcting geometric distortion, performing atmospheric correction, etc., to ensure the accuracy and consistency of the data. After data processing, the remote sensing data is used to construct a rural energy geographic model, and the remote sensing data is converted into a geographic structure model through a geographic information system (GIS) tool. The model contains various energy resource distribution and geographic structure information in the rural area, which provides a geographical basis for subsequent energy feature analysis and optimization.

[0035] Step S2: collecting energy data in the rural management area according to the sensor integration device to generate rural energy data; performing feature analysis on the rural energy data to obtain rural energy feature data; mapping the rural energy feature data to a rural energy geographic model to perform rural energy feature mapping processing to generate a rural energy feature model;

[0036] In an embodiment of the present invention, various types of sensor integrated devices are deployed in rural management areas, such as solar radiation sensors, wind speed sensors, humidity sensors and temperature sensors, energy monitoring sensors, etc. These sensors can collect energy-related data in rural areas in real time, including solar radiation intensity, environmental humidity and temperature, and energy production conditions. Through the data interface of the integrated device, these energy data are collected and summarized to form a rural energy data set, and feature analysis is performed on these energy data to extract representative energy feature data. For example, feature indicators such as the average value, maximum value, minimum value, and change trend of the data are extracted through statistical analysis methods. After completing the feature analysis, the extracted rural energy feature data is mapped to the previously generated rural energy geographic model, and the feature data is matched with the geographical location to form a rural energy feature model, which includes not only geographical structure information but also energy feature data.

[0037] Step S3: Analyze the multidimensional physical characteristics of rural energy based on the rural energy characteristic model to generate multidimensional physical characteristics data of rural energy; perform rural energy physical characteristics integration processing on the rural energy multidimensional physical characteristics data to generate rural energy physical characteristics integrated data;

[0038] In the embodiment of the present invention, a rural energy characteristic model is used to perform multi-dimensional physical characteristic analysis. According to the energy data in the model, the production characteristics, energy storage characteristics and transmission characteristics of different energy sources are analyzed. Through physical modeling and data analysis technology, the physical characteristic data such as production efficiency, energy storage capacity, transmission loss, etc. of these energy sources are obtained respectively. The physical characteristic data from different sources are integrated and processed. The integrated processing adopts methods such as physical characteristic interactive characteristic analysis and data fusion to integrate the physical characteristic data of different types of energy into unified rural energy physical characteristic integrated data. These integrated data can provide a global physical characteristic view of the rural energy system and provide a basis for subsequent nonlinear driving characteristic modeling.

[0039] Step S4: Based on the integrated data of rural energy physical characteristics, nonlinear driving characteristics of rural energy are modeled and processed to generate a rural energy driving model; based on the rural energy driving model, a rural energy driving engine is constructed, and the rural energy characteristic model is analyzed by the rural energy driving engine to generate a rural energy digital twin model;

[0040] In the embodiment of the present invention, nonlinear driving characteristic modeling is performed based on the integrated data of rural energy physical characteristics. The integrated data of rural energy physical characteristics is modeled by using machine learning algorithms (such as decision trees, support vector machines, etc.) and physical modeling methods (such as dynamic system simulation, etc.) to perform energy driving characteristic modeling. A driving model that reflects the complex relationship between various physical characteristics in the rural energy system is established. After the driving model is generated, a rural energy driving engine is constructed using the model. The driving engine is a computing tool that can calculate and simulate the dynamic behavior of the rural energy system under different conditions in real time. Then, the driving engine is used to analyze the rural energy characteristic model to generate a rural energy digital twin model. The digital twin model can accurately reflect the operating status and characteristics of the real rural energy system and provide support for intelligent energy management and scheduling.

[0041] Step S5: Perform digital energy supply scheduling analysis and processing based on the rural energy digital twin model to generate digital energy supply scheduling data; design an intelligent optimization model for rural energy supply scheduling based on the digital energy supply scheduling data to obtain a rural energy supply scheduling optimization model; and perform rural energy supply scheduling intelligent optimization operations based on the rural energy supply scheduling optimization model.

[0042] In an embodiment of the present invention, a rural energy digital twin model is used to perform digital energy supply scheduling analysis, the implementation effects of different energy scheduling schemes are simulated through the digital twin model, the energy supply efficiency, cost and stability under different schemes are calculated, digital energy supply scheduling data is generated, and an intelligent optimization model is designed based on the energy supply scheduling data. By introducing an optimization algorithm (such as a reinforcement learning algorithm, etc.), a model that can optimize energy scheduling under various constraints (such as supply and demand balance, efficiency maximization, etc.) is designed, and the optimal supply scheduling scheme is formulated according to the optimization model. The supply scheduling optimization operation is performed according to the intelligent optimization model. According to the real-time supply and demand situation and the optimization results, the energy supply strategy is dynamically adjusted to ensure the efficient operation and stable supply of the rural energy system, which is achieved through an automated control system, making the supply scheduling more flexible and intelligent.

[0043] Preferably, step S1 comprises the following steps:

[0044] Step S11: Acquire rural remote sensing data of the rural management area;

[0045] Step S12: performing remote sensing data synchronization correction processing on the rural remote sensing data to generate corrected rural remote sensing data; performing multispectral feature extraction on the corrected rural remote sensing data to generate rural remote sensing feature data;

[0046] Step S13: Performing rural geographical structure modeling processing based on rural remote sensing feature data to generate a rural geographical model;

[0047] Step S14: Perform rural energy spatial feature identification processing on the rural geographic model to generate a rural energy geographic model.

[0048] The present invention obtains remote sensing data of rural management areas through remote sensing technology, covering large areas, including areas that cannot be directly entered or are difficult to monitor. This makes the data collection more comprehensive and can better reflect the actual situation of rural energy distribution. Remote sensing data provides a large amount of geographical and environmental information, greatly improves the efficiency and speed of data acquisition, and provides sufficient time and data support for subsequent data analysis and modeling. Through synchronous correction processing, the data deviation caused by errors or atmospheric effects of satellite images, drone images, etc. is corrected to ensure the accuracy and reliability of remote sensing data, thereby improving the accuracy of the model. Multispectral feature extraction technology can obtain spectral information of different bands, reveal the spectral characteristics of different objects, extract more geographical and environmental features, such as soil type, vegetation coverage, water distribution, etc., and provide more abundant data support for subsequent energy geographic structure modeling. Using corrected and feature-extracted remote sensing data for geographic structure modeling, a more refined and realistic geographic model is constructed to reflect important features such as topography and land use in rural areas, and provide guarantee for the accuracy of energy models. The detailed geographic model can reflect the distribution and characteristics of rural energy infrastructure, making the model suitable for various rural terrains and environmental conditions, and improving the universality and applicability of the model in different regions. The energy spatial characteristics of the rural geographic model are marked, and the energy distribution characteristics of specific areas (such as the location of solar photovoltaic arrays, the distribution area of ​​wind energy resources, etc.) are identified and marked to help more accurately manage and optimize energy utilization. The identification of energy spatial characteristics provides a data basis for subsequent energy planning, layout and optimization, and can better carry out the site selection and planning of energy facilities, thereby improving the overall efficiency and benefits of the rural energy system.

[0049] In the embodiment of the present invention, remote sensing technology is used to obtain remote sensing data of a rural management area, and a satellite remote sensing device or a remote sensing sensor carried by an unmanned aerial vehicle is used to obtain high-resolution remote sensing images covering the target area. These images include image data in different spectral ranges, such as visible light, infrared and near-infrared spectra, which can reflect various features of the surface, such as vegetation, soil, water bodies and buildings. The acquisition of these remote sensing data can be performed at a predetermined time frequency to ensure the timeliness and continuity of the data. The acquired remote sensing data is synchronously corrected. The synchronous correction includes two parts: geometric correction and radiation correction. The geometric correction is used to eliminate the spatial deformation of the image and ensure that the image coordinates match the actual geographic coordinates; the radiation correction is used to correct the image brightness difference caused by atmospheric conditions, sensor characteristics and other factors. The image data generated after correction is called corrected rural remote sensing data. Multispectral feature extraction is performed on the corrected remote sensing data. Multispectral analysis technology is used to extract spectral features of different bands in the image, such as vegetation index (such as NDVI), water index, soil brightness, etc. These features can effectively reflect the types and distribution of objects in rural areas, generate rural remote sensing feature data, and provide rich feature information for subsequent geographic modeling. The use of rural remote sensing feature data for geographic structure modeling includes combining multispectral feature data with geographic information using geographic information system (GIS) software to build a geographic structure model of rural areas. The model can reflect the terrain characteristics, land use types, vegetation coverage, water distribution, etc. of rural areas. It is further refined through spatial analysis and classification algorithms (such as image classification algorithms or rule sets based on convolutional neural networks). The generated rural geographic model can accurately describe the natural and human geographical structure of rural areas. The generated rural geographic model is further processed to identify the spatial characteristics of rural energy. According to the energy resource distribution data of rural areas, these data are superimposed and analyzed with the geographic model. Through spatial analysis methods, it is determined which areas have high solar energy utilization potential or rich wind energy resources.

[0050] Preferably, step S14 comprises the following steps:

[0051] Perform remote sensing feature segmentation processing of land use type on rural remote sensing feature data to generate segmented rural remote sensing feature data;

[0052] Perform rural spatial feature analysis based on segmented rural remote sensing feature data to generate rural spatial feature data; perform rural energy spatial feature analysis based on rural spatial feature data to generate rural energy spatial feature data;

[0053] The rural energy spatial characteristics of the rural geographic model are identified according to the rural energy spatial characteristics data to generate a rural energy geographic model.

[0054] The present invention performs land use type segmentation processing on remote sensing feature data, accurately classifies different types of land (such as cultivated land, forest land, water bodies, residential areas, etc.), provides a basis for subsequent energy distribution and utilization analysis, better identifies the applicable energy type of each land type, and the land use type segmentation processing helps to remove mixed background information, improve the focus on target areas and specific objects, ensure the accuracy and effectiveness of feature extraction, and improve the quality of data analysis. Spatial feature analysis is performed based on segmented remote sensing data to fully reveal the spatial distribution characteristics in rural areas, such as terrain changes, vegetation coverage, roads and water body networks, etc., to more accurately divide and manage regions, help to formulate more sophisticated energy allocation and utilization strategies, and improve the accuracy and efficiency of rural energy management. Energy spatial feature analysis is performed on rural spatial feature data to identify the energy utilization potential (such as highlands rich in wind energy, open flat land suitable for photovoltaics) and limitations (such as shadowed areas, remote areas that are difficult to access), provide data support for energy layout and use, and provide vital digital information for key tasks such as energy production forecasting and supply and demand balance analysis, ensuring that the model can reflect the actual rural energy utilization situation. Combining energy spatial characteristic data with geographic models to accurately identify the energy characteristics of each region is crucial for formulating energy development and utilization strategies. Based on a detailed rural energy geographic model, detailed information on digital rural energy geographic characteristics can be provided to provide data support for subsequent optimization of energy utilization efficiency and improve the scientific nature and economic benefits of rural energy management.

[0055] In an embodiment of the present invention, remote sensing image processing technology is used to perform land use type segmentation processing on rural remote sensing feature data, and a classification algorithm (such as a supervised classification algorithm, an unsupervised classification algorithm or a deep learning method) is applied to the acquired multispectral remote sensing image to identify and segment the land use type. Different land use types (such as cultivated land, forest land, water area, building area, etc.) have different spectral characteristics, which are segmented from the remote sensing data through a classification algorithm. After the segmentation processing, each pixel point in the generated segmented rural remote sensing feature data is assigned a corresponding land use type label. These labeled data can clearly show the distribution of different types of land in rural areas, providing a basis for further spatial feature analysis. The segmented remote sensing feature data is used to analyze rural spatial features, and the area, shape, boundary features, etc. of different land use types are counted and analyzed to generate rural spatial feature data. Spatial analysis tools (such as vector analysis tools in GIS software) are used to calculate the area ratio, spatial distribution pattern, boundary complexity, and adjacent relationship of each land use type. These analysis results help identify the spatial characteristics of different types of land in rural areas, such as large continuous cultivated land areas, scattered forest land, and concentrated construction areas. The generated rural spatial feature data can accurately reflect the spatial layout and characteristics of land use types, and provide necessary information support for energy spatial feature analysis. Rural energy spatial feature analysis is carried out based on rural spatial feature data, and these energy data are combined with land use types and spatial features for superposition analysis in combination with energy resource data in rural areas. Analyze which areas have land types for the spatial location of energy production equipment, transmission equipment, and energy fire fighting equipment, and integrate these analysis results to generate rural energy spatial feature data. These data describe the spatial distribution characteristics and utilization potential of different energy types in rural areas, providing a basis for subsequent energy management and planning. Using the rural energy spatial characteristic data, the previously established rural geographic model is processed for energy spatial characteristic identification, and the rural energy spatial characteristic data is superimposed on the rural geographic model to ensure that the energy characteristic information of each region can be accurately reflected in the geographic model. According to the energy spatial characteristic data, the spatial identification tool (such as the identification function in GIS) is used to identify the energy production area, energy transmission area, etc. in the geographic model. The specific identification process can include assigning energy characteristic labels to different areas and visually displaying and classifying these areas. Finally, the generated rural energy geographic model not only contains the geographical structure information of the rural area, but also clearly identifies the utilization areas of different energy types, providing a comprehensive reference for the development, utilization and optimization of rural energy, and providing strong support for energy management decisions.

[0056] Preferably, step S2 comprises the following steps:

[0057] Step S21: Designing an energy data integration interface for a rural management area according to the sensor integration device to generate a rural energy data integration interface;

[0058] Step S22: collecting rural energy data according to the rural energy data integration interface to generate rural energy data; performing feature analysis on the rural energy data to obtain rural energy feature data;

[0059] Step S23: Mapping the rural energy characteristic data to the rural energy geographic model to perform rural energy characteristic mapping processing to generate a rural energy characteristic model.

[0060] The present invention can be compatible with different types of sensors and data acquisition equipment by designing a data integration interface, thereby ensuring the diversity and comprehensiveness of energy data, so that the system can flexibly adapt to different types of equipment and data sources. The design of the integrated interface effectively regulates and unifies the format and standard of data acquisition, reduces errors and redundancy in the data acquisition process, ensures the efficiency and accuracy of data acquisition, and provides a reliable data basis for subsequent data analysis and modeling. Energy data is collected through the integrated interface, various energy data (such as photovoltaic power generation, wind speed, energy consumption data, etc.) are collected in real time, the timeliness and accuracy of the data are ensured, the operating status and change trend of the rural energy system are dynamically analyzed, the collected energy data are feature analyzed, and multi-dimensional energy feature data (such as energy consumption characteristics, power generation characteristics, weather conditions, etc.) are extracted to provide rich data support for the subsequent energy feature model, and the multi-dimensionality and accuracy of the model are improved. The rural energy feature data is mapped to the geographical model, and the distribution of energy in the rural area and its characteristics (such as efficient utilization areas, inefficient areas, etc.) are intuitively displayed, which is helpful to identify the advantages and disadvantages of energy utilization and optimize the energy management strategy. The rural energy feature model can comprehensively consider geographical characteristics and energy data to provide refined energy management information.

[0061] In an embodiment of the present invention, the sensor equipment in the rural management area is analyzed and selected, and the sensor type (such as solar radiation sensor, photovoltaic sensor, electric energy meter, etc.) suitable for the energy management needs of the area is selected. According to the type and quantity of the selected sensor equipment, a data integration interface is designed. The design of the data integration interface needs to consider the communication protocol, data format and transmission rate of the sensor equipment to ensure that the data of different sensors can be uniformly collected and processed. For example, the Internet of Things (IoT) technology is used to build an integrated interface compatible with a variety of sensor devices, and data is transmitted in real time through wireless networks (such as LoRa, NB-IoT). The rural energy data integration interface finally generated can seamlessly access various sensors and provide efficient and reliable data collection and transmission functions. The energy data integration interface is used to start collecting energy data in the rural management area. The sensor integration device collects various energy data in real time, such as solar radiation intensity, wind speed, temperature, humidity, electricity usage, etc., and summarizes them to a centralized management platform through the data integration interface. Next, the collected energy data is subjected to feature analysis, including data cleaning (removing outliers and noise), data conversion (normalization or standardization processing) and feature extraction (such as calculating statistical features such as average, maximum, minimum, standard deviation, etc.). Through these analysis steps, representative energy feature data are extracted to reflect the energy utilization status and change trend in rural areas. The rural energy feature data is mapped to the previously created rural energy geographic model, and the energy feature data and the geographic model are spatially aligned and coordinate matched to ensure that the geographical location of the data and the model location accurately correspond. The energy feature data is mapped to the corresponding area of ​​the geographic model according to geographic units (such as villages, fields, wind farms, etc.) using geographic information system (GIS) tools. For example, the characteristic data such as solar radiation intensity, wind speed and electricity usage of each village are assigned to the corresponding area of ​​the model. Through this mapping process, a rural energy feature model that integrates geographic information and energy feature data is generated.

[0062] Preferably, step S23 includes the following steps:

[0063] Step S231: performing data traceability feature analysis on rural energy data according to the rural energy data integration interface to generate data traceability feature information;

[0064] Step S232: performing rural energy traceability mapping relationship analysis based on the data traceability feature information and the rural energy spatial feature data of the rural energy geographic model to generate rural energy traceability mapping relationship data;

[0065] Step S233: Based on the rural energy traceability mapping relationship data, the rural energy characteristic data is mapped to the rural energy geographic model to perform rural energy characteristic mapping processing to generate a rural energy characteristic model.

[0066] The present invention uses data traceability feature analysis to trace back key information such as the source, generation time, and collection conditions of energy data, verify the authenticity and integrity of the data, ensure data quality, and provide a reliable basis for subsequent analysis. In addition, data traceability feature information can help identify potential problems in the data collection process (such as data loss, collection errors, etc.), and make timely corrections and optimizations, thereby improving the accuracy and effectiveness of data management. Through traceability mapping relationship analysis, the traceability feature information is associated with the spatial feature data in the rural energy geographic model, which can accurately locate the geographical location of energy data and its spatial characteristics, deeply understand the energy characteristics and usage of different geographical locations, not only identify the direct relationship between energy data and its geographical location, but also reveal the impact of potential spatial characteristics on energy use, provide deeper insights, and support more complex energy management and optimization decisions. By using traceability mapping relationship data to accurately map energy characteristic data to the geographic model, a characteristic model that reflects actual energy usage can be constructed more finely, which can reflect the spatial distribution of energy and its usage characteristics. By constructing an accurate energy characteristic model, it is easier to formulate more detailed energy management and optimization strategies based on the specific conditions of different regions, thereby improving the refinement and intelligence of energy management. Detailed characteristic models help identify areas of efficient energy utilization and waste, and support decision makers to make better decisions in planning and resource allocation, thereby improving the efficiency and sustainability of the overall energy system.

[0067] In the embodiment of the present invention, the energy data obtained through the rural energy data integration interface is subjected to data traceability feature analysis. The goal of the data traceability feature analysis is to determine the source of each piece of data and the relevant information in the collection process, including the data collection time, collection location, sensor type, sensor status, and data transmission path. The specific operation includes classifying the data provided by the data integration interface and marking the source according to the metadata of the data (such as timestamp, location coordinates, device ID, etc.). For example, if a data record shows that the data from a specific sensor at a certain time point is abnormal, the operating status or environmental conditions of the sensor can be understood through traceability analysis to determine the cause of the data abnormality. Through this analysis, the generated data traceability feature information includes the collection source, error source, and collection environment characteristics of each piece of data, providing a basis for further data quality control and analysis. The data traceability feature information and the rural energy spatial feature data in the rural energy geographic model are used to perform traceability mapping relationship analysis. The purpose of the analysis is to establish the relationship between the data source and its geographic spatial location, and match the location data in the data traceability feature information with the spatial feature data in the rural energy geographic model. Spatial analysis methods (such as buffer analysis, point-to-polygon mapping, etc.) are used to associate the data collection points with the geographic feature areas in the model. For example, if a sensor data records the photovoltaic intensity of a specific area, it is necessary to confirm that the traceability information of the sensor data corresponds to the specific location of the area in the geographic model, analyze the errors or deviations that may exist in the matching process, and ensure the accuracy of the traceability mapping. The rural energy traceability mapping relationship data finally generated clearly reflects the source of each data and its spatial position relationship in the geographic model, providing a basis for accurately mapping energy feature data to the geographic model. Based on the rural energy traceability mapping relationship data, the rural energy feature data is accurately mapped to the rural energy geographic model, and the energy feature data is matched with its corresponding traceability mapping relationship data. Ensure that each energy feature data can find its exact location in the geographic model through the traceability mapping relationship. Using GIS software or other spatial analysis tools, the energy feature data (such as solar radiation intensity, wind energy density, electricity consumption, etc.) is located to the corresponding area of ​​the geographic model according to the traceability mapping relationship. The geographic model of each area is given a specific energy feature value to reflect the energy characteristics of the area. After the mapping of characteristic data, the generated rural energy characteristic model integrates energy characteristic data and geographic space information. The model can intuitively display the spatial distribution of different energy characteristics in rural areas, providing accurate basic data for further energy management and optimization decision-making.

[0068] Preferably, step S3 comprises the following steps:

[0069] Step S31: performing a rural energy multi-dimensional physical characteristic analysis based on a rural energy characteristic model to generate rural energy multi-dimensional physical characteristic data, wherein the rural energy multi-dimensional physical characteristic data includes rural energy production physical characteristic data, rural energy storage characteristic data, and rural energy transmission behavior characteristic data;

[0070] Step S32: performing rural energy multi-dimensional physical characteristic interaction feature analysis based on the rural energy multi-dimensional physical characteristic data to generate rural energy multi-dimensional physical characteristic interaction feature data;

[0071] Step S33: performing rural energy physical characteristic integration processing on the rural energy multi-dimensional physical characteristic data according to the rural energy multi-dimensional physical characteristic interactive feature data to generate rural energy physical characteristic integrated data.

[0072] By analyzing the multidimensional physical characteristics of rural energy, the present invention can systematically understand the various physical characteristics of the energy system in the process of production, energy storage and transmission, and can reveal the performance and potential problems of each link of the energy system, providing an important reference for optimizing energy management, respectively obtaining the characteristic data of production, energy storage and transmission behavior, and providing targeted analysis data for the optimization of different links. For example, production characteristic data can be used to optimize energy production efficiency, energy storage characteristic data can help improve the utilization rate of energy storage system, and transmission behavior characteristic data can help reduce transmission loss. Through the analysis of multidimensional physical characteristic data, the various dimensions of the energy characteristic model can be refined, the simulation accuracy and reliability of the model for the actual energy system can be improved, and the model can be effectively operated in different usage scenarios. Multidimensional physical characteristic interaction feature analysis can reveal the mutual influence relationship between different physical characteristics, such as the dynamic relationship between production and energy storage, energy storage and transmission. This interactive analysis helps to understand the coupling effect of each link, identify the key factors affecting the overall performance of the system, and better coordinate the operation of each link by understanding the interaction between each physical characteristic, so as to improve the overall performance and efficiency of the entire energy system. For example, by optimizing the interactive characteristics of energy production and energy storage, while maintaining high production efficiency, reducing energy waste, in the complex and changing rural energy environment, interactive characteristic data provides decision makers with a more comprehensive perspective, helping to make energy optimization and configuration decisions under multi-objective and multi-constraint conditions. The integrated processing of multi-dimensional physical property data builds a comprehensive energy characteristic view, comprehensively displays the comprehensive performance of rural energy systems in different dimensions, and better understands and manages the overall behavior and performance of energy systems. Physical property integrated data provides the necessary data support for the digital integration of the entire energy system, identifies the key points for overall performance improvement and key information driven by rural energy data, thereby achieving a comprehensive analysis of the rural energy system.

[0073] As an embodiment of the present invention, refer to Figure 2As shown, Figure 1 FIG. 1 is a flow chart of detailed implementation steps of step S3 in FIG. 1 . In this embodiment, step S3 includes:

[0074] Step S31: performing a rural energy multi-dimensional physical characteristic analysis based on a rural energy characteristic model to generate rural energy multi-dimensional physical characteristic data, wherein the rural energy multi-dimensional physical characteristic data includes rural energy production physical characteristic data, rural energy storage characteristic data, and rural energy transmission behavior characteristic data;

[0075] In an embodiment of the present invention, for different types of energy production methods such as solar energy and wind energy, relevant physical characteristic data in the geographic model are extracted. For solar energy, the radiation intensity, sunshine time, tilt angle, etc. of photovoltaic panels at different locations are calculated; for wind energy, the wind speed distribution, wind force density, turbine efficiency, etc. are analyzed; the performance characteristics of various energy storage devices such as battery energy storage in rural areas are analyzed, and according to the location of energy storage nodes in the geographic model, the energy storage capacity, charging and discharging efficiency, energy loss rate, response time and other energy storage characteristic data are calculated. The physical characteristics of energy transmission systems (such as power grids, cables, pipelines, etc.) are analyzed. Data such as transmission path length, transmission loss, load capacity and transmission speed are calculated, and possible bottlenecks and inefficient links in the transmission process are identified, and finally multi-dimensional physical characteristic data of rural energy is generated.

[0076] Step S32: performing rural energy multi-dimensional physical characteristic interaction feature analysis based on the rural energy multi-dimensional physical characteristic data to generate rural energy multi-dimensional physical characteristic interaction feature data;

[0077] In an embodiment of the present invention, the relationship between the production of different types of energy and the energy storage system is analyzed. For example, by simulating the interaction between solar power generation and battery energy storage in different time periods, it is studied how to maximize energy storage and reduce energy waste during peak power generation. The interactive relationship between energy production and transmission systems is analyzed. For example, when photovoltaic power generation increases under the condition of solar photovoltaic power generation, how to optimize the dispatching strategy of the power grid to reduce transmission losses and avoid overload is studied, and the dynamic interaction between energy storage systems and transmission systems is studied. For example, during the peak load period of the power grid, it is analyzed how to use the energy storage system for load balancing to reduce power transmission losses and power grid pressure, and finally generate multi-dimensional physical characteristics interactive characteristic data of rural energy.

[0078] Step S33: performing rural energy physical characteristic integration processing on the rural energy multi-dimensional physical characteristic data according to the rural energy multi-dimensional physical characteristic interactive feature data to generate rural energy physical characteristic integrated data.

[0079] In the embodiment of the present invention, the physical characteristic data of each energy production mode, the characteristic data of the energy storage system, and the behavioral characteristic data of the transmission system are uniformly processed. Through methods such as data fusion and machine learning, the various characteristic data are integrated to identify the key factors affecting the performance of the entire rural energy system. Based on integrated processing, comprehensive characteristic indicators reflecting the overall performance of the rural energy system are constructed, such as total energy production efficiency, comprehensive energy storage efficiency, system transmission loss rate, etc. These indicators can provide a global view of the performance of the entire energy system, which is convenient for system optimization and management. All processed data are organized into a complete rural energy physical characteristic integrated data set, which covers the integrated characteristics of various aspects of production, energy storage and transmission in the rural energy system, and provides important data support for subsequent optimization modeling and digital twin analysis.

[0080] Preferably, step S31 includes the following steps:

[0081] Step S311: collecting rural energy production-related data for the rural energy characteristic model to obtain rural energy production-related data, wherein the rural energy production-related data includes rural energy production data and rural energy production environment data;

[0082] Step S312: using finite element analysis technology to perform rural energy production environmental impact analysis on rural energy production environmental data to generate rural energy production environmental impact data;

[0083] Step S313: Analyzing the rural energy production physical characteristics of the rural energy production data according to the rural energy production environmental impact data to generate rural energy production physical characteristics data;

[0084] Step S314: Analyze rural energy storage nodes according to the rural energy characteristic model to generate rural energy storage node data, wherein the rural energy storage node data includes rural energy storage data and rural energy storage node status data;

[0085] Step S315: performing rural energy storage effectiveness analysis based on rural energy production data and rural energy storage data to generate rural energy storage effectiveness data;

[0086] Step S316: performing rural energy storage characteristic analysis on the rural energy storage effectiveness data based on the rural energy storage node status data to generate rural energy storage characteristic data;

[0087] Step S317: Analyze rural energy transmission characteristics according to the rural energy characteristic model to generate rural energy transmission characteristic data;

[0088] Step S318: performing energy transmission time-domain finite difference analysis according to the rural energy transmission characteristic data to generate energy transmission time-domain finite difference data;

[0089] Step S319: Analyze rural energy transmission behavior characteristics based on energy transmission time-domain finite difference data to generate rural energy transmission behavior characteristic data.

[0090] The present invention collects rural energy production-related data on the rural energy characteristic model to collect rural energy production data and rural energy production environment data, comprehensively understand all key factors in the energy production process, including energy type, production volume, production equipment status and environmental conditions (such as temperature, humidity, wind speed, etc.), ensure the integrity and representativeness of production data, help identify key factors affecting energy production efficiency, formulate targeted optimization strategies, improve energy production efficiency and stability, and reduce waste. The rural energy production environment data is analyzed for rural energy production environmental impact using finite element analysis technology. The finite element analysis technology performs high-precision simulation of various complex factors in the production environment (such as terrain, climate change, facility layout, etc.), provides a deep understanding of the rural energy production environment, and reveals the specific impact of the environment on the energy production process. According to the rural energy production environment impact data, the rural energy production data is analyzed for the physical characteristics of rural energy production, and the physical characteristics in the production process, such as the response characteristics of the production equipment, energy conversion efficiency, etc., are more accurately identified. According to the rural energy characteristic model, the rural energy storage node analysis is performed to obtain key data such as the operating status and storage capacity of the energy storage equipment, comprehensively evaluate the performance and reliability of the energy storage system, and ensure the stability of energy storage and other characteristics. According to the rural energy production data and rural energy storage data, the rural energy storage effectiveness analysis is carried out to understand the matching degree between energy production and energy storage, identify the efficiency loss in the energy storage process, and provide detailed information on rural energy storage to optimize the scheduling strategy of the energy storage system, ensure that energy can be fully stored during the peak production period, and can be efficiently released during the peak demand period, so as to improve energy utilization. Based on the rural energy storage node status data, the rural energy storage effectiveness data is analyzed for rural energy storage characteristics. By analyzing the energy storage node status data and the energy storage effectiveness data, the characteristics of the energy storage system under different operating conditions (such as charging and discharging rate, energy density, attenuation characteristics, etc.) are revealed. According to the rural energy characteristic model, the rural energy transmission characteristics are analyzed. Through the transmission characteristics analysis, the characteristics of energy in the transmission process, such as transmission loss, transmission speed, pipeline and line load capacity, etc., are identified. According to the rural energy transmission characteristic data, the energy transmission time domain finite difference analysis is carried out. The time domain finite difference analysis can dynamically simulate the changes of various conditions in the energy transmission process (such as voltage fluctuations, pressure changes, etc.), provide a detailed analysis of the transmission process, and help identify potential problems and bottlenecks. By understanding the dynamic characteristics of the transmission process, more detailed information on rural energy transmission characteristics is provided, which improves the stability of the system and the response speed to external disturbances. The rural energy transmission behavior characteristics are analyzed based on the energy transmission time-domain finite difference data, and the comprehensive understanding of the behavior characteristics of the transmission system under different conditions, such as transmission efficiency, delay, reliability, etc., is helpful for the optimal design and operation management of the system.

[0091] In an embodiment of the present invention, a rural energy characteristic model is used to collect detailed data on the rural energy production process. Rural energy production data is collected, including production data of different types of energy such as solar energy, wind energy, and biomass energy. For example, for solar energy, photovoltaic power generation, battery panel efficiency, sunshine duration, etc. are collected; for wind energy, wind power generation, wind speed and direction, etc. are collected. Rural energy production environment data is collected, including environmental parameters such as temperature, humidity, air pressure, and terrain characteristics. These environmental factors will directly affect the efficiency of energy production. For example, by installing environmental sensors, data such as wind speed, solar radiation intensity, air humidity, and temperature are collected to obtain energy production efficiency under different environmental conditions. Finally, the rural energy production data and rural energy production environment data obtained are integrated to form rural energy production related data, which provides a basis for subsequent analysis. Finite element analysis technology is used to analyze the environmental impact of rural energy production environmental data, and a finite element model containing rural energy production environmental data is established. The model includes the terrain, meteorological conditions and surrounding facility layout of the rural energy production site. Through finite element analysis, the impact of various environmental conditions (such as wind speed changes, temperature fluctuations, rainfall, etc.) on energy production equipment (such as solar panels, wind turbines, etc.) is simulated, and the impact of different wind speeds on the output power of wind turbines or the changes in the efficiency of solar panels under different temperature and humidity conditions are analyzed to generate rural energy production environmental impact data. These data are used to evaluate the specific impact of environmental factors on the energy production process. Rural energy production environmental impact data are used to analyze the physical characteristics of rural energy production data, and analyze the energy production efficiency and equipment performance under different environmental conditions (such as wind speed, temperature, humidity, etc.). By establishing mathematical models or using statistical analysis methods, the impact of environmental factors on energy production, equipment efficiency and service life are studied. The physical characteristics of energy production such as the attenuation rate of photovoltaic panel power output under high temperature are analyzed to generate rural energy production physical characteristics data, which reflects the physical characteristics and performance of energy production equipment under different environmental conditions. According to the rural energy characteristic model, the energy storage nodes in the rural energy system are identified and analyzed, and the location and type of energy storage equipment, such as battery energy storage system, pumped storage system, etc., are determined. Through sensors and monitoring equipment, the operation data of the energy storage system, including charging and discharging rate, energy storage amount, equipment temperature, etc., are collected. At the same time, the status data of the energy storage nodes are collected to reflect the health status, operating efficiency and available capacity of the energy storage equipment. For example, the voltage, current and temperature of the battery are monitored, and the number of charge and discharge cycles and capacity decay rate are analyzed to generate rural energy storage node data, which contains real-time operation data and status information of the energy storage system, providing data support for energy storage performance analysis. Using rural energy production data and energy storage data, the effectiveness of the rural energy storage system is analyzed, and the charging and discharging efficiency, energy loss and storage time of the energy storage system in different time periods are calculated.For example, analyze the time difference between solar power generation and battery energy storage, evaluate the energy storage and release efficiency of energy storage systems during the day and night, study the matching between production and energy storage, and evaluate the effectiveness of energy storage systems during production peaks and valleys. For example, by analyzing the battery charging rate during the peak period of photovoltaic power generation and the discharge rate at night, determine whether the capacity of the energy storage system can meet the nighttime load demand, and generate rural energy storage effectiveness data. Using energy storage node status data and rural energy storage effectiveness data, analyze the characteristics of the energy storage system, analyze the charging and discharging behavior and state changes of the energy storage system, analyze the voltage and current changes of the energy storage nodes through time series, evaluate its charging and discharging cycle efficiency and reliability, evaluate the performance of the energy storage system under different environmental and load conditions, such as energy storage efficiency and equipment life under extreme temperatures or high loads, and generate rural energy storage characteristic data, which provides important information about the behavior and characteristics of the energy storage system, and helps to optimize energy storage management strategies and equipment selection. Based on the rural energy characteristic model, the transmission characteristics of rural energy are analyzed, the structure and configuration of the rural energy transmission network, such as power transmission lines, pipeline networks, etc., are determined, and the operation data of the transmission network, including voltage, current, flow and transmission loss, etc., are collected. Through simulation analysis or measured data, the efficiency, stability and reliability of the transmission network are evaluated. For example, the voltage drop and power loss of power transmission lines under high load, or the flow change and leakage of pipeline networks under different pressure conditions are analyzed to generate rural energy transmission characteristic data. Using rural energy transmission characteristic data, time domain finite difference analysis is performed to establish a time domain model of the transmission network and simulate the behavior of energy changing over time during transmission. Using the finite difference method, the time step and difference of energy change during transmission are calculated, the state of the transmission system at different time points is analyzed, the instantaneous current change and voltage fluctuation of power transmission lines at peak load, or the time domain change of pressure of pipelines during flow regulation are calculated, and energy transmission time domain finite difference data are generated, which describe the dynamic behavior and time characteristics of the transmission system. The behavioral characteristics of the rural energy transmission system are analyzed using the energy transmission time-domain finite difference data, the response characteristics and stability of the transmission system at different time points are analyzed, such as the response time and recovery speed when the load suddenly increases or decreases, the efficiency and reliability of the transmission system are evaluated, and potential bottlenecks and failure points are identified, such as analyzing the transmission loss and voltage drop of high-load or long-distance transmission lines, or evaluating the leakage risk and pressure loss of pipeline networks under high flow conditions, to generate rural energy transmission behavioral characteristic data.

[0092] Preferably, step S4 comprises the following steps:

[0093] Step S41: Acquire historical rural energy driving data;

[0094] Step S42: Analyze the multi-scale driving factors of rural energy based on the integrated data of rural energy physical characteristics to generate multi-scale driving factor data of rural energy;

[0095] Step S43: Perform nonlinear driving characteristic modeling of rural energy based on the decision tree algorithm and rural energy multi-scale driving factor data to generate a rural energy driving model;

[0096] Step S44: performing model training optimization on the rural energy driving model through historical rural energy driving data to generate an optimized rural energy driving model;

[0097] Step S45: construct a rural energy driving engine based on the optimized rural energy driving model, and perform rural energy digital twin analysis on the rural energy characteristic model based on the rural energy driving engine to generate a rural energy digital twin model.

[0098] The present invention obtains historical rural energy driving data, which is helpful to establish the initial state of the driving model, provides a detailed record of the past behavior and state of the energy system, and provides rich data support for subsequent analysis and model optimization. By analyzing historical data, the long-term trends and patterns in the energy system are identified, and this information is very important for predicting future energy demand and supply. Multi-scale driving factor analysis can reveal factors of different scales that affect the rural energy system, including micro (such as equipment efficiency, individual energy consumption), meso (such as village-level energy flow) and macro (such as regional climate change) factors. This comprehensive analysis can help better understand how various factors jointly affect the operation of the energy system. By identifying driving factors of different scales, a more comprehensive optimization strategy is formulated, which considers both local optimization and overall coordination to ensure the efficient operation of the energy system. Based on the decision tree algorithm and rural energy multi-scale driving factor data, the nonlinear driving feature modeling of rural energy is processed. The decision tree algorithm is suitable for processing complex nonlinear relationships. By using the algorithm to model the driving factors, the complex relationship between the various driving factors in the rural energy system is accurately captured. The decision tree algorithm has a clear structure, which is convenient for explaining how each driving factor affects the behavior of the energy system, helps to understand the prediction results of the model, and supports a transparent decision-making process. The rural energy driving model is trained and optimized through historical rural energy driving data, and the parameters and structure of the model are continuously adjusted to improve the model's fit to the actual energy system and the prediction accuracy, ensuring the reliability of the model under different conditions. The optimized model can better adapt to various changing energy usage scenarios, provide more robust prediction and analysis results, and support intelligent energy management decisions. According to the optimized rural energy driving model, a rural energy driving engine is constructed. The constructed driving engine can perform real-time digital twin simulation, accurately reflect the dynamic behavior of the rural energy system under different conditions, quickly identify and respond to problems in the system, and improve the system's response speed and adaptability. The digital twin model provides a virtual environment in which energy system optimization experiments under different scenarios are carried out, the effects of various optimization strategies are predicted and analyzed, more intelligent and scientific energy management decisions are supported, real-time monitoring and overall driving characteristics of rural energy are monitored, and intelligent control of the energy system is realized to improve the efficiency and stability of system operation.

[0099] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation step flow chart of step S4 in the embodiment, step S4 in this embodiment includes:

[0100] Step S41: Acquire historical rural energy driving data;

[0101] In an embodiment of the present invention, historical rural energy driving data is extracted from the historical database of the rural energy system, including energy production, storage, transmission and consumption data in the past period of time, as well as environmental and operating condition data related to these processes. Specific data items may include solar power generation, wind power generation, battery energy storage charging and discharging records, power transmission, load changes, temperature, humidity and other environmental data. The time span of historical data is determined according to the analysis requirements, and it is usually necessary to cover multiple seasonal cycles to fully reflect the long-term change characteristics and trends of the energy system. For example, all relevant data in the past three years are collected to capture the impact of seasonal and interannual changes, and provide data support for subsequent multi-scale driving factor analysis.

[0102] Step S42: Analyze the multi-scale driving factors of rural energy based on the integrated data of rural energy physical characteristics to generate multi-scale driving factor data of rural energy;

[0103] In the embodiment of the present invention, the integrated data of rural energy physical characteristics is used to perform multi-scale driving factor analysis, and the physical characteristic data is decomposed into different scales to identify driving factors at different scales. Statistical analysis and data mining techniques (such as correlation analysis and principal component analysis) are used to identify key driving factors affecting the performance of rural energy systems, such as key factors such as energy production, transmission, and consumption. Through these analyses, rural energy multi-scale driving factor data are generated, which can reflect the factors that have a significant impact on the performance of the energy system at different scales when the rural energy system is running.

[0104] Step S43: Perform nonlinear driving characteristic modeling of rural energy based on the decision tree algorithm and rural energy multi-scale driving factor data to generate a rural energy driving model;

[0105] In an embodiment of the present invention, multi-scale driving factor data is used to establish a nonlinear driving feature model based on a decision tree algorithm, an appropriate decision tree algorithm (such as CART, random forest, etc.) is selected as a modeling tool, and the multi-scale driving factor data is used as an input feature variable to construct a decision tree model. The decision tree algorithm recursively splits the data set to map the complex nonlinear relationship between the input variables (such as environmental conditions, historical energy consumption) and the output variables (such as current production efficiency, energy storage status) of the energy system. During the training process, the depth and structure of the decision tree are optimized through cross-validation and pruning techniques to prevent overfitting and improve the generalization ability of the model. The generated rural energy driving model can accurately reflect the nonlinear effects of different driving factors on the performance of the energy system.

[0106] Step S44: performing model training optimization on the rural energy driving model through historical rural energy driving data to generate an optimized rural energy driving model;

[0107] In an embodiment of the present invention, the rural energy drive model is trained and optimized using historical rural energy drive data, and the historical data is divided into a training set, a validation set, and a test set to ensure the comprehensiveness and reliability of the model optimization process. The training set data is used to further train the drive model, adjust the parameters and structure of the model, enhance the model's ability to fit the historical data, and verify the model through the validation set data to evaluate the model's prediction accuracy and generalization ability to prevent overfitting. During the optimization process, algorithms such as gradient descent and grid search are used to optimize the model's hyperparameters to ensure the best performance of the model. Finally, the optimized model is performance tested based on the test set data to confirm the accuracy and stability of the model. The generated optimized rural energy drive model can more accurately predict and feedback the behavior and performance of the rural energy system under different conditions.

[0108] Step S45: construct a rural energy driving engine based on the optimized rural energy driving model, and perform rural energy digital twin analysis on the rural energy characteristic model based on the rural energy driving engine to generate a rural energy digital twin model.

[0109] In an embodiment of the present invention, an optimized rural energy driving model is used to construct a rural energy driving engine. The rural energy driving engine is a computing platform that can run the optimized driving model in real time, simulate and predict the dynamic behavior of the rural energy system under different operating conditions, and deploy the optimized driving model to the driving engine to ensure that the engine has sufficient computing power and storage capacity to handle a large amount of input data and simulation processes. Based on the rural energy driving engine, a digital twin analysis is performed on the rural energy characteristic model. The digital twin analysis simulates the real-time state and future evolution of the rural energy system by inputting the latest environmental data, operation data and demand data in real time, and generates a rural energy digital twin model. The model can dynamically reflect the current state and future trends of the energy system, and provide a scientific basis for optimizing energy management strategies and decisions. The digital twin model provides a powerful tool for the intelligent and dynamic management of rural energy systems, constantly adapting to changes in the environment and demand, and improving the efficiency and stability of the energy system.

[0110] Preferably, step S5 comprises the following steps:

[0111] Step S51: performing digital energy supply scheduling analysis and processing based on the rural energy digital twin model to generate digital energy supply scheduling data;

[0112] Step S52: performing a digital energy supply scheduling demand characteristic analysis based on the digital energy supply scheduling data to generate digital energy supply scheduling demand characteristic data;

[0113] Step S53: Based on the preset long short-term memory network model, the digital energy supply scheduling data is subjected to a time series benefit prediction of the digital energy supply scheduling, and the digital energy supply scheduling time series benefit data is generated;

[0114] Step S54: formulating rural energy supply scheduling optimization constraint conditions through digital energy supply scheduling demand characteristic data, and formulating rural energy supply scheduling optimization target conditions through digital energy supply scheduling time series benefit data;

[0115] Step S55: Designing an intelligent optimization model for rural energy supply scheduling based on the preset reinforcement learning algorithm for the rural energy supply scheduling optimization constraint conditions and the rural energy supply scheduling optimization target conditions to obtain a rural energy supply scheduling optimization model;

[0116] Step S56: Execute the rural energy supply scheduling intelligent optimization operation according to the rural energy supply scheduling optimization model.

[0117] The present invention performs digital energy supply scheduling analysis and processing based on the rural energy digital twin model, can simulate and analyze the performance of different energy supply schemes in actual operation, and quickly generate energy supply scheduling data. The digital twin model provides a virtual environment, on which a variety of supply scheduling schemes are tested and optimized, the accuracy of the scheduling strategy is improved, and the balance of supply and demand is ensured. According to the digital energy supply scheduling data, the digital energy supply scheduling demand feature analysis is performed to identify the key demand features in the energy supply, such as peak load, valley load, seasonal demand changes, etc., which helps to optimize the scheduling strategy to cope with the variable energy demand. The feature analysis data can help identify potential scheduling needs, support the optimization of scheduling strategies based on demand characteristics, and ensure the stability and sustainability of energy supply. Based on the preset long short-term memory network model, the digital energy supply scheduling data is used to predict the time series benefit of digital energy supply scheduling. The long short-term memory network (LSTM) model is suitable for processing time series data. By using the LSTM model, the time series benefit of energy supply scheduling is more accurately predicted, the influence of long and short-term factors on supply is captured, the time arrangement of energy supply is optimized, resource waste and supply and demand imbalance are avoided, and energy is effectively used at the right time. The optimization constraints of rural energy supply scheduling are formulated through the demand characteristic data of digital energy supply scheduling, ensuring that the optimization model takes into account multiple factors such as supply and demand balance, cost control, reliability, etc., and constructing a more comprehensive optimization model. The optimization target conditions of rural energy supply scheduling are formulated through the time series benefit data of digital energy supply scheduling, so as to better focus on specific optimization goals (such as minimum cost, maximum efficiency, maximum stability, etc.), thereby improving the effect of supply scheduling optimization. Based on the preset reinforcement learning algorithm, the intelligent optimization model of rural energy supply scheduling is designed for the optimization constraints and target conditions of rural energy supply scheduling. The reinforcement learning algorithm can adaptively learn and optimize the supply scheduling strategy, gradually improve the system performance, adapt to the complex and changing energy demand and supply environment, ensure the optimality of supply scheduling, and continuously improve the scheduling strategy in a dynamic environment through repeated trials and adjustments. It has self-learning and adaptive capabilities, and improves the flexibility and robustness of the energy supply system. By executing the supply scheduling strategy formulated by the optimization model, verifying and adjusting the optimization effect in actual operations, and ensuring the effectiveness of the theoretical model in practical applications, intelligent optimization operations can dynamically respond to actual changes in supply and demand, automatically adjust the supply strategy, ensure that the energy system always operates in the optimal state, and improve the overall intelligence level of energy management.

[0118] In the embodiment of the present invention, a rural energy digital twin model is used to perform digital energy supply scheduling analysis. The rural energy digital twin model is used to analyze the characteristics of energy production, energy storage status, and transmission data, simulate the current energy system state, and analyze the performance of different scheduling strategies in various scenarios (such as different weather conditions, energy demand changes, equipment status, etc.), simulate the supply curve and demand curve of various energy sources, evaluate the impact of different strategies on energy supply balance, and identify possible bottlenecks and optimization space. Digital energy supply scheduling data is generated according to the analysis results, and the data includes quantitative information and scheduling status of real-time energy supply scheduling. According to the digital energy supply scheduling data, the digital energy supply scheduling demand feature analysis is performed, the scheduling data is classified, and the energy demand characteristics under different time periods and different load conditions are identified, such as peak load, valley load, seasonal demand fluctuations, etc., and the demand characteristics and patterns in historical data are analyzed through data clustering and pattern recognition technology. For example, energy peak demand or supply and demand imbalance in certain specific time periods is identified. Combined with these feature information, digital energy supply scheduling demand feature data is generated to provide decision support for further optimizing the supply strategy. Based on the preset long short-term memory network model, the digital energy supply scheduling data is used to predict the time series benefits of digital energy supply scheduling. The digital energy supply scheduling data is organized into a time series format and input into the LSTM model. The LSTM model is preset to be able to handle complex time dependencies and predict future energy supply and demand trends. By training the past scheduling data, the model learns the supply and demand change rules and time series characteristics of the energy system. In the prediction process, the model predicts the energy supply benefits and possible demand changes in the future based on the current system status and historical trends, and generates digital energy supply scheduling time series benefit data, which provides a forward-looking reference for the formulation of scheduling optimization strategies. The optimization constraints of rural energy supply scheduling are formulated through the demand characteristic data of digital energy supply scheduling, such as defining the optimization constraints of supply scheduling, such as supply and demand balance, cost control, system reliability, etc., and the optimization target conditions of rural energy supply scheduling are formulated through the time series benefit data of digital energy supply scheduling, such as determining the specific parameters of various constraints (such as maximum load, minimum energy storage capacity, etc.) to ensure that the scheduling strategy can meet the demand changes under different conditions. At the same time, the time series benefit data is used to set the optimization target conditions (such as maximizing economic benefits, minimizing energy loss, optimizing equipment utilization, etc.). These conditions define the boundaries and goals of scheduling optimization and provide a clear direction for the subsequent design of intelligent optimization models.Based on the preset reinforcement learning algorithm, the intelligent optimization model of rural energy supply scheduling is designed for the optimization constraints and target conditions of rural energy supply scheduling. The state space, action space and reward function of reinforcement learning are defined to ensure that the model can learn the optimal strategy under various scheduling conditions. The state space includes the current energy production, energy storage and demand status, and the action space includes possible energy scheduling strategies (such as adjusting the power supply, changing the energy storage strategy, etc.). The reward function is defined according to the optimization objectives (such as maximizing benefits and minimizing losses), and different scheduling strategies are scored. Through multiple simulations and training, the reinforcement learning model is repeatedly learned and optimized in different environments. During the training process, the model gradually masters the optimal scheduling strategy under different conditions and generates a supply scheduling optimization model. According to the rural energy supply dispatch optimization model, the rural energy supply dispatch intelligent optimization operation is performed. The model obtains the current state of the energy system and external environmental data in real time, and automatically selects the optimal dispatch strategy based on these inputs. The model combines the automated energy dispatch system and dynamically adjusts the output of photovoltaic panels, the charge and discharge rate of batteries, etc. according to real-time weather data, current energy storage levels and predicted electricity demand. The model's decision is driven by a reinforcement learning algorithm to ensure that the system's energy supply and demand can maintain the best balance in any given time period. Intelligent optimization operations can improve energy utilization efficiency, reduce costs, and ensure the stability and reliability of the supply system. This intelligent and automated dispatching method greatly enhances the management level and adaptability of rural energy systems.

[0119] This specification provides a digital twin analysis system for rural energy, which is used to execute the digital twin analysis method for rural energy described above. The digital twin analysis system for rural energy includes:

[0120] The rural energy geographic structure modeling module is used to obtain rural remote sensing data of rural management areas; based on rural remote sensing data, rural energy geographic structure modeling is performed to generate a rural energy geographic model;

[0121] The rural energy mapping module is used to collect energy data in the rural management area according to the sensor integrated device to generate rural energy data; perform feature analysis on the rural energy data to obtain rural energy feature data; map the rural energy feature data to the rural energy geographic model to perform rural energy feature mapping processing to generate a rural energy feature model;

[0122] A rural energy physical characteristics analysis module is used to analyze the multi-dimensional physical characteristics of rural energy based on a rural energy characteristic model to generate rural energy multi-dimensional physical characteristics data; to perform rural energy physical characteristics integration processing on the rural energy multi-dimensional physical characteristics data to generate rural energy physical characteristics integration data;

[0123] The rural energy driving characteristic analysis module is used to model and process the nonlinear driving characteristics of rural energy based on the integrated data of rural energy physical characteristics to generate a rural energy driving model; a rural energy driving engine is constructed based on the rural energy driving model, and a rural energy digital twin analysis is performed on the rural energy characteristic model based on the rural energy driving engine to generate a rural energy digital twin model;

[0124] The rural energy supply scheduling optimization module is used to perform digital energy supply scheduling analysis and processing based on the rural energy digital twin model to generate digital energy supply scheduling data; design an intelligent optimization model for rural energy supply scheduling based on the digital energy supply scheduling data to obtain a rural energy supply scheduling optimization model; and perform rural energy supply scheduling intelligent optimization operations based on the rural energy supply scheduling optimization model.

[0125] The beneficial effect of the present application is that, by designing a data integration interface and optimizing the data collection process, the present invention can efficiently collect and integrate multi-source heterogeneous data from remote sensing, sensors, historical energy consumption, etc., ensure the comprehensiveness and diversity of the data, improve the timeliness and integrity of the data, and provide more accurate basic data support for digital twin analysis. And the rural energy characteristic data is mapped to the corresponding geographic model, which fully reflects the digital information of the overall rural energy system and realizes the comprehensive collection and integration of rural energy-related information. By constructing a digital twin model based on multi-dimensional physical properties and multi-scale driving factors, the dynamic change characteristics of the rural energy system are reflected in real time. Unlike the traditional analysis method that relies on fixed models and empirical rules, the digital twin model can capture and simulate the complex dynamic behavior of the rural energy system in real time, improving the accuracy and adaptability of the analysis. Advanced artificial intelligence algorithms, including decision trees, long short-term memory networks (LSTM), reinforcement learning, etc., are used to model nonlinear driving characteristics and optimize supply scheduling. These intelligent means automatically learn and adapt to different energy usage scenarios, improve the intelligence level of rural energy management, and achieve more accurate and effective analysis and decision support. Taking into account the multi-dimensional physical characteristics of production, storage, and transmission in rural energy systems, the intelligent optimization and dynamic adjustment of energy supply scheduling are realized, which improves the overall efficiency and reliability of rural energy systems and ensures the optimal allocation and utilization of resources. Through modular design and digital data-driven analysis methods, the rural energy digital twin model has high flexibility and scalability. It can flexibly adjust and expand its functions according to different regions and demand changes, and support the management needs of multiple energy types and complex energy systems.

[0126] 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.

[0127] 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 digital twin analysis method for rural energy, characterized in that: The following steps are involved: Step S1: Acquire rural remote sensing data of rural management areas; perform rural energy geographic structure modeling based on the rural remote sensing data to generate a rural energy geographic model; Step S2: collecting energy data in the rural management area according to the sensor integration device to generate rural energy data; performing feature analysis on the rural energy data to obtain rural energy feature data; mapping the rural energy feature data to a rural energy geographic model to perform rural energy feature mapping processing to generate a rural energy feature model; Step S3: Analyze the multidimensional physical characteristics of rural energy based on the rural energy characteristic model to generate multidimensional physical characteristics data of rural energy; perform rural energy physical characteristics integration processing on the rural energy multidimensional physical characteristics data to generate rural energy physical characteristics integrated data; Step S4: Based on the integrated data of rural energy physical characteristics, nonlinear driving characteristics of rural energy are modeled and processed to generate a rural energy driving model; Based on the rural energy driving model, a rural energy driving engine is constructed, and the rural energy digital twin analysis is performed on the rural energy characteristic model according to the rural energy driving engine to generate a rural energy digital twin model; Step S5: Perform digital energy supply scheduling analysis and processing based on the rural energy digital twin model to generate digital energy supply scheduling data; design an intelligent optimization model for rural energy supply scheduling based on the digital energy supply scheduling data to obtain a rural energy supply scheduling optimization model; and perform rural energy supply scheduling intelligent optimization operations based on the rural energy supply scheduling optimization model.

2. The digital twin analysis method for rural energy according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire rural remote sensing data of the rural management area; Step S12: performing remote sensing data synchronization correction processing on the rural remote sensing data to generate corrected rural remote sensing data; performing multispectral feature extraction on the corrected rural remote sensing data to generate rural remote sensing feature data; Step S13: Performing rural geographical structure modeling processing based on rural remote sensing feature data to generate a rural geographical model; Step S14: Perform rural energy spatial feature identification processing on the rural geographic model to generate a rural energy geographic model.

3. The digital twin analysis method for rural energy according to claim 2 is characterized in that: Step S14 includes the following steps: Perform remote sensing feature segmentation processing of land use type on rural remote sensing feature data to generate segmented rural remote sensing feature data; Perform rural spatial feature analysis based on segmented rural remote sensing feature data to generate rural spatial feature data; perform rural energy spatial feature analysis based on rural spatial feature data to generate rural energy spatial feature data; The rural energy spatial characteristics of the rural geographic model are identified according to the rural energy spatial characteristics data to generate a rural energy geographic model.

4. The digital twin analysis method for rural energy according to claim 3 is characterized in that: Step S2 includes the following steps: Step S21: Designing an energy data integration interface for a rural management area according to the sensor integration device to generate a rural energy data integration interface; Step S22: collecting rural energy data according to the rural energy data integration interface to generate rural energy data; performing feature analysis on the rural energy data to obtain rural energy feature data; Step S23: Mapping the rural energy characteristic data to the rural energy geographic model to perform rural energy characteristic mapping processing to generate a rural energy characteristic model.

5. The digital twin analysis method for rural energy according to claim 4 is characterized in that: Step S23 includes the following steps: Step S231: performing data traceability feature analysis on rural energy data according to the rural energy data integration interface to generate data traceability feature information; Step S232: performing rural energy traceability mapping relationship analysis based on the data traceability feature information and the rural energy spatial feature data of the rural energy geographic model to generate rural energy traceability mapping relationship data; Step S233: Based on the rural energy traceability mapping relationship data, the rural energy characteristic data is mapped to the rural energy geographic model to perform rural energy characteristic mapping processing to generate a rural energy characteristic model.

6. The digital twin analysis method for rural energy according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing a rural energy multi-dimensional physical characteristic analysis based on a rural energy characteristic model to generate rural energy multi-dimensional physical characteristic data, wherein the rural energy multi-dimensional physical characteristic data includes rural energy production physical characteristic data, rural energy storage characteristic data, and rural energy transmission behavior characteristic data; Step S32: performing rural energy multi-dimensional physical characteristic interaction feature analysis based on the rural energy multi-dimensional physical characteristic data to generate rural energy multi-dimensional physical characteristic interaction feature data; Step S33: performing rural energy physical characteristic integration processing on the rural energy multi-dimensional physical characteristic data according to the rural energy multi-dimensional physical characteristic interactive feature data to generate rural energy physical characteristic integrated data.

7. The digital twin analysis method for rural energy according to claim 6 is characterized in that: Step S31 includes the following steps: Step S311: collecting rural energy production-related data for the rural energy characteristic model to obtain rural energy production-related data, wherein the rural energy production-related data includes rural energy production data and rural energy production environment data; Step S312: using finite element analysis technology to perform rural energy production environmental impact analysis on rural energy production environmental data to generate rural energy production environmental impact data; Step S313: Analyzing the rural energy production physical characteristics of the rural energy production data according to the rural energy production environmental impact data to generate rural energy production physical characteristics data; Step S314: Analyze rural energy storage nodes according to the rural energy characteristic model to generate rural energy storage node data, wherein the rural energy storage node data includes rural energy storage data and rural energy storage node status data; Step S315: performing rural energy storage effectiveness analysis based on rural energy production data and rural energy storage data to generate rural energy storage effectiveness data; Step S316: performing rural energy storage characteristic analysis on the rural energy storage effectiveness data based on the rural energy storage node status data to generate rural energy storage characteristic data; Step S317: Analyze rural energy transmission characteristics according to the rural energy characteristic model to generate rural energy transmission characteristic data; Step S318: performing energy transmission time-domain finite difference analysis according to the rural energy transmission characteristic data to generate energy transmission time-domain finite difference data; Step S319: Analyze rural energy transmission behavior characteristics based on energy transmission time-domain finite difference data to generate rural energy transmission behavior characteristic data.

8. The digital twin analysis method for rural energy according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Acquire historical rural energy driving data; Step S42: Analyze the multi-scale driving factors of rural energy based on the integrated data of rural energy physical characteristics to generate multi-scale driving factor data of rural energy; Step S43: Perform nonlinear driving characteristic modeling of rural energy based on the decision tree algorithm and rural energy multi-scale driving factor data to generate a rural energy driving model; Step S44: performing model training optimization on the rural energy driving model through historical rural energy driving data to generate an optimized rural energy driving model; Step S45: construct a rural energy driving engine based on the optimized rural energy driving model, and perform rural energy digital twin analysis on the rural energy characteristic model based on the rural energy driving engine to generate a rural energy digital twin model.

9. The digital twin analysis method for rural energy according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing digital energy supply scheduling analysis and processing based on the rural energy digital twin model to generate digital energy supply scheduling data; Step S52: performing a digital energy supply scheduling demand characteristic analysis based on the digital energy supply scheduling data to generate digital energy supply scheduling demand characteristic data; Step S53: Based on the preset long short-term memory network model, the digital energy supply scheduling data is subjected to a time series benefit prediction of the digital energy supply scheduling, and the digital energy supply scheduling time series benefit data is generated; Step S54: formulating rural energy supply scheduling optimization constraint conditions through digital energy supply scheduling demand characteristic data, and formulating rural energy supply scheduling optimization target conditions through digital energy supply scheduling time series benefit data; Step S55: Designing an intelligent optimization model for rural energy supply scheduling based on the preset reinforcement learning algorithm for the rural energy supply scheduling optimization constraint conditions and the rural energy supply scheduling optimization target conditions to obtain a rural energy supply scheduling optimization model; Step S56: Execute the rural energy supply scheduling intelligent optimization operation according to the rural energy supply scheduling optimization model.

10. A digital twin analysis system for rural energy, characterized in that: Used to execute the digital twin analysis method for rural energy as claimed in claim 1, the digital twin analysis system for rural energy comprises: The rural energy geographic structure modeling module is used to obtain rural remote sensing data of rural management areas; based on rural remote sensing data, rural energy geographic structure modeling is performed to generate a rural energy geographic model; The rural energy mapping module is used to collect energy data in the rural management area according to the sensor integrated device to generate rural energy data; perform feature analysis on the rural energy data to obtain rural energy feature data; map the rural energy feature data to the rural energy geographic model to perform rural energy feature mapping processing to generate a rural energy feature model; A rural energy physical characteristics analysis module is used to analyze the multi-dimensional physical characteristics of rural energy based on a rural energy characteristic model to generate rural energy multi-dimensional physical characteristics data; to perform rural energy physical characteristics integration processing on the rural energy multi-dimensional physical characteristics data to generate rural energy physical characteristics integration data; The rural energy driving characteristic analysis module is used to model and process the nonlinear driving characteristics of rural energy based on the integrated data of rural energy physical characteristics to generate a rural energy driving model; a rural energy driving engine is constructed based on the rural energy driving model, and a rural energy digital twin analysis is performed on the rural energy characteristic model based on the rural energy driving engine to generate a rural energy digital twin model; The rural energy supply scheduling optimization module is used to perform digital energy supply scheduling analysis and processing based on the rural energy digital twin model to generate digital energy supply scheduling data; design an intelligent optimization model for rural energy supply scheduling based on the digital energy supply scheduling data to obtain a rural energy supply scheduling optimization model; and perform rural energy supply scheduling intelligent optimization operations based on the rural energy supply scheduling optimization model.

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