Energy entity resource sharing economic system and method thereof

Abstract

The invention relates to the technical field of resource sharing, in particular to an energy entity resource sharing economic system and method, and the system comprises a resource monitoring module, a resource flow analysis module, a demand response strategy module, a cost evaluation module, a periodicity analysis module and a stability evaluation module. According to the method, the dynamic adaptability and the accuracy of resource allocation are effectively improved by collecting and analyzing the use condition and the flow data of the entity resources, the periodic change and the flow direction of the resources are analyzed through the data recorded in real time by the geographic position and the time label, and the dynamic monitoring and analysis not only contributes to predicting the resource demand peak, but also contributes to improving the resource allocation efficiency. In addition, a more effective sharing and allocation strategy can be designed for a shortage area, by simulating various allocation schemes, a response strategy can be optimized, the resource use efficiency can be improved, meanwhile, the operation cost can be reduced, long-term benefits and sustainable development of a resource sharing economic system can be ensured, cooperation and interaction among communities can be improved, and environment sustainability can be supported.

Description

Technical Field

[0001] The present invention relates to the technical field of resource sharing, and in particular to an economic system and method for sharing physical resources. Background Art

[0002] The field of resource sharing technology involves the development and use of platforms to efficiently allocate and use idle physical and digital resources. It includes various systems and models, such as sharing economy platforms, P2P networks, and integrated services, which aim to maximize the efficiency and accessibility of resource use. The technical aspects mainly involve resource scheduling, data analysis, and user interaction optimization to achieve efficient circulation of resources among users. Applications of this technology include sharing of transportation, accommodation, office space and materials, as well as sharing of digital resources such as storage space and computing power.

[0003] The physical resource sharing economic system refers to a specific resource sharing model used to optimize the allocation and use of physical assets. This system allows individuals or organizations to share physical resources, such as tools, vehicles or equipment, and manages the borrowing and return of resources through an online platform. The purpose is to reduce resource idle time, improve resource utilization, and promote environmental sustainability. It can also help reduce costs and increase interaction and cooperation among communities.

[0004] Existing resource sharing technologies lack an in-depth understanding of resource flows and cyclical demand changes, resulting in resource allocation that cannot respond to market demand changes in real time, and low efficiency and utilization. The lack of comprehensive analysis of real-time data means that resource allocation often relies on experience, which can easily lead to problems of excess or shortage of resources, increase operating costs and reduce user satisfaction. For example, the lack of effective demand forecasting tools leads to insufficient shared cars during peak demand periods, or too many cars being idle during low demand periods. This not only affects the economic benefits of service providers, but also reduces users' trust and reliance on shared resource platforms, further hindering the promotion and development of shared economic models. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an economic system and method for sharing physical resources.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution, an energy entity resource sharing economic system comprises: The resource monitoring module uses geographic location and time tags to collect physical resource usage and flow data, records physical resource flows in different regions and times in real time, and generates a dynamic resource map; The resource flow analysis module adopts the dynamic resource map to analyze the flow and periodic changes of regional entity resources, and obtains the resource flow trend analysis results by comparing the inflow and outflow differences of regional resources; The demand response strategy module identifies the peak usage and shortage areas of physical resources based on the resource flow trend analysis results, simulates the response effect of the physical resource sharing and allocation scheme on demand, and obtains the response strategy optimization scheme; The cost evaluation module implements the response strategy optimization scheme, performs entity resource sharing and allocation simulation, evaluates the effect and cost-effectiveness of the differentiation scheme, and obtains the resource allocation simulation result; The periodicity analysis module determines the optimal frequency and scale of entity resource sharing and allocation through the resource allocation simulation results, makes adjustments for differentiated resource types, and generates periodicity adjustment indicators; The stability evaluation module uses the periodic adjustment index to analyze the entity resource sharing situation under the differentiated allocation strategy, and obtains the stability prediction result by long-term tracking and evaluating the resource allocation effect and stability.

[0007] As a further solution of the present invention, the dynamic resource map includes a resource usage frequency map, a resource flow speed map and a resource distribution hotspot map; the resource flow trend analysis results include a periodic demand fluctuation map, a regional resource inflow and outflow comparison table and a time series change map; the response strategy optimization plan includes a peak resource allocation plan, an emergency response plan for shortage areas and a long-term resource balance strategy; the resource allocation simulation results include a cost-effectiveness analysis map, an expected and real-time effect comparison table and a resource utilization calculation result; the periodic adjustment indicators include resource allocation frequency settings, resource type differentiation processing parameters and optimal scale estimation values; the stability prediction results include a long-term resource allocation effect map, a stability scoring table and a resource supply and demand balance prediction map.

[0008] As a further solution of the present invention, the resource monitoring module includes: The geo-tag collection submodule uses geographic information system technology to capture and record the location of physical resources, extract geographic coordinates from the location data of physical resources, standardize geographic information, and integrate the geographic tags of physical resources to generate geographic information summaries; The time tag analysis submodule collects time tags of entity resources based on the geographic information aggregation, identifies the usage frequency and duration through time series analysis, performs trend and pattern recognition on time data, and obtains time pattern analysis results; The resource flow mapping submodule identifies the flow status of physical resources at differentiated times and locations based on the time pattern analysis results, and obtains a dynamic resource map by comparing the resource usage differences in differentiated areas and time periods.

[0009] As a further solution of the present invention, the resource flow analysis module includes: The resource trend calculation submodule extracts the inflow and outflow data of physical resources in different regions based on the dynamic resource map, calculates the total flow of resources in each region, identifies the key resource flow direction, and builds a resource flow baseline; The seasonal change analysis submodule performs periodic analysis on the data in the resource flow baseline, detects seasonal and periodic fluctuations in resource flow, determines regular changes in resource flow direction through periodic data, and obtains a periodic change map; The regional difference comparison submodule uses the periodic change map to compare the differences in the inflow and outflow of physical resources between differentiated regions, and through data comparison and analysis, records the trends and characteristics of regional physical resource flows to obtain resource flow trend analysis results.

[0010] As a further solution of the present invention, the demand response strategy includes: The usage analysis submodule analyzes the usage of physical resources in different regions based on the resource flow trend analysis results, identifies peak and shortage areas of physical resource usage, and generates a resource demand analysis diagram; The resource sharing simulation submodule uses a genetic algorithm to optimize and iterate the sharing and allocation scheme of physical resources according to the resource demand analysis diagram, and simulates the execution effect of the scheme under differentiated scenarios to obtain the resource allocation simulation result; The strategy optimization submodule uses the resource allocation simulation results to evaluate the response effect of the differentiated allocation plan to regional resource demand, optimizes resource utilization efficiency and matches regional demand through a cyclic adjustment plan, determines the optimal resource sharing and allocation strategy, and obtains a response strategy optimization plan.

[0011] As a further solution of the present invention, the formula of the genetic algorithm is as follows: ; Among them, R is the resource allocation efficiency, represents the total demand at the current time t, is the weight coefficient of demand, Represents the supply of resources, is the weight coefficient of supply, Indicates the number of nodes participating in resource configuration, is the adjustment factor for the number of nodes, Represents the amount of constraint violation for the current configuration.

[0012] As a further solution of the present invention, the cost assessment module includes: The solution simulation submodule executes the entity resource sharing and deployment in the response strategy optimization solution, simulates the resource allocation process under the differentiated scenario, evaluates the implementation feasibility of the differentiated solution, identifies potential execution obstacles and deployment efficiency, and generates implementation simulation results; The effect quantification submodule quantifies and evaluates the real-time effect of resource allocation based on the implementation simulation results, compares the matching degree of differentiated solutions to resource requirements, identifies the advantages and limitations of multiple solutions, and obtains the allocation effect evaluation results; The cost-benefit analysis submodule calculates the cost-benefit ratio of the differentiated solution through the allocation effect evaluation result, evaluates the economic feasibility of the differentiated solution, and obtains the resource allocation simulation result.

[0013] As a further solution of the present invention, the periodicity analysis module includes: The frequency determination submodule determines the optimal allocation frequency through trend analysis and effect comparison based on the resource allocation simulation result and with reference to the effect and continuity of resource sharing and allocation under differentiated time intervals, and generates the optimal frequency index; The performance evaluation submodule adopts the optimal frequency index to analyze the resource sharing and deployment effects under differentiated scales, evaluate the efficiency and feasibility of resource sharing, and obtain the deployment scale evaluation results through comparative analysis; The type adjustment submodule uses the allocation scale assessment results to adjust the physical resources of differentiated types, customizes allocation strategies for consumables and durable goods, optimizes resource utilization efficiency through simulation and adjustment cycles, and obtains periodic adjustment indicators.

[0014] As a further solution of the present invention, the stability assessment module includes: The differentiated strategy analysis submodule analyzes the sharing effect of differentiated entity resource types under the implementation of the deployment strategy based on the periodic adjustment index, evaluates the response speed and efficiency of the strategy, and generates a strategy adaptability analysis result; The tracking and evaluation submodule uses the results of the strategy adaptability analysis to conduct long-term tracking of entity resource allocation, collect continuous data on resource sharing and allocation, detect the sustained effect and change trend of resource allocation through time series analysis, and obtain long-term allocation effect evaluation results; The risk prediction submodule analyzes the stability of resource allocation based on the long-term allocation effect evaluation results, predicts the sustainability and potential risks of resource allocation in the next week, and obtains stability prediction results.

[0015] An energy entity resource sharing economic method, the energy entity resource sharing economic method is executed based on the energy entity resource sharing economic system, and comprises the following steps: S1: Collect physical resource usage data in multiple regions and at different times, locate resource flows and usage peaks through geographic locations and time tags, synchronize and organize the data, and generate a dynamic resource map; S2: Based on the dynamic resource map, compare the inflow and outflow of resources in multiple regions, analyze the resource flow direction and periodic changes, and obtain the resource flow trend analysis results; S3: Based on the resource flow trend analysis results, identify the peak period and shortage area of ​​resource use, simulate the physical resource sharing and allocation plan, capture the impact of the differentiated plan on demand response, and generate a response strategy optimization plan; S4: Implement the response strategy optimization plan, simulate the sharing and deployment of physical resources, use the simulation results to evaluate the cost-effectiveness of the differentiation strategy, and obtain the resource deployment simulation results; S5: Utilize the resource allocation simulation results to analyze the implementation frequency and scale of various resource sharing and allocation schemes, make periodic adjustments based on differentiated resource types and regional demands, and generate stability prediction results through long-term tracking and analysis.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by collecting and analyzing the usage and flow data of physical resources, the dynamic adaptability and accuracy of resource allocation can be effectively improved. Through the data recorded in real time with geographic location and time tags, an accurate resource flow map can be generated to analyze the periodic changes and flow direction of resources. This dynamic monitoring and analysis not only helps to predict peak resource demand, but also can design more effective sharing and allocation strategies for shortage areas. By simulating various allocation schemes, response strategies can be optimized, resource utilization efficiency can be improved, and operating costs can be reduced. Periodic analysis and stability assessment further ensure the long-term benefits and sustainable development of the resource sharing economic system, improve cooperation and interaction among communities, and support environmental sustainability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system flow chart of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention; Figure 3 It is a flow chart of the resource monitoring module of the present invention; Figure 4 It is a flow chart of the resource flow analysis module of the present invention; Figure 5 It is a flow chart of the demand response strategy module of the present invention; Figure 6 is a flow chart of the cost assessment module of the present invention; Figure 7 It is a flow chart of the periodic analysis module of the present invention; Figure 8 is a flow chart of the stability assessment module of the present invention; Fig. 9 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0020] See also Figure 1 to Figure 2 The present invention provides a technical solution, a physical resource sharing economic system comprising: The resource monitoring module uses geographic location and time tags to collect physical resource usage and flow data, records physical resource flows in different regions and times in real time, and generates a dynamic resource map; The resource flow analysis module uses dynamic resource maps to analyze the flow and periodic changes of physical resources in different regions. By comparing the inflow and outflow differences of regional resources, the resource flow trend analysis results are obtained; The demand response strategy module identifies the peak and shortage areas of physical resource usage in differentiated regions based on the results of resource flow trend analysis, simulates the response effect of differentiated physical resource sharing and allocation schemes on demand, and obtains the response strategy optimization scheme; The cost assessment module implements the response strategy optimization plan, conducts entity resource sharing and deployment simulation, evaluates the effect and cost-effectiveness of the differentiation plan, and obtains the resource deployment simulation results; The periodic analysis module determines the optimal frequency and scale of entity resource sharing and allocation through the resource allocation simulation results, performs periodic adjustments for differentiated resource types, matches long-term demand changes, optimizes economic benefits, and generates periodic adjustment indicators; The stability assessment module uses periodic adjustment indicators to analyze the entity resource sharing under differentiated allocation strategies, and obtains stability prediction results through long-term tracking and evaluation of resource allocation effects and stability.

[0021] The dynamic resource map includes a resource usage frequency map, a resource flow speed map and a resource distribution hotspot map. The resource flow trend analysis results include a periodic demand fluctuation map, a regional resource inflow and outflow comparison table and a time series change map. The response strategy optimization plan includes a peak resource allocation plan, an emergency response plan for shortage areas and a long-term resource balance strategy. The resource allocation simulation results include a cost-benefit ratio analysis map, a comparison table of expected and real-time effects and resource utilization calculation results. The periodic adjustment indicators include resource allocation frequency settings, resource type differentiation processing parameters and optimal scale estimates. The stability prediction results include a long-term resource allocation effect map, a stability score table and a resource supply and demand balance prediction map.

[0022] See also Figure 2 , 3 , the resource monitoring module includes: The geo-tag collection submodule uses geographic information system technology to capture and record the location of physical resources, extract geographic coordinates from the location data of physical resources, standardize geographic information, and integrate the geographic tags of physical resources to generate the execution process of geographic information summary as follows; Use geographic information system technology to accurately record the location of physical resources, collect location data through specialized sensor equipment, and generate geographic coordinates. Data standardization in the process involves converting geographic information in different formats into a unified format and using standardized algorithms to process coordinate data for more accurate geographic information integration. The acquisition and standardization of geographic coordinates are the basis for realizing geographic information integration. Through the integration of geographic information, accurate geographic tags can be established for physical resources, providing an accurate geographic data foundation for subsequent data application and analysis, and generating geographic information summaries.

[0023] The time tag analysis submodule collects time tags of entity resources based on geographic information aggregation, identifies usage frequency and duration through time series analysis, and performs trend and pattern recognition on time data. The execution process of obtaining time pattern analysis results is as follows; Using time series analysis to identify the frequency and duration of use, we can use the formula , calculate the time pattern analysis results. In the formula, f(t) represents the time series function, t represents time, and a, b and c represent model parameters. Formula explanation and formula calculation derivation process: Suppose there is a set of time series data ,in represents the data at the gth time point. The parameters a, b, and c are obtained by fitting the data, for example, using the least squares method. The specific calculation is as follows: set a=5, b=0.03, c=2, and fit the data for t=1, 2, 3, ..., 10. By calculating f(t) corresponding to each g, the time pattern is analyzed. This analysis shows that the frequency of use increases exponentially over time.

[0024] The resource flow mapping submodule identifies the flow status of physical resources at differentiated times and locations based on the time pattern analysis results, and obtains the dynamic resource map by comparing the resource usage differences in differentiated areas and time periods. The execution process is as follows; According to the results of time pattern analysis, the flow status of physical resources at different times and places is determined. The process collects and compares resource usage data in different time periods and regions, conducts in-depth analysis of the data, and uses data comparison methods such as differentiation analysis and flow simulation to identify patterns and trends in resource flow. The difference in resource usage in different locations illustrates the uneven distribution of resources. The analysis results are crucial to understanding the dynamic distribution of resources and optimizing resource allocation. In this way, a dynamic resource map is obtained.

[0025] See also Figure 2 , 4 , the resource flow analysis module includes: The resource trend calculation submodule extracts the inflow and outflow data of physical resources in different regions based on the dynamic resource map, calculates the total flow of resources in each region, identifies the key resource flow direction, and constructs the resource flow baseline. The execution process is as follows; The resource trend calculation submodule calculates the total flow of resources in each area according to the formula , calculate the key resource flow. In the formula, Q represents the total resource flow in the region, and Represent the resource inflow and outflow of the i-th region respectively, and n is the number of regions. Formula explanation and formula calculation derivation process: Set the resource inflow of the three regions to , , , and the resource outflows are , , According to the formula, Q = (100-80) + (150-110) + (120-100) = 20 + 40 + 20 = 80. This result shows that the total flow of resources in the entire region is positive, indicating that the overall resources are in a net inflow state.

[0026] The seasonal change analysis submodule performs periodic analysis on the data in the resource flow baseline, detects seasonal and periodic fluctuations in resource flow, determines the regular changes in resource flow direction through periodic data, and obtains the execution process of the periodic change map as follows; Statistical methods are used to conduct a periodic analysis of the resource flow baseline, and the statistical characteristics of the periodic fluctuations in resource flows, such as the mean, standard deviation, and peak, are calculated. Time series analysis tools are used to detect and confirm the periodic fluctuations in resource flows. The results of these periodic analyses help determine the regular changes in resource flows and reveal the seasonal patterns of resource flows, which are of great significance for formulating future resource allocation and management policies, providing a basis for predicting future resource flows and obtaining a map of periodic changes.

[0027] The regional difference comparison submodule uses the periodic change map to compare the differences in the inflow and outflow of physical resources between differentiated regions. Through data comparison and analysis, the trend and characteristics of regional physical resource flow are recorded, and the execution process of obtaining the resource flow trend analysis results is as follows; Using the periodic change map as a basis, the resource inflow and outflow data between different regions are compared. Through horizontal and vertical comparative analysis of the data, and the comprehensive use of statistical analysis methods, such as variance analysis and correlation analysis, the characteristics and trends of resource flows in each region are clarified. Through comparative analysis, significant differences in resource flows between regions can be observed. These differences reflect the imbalance of regional economic activities. The results of regional difference comparative analysis provide data support for the formulation of resource management and optimization strategies, and construct a resource flow trend map.

[0028] See also Figure 2 , 5 , demand response strategies include: The usage analysis submodule analyzes the usage of physical resources in differentiated areas based on the resource flow trend analysis results, identifies the peak and shortage areas of physical resource usage, and generates a resource demand analysis diagram. The execution process is as follows; Starting from the results of resource flow trend analysis, we specifically analyze the usage of physical resources in differentiated regions. In the process, we identify the peak and shortage of resource usage in each region. Through statistical analysis methods, such as demand forecasting and demand gap analysis, we cluster and classify the data, and conduct a deeper interpretation and analysis of the data in peak areas and shortage areas. These analyses help to depict a detailed map of resource usage, showing the specific situation of resource utilization in each region, providing a basis for resource reallocation and optimization, and providing data support and decision-making basis for the effective management and optimization of physical resources, thus obtaining a resource demand analysis diagram.

[0029] The resource sharing simulation submodule uses genetic algorithms to optimize and iterate the sharing and allocation scheme of physical resources according to the resource demand analysis diagram, and simulates the execution effect of the scheme under differentiated scenarios. The execution process of obtaining the resource allocation simulation results is as follows; The formula of genetic algorithm is as follows: ; Among them, R is the resource allocation efficiency, represents the total demand at the current time t, is the weight coefficient of demand, Represents the supply of resources, is the weight coefficient of supply, Indicates the number of nodes participating in resource configuration. is the adjustment factor for the number of nodes, Represents the amount of constraint violation for the current configuration.

[0030] The parameters have the following meanings and are obtained as follows: Indicates the total resource demand at time t. It is accumulated from the demand data obtained through real-time monitoring in the resource management system and is set to 1200 units at a certain moment.

[0031] is the weight coefficient of demand, which is set to 1.5. The value is obtained based on data analysis and is proportional to the importance of demand.

[0032] It represents the resource supply at time t, which is also obtained through the real-time monitoring data of the resource management system. The current supply is set to 800 units.

[0033] It is the weight coefficient of supply, which is set to 0.8. The value is obtained based on the analysis of resource circulation speed and supply stability.

[0034] It is the number of nodes participating in resource configuration. It is the number of active nodes directly queried from the network resource database. The current setting is 25.

[0035] is the adjustment coefficient of the number of nodes, which is set to 0.3. The coefficient is adjusted based on the statistical analysis of network size and configuration efficiency.

[0036] Represents the current configuration constraint violation amount, which is calculated through exception reports and violation records in the configuration system and is set to -20 (negative values ​​indicate violations).

[0037] Substituting the actual value into the formula for calculation, we get:

[0038] The results show that in the current resource allocation model, the allocation efficiency is 205.04, which reflects the performance of the resource allocation scheme optimized by the genetic algorithm under given resource supply, demand, node participation and violation conditions. The performance index provides a numerical basis for resource management decisions and demonstrates the effectiveness of the optimization algorithm in actual operations. Through further analysis of the numerical values, the resource allocation strategy can be adjusted in time to cope with dynamically changing demand and supply conditions.

[0039] The strategy optimization submodule uses the resource allocation simulation results to evaluate the response effect of the differentiated allocation scheme to regional resource demand, optimizes resource utilization efficiency and matches regional demand through cyclic adjustment schemes, determines the optimal resource sharing and allocation strategy, and obtains the execution process of the response strategy optimization scheme as follows; Optimize resource utilization efficiency through a cyclic adjustment scheme, according to the formula , determine the optimal resource sharing and allocation strategy. In the formula, E represents resource utilization efficiency, represents the resource usage of the ith region, represents the resource demand of the ith region, and n is the number of regions. Formula explanation and formula calculation derivation process: Assume that the resource usage of three regions is , , , the resource demand is , , Calculated according to the formula This efficiency value indicates that the matching degree of resource utilization is close to optimization. By further adjusting the resource allocation plan, the efficiency of resource utilization can be further improved to ensure a more accurate matching of resource supply and demand. The response strategy optimization plan ensures the effective allocation and utilization of resources in various regions.

[0040] See also Figure 2 , 6 , the cost assessment module includes: The solution simulation submodule executes the entity resource sharing and deployment in the response strategy optimization solution, simulates the resource allocation process under the differentiated scenario, evaluates the implementation feasibility of the differentiated solution, identifies potential implementation obstacles and deployment efficiency, and generates the implementation simulation results. The execution process is as follows; Responsible for the sharing and deployment of physical resources in the execution response strategy optimization plan. In the process, it simulates resource allocation in different scenarios. By using the simulation system to dynamically display the flow of resources from one area to another, it evaluates the feasibility of the implementation of the plan, which involves the efficiency of resource allocation and the obstacles encountered during the implementation process, such as logistics delays or resource matching problems. In this way, it reflects the operational details and potential problems of the plan in different scenarios, provides actual operational data and predictive analysis for the optimization of the deployment strategy, shows the performance of different resource allocation plans in actual operations and potential implementation difficulties, and generates implementation simulation results.

[0041] The effect quantification submodule quantifies and evaluates the real-time effect of resource allocation based on the implementation simulation results, compares the matching degree of differentiated solutions to resource requirements, identifies the advantages and limitations of multiple solutions, and obtains the execution process of the allocation effect evaluation results as follows; By comparing the matching degree of differentiated solutions to resource requirements, according to the formula , identify the advantages and limitations of multiple solutions. In the formula, M represents the degree of matching, represents the amount of resources that the z-th solution can meet the demand, represents the total resources of the zth solution, is the number of solutions. Detailed explanation of the formula and the process of formula calculation: Set the amount of resources that meet the demand in the three solutions to , , The total resources are , , Calculated according to the formula This matching degree shows that the scheme has met the demand well as a whole, but there is still room for improvement. The evaluation results of the deployment effect provide a basis for improving and selecting the scheme to ensure that resources are used to achieve the best effect.

[0042] The cost-benefit analysis submodule calculates the cost-benefit ratio of the differentiation plan through the allocation effect evaluation results, evaluates the economic feasibility of the differentiation plan, and obtains the execution process of the resource allocation simulation results as follows; The cost-benefit ratio is calculated using the allocation effect evaluation results. The process analyzes the economic data and resource usage data of each plan in detail, and adopts cost-benefit analysis methods, such as internal rate of return or net present value method, to evaluate the economic feasibility of different plans, examine the proportional relationship between input and output, and evaluate the economic feasibility of the plan. It provides decision makers with an economic evaluation of implementing differentiated resource allocation plans, so that resource allocation decisions can be optimized and adjusted on the basis of economic benefits, and resource allocation simulation results can be obtained.

[0043] See also Figure 2 , 7, the periodicity analysis module includes: The frequency determination submodule determines the optimal allocation frequency based on the resource allocation simulation results, and refers to the effect and continuity of resource sharing and allocation under differentiated time intervals, through trend analysis and effect comparison, and the execution process of generating the optimal frequency index is as follows; Determine the optimal deployment frequency through trend analysis and effect comparison, according to the formula , generating the optimal frequency index. Where F represents the optimal frequency, represents the change in the effect at the oth time interval, Represents the number of time intervals analyzed. Detailed explanation of the formula and the process of formula calculation: The effect change under four time intervals is set to , , , Calculated according to the formula Calculations show that the allocation frequency should be adjusted to increase the effect of 0.15 per unit time interval to ensure the continuity and maximum effect of resource allocation. The optimal frequency index ensures the effective allocation and use of resources at different time intervals.

[0044] The performance evaluation submodule uses the optimal frequency index to analyze the resource sharing and allocation effects under differentiated scales, evaluate the efficiency and feasibility of resource sharing, and obtain the allocation scale evaluation results through comparative analysis. The execution process is as follows; The optimal frequency index is used to analyze the resource sharing and allocation effects at different scales. In the process, the efficiency and feasibility of resource sharing are evaluated through statistical analysis and performance comparison. Data analysis techniques, such as variance analysis and correlation coefficient calculation, are used to compare the resource allocation results at different scales to reflect the actual efficiency of resource sharing and allocation at different scales. This provides a quantitative basis for the optimization and adjustment of resource allocation strategies, helps identify the advantages and limitations of resource sharing at a specific scale, provides strategic suggestions for resource management, and obtains allocation scale evaluation results.

[0045] The type adjustment submodule uses the allocation scale assessment results to adjust the physical resources of differentiated types, customizes the allocation strategy for consumables and durable goods, optimizes resource utilization efficiency through simulation and adjustment cycles, and obtains the execution process of periodic adjustment indicators as follows; The results of the allocation scale assessment are used to adjust different types of physical resources, including customizing different allocation strategies for consumables and durable goods, optimizing resource utilization efficiency through data simulation and strategy adjustment cycles, conducting resource allocation simulations, comparing the utilization efficiency and consumption of different types of resources, and adjusting resource allocation strategies based on these simulation results. This provides an adjustment basis for the continued effective use of resources, and through continuous optimization cycles, ensures that resource type adjustments are more in line with actual needs, improves overall resource utilization efficiency, and obtains periodic adjustment indicators.

[0046] See also Figure 2 , 8 , the stability assessment module includes: The differentiation strategy analysis submodule analyzes the sharing effect of differentiated entity resource types under the implementation of the deployment strategy based on the periodic adjustment indicators, evaluates the response speed and efficiency of the strategy, and generates the execution process of the strategy adaptability analysis results as follows; Based on the periodic adjustment indicators, the sharing effect analysis is conducted for different types of physical resources under the implementation of the allocation strategy. The process involves the evaluation of the response speed and efficiency of the strategy. Through the implementation of simulation and effect tracking, the use of data analysis techniques such as regression analysis and efficiency testing, the effects of the allocation strategies of different resource types are compared, revealing the adaptability and effectiveness of the strategy to various types of resources, providing improvement directions and decision-making support for resource sharing and allocation, showing the response and adaptability of the strategy in actual operations, providing a basis for further optimization, and generating strategy adaptability analysis results.

[0047] The tracking and evaluation submodule uses the results of the strategy adaptability analysis to conduct long-term tracking of entity resource allocation, collect continuous data on resource sharing and allocation, and detect the sustained effect and change trend of resource allocation through time series analysis. The execution process of obtaining the long-term configuration effect evaluation results is as follows; Through time series analysis to detect the sustained effect of resource allocation, according to the formula , and obtain the long-term configuration effect evaluation result. In the formula, Z(t) represents the resource configuration effect at time t, Z(t-1) represents the resource configuration effect at the previous moment, and X(t) represents the new input data at time t. is the smoothing coefficient, and α is the weight coefficient of demand. , if the resource allocation effect at time t-1 is Z(t-1)=50 and new input data is received at time t , calculated according to the formula The results show that the effect of resource allocation is continuously improving, demonstrating the stability and adaptability of resource allocation in long-term tracking. The long-term allocation effect evaluation results provide a basis for continuous monitoring and adjustment of resource allocation strategies.

[0048] The risk prediction submodule analyzes the stability of resource allocation based on the long-term allocation effect evaluation results, predicts the sustainability and potential risks of resource allocation in the next week, and obtains the execution process of the stability prediction results as follows; The stability analysis of resource allocation is carried out using the results of long-term allocation effect evaluation. In the process, the sustainability and potential risks of resource allocation in the next week are predicted. By using risk analysis methods and prediction models, such as probability models and risk assessment matrices, the potential risks of resource allocation are quantified and evaluated, providing forward-looking risk information and early warnings for resource management, helping decision makers to formulate preventive measures and optimization strategies, and demonstrating the risk prediction and sustainability assessment of resource allocation in the future, providing a scientific basis for the continuous optimization of resource allocation and risk management, and obtaining stability prediction results.

[0049] See also Fig. 9 , a method for sharing an energy entity resource economy, the method for sharing an energy entity resource economy is executed based on the above energy entity resource sharing economy system, and includes the following steps: S1: Collect physical resource usage data in multiple regions and at different times, locate resource flows and usage peaks through geographic locations and time tags, synchronize and organize the data, and generate a dynamic resource map; S2: Based on the dynamic resource map, compare the inflow and outflow of resources in multiple regions, analyze the resource flow direction and periodic changes, and obtain the resource flow trend analysis results; S3: Based on the results of resource flow trend analysis, identify the peak periods and shortage areas of resource use, simulate physical resource sharing and allocation plans, capture the impact of differentiated plans on demand response, and generate response strategy optimization plans; S4: Implement the response strategy optimization plan, simulate the sharing and allocation of physical resources, use the simulation results to evaluate the cost-effectiveness of the differentiation strategy, and obtain the resource allocation simulation results; S5: Use the resource allocation simulation results to analyze the implementation frequency and scale of various resource sharing and allocation schemes, make periodic adjustments based on differentiated resource types and regional needs, and generate stability prediction results through long-term tracking and analysis.

[0050] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An energy entity resource sharing economic system, characterized in that: The system comprises: The resource monitoring module uses geographic location and time tags to collect physical resource usage and flow data, records physical resource flows in different regions and times in real time, and generates a dynamic resource map; The resource flow analysis module adopts the dynamic resource map to analyze the flow and periodic changes of regional entity resources, and obtains the resource flow trend analysis results by comparing the inflow and outflow differences of regional resources; The demand response strategy module identifies the peak usage and shortage areas of physical resources based on the resource flow trend analysis results, simulates the response effect of the physical resource sharing and allocation scheme on demand, and obtains the response strategy optimization scheme; The cost evaluation module implements the response strategy optimization scheme, performs entity resource sharing and allocation simulation, evaluates the effect and cost-effectiveness of the differentiation scheme, and obtains the resource allocation simulation result; The periodicity analysis module determines the optimal frequency and scale of entity resource sharing and allocation through the resource allocation simulation results, makes adjustments for differentiated resource types, and generates periodicity adjustment indicators; The stability evaluation module uses the periodic adjustment index to analyze the entity resource sharing situation under the differentiated allocation strategy, and obtains the stability prediction result by long-term tracking and evaluating the resource allocation effect and stability.

2. The energy entity resource sharing economic system according to claim 1 is characterized in that: The dynamic resource map includes a resource usage frequency map, a resource flow speed map and a resource distribution hotspot map; the resource flow trend analysis results include a periodic demand fluctuation map, a regional resource inflow and outflow comparison table and a time series change map; the response strategy optimization plan includes a peak resource allocation plan, an emergency response plan for shortage areas and a long-term resource balance strategy; the resource allocation simulation results include a cost-effectiveness analysis map, a comparison table of expected and real-time effects and resource utilization calculation results; the periodic adjustment indicators include resource allocation frequency settings, resource type differentiation processing parameters and optimal scale estimates; the stability prediction results include a long-term resource allocation effect map, a stability scoring table and a resource supply and demand balance prediction map.

3. The energy entity resource sharing economic system according to claim 1 is characterized in that: The resource monitoring module includes: The geo-tag collection submodule uses geographic information system technology to capture and record the location of physical resources, extract geographic coordinates from the location data of physical resources, standardize geographic information, and integrate the geographic tags of physical resources to generate geographic information summaries; The time tag analysis submodule collects time tags of entity resources based on the geographic information aggregation, identifies the usage frequency and duration through time series analysis, performs trend and pattern recognition on time data, and obtains time pattern analysis results; The resource flow mapping submodule identifies the flow status of physical resources at differentiated times and locations based on the time pattern analysis results, and obtains a dynamic resource map by comparing the resource usage differences in differentiated areas and time periods.

4. The energy entity resource sharing economic system according to claim 1 is characterized in that: The resource flow analysis module includes: The resource trend calculation submodule extracts the inflow and outflow data of physical resources in different regions based on the dynamic resource map, calculates the total flow of resources in each region, identifies the key resource flow direction, and builds a resource flow baseline; The seasonal change analysis submodule performs periodic analysis on the data in the resource flow baseline, detects seasonal and periodic fluctuations in resource flow, determines regular changes in resource flow direction through periodic data, and obtains a periodic change map; The regional difference comparison submodule uses the periodic change map to compare the differences in the inflow and outflow of physical resources between differentiated regions, and through data comparison and analysis, records the trends and characteristics of regional physical resource flows to obtain resource flow trend analysis results.

5. The energy entity resource sharing economic system according to claim 1 is characterized in that: The demand response strategy includes: The usage analysis submodule analyzes the usage of physical resources in different regions based on the resource flow trend analysis results, identifies peak and shortage areas of physical resource usage, and generates a resource demand analysis diagram; The resource sharing simulation submodule uses a genetic algorithm to optimize and iterate the sharing and allocation scheme of physical resources according to the resource demand analysis diagram, and simulates the execution effect of the scheme under differentiated scenarios to obtain the resource allocation simulation result; The strategy optimization submodule uses the resource allocation simulation results to evaluate the response effect of the differentiated allocation plan to regional resource demand, optimizes resource utilization efficiency and matches regional demand through a cyclic adjustment plan, determines the optimal resource sharing and allocation strategy, and obtains a response strategy optimization plan.

6. The energy entity resource sharing economic system according to claim 5 is characterized in that: The formula of the genetic algorithm is as follows: ； Among them, R is the resource allocation efficiency, represents the total demand at the current time t, is the weight coefficient of demand, Represents the supply of resources, is the weight coefficient of supply, Indicates the number of nodes participating in resource configuration, is the adjustment factor for the number of nodes, Represents the amount of constraint violation for the current configuration.

7. The energy entity resource sharing economic system according to claim 1 is characterized in that: The cost assessment module includes: The solution simulation submodule executes the entity resource sharing and deployment in the response strategy optimization solution, simulates the resource allocation process under the differentiated scenario, evaluates the implementation feasibility of the differentiated solution, identifies potential execution obstacles and deployment efficiency, and generates implementation simulation results; The effect quantification submodule quantifies and evaluates the real-time effect of resource allocation based on the implementation simulation results, compares the matching degree of differentiated solutions to resource requirements, identifies the advantages and limitations of multiple solutions, and obtains the allocation effect evaluation results; The cost-benefit analysis submodule calculates the cost-benefit ratio of the differentiated solution through the allocation effect evaluation result, evaluates the economic feasibility of the differentiated solution, and obtains the resource allocation simulation result.

8. The energy entity resource sharing economic system according to claim 1 is characterized in that: The periodicity analysis module comprises: The frequency determination submodule determines the optimal allocation frequency through trend analysis and effect comparison based on the resource allocation simulation results and referring to the effect and continuity of resource sharing and allocation under differentiated time intervals, and generates the optimal frequency index; The performance evaluation submodule adopts the optimal frequency index to analyze the resource sharing and deployment effects under differentiated scales, evaluate the efficiency and feasibility of resource sharing, and obtain the deployment scale evaluation results through comparative analysis; The type adjustment submodule uses the allocation scale assessment results to adjust the physical resources of differentiated types, customizes allocation strategies for consumables and durable goods, optimizes resource utilization efficiency through simulation and adjustment cycles, and obtains periodic adjustment indicators.

9. The energy entity resource sharing economic system according to claim 1, characterized in that: The stability assessment module comprises: The differentiated strategy analysis submodule analyzes the sharing effect of differentiated entity resource types under the implementation of the deployment strategy based on the periodic adjustment index, evaluates the response speed and efficiency of the strategy, and generates a strategy adaptability analysis result; The tracking and evaluation submodule uses the results of the strategy adaptability analysis to conduct long-term tracking of entity resource allocation, collect continuous data on resource sharing and allocation, detect the sustained effect and change trend of resource allocation through time series analysis, and obtain long-term allocation effect evaluation results; The risk prediction submodule analyzes the stability of resource allocation based on the long-term allocation effect evaluation results, predicts the sustainability and potential risks of resource allocation in the next week, and obtains stability prediction results.

10. An energy entity resource sharing economic method, characterized in that: The implementation of the physical resource sharing economic system according to any one of claims 1 to 9 includes the following steps: Collect physical resource usage data in multiple regions and at different times, locate resource flows and usage peaks through geographic locations and time tags, synchronize and organize the data, and generate a dynamic resource map; Based on the dynamic resource map, compare the inflow and outflow of resources in multiple regions, analyze the resource flow direction and periodic changes, and obtain the resource flow trend analysis results; Based on the resource flow trend analysis results, identify the peak periods and shortage areas of resource use, simulate physical resource sharing and allocation plans, capture the impact of differentiated plans on demand response, and generate response strategy optimization plans; Implement the response strategy optimization plan, simulate the sharing and deployment of physical resources, use the simulation results to evaluate the cost-effectiveness of the differentiation strategy, and obtain the resource deployment simulation results; The resource allocation simulation results are used to analyze the implementation frequency and scale of various resource sharing and allocation schemes, and periodic adjustments are made according to differentiated resource types and regional demands. Through long-term tracking and analysis, stability prediction results are generated.

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