Geothermal compensation method, system, device and storage medium based on region

Abstract

The present application provides a geothermal compensation method, system, device and storage medium based on region, which relates to the field of geothermal compensation. The method includes: obtaining geothermal resource data and a first spatiotemporal distribution map of energy consumption in the target area, and performing geothermal balance analysis based on the geothermal resource data and the spatiotemporal distribution map of regional energy consumption to determine the geothermal supply and demand imbalance area; determining the second spatiotemporal distribution map of geothermal supply and demand imbalance area based on the geothermal supply and demand imbalance area and the spatiotemporal distribution map of regional energy consumption, and determining the first compensation strategy based on the second spatiotemporal distribution map of energy consumption; obtaining multi-source historical data of the geothermal supply and demand imbalance area, and compensating the first compensation strategy based on the multi-source historical data to obtain the second compensation strategy; obtaining multi-source compensation data in real time, and supplementing the second compensation strategy based on the multi-source compensation data to obtain the target compensation strategy. The above method improves the accuracy of geothermal resource allocation compensation.

Description

Technical Field

[0001] The present application relates to the field of geothermal compensation, and in particular to a method, system, device and storage medium for geothermal compensation based on a region. Background Art

[0002] In recent years, with the rapid growth of energy demand and the increasing depletion of fossil energy, the development and utilization of renewable energy such as geothermal energy has become an important way to achieve sustainable energy development. Geothermal resources have the advantages of being renewable, clean, and stable, and have broad application prospects in building heating, industrial and agricultural production, tourism and health care. However, due to the constraints of natural endowments, development technology, supporting facilities and other factors, it is urgent to take effective measures to optimize the supply and demand relationship of geothermal energy and achieve a dynamic balance between supply and demand.

[0003] At present, the extensive management mode of "total control and quota allocation" is generally adopted for the compensation and regulation of geothermal resources. The total mining volume indicators are set according to the total resource endowment and mining technical conditions of various geothermal fields, and the indicators are decomposed into various districts and counties and utilization projects according to the idea of ​​"egalitarianism". Although this approach controls the intensity of geothermal mining at a macro level, it ignores the spatiotemporal differentiation characteristics of geothermal resource endowment, regional energy consumption demand, technical and economic conditions, etc., making it difficult to achieve precise and differentiated resource optimization allocation, which often causes imbalance between supply and demand in local areas. Summary of the invention

[0004] The present application provides a geothermal compensation method, system, device and storage medium based on region, which can achieve more precise and differentiated resource optimization allocation and avoid imbalance in geothermal supply and demand in local areas.

[0005] In a first aspect, the present application provides a geothermal compensation method based on a region, the method comprising:

[0006] Obtaining geothermal resource data and a first spatiotemporal distribution map of energy consumption in a target area, and performing geothermal balance analysis based on the geothermal resource data and the spatiotemporal distribution map of regional energy consumption to determine the geothermal supply and demand imbalance area in the target area;

[0007] Based on the geothermal supply and demand imbalance area and the regional energy consumption spatiotemporal distribution map, determine a second energy consumption spatiotemporal distribution map of the geothermal supply and demand imbalance area, and determine a first compensation strategy based on the second energy consumption spatiotemporal distribution map;

[0008] Acquire multi-source historical data of the geothermal supply and demand imbalance area, and compensate the first compensation strategy based on the multi-source historical data to obtain a second compensation strategy;

[0009] The multi-source compensation data is acquired in real time, and the second compensation strategy is supplemented based on the multi-source compensation data to obtain a target compensation strategy.

[0010] By adopting the above technical solution, by obtaining the geothermal resource data and the first spatiotemporal distribution map of energy consumption in the target area, and conducting geothermal balance analysis based on the two, it is possible to systematically characterize the matching relationship between geothermal resources and energy consumption demand in the time and space dimensions, and accurately identify the geothermal supply and demand imbalance area. On this basis, this method focuses on the geothermal supply and demand imbalance area, specifically constructs the second spatiotemporal distribution map of energy consumption, and determines the first compensation strategy based on it, realizing the formulation of strategies that are adapted to local conditions and prescribe the right medicine for the right disease, greatly improving the pertinence and effectiveness of compensation. At the same time, this method fully absorbs the past lessons reflected by the multi-source historical data of the geothermal supply and demand imbalance area, and optimizes and iterates the first compensation strategy based on this, forming the second compensation strategy, and further strengthening the rationality and feasibility of the strategy. Furthermore, this method obtains multi-source compensation data in real time, and continuously optimizes and improves the second compensation strategy based on this, and finally forms a dynamically iterative target compensation strategy. It can be seen that this application adopts a step-by-step progressive method architecture of "geothermal resource data + first energy consumption spatiotemporal distribution map → geothermal balance analysis → imbalance area identification → second energy consumption spatiotemporal distribution map → first compensation strategy → multi-source historical data → second compensation strategy → multi-source compensation data → target compensation strategy" and a thinking from local to overall, from static to dynamic, forming a set of systematic solutions from data collection, feature analysis to strategy generation and strategy optimization, which better overcomes the shortcomings of insufficient data support, rough analysis methods, and "one-size-fits-all" compensation strategies of existing models, and the accuracy, scientificity, and dynamism of compensation have been significantly improved.

[0011] In a second aspect of the present application, a geothermal compensation method system based on a region is provided, comprising:

[0012] A data acquisition module, used to acquire geothermal resource data and a first spatiotemporal distribution map of energy consumption in a target area, and perform geothermal balance analysis based on the geothermal resource data and the spatiotemporal distribution map of regional energy consumption to determine the geothermal supply and demand imbalance area in the target area;

[0013] A first compensation strategy generating module is used to determine a second energy consumption spatiotemporal distribution map of the geothermal supply and demand imbalance region based on the geothermal supply and demand imbalance region and the regional energy consumption spatiotemporal distribution map, and determine a first compensation strategy based on the second energy consumption spatiotemporal distribution map;

[0014] A second compensation strategy generation module is used to obtain multi-source historical data of the geothermal supply and demand imbalance area, and adjust the first compensation strategy based on the multi-source historical data to obtain a second compensation strategy;

[0015] A target compensation strategy generation module is used to obtain multi-source compensation data in real time, and adjust the second compensation strategy based on the multi-source compensation data to obtain a target compensation strategy;

[0016] The imbalance area compensation module is used to compensate for the imbalance area of ​​geothermal supply and demand by adopting a target compensation strategy.

[0017] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the above method steps.

[0018] In the fourth aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the above method.

[0019] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. This application obtains geothermal resource data and the first energy consumption spatiotemporal distribution map of the target area, and conducts geothermal balance analysis based on the two. It can systematically characterize the matching relationship between geothermal resources and energy consumption demand in time and space dimensions, accurately identify areas of geothermal supply and demand imbalance, and lay a solid foundation for subsequent precise compensation.

[0021] 2. This application focuses on areas with an imbalance in geothermal supply and demand, specifically constructs a second spatiotemporal distribution map of energy consumption, and determines the first compensation strategy based on this, achieving a strategy formulation that is tailored to local conditions and the right medicine for the right situation, greatly improving the pertinence and effectiveness of compensation. At the same time, this method fully absorbs past lessons learned from multi-source historical data in areas with an imbalance in geothermal supply and demand, and optimizes and iterates the first compensation strategy based on this, forming a second compensation strategy, which is further strengthened in terms of strategy rationality and feasibility. Furthermore, this method obtains multi-source compensation data in real time, and continuously optimizes and improves the second compensation strategy based on this, ultimately forming a dynamically iterative target compensation strategy.

[0022] 3. This application adopts a step-by-step progressive method architecture of "geothermal resource data + first energy consumption spatiotemporal distribution map → geothermal balance analysis → imbalance area identification → second energy consumption spatiotemporal distribution map → first compensation strategy → multi-source historical data → second compensation strategy → multi-source compensation data → target compensation strategy" and a thinking from local to overall, from static to dynamic, forming a set of systematic solutions from data collection, feature analysis to strategy generation and strategy optimization, which better overcomes the shortcomings of the existing model such as insufficient data support, rough analysis methods, and "one-size-fits-all" compensation strategies, and the accuracy, scientificity, and dynamism of compensation have been significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic flow chart of a geothermal compensation method based on region provided in an embodiment of the present application;

[0024] Figure 2 An architectural diagram of a geothermal compensation system based on a region provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0026] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0028] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] In order to facilitate understanding of the method and system provided by the embodiments of the present application, before introducing the embodiments of the present application, the background of the embodiments of the present application is first introduced.

[0030] In recent years, with the dual pressures of the rapid growth of global energy demand and the increasing depletion of fossil energy, the development and utilization of renewable energy represented by geothermal energy has become an important way and inevitable choice to achieve sustainable energy development. Geothermal resources, with their outstanding advantages such as renewable, clean and stable, have shown broad application prospects and huge development potential in the fields of building heating, industrial and agricultural production, tourism and health care. However, they are restricted by multiple factors such as natural resource endowment, development technology level, and supporting facilities construction. Optimizing the spatial allocation of geothermal resources and coordinating the dynamic balance of regional supply and demand have become practical problems and urgent tasks that need to be solved.

[0031] At present, in the practice of compensation and regulation of geothermal resources, the extensive management mode of "total control and quota allocation" is generally followed. According to the total amount of geothermal resources and mining technical conditions in the region, the thermal field management departments of various places set annual or longer-term mining total control indicators, and adhere to the idea of ​​"egalitarianism" to decompose and issue indicators layer by layer according to administrative divisions or utilization projects. Although this approach controls the intensity of geothermal resource exploitation at the macro level and curbs the trend of over-exploitation, it also has obvious limitations and shortcomings. It ignores the objective differences in geothermal resource endowment, terminal energy demand, technical and economic conditions, etc. in different regions, fails to fully take into account the temporal and spatial differentiation characteristics of resources, demand, technology and other factors, and is difficult to implement precise policies according to local conditions to achieve differentiated and refined resource optimization allocation, which often causes serious imbalances in supply and demand in local areas and low resource utilization efficiency. As a whole, it fails to truly achieve the optimal allocation and high-quality development of geothermal resources.

[0032] After the background introduction of the above content, those skilled in the art can understand the problems existing in the prior art. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0033] Based on the above background technology, please refer to Figure 1 , Figure 1 A flow chart of a geothermal compensation method based on region provided in an embodiment of the present application. The system can be implemented by a computer program or can be run as an independent tool application. Specifically, in an embodiment of the present application, the method can be applied on a server, but can also be applied on electronic devices such as a server. A geothermal compensation method based on region includes the following steps:

[0034] S101, obtaining geothermal resource data and a first spatiotemporal distribution map of energy consumption in a target area, and performing geothermal balance analysis based on the geothermal resource data and the spatiotemporal distribution map of regional energy consumption to determine a geothermal supply and demand imbalance area in the target area;

[0035] Specifically, in order to achieve accurate compensation for geothermal resources, it is first necessary to obtain geothermal resource data and the first spatiotemporal distribution map of energy consumption in the target area, and on this basis, conduct geothermal balance analysis to identify key geothermal supply and demand imbalance areas. Specifically, geothermal resource data can be obtained through field surveys, data collection, remote sensing and other means, covering multiple dimensions such as the spatial distribution of geothermal fields, resource endowment, geological characteristics, and hydrochemical characteristics, forming a comprehensive and three-dimensional resource inventory. The first spatiotemporal distribution map of energy consumption comprehensively considers data such as population distribution, industrial layout, total energy consumption, and load curves, and meticulously depicts the scale level and dynamic change characteristics of energy consumption in the target area in terms of time and space, revealing the spatiotemporal differentiation of regional energy consumption. Based on the above two types of data, the energy balance method, supply-demand matching evaluation and other methods are used to quantitatively analyze the matching relationship between the supply capacity of geothermal resources and regional energy demand in the temporal and spatial dimensions, identify counties, towns, and geothermal fields with significant imbalances in resource endowment, development and utilization intensity, energy demand scale, peak and valley distribution, and determine the key areas of geothermal supply and demand imbalance.

[0036] Based on the above embodiment, as an optional embodiment, obtaining geothermal resource data of the target area and a first energy consumption spatiotemporal distribution map includes:

[0037] S201, obtaining geothermal resource distribution data of a target area, and classifying and cleaning the geothermal resource distribution data to obtain geothermal resource data;

[0038] Specifically, through geological surveys, geophysical exploration, drilling sampling and other means, we can obtain multi-dimensional geothermal resource distribution data such as the spatial distribution of regional geothermal fields, resource reserves, burial depth, water temperature and quality, and permeability. However, the above data usually come from a wide range of sources and in different formats, and may have quality problems such as redundancy, errors, and missing, which is not conducive to subsequent analysis and application. Therefore, it is also necessary to systematically classify and clean the acquired geothermal resource distribution data. First, according to the characteristics of the data content, it is divided into several categories such as resource quantity data, spatial distribution data, attribute parameter data, etc., and stored in corresponding data tables respectively. Then, for each type of data, data mining, machine learning and other technologies are used, combined with expert knowledge, to identify outliers and outliers in the data, and interpolation, elimination, correction and other measures are taken to clean it. At the same time, similar data from different sources and different periods are compared and checked, conflicting data are eliminated, and logical contradictions between data are eliminated. In addition, data fusion, map construction and other methods are used to spatially and semantically associate the scattered well point data and attribute parameter data, and finally form structured and high-quality geothermal resource data.

[0039] S202, collecting energy consumption data of various buildings and facilities in the target area, and preprocessing the energy consumption data to obtain preprocessed energy consumption data;

[0040] Specifically, through energy consumption monitoring, statistical reports, meter reading systems and other channels, we can widely obtain energy consumption data such as electricity, gas, and heat from various end users in industrial parks, commercial complexes, residential communities, municipal facilities, etc., covering multiple dimensions such as energy consumption time, energy consumption, load curves, and energy quality. However, the above energy consumption data are usually large in volume and complex in source, and may have problems such as different dimensions, different frequencies, measurement errors, and missing measurement points, which are not conducive to subsequent unified analysis and regularity mining. Therefore, it is also necessary to carry out systematic preprocessing of the collected energy consumption data. First, the energy consumption data is classified and sorted according to key attributes such as time, space, and energy type, and combined with energy measurement specifications and physical common sense, outliers in the data are identified and marked. Then, data cleaning technology is used to eliminate obviously erroneous abnormal data, and interpolation correction is performed on truly invalid data. At the same time, according to the differences in dimensions and frequencies of energy consumption data, a reasonable conversion algorithm is designed to achieve normalization and scale unification of energy consumption data. On this basis, data compression, data modeling and other technologies are used to further optimize the data storage format, improve data retrieval and computing efficiency, and finally obtain a regular, unified, and quality-controlled pre-processed energy consumption data set. Through this step, the value of multi-source heterogeneous energy consumption data is fully explored, data redundancy and noise are reduced, data quality is improved, and data interoperability is enhanced, laying a data foundation for the subsequent in-depth analysis and application of energy consumption data. On the one hand, the degree of normalization and standardization of energy consumption data has been greatly improved, which can effectively support unified data analysis and comprehensive utilization; on the other hand, the spatiotemporal attributes and energy attributes of the data have been highlighted, creating favorable conditions for exploring the spatiotemporal distribution of regional energy consumption.

[0041] S203, aggregate and count the preprocessed energy consumption data according to time and space dimensions to generate a spatiotemporal distribution matrix that reflects the dynamic changes in regional energy consumption demand and spatial distribution characteristics, and construct a distribution map based on the spatiotemporal distribution matrix to obtain a first spatiotemporal distribution map of energy consumption.

[0042] Specifically, firstly, a reasonable aggregation granularity is set for energy consumption data according to the time dimension (such as monthly, quarterly, annual, etc.) and the spatial dimension (such as streets, communities, parks, etc.), and the energy consumption data in each spatiotemporal unit is summarized and counted by using data cube, crowd intelligence computing and other technologies to generate a spatiotemporal distribution matrix reflecting the regional energy consumption level under different time periods and different spatial scales. The rows of the matrix represent spatial objects, the columns represent time objects, and the matrix element values ​​represent the energy consumption or intensity of the corresponding spatiotemporal unit. On this basis, reasonable color scales, scales, legends and other elements are designed by using data visualization, geographic information system and other technologies to render the spatiotemporal distribution matrix into an intuitive and vivid first energy consumption spatiotemporal distribution map. For example, the depth of color is used to represent the difference in energy consumption intensity, the curve chart is used to show the fluctuation trend of energy consumption demand over time, and the pie chart and bar chart are used to show the composition ratio of different energy consumption varieties, thereby multi-dimensionally and three-dimensionally depicting the spatiotemporal differentiation characteristics of regional energy consumption demand, and finally obtaining the first energy consumption spatiotemporal distribution map covering the total energy consumption, energy consumption intensity, load curve, energy structure, demand forecast and other contents.

[0043] Based on the above embodiment, as an optional embodiment, geothermal balance analysis is performed based on geothermal resource data and regional energy consumption spatiotemporal distribution map to determine the geothermal supply and demand imbalance area in the target area, including:

[0044] S301, evaluating the resource potential of each geothermal field in the target area based on the geothermal resource data, obtaining a geothermal resource potential evaluation result, and constructing a geothermal resource potential map of the target area based on the geothermal resource potential evaluation result;

[0045] Specifically, based on the key parameters such as temperature, pressure, permeability, burial depth, etc. in the geothermal resource data, the volume method, analogy method, numerical simulation and other methods are used to quantitatively evaluate the geothermal resources, recoverable resources, development difficulty and other indicators of the geothermal fields in various places, and supplemented by qualitative analysis, to obtain the evaluation results that fully reflect the resource potential of the geothermal fields in various places. During the evaluation process, on the one hand, it is necessary to strengthen the integration and application of geological experts' knowledge to improve the geological interpretability of the evaluation; on the other hand, it is necessary to pay attention to uncertainty analysis and clarify the confidence interval of resource potential. On this basis, using GIS spatial analysis, data visualization and other technologies, the resource potential evaluation results of the geothermal fields in various places are associated and mapped with their spatial location information, and reasonable color scales, scales, classification standards and other elements are designed to finally construct a geothermal resource potential map of the target area. The map intuitively displays the resource potential level and development difficulty of the geothermal fields in various places through colors, blocks, annotations, etc., and reveals the differentiation law of resource potential in regional space.

[0046] S302, using the first energy consumption spatiotemporal distribution diagram, analyzing the temporal dynamic characteristics and spatial distribution characteristics of energy consumption demand in the target area;

[0047] Specifically, in order to gain a deeper insight into the changing trend and spatial pattern of energy consumption in the target area, it is necessary to systematically analyze the temporal dynamic characteristics and spatial distribution characteristics of regional energy consumption demand based on the first energy consumption spatiotemporal distribution map. Specifically, first focus on the time dimension of the map, use time series analysis, trend extrapolation and other methods to depict the year-on-year trend of the total energy consumption in the entire region, and build a quarterly and monthly energy consumption demand time series model to reveal the fluctuation law and periodic characteristics of energy consumption demand on different time scales. Then, focus on the spatial dimension of the map, use spatial statistics, geological analysis and other methods to compare and analyze the differences in per capita energy consumption levels in different blocks and towns, study the heterogeneity of energy consumption growth rates in different regions, and identify high-energy consumption and high-growth spatial agglomeration areas. On this basis, further use correlation analysis, regression analysis and other methods to explore the spatial correlation between regional energy consumption intensity and population distribution, industrial structure, economic level, etc., and reveal the internal driving mechanism for the formation of the spatial differentiation pattern of energy consumption. Through this step, we can make full use of the rich spatiotemporal information carried by the map, quantitatively describe the evolution trend of regional energy demand in the time dimension, depict the differentiation pattern of regional energy demand in the spatial dimension, and deepen the understanding of the spatiotemporal distribution law of energy consumption and its internal mechanism. On the one hand, we have mastered the dynamic change law of regional "energy load", which is helpful for prospective optimization of regional energy use strategy; on the other hand, we have identified the hot and cold distribution of regional energy demand in space, which is helpful for implementing energy-saving and consumption-reducing measures according to local conditions.

[0048] S303, constructing an energy demand classification map according to the spatiotemporal distribution characteristics, and performing spatial superposition analysis on the geothermal resource potential map and the energy demand classification map to obtain a spatial matching relationship between geothermal resource distribution and energy demand distribution;

[0049] Specifically, firstly, based on multiple indicators such as total energy consumption, energy intensity, growth rate, load curve, etc., cluster analysis, pattern recognition and other methods are used to divide the energy consumption demands of different blocks in the region into multiple categories such as high, medium and low, and integrate them with the spatial boundaries of the blocks to construct an energy consumption demand classification map that directly reflects the spatial differentiation pattern of regional energy consumption demand. Then, using the geographic information system as a platform, the classification map is spatially registered and layered with the geothermal resource potential map constructed in the early stage. Using spatial topological analysis, buffer zone analysis and other methods, the spatial relationship indicators such as overlap and proximity of the two types of maps in spatial distribution are quantitatively compared to form a quantitative matrix reflecting the spatial matching degree of geothermal resource distribution and energy consumption demand distribution. On this basis, referring to the development radius, transportation radius and other parameters of regional geothermal resources, we identify the areas where geothermal resource-rich areas and high energy consumption demand areas are well matched in space, as the priority layout areas for geothermal resources; at the same time, we identify the areas where geothermal resource-rich areas and high energy consumption demand areas are spatially separated, as long-distance hot spots, and specifically judge the economic feasibility of long-distance transportation of geothermal resources. Through this step, the geothermal resource endowment characteristics are associated with the regional energy demand characteristics, extending from the static resource distribution evaluation to the dynamic supply and demand matching analysis, and scientifically identifying the advantageous areas and weak areas of geothermal resource utilization in the region, which can not only provide an intuitive basis for the optimization of the spatial layout of geothermal resources, but also provide a judgment reference for the formulation of resource allocation plans for long-distance hot spots.

[0050] S304, calculating the spatial matching degree between the geothermal resource potential and the energy consumption demand in the target area based on the spatial matching relationship, generating a spatial matching degree map reflecting the spatial matching status between geothermal resources and energy consumption demand, and preliminarily determining the spatial imbalance area between geothermal resource supply and energy consumption demand based on the spatial matching degree map;

[0051] Specifically, firstly, based on the spatial superposition analysis results of the geothermal resource potential map and the energy consumption demand classification map, the overlapping areas, buffer zones, adjacent areas, etc. of the two types of maps are spatially divided, and the spatial topological relationship matrix of each sub-area is established. Then, from the two scales of the region as a whole and each sub-area, the resource potential index and energy consumption demand index are constructed, and the spatial correlation intensity of the two types of indexes is quantitatively measured using the spatial matching degree model to form a multi-scale, multi-indicator spatial matching degree matrix. On this basis, the mapping function of the geographic information system is used to reasonably set the classification standards and color schemes, and the spatial matching degree matrix is ​​converted into a spatial matching degree thematic map, which intuitively shows the degree of fit between the geothermal resource endowment and energy consumption demand in the region in spatial configuration. Furthermore, the areas with low spatial matching degrees are identified as the imbalance areas of geothermal resource supply and demand in the spatial dimension, representing the areas where the spatial layout of resources is not compatible with the spatial pattern of energy consumption. Through this step, the spatial correspondence between geothermal resources and energy consumption demand is quantified into an intuitive spatial matching index, and is spatially expressed through mapping, so that the resource endowment status and demand distribution situation are deeply integrated in the spatial dimension, forming an adaptability analysis of the spatial configuration of the resource side and the demand side.

[0052] S305, based on the time variation characteristics, evaluating the temporal matching degree between geothermal resource supply and energy consumption demand at different time scales, and constructing a matching degree time series diagram reflecting the temporal matching status between geothermal resource supply and energy consumption demand based on the temporal matching degree;

[0053] Specifically, according to the current status of regional geothermal resource development and utilization and the spatiotemporal distribution map of energy consumption demand, the time series data reflecting the geothermal resource supply capacity and energy consumption demand load are extracted respectively, and the resource supply time series and energy consumption demand time series of multiple time scales such as day, month, season and year are constructed. Then, the correlation strength of the two types of time series under different time lags is quantitatively measured by using methods such as cross-correlation analysis and dynamic time warping, forming a quantitative matrix reflecting the time matching degree between geothermal resource supply and energy consumption demand. On this basis, the time series data visualization technology is used to reasonably set the time axis, ordinate and other chart elements to transform the time matching degree matrix into a matching degree time series diagram, which intuitively displays the dynamic adaptability of the geothermal resource supply capacity and energy consumption demand load in the time dimension in the region. Then, the time period with low time matching degree is focused on as the imbalance period of geothermal resource supply and demand in the time dimension, which represents the time period when the resource supply rhythm is not coordinated with the load demand rhythm. Through this step, the dynamic correlation between geothermal resource supply and energy consumption demand is quantified as a time matching index and dynamically expressed in the form of a time series diagram, so that the resource guarantee capability and load demand level are deeply integrated on the time scale, forming an adaptability analysis of the supply guarantee rhythm between the resource side and the demand side.

[0054] S306, performing matching analysis on the matching degree time series diagram and the spatial imbalance area to obtain the geothermal supply and demand imbalance area.

[0055] Specifically, in order to accurately identify the imbalanced areas between geothermal resource supply and energy demand in the spatial and temporal dimensions, it is necessary to verify the matching degree time series diagram with the spatial imbalanced area on the basis of revealing the temporal matching characteristics and spatial matching characteristics of resource supply and demand, and comprehensively determine the temporal and spatial mismatched areas of resource guarantee and load demand. Specifically, firstly, a comprehensive interpretation of the matching degree time series diagram is carried out to identify the intervals with low overall temporal matching degree and serious imbalance in the key period as the imbalanced period of resource supply and demand in the temporal dimension. Then, the spatial matching degree map is superimposed and analyzed to identify the areas where the spatial correlation between the resource potential index and the energy demand index is generally weak, as the imbalanced areas of resource supply and demand in the spatial dimension. On this basis, the temporal imbalanced period and the spatial imbalanced area are spatiotemporally coupled, and the supply and demand matching status of the key areas in the key period is quantitatively measured by using methods such as geostatistics and spatial econometrics. Then, the areas and their key periods where the temporal and spatial dimensions are unbalanced at the same time are identified as the imbalanced areas of geothermal resource supply and energy demand in the region, characterizing the weak areas in the temporal and spatial matching of resource guarantee capacity and load demand level. Through this step, we make full use of the time matching characteristics and space matching characteristics to build a spatiotemporal coupling analysis framework that reflects the imbalance between regional geothermal resource supply and demand. The supply and demand rhythm mismatch problem revealed by the matching degree time series diagram is verified with the supply and demand layout misalignment problem revealed by the spatial matching degree map. The regional resource utilization system is diagnosed in three dimensions from the time and space dimensions. Objectively, it can panoramically depict the adaptability defects of resource supply guarantee and load response demand in the spatiotemporal scale, and subjectively provide a spatiotemporal decision-making basis for regional energy planning.

[0056] S102, determining a second spatiotemporal energy consumption distribution map of the geothermal supply and demand imbalance region based on the geothermal supply and demand imbalance region and the regional spatiotemporal energy consumption distribution map, and determining a first compensation strategy based on the second spatiotemporal energy consumption distribution map;

[0057] Specifically, on the basis of constructing the second spatiotemporal distribution map of energy consumption, multi-criteria decision-making, scenario analysis, optimization algorithm and other methods are adopted, combined with multiple constraints such as geothermal resources, technical economy, ecological environment, policy mechanism, etc., to form the first compensation strategy. Specifically, firstly, key attribute parameters such as total energy consumption, peak-to-valley ratio, heat demand density, etc. are extracted from the second spatiotemporal distribution map of energy consumption as the basic data for formulating compensation strategies. Then, for different types of imbalance, corresponding strategy generation models are constructed. For example, for resource-scarce areas, factors such as geothermal resource exploration and development and cascade utilization are focused on; for areas with prominent energy consumption peaks, means such as thermal storage peak regulation and demand response are focused on. On this basis, with the goal of improving the matching degree of regional supply and demand and optimizing the efficiency of resource allocation, key parameters are input into the model, and through parameter optimization, strategy simulation, effect evaluation and other steps, the optimal combination of various compensation means such as resources, technology, management, and policies is explored to form a first compensation strategy customized for the region.

[0058] Based on the above embodiment, as an optional embodiment, based on the geothermal supply and demand imbalance area and the regional energy consumption spatiotemporal distribution map, a second energy consumption spatiotemporal distribution map of the geothermal supply and demand imbalance area is determined, including:

[0059] S401, according to the regional distribution map of geothermal supply and demand imbalance, extract the spatial boundary and location information of each imbalanced area, and extract the imbalanced energy consumption demand data of the corresponding area from the regional energy consumption spatiotemporal distribution map based on the spatial boundary and location information, and generate the imbalanced energy consumption demand time series data and imbalanced spatial distribution data of each imbalanced area based on the imbalanced energy consumption demand data;

[0060] Specifically, firstly, based on the regional distribution map of geothermal supply and demand imbalance, the spatial query and overlay analysis functions of the geographic information system are used to extract the spatial boundary coordinates and location information data of each imbalance zone. Then, with the spatial boundary of the imbalance zone as the constraint condition, spatial clipping is performed from the regional energy consumption spatiotemporal distribution map, and the clipped energy consumption spatiotemporal data is merged with the spatial boundary of the imbalance zone to generate the unbalanced energy consumption demand spatiotemporal distribution data set for each imbalance zone. On this basis, the time series of indicators such as the total energy consumption demand and energy intensity are further extracted from the data set to form the unbalanced energy consumption demand time series data reflecting the time dynamic characteristics of the energy consumption load of each imbalance zone; at the same time, the spatial distribution information of energy load of different blocks and different energy consumption types is extracted to form the unbalanced spatial distribution data reflecting the spatial differentiation characteristics of the energy consumption pattern of each imbalance zone. Through the above steps, the existing regional energy consumption spatiotemporal distribution information and the identification results of geothermal supply and demand imbalance zones are fully utilized to locate the key spatiotemporal analysis units. Through precise constraints on spatial boundaries and selective extraction of data, the spatiotemporal data of energy consumption in the imbalanced areas are separated, and the focus of the macro-regional picture is shifted to the picture of the key imbalanced areas. This can not only reduce the data scale of subsequent spatiotemporal analysis, but also provide data support for formulating energy consumption optimization strategies for imbalanced areas according to local conditions.

[0061] S402, performing cluster analysis on the imbalanced area according to the imbalanced energy consumption demand time series data and the imbalanced spatial distribution data to obtain a cluster analysis result;

[0062] Specifically, firstly, according to the time fluctuation characteristics of the energy demand of each imbalance zone, the key parameters reflecting its peak and valley characteristics, fluctuation frequency, etc. are extracted, and the time similarity of different imbalance intervals is calculated accordingly, and the time similarity matrix is ​​constructed. At the same time, according to the spatial differentiation characteristics of the energy demand intensity of each imbalance zone, the key parameters reflecting its spatial autocorrelation and spatial heterogeneity are extracted, and the spatial proximity of different imbalance intervals is calculated accordingly, and the spatial proximity matrix is ​​constructed. On this basis, the time similarity matrix and the spatial proximity matrix are integrated, and the imbalance zones with similar spatiotemporal distribution patterns are divided into the same category by using system clustering, K-means and other methods to form the clustering results of the imbalance zones. Then, the class mean of each type of imbalance zone is calculated, which is used as the typical representative of the imbalance zone, and the spatiotemporal distribution pattern of energy demand of each type of imbalance zone is summarized accordingly to form the clustering analysis results. Through the above steps, we can make full use of the time series characteristics and spatial distribution characteristics of the energy demand in the imbalanced areas, and group and classify the imbalanced areas from the time dimension and space dimension. This can not only reveal the differences in the spatiotemporal distribution of energy demand in different imbalanced areas and grasp the personalized imbalance inducements, but also summarize the various patterns of spatiotemporal distribution of energy demand in the imbalanced areas, and provide a basis for formulating energy consumption optimization strategies with classified guidance and precise policy implementation.

[0063] S403, extracting features from the unbalanced energy consumption demand time series data and the unbalanced spatial distribution data to obtain unbalanced spatiotemporal feature data, and constructing a second energy consumption spatiotemporal distribution diagram according to the cluster analysis results and the unbalanced spatiotemporal feature data.

[0064] Specifically, firstly, based on the time series data of unbalanced energy demand, the time series analysis method is used to extract key indicators reflecting the time dynamic characteristics of energy demand in the unbalanced area, such as annual average heat consumption, peak-to-valley ratio of heating season, load duration, etc., to form the time feature set of unbalanced energy demand. At the same time, based on the unbalanced spatial distribution data, the key indicators reflecting the spatial pattern characteristics of energy demand in the unbalanced area, such as demand center of gravity, spatial dispersion, local autocorrelation index, etc., are extracted by using methods such as spatial autocorrelation analysis to form the spatial feature set of unbalanced energy demand. On this basis, the time feature set and the spatial feature set are standardized, and the dimensionality reduction methods such as principal component analysis are used to screen out the key features that have a significant impact on the spatiotemporal distribution of energy demand in the unbalanced area, and to construct the unbalanced spatiotemporal feature data. Then, the cluster analysis results are combined with the unbalanced spatiotemporal feature data to visualize the typical spatiotemporal characteristics of each type of unbalanced area. Through time series diagrams, spatial distribution diagrams and other charts, a second spatiotemporal distribution diagram of energy consumption reflecting the spatiotemporal configuration characteristics of energy demand in different types of unbalanced areas is constructed.

[0065] Based on the above embodiment, as an optional embodiment, determining the first compensation strategy based on the second energy consumption spatiotemporal distribution graph includes:

[0066] S501, analyzing the second energy consumption spatiotemporal distribution diagram to obtain the peak time period and peak area of ​​energy consumption demand, and determining compensation demand time distribution data and compensation demand spatial distribution data according to the peak time period and peak area of ​​energy consumption demand;

[0067] S502, determining a compensation priority level according to the compensation demand time distribution data and the compensation demand space distribution data;

[0068] Specifically, the second spatiotemporal distribution map of energy consumption is first comprehensively interpreted. By comparing and analyzing the energy demand intensity of different time periods and different regions, the key spatiotemporal units with significantly higher energy demand than other time periods and regions are identified, and they are determined as the peak time periods and peak areas of energy demand in the imbalanced area. On this basis, the energy demand data of each time section in the peak period are further extracted to construct the compensation demand time distribution data reflecting the dynamic changes of resource compensation demand during the peak period. At the same time, the energy demand data of each block in the peak area are extracted to construct the compensation demand spatial distribution data reflecting the spatial differences of resource compensation demand during the peak period. Through the above steps, the spatiotemporal distribution law of energy demand in the imbalanced area is fully utilized, and the key spatiotemporal units with the most significant impact on resource security are focused on, so that the determination of compensation demand is more targeted. By constructing the time distribution data and spatial distribution data of compensation demand respectively, it can provide an important reference for the optimal configuration of resource compensation schemes in time and space scales.

[0069] S503, performing compensation resource analysis based on geothermal resource data to determine the temporal and spatial distribution characteristics of supplementary resources of the compensation resources;

[0070] Specifically, first, based on the time distribution data of compensation demand, the resource shortage intensity of each time section during the peak period is calculated, and the time sections are sorted accordingly to form priority time distribution data reflecting the priority of compensation time. At the same time, based on the spatial distribution data of compensation demand, the resource shortage intensity of each block in the peak area is calculated, and the blocks are sorted accordingly to form priority spatial distribution data reflecting the priority of compensation space. On this basis, the priority time distribution data and the priority spatial distribution data are comprehensively integrated, and the compensation priority level reflecting the coordinated optimization of the time and space dimensions is finally determined by constructing a two-dimensional weight matrix of time and space, and combining methods such as hierarchical analysis. Through the above steps, the time and space distribution information of compensation demand can be fully utilized. On the basis of ensuring priority to satisfying the shortage of resources in key periods and key areas, the coupling effect of compensation benefits in the time dimension and the space dimension is comprehensively considered, so that the configuration of the compensation plan is more matched with the actual demand, and "supply and demand balance" is achieved in the time and space scale.

[0071] S504: Determine a first supplementation strategy based on the compensation priority level and the temporal and spatial distribution characteristics of the supplementation resources.

[0072] Specifically, firstly, based on the compensation priority level, the key time periods and key areas in urgent need of compensation are identified, and the corresponding compensation demand time series and compensation demand spatial distribution data are extracted. At the same time, based on the spatiotemporal distribution characteristics of supplementary resources, the status of supplementary resources available in these key time periods and key areas is analyzed, and the corresponding supplementary resource time series and supplementary resource spatial distribution data are extracted. On this basis, through the cross-comparison analysis of compensation demand and supplementary resources in time scale and space scale, the matching degree between the two in key time periods and key areas is identified, and a two-dimensional spatiotemporal matching matrix is ​​constructed accordingly. Then, the optimization decision-making method is adopted to solve the resource optimization allocation model under the constraints of the spatiotemporal matching matrix, and finally the optimal compensation strategy that takes into account time allocation and spatial layout is determined. Through the above steps, the compensation priority information is fully utilized to guide the key direction of compensation, focusing on the key objects that need to be protected in the spatiotemporal dimension, so that the formulation of compensation plan is more targeted.

[0073] S103, obtaining multi-source historical data of the geothermal supply and demand imbalance area, and adjusting the first compensation strategy based on the multi-source historical data to obtain a second compensation strategy;

[0074] Specifically, after obtaining the first compensation strategy, in order to further improve the scientificity and feasibility of the strategy, it is also necessary to fully absorb the historical experience data of geothermal development, energy utilization and other aspects in the geothermal supply and demand imbalance area, and optimize and iterate the first compensation strategy accordingly, and finally form a more mature and complete second compensation strategy. Specifically, firstly, the historical data of geological exploration, resource exploitation, energy flow metering, equipment operation and maintenance, energy efficiency statistics, policy implementation and other links in the geothermal supply and demand imbalance area are widely collected, and through data cleaning, fusion, correlation analysis and other processing, a historical data set covering multiple dimensions such as resources, technology, economy and policy is formed. Then, intelligent algorithms such as machine learning and data mining are used to deeply explore the key information such as resource distribution law, mining efficiency influencing factors, supply and demand imbalance causes, policy effect evaluation and so on contained in the historical data, and accordingly, the target parameters, weight coefficients, constraints and so on in the first compensation strategy are adjusted and optimized in a targeted manner, such as adjusting the key areas and target layers of geothermal resource exploration and development, optimizing the single well layout parameters, introducing new production increase measures, and improving the peak and valley adjustment scheme, so as to obtain the second compensation strategy.

[0075] Based on the above embodiment, as an optional embodiment, the second compensation strategy is adjusted based on multi-source compensation data to obtain a target compensation strategy, including:

[0076] S501, reconstructing the historical energy supply and demand balance and historical geothermal resource development and utilization in the target area based on multi-source historical data;

[0077] Specifically, firstly, historical data in multiple fields such as meteorology, economy, society, energy, and geography in the target area are widely collected, and data cleaning, data integration and other technologies are used for fusion processing to establish a multi-source heterogeneous historical database. On this basis, from the two aspects of energy supply and consumption demand, key indicators reflecting the historical energy supply and demand situation in the target area are extracted, and through supply and demand comparison analysis, the energy supply and demand gap situation in different historical periods is quantitatively characterized, and the historical energy supply and demand balance time series of the target area is reconstructed. At the same time, from the aspects of geothermal resource exploration, mining, and utilization, key indicators reflecting the development status of geothermal resources in the target area are extracted, and through comprehensive integrated analysis, the characteristics of geothermal resource development stages in different historical periods are quantitatively characterized, and the spatiotemporal distribution map of historical geothermal resource development and utilization in the target area is reconstructed. Through the above steps, multi-source historical data are fully utilized to objectively reproduce the historical energy supply and demand situation and geothermal resource utilization pattern in the target area, revealing its spatiotemporal dynamic evolution law, which can provide important historical reference for resource compensation.

[0078] S502, based on the historical energy supply and demand balance and the historical geothermal resource development and utilization, the first compensation strategy is simulated and evaluated to obtain a simulation evaluation result, and the first compensation strategy is compensated based on the simulation evaluation result to obtain a second compensation strategy.

[0079] Specifically, firstly, based on the reconstructed historical energy supply and demand balance time series and the spatiotemporal distribution map of geothermal resource development and utilization, an energy system simulation model reflecting the historical conditions of the target area is constructed. On this basis, the key parameters in the first compensation strategy are input into the simulation model to simulate the energy supply and demand balance effect and geothermal resource utilization pattern that would be produced if the strategy was adopted in different historical periods. By comparing and analyzing with the actual historical conditions, the theoretical effect of the first compensation strategy under different historical backgrounds is quantitatively evaluated to form a comprehensive simulation evaluation result. Then, from the aspects of energy security improvement and geothermal resource utilization efficiency, the problems and deficiencies in the simulation evaluation results are identified, and the time configuration and spatial layout of the first compensation strategy are dynamically adjusted in a targeted manner, and finally a second compensation strategy that takes into account historical experience and current needs is formed. Through the above steps, historical data resources are fully utilized, and wisdom and experience are extracted from the evolution of historical conditions, so that the formulation of the compensation plan fully considers the development process and resource and environmental background of the target area, and avoids the "idealized" plan design that is divorced from reality. At the same time, a simulation evaluation method is used to backtest the preliminary plan using historical data to comprehensively judge its applicability and effectiveness in different contexts, which can provide important feedback for the optimization and improvement of the plan.

[0080] S104, acquiring multi-source compensation data in real time, and adjusting the second compensation strategy based on the multi-source compensation data to obtain a target compensation strategy;

[0081] Specifically, after forming the second compensation strategy, in order to further improve the dynamic adaptability and real-time optimization capabilities of the strategy, it is also necessary to establish a real-time perception and strategy iteration mechanism. Specifically, through the Internet of Things, cloud computing and other technical means, the geothermal field mining data, pipeline transmission parameters, user-side load data and other multi-source compensation data in the geothermal supply and demand imbalance area are collected in real time, and transmitted and aggregated to the cloud management platform. In the cloud platform, through algorithms such as stream data processing, time series analysis, and anomaly diagnosis, the multi-source compensation data are fused and calculated, and intelligently analyzed to dynamically perceive the real-time operating status of the regional geothermal system, and timely discover potential risks such as resource shortages, equipment anomalies, pipeline bottlenecks, and user-side peaks. Based on real-time status data and risk warning results, the relevant target parameters, operating thresholds, and scheduling plans in the second compensation strategy are dynamically adjusted to dynamically optimize with changes in the actual operating conditions of the region, thereby forming a target compensation strategy with real-time perception and dynamic optimization capabilities. For example, when real-time data reflects the risk of shortage of regional geothermal resources, timely increase the exploration and development of geothermal resources in the target compensation strategy; when the user-side load data warns of peak risk, correspondingly strengthen heat storage peak regulation, demand response and other measures; when online diagnosis finds pipeline transmission bottlenecks, timely optimize the pipeline architecture and operating parameters in the compensation strategy. Through the above steps, make full use of new generation information technologies such as the Internet of Things and big data to obtain all-round and dynamic regional compensation data, and timely feedback and optimize it into the compensation strategy, forming a compensation mechanism of real-time perception, dynamic optimization, and closed-loop iteration, so as to promote the compensation strategy to continuously optimize and improve itself in the dynamic evolution of the regional geothermal system, and always maintain strong adaptability, thereby significantly improving the allocation efficiency and utilization efficiency of regional geothermal resources.

[0082] Based on the above embodiment, as an optional embodiment, the second compensation strategy is supplemented based on multi-source compensation data to obtain a target compensation strategy, including:

[0083] S601, analyze multi-source compensation data to obtain the target positioning, key tasks and priority areas of geothermal resource compensation;

[0084] Specifically, we first collect multi-source compensation data such as the national energy development plan, geothermal resource compensation standards, and local compensation work plans to build a top-level document library for geothermal resource compensation. On this basis, we use text mining, content analysis and other technologies to systematically sort out compensation-related policy documents, quantitatively characterize the internal context of the documents, and analyze them through keyword extraction and semantic clustering. At the same time, through task matrix decomposition, association rule mining and other analyses, we identify the key tasks of geothermal resource compensation and clarify its key directions in energy transformation and development. In addition, combined with compensation standards, local experience and other data, a value judgment model is used to evaluate the urgency and importance of compensation work in different fields, and based on this, the priority compensation areas at the current stage are determined. The above steps make the formulation of compensation plans more clearly goal-oriented.

[0085] S602, optimizing the second compensation strategy based on target positioning, key tasks and priority areas to obtain a target compensation strategy.

[0086] Specifically, firstly, the main contents of the second compensation strategy are sorted out, and the target positioning of geothermal resource compensation is compared to achieve the degree of fit in the compensation target, identify the deviations and deficiencies, and adjust and optimize the overall ideas and basic principles of the compensation strategy accordingly. At the same time, according to the clear key compensation tasks, the compensation strategy is refined and deconstructed by using methods such as task decomposition and key paths, and the various tasks are matched and analyzed to identify the weak links in the implementation of key tasks by the compensation strategy, and then the relevant measures are supplemented and improved in a targeted manner to build a task implementation mechanism with clear responsibilities and interlocking links. In addition, based on the current priority compensation areas, the resource allocation optimization model is adopted to comprehensively evaluate the resource input intensity and guarantee level of the compensation strategy in key areas, find out the "short board" of resource allocation, and optimize it through resource overall allocation, policy support and other methods, promote the concentration of compensation resources in priority areas, and form a compensation synergy. Through the above steps, the second compensation strategy is optimized and shaped in all aspects, and finally a target compensation strategy with accurate positioning, strict logic, strong measures and prominent focus is formed.

[0087] S105, using a target compensation strategy to compensate for the geothermal supply and demand imbalance area.

[0088] Specifically, after obtaining the target compensation strategy, the thermal energy resources in the target area are regulated based on the target compensation strategy to compensate for the geothermal supply and demand imbalance area.

[0089] See also Figure 2 , Figure 2 An architecture diagram of a geothermal compensation system based on a region is provided in an embodiment of the present application. The geothermal compensation system based on a region may include:

[0090] Data acquisition module 1, used to acquire geothermal resource data and a first spatiotemporal distribution map of energy consumption in a target area, and perform geothermal balance analysis based on the geothermal resource data and the spatiotemporal distribution map of regional energy consumption to determine the geothermal supply and demand imbalance area;

[0091] A first compensation strategy generating module 2 is used to determine a second energy consumption spatiotemporal distribution map of the geothermal supply and demand imbalance region based on the geothermal supply and demand imbalance region and the regional energy consumption spatiotemporal distribution map, and determine a first compensation strategy based on the second energy consumption spatiotemporal distribution map;

[0092] The second compensation strategy generating module 3 is used to obtain multi-source historical data of the geothermal supply and demand imbalance area, and adjust the first compensation strategy based on the multi-source historical data to obtain the second compensation strategy;

[0093] A target compensation strategy generating module 4 is used to obtain multi-source compensation data in real time, and adjust the second compensation strategy based on the multi-source compensation data to obtain a target compensation strategy;

[0094] The imbalanced area compensation module 5 is used to compensate for the imbalanced area of ​​geothermal supply and demand by adopting a target compensation strategy.

[0095] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0096] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302 or end-to-end wireless communication.

[0097] The communication bus 302 is used to realize the connection and communication between these components.

[0098] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0099] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0100] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field~Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0101] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read~Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage system located away from the aforementioned processor 301. Refer to Figure 3 The memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a regional-based geothermal compensation method.

[0102] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program storing the geothermal compensation method based on the region in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application. In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0103] In the several implementations provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are only schematic, such as the division of modules, which is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, indirect coupling or communication connection of systems or modules, which can be electrical or other forms.

[0104] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] The present application also provides a computer storage medium that can store multiple instructions, which are suitable for being loaded and executed by a processor as described above. Figure 1 The geothermal compensation method based on the region in the embodiment shown, the specific execution process can be found in Figure 1 The specific description of the illustrated embodiment will not be repeated here.

[0106] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software functional modules.

[0107] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0108] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0109] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A geothermal compensation method based on region, characterized in that: The method comprises: obtaining geothermal resource data and a first spatiotemporal distribution map of energy consumption in a target area, and performing geothermal balance analysis based on the geothermal resource data and the spatiotemporal distribution map of regional energy consumption to determine the geothermal supply and demand imbalance area in the target area; the obtaining of the geothermal resource data and the first spatiotemporal distribution map of energy consumption in the target area comprises: obtaining the geothermal resource distribution data of the target area, and classifying and cleaning the geothermal resource distribution data to obtain the geothermal resource data; collecting energy consumption data of various buildings and facilities in the target area, and preprocessing the energy consumption data to obtain preprocessed energy consumption data; aggregating and statistically analyzing the preprocessed energy consumption data according to time and space dimensions to generate a spatiotemporal distribution matrix reflecting the dynamic changes and spatial distribution characteristics of regional energy consumption demand, and constructing a distribution map based on the spatiotemporal distribution matrix to obtain the first spatiotemporal distribution map of energy consumption; determining the second energy consumption of the geothermal supply and demand imbalance area based on the geothermal supply and demand imbalance area and the regional spatiotemporal distribution map of energy consumption; spatiotemporal distribution map, and determining a first compensation strategy based on the second spatiotemporal distribution map of energy consumption; the second spatiotemporal distribution map of energy consumption in the geothermal supply and demand imbalance area is determined based on the geothermal supply and demand imbalance area and the regional energy consumption spatiotemporal distribution map, including: extracting the spatial boundaries and location information of each imbalance area according to the geothermal supply and demand imbalance area distribution map, and extracting the imbalance energy consumption demand data of the corresponding area from the regional energy consumption spatiotemporal distribution map based on the spatial boundaries and the location information, and generating the imbalance energy consumption demand time series data and imbalance spatial distribution data of each imbalance area based on the imbalance energy consumption demand data; performing cluster analysis on the imbalance area according to the imbalance energy consumption demand time series data and the imbalance spatial distribution data to obtain cluster analysis results; performing feature extraction on the imbalance energy consumption demand time series data and the imbalance spatial distribution data to obtain imbalance spatiotemporal feature data, and constructing the second spatiotemporal distribution map of energy consumption according to the cluster analysis results and the imbalance spatiotemporal feature data; Acquire multi-source historical data of the geothermal supply and demand imbalance area, and adjust the first compensation strategy based on the multi-source historical data to obtain a second compensation strategy; acquire multi-source compensation data in real time, and adjust the second compensation strategy based on the multi-source compensation data to obtain a target compensation strategy; use the target compensation strategy to compensate for the geothermal supply and demand imbalance area.

2. The method according to claim 1, characterized in that The geothermal equilibrium analysis based on the geothermal resource data and the regional energy consumption spatiotemporal distribution map is performed to determine the geothermal supply and demand imbalance area in the target area, including: evaluating the resource potential of each geothermal field in the target area based on the geothermal resource data to obtain a geothermal resource potential evaluation result, and constructing a geothermal resource potential map of the target area based on the geothermal resource potential evaluation result; using the first energy consumption spatiotemporal distribution map to analyze the temporal dynamic characteristics and spatial distribution characteristics of energy consumption demand in the target area; constructing an energy consumption demand classification map according to the spatiotemporal distribution characteristics, and performing spatial superposition analysis on the geothermal resource potential map and the energy consumption demand classification map to obtain a geothermal resource classification map. The spatial matching relationship between the distribution of geothermal resources and the distribution of energy consumption demand is calculated; based on the spatial matching relationship, the spatial matching degree of the geothermal resource potential and the energy consumption demand in the target area is calculated, and a spatial matching degree map reflecting the spatial matching status of geothermal resources and energy consumption demand is generated, and based on the spatial matching degree map, the spatial imbalance area between the supply of geothermal resources and the energy consumption demand is judged; based on the time variation characteristics, the time matching degree of the geothermal resource supply and the energy consumption demand on different time scales is evaluated, and based on the time matching degree, a matching degree time series diagram reflecting the time matching status of geothermal resources and energy consumption demand is constructed; matching analysis is performed on the matching degree time series diagram and the spatial imbalance area to obtain the geothermal supply and demand imbalance area.

3. The method according to claim 1, characterized in that The method of determining the first compensation strategy based on the second spatiotemporal distribution diagram of energy consumption includes: analyzing the second spatiotemporal distribution diagram of energy consumption to obtain peak time periods and peak areas of energy consumption demand, and determining compensation demand time distribution data and compensation demand spatial distribution data according to the peak time periods and peak areas of energy consumption demand; determining a compensation priority level according to the compensation demand time distribution data and the compensation demand spatial distribution data; performing compensation resource analysis based on the geothermal resource data to determine the spatiotemporal distribution characteristics of supplementary resources of the compensation resources; and determining the first compensation strategy based on the compensation priority level and the spatiotemporal distribution characteristics of the supplementary resources.

4. The method according to claim 1, characterized in that The adjusting the first compensation strategy based on the multi-source historical data to obtain the second compensation strategy includes: reconstructing the historical energy supply and demand balance and the historical geothermal resource development and utilization in the target area based on the multi-source historical data; simulating and evaluating the first compensation strategy based on the historical energy supply and demand balance and the historical geothermal resource development and utilization to obtain a simulation evaluation result, and compensating the first compensation strategy based on the simulation evaluation result to obtain the second compensation strategy.

5. The method according to claim 1, characterized in that: The adjusting the second compensation strategy based on the multi-source compensation data to obtain the target compensation strategy includes: analyzing the multi-source compensation data to obtain the target positioning, key tasks and priority areas of geothermal resource compensation; optimizing the second compensation strategy based on the target positioning, the key tasks and the priority areas to obtain the target compensation strategy.

6. A geothermal compensation system based on a region, characterized in that: The system includes: a data acquisition module, which is used to acquire geothermal resource data and a first spatiotemporal distribution map of energy consumption in a target area, and perform geothermal balance analysis based on the geothermal resource data and the spatiotemporal distribution map of regional energy consumption to determine the geothermal supply and demand imbalance area in the target area; the acquisition of geothermal resource data and the first spatiotemporal distribution map of energy consumption in the target area includes: acquiring geothermal resource distribution data in the target area, and classifying and cleaning the geothermal resource distribution data to obtain the geothermal resource data; collecting energy consumption data of various buildings and facilities in the target area, and preprocessing the energy consumption data to obtain the preprocessed energy consumption data. data; aggregate and count the preprocessed energy consumption data according to time and space dimensions to generate a spatiotemporal distribution matrix that reflects the dynamic changes and spatial distribution characteristics of regional energy consumption demand, and construct a distribution map based on the spatiotemporal distribution matrix to obtain the first spatiotemporal distribution map of energy consumption; a first compensation strategy generation module, for determining a second spatiotemporal distribution map of energy consumption in the geothermal supply and demand imbalance area based on the geothermal supply and demand imbalance area and the regional spatiotemporal distribution map of energy consumption, and determining a first compensation strategy based on the second spatiotemporal distribution map of energy consumption; the first compensation strategy generation module, for determining a second spatiotemporal distribution map of energy consumption in the geothermal supply and demand imbalance area based on the geothermal supply and demand imbalance area and the regional spatiotemporal distribution map of energy consumption The second energy consumption spatiotemporal distribution map comprises: extracting the spatial boundaries and location information of each imbalanced area according to the geothermal supply and demand imbalanced area distribution map, extracting the imbalanced energy consumption demand data of the corresponding area from the regional energy consumption spatiotemporal distribution map based on the spatial boundaries and the location information, and generating the imbalanced energy consumption demand time series data and imbalanced spatial distribution data of each imbalanced area based on the imbalanced energy consumption demand data; performing cluster analysis on the imbalanced area according to the imbalanced energy consumption demand time series data and the imbalanced spatial distribution data to obtain cluster analysis results; and clustering the imbalanced energy consumption demand time series data and the imbalanced spatial distribution data. The invention relates to a method for generating a geothermal energy consumption spatial-temporal distribution diagram, ...

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 5.

8. An electronic device, characterized in that: It includes a processor, a memory and a transceiver, the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 5.

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