An entity resource sharing economy system and its method

Abstract

The present invention relates to the technical field of resource sharing, and specifically to an entity resource sharing economic system and its method. The system includes a resource monitoring module, a resource flow analysis module, a demand response strategy module, a cost assessment module, a periodic analysis module, and a stability assessment module. In the present invention, by collecting and analyzing the usage and flow data of entity resources, the dynamic adaptability and accuracy of resource allocation are effectively improved. Through the data recorded in real time with geographical location and time tags, the periodic changes and flows of resources are analyzed. This dynamic monitoring and analysis not only helps to predict peak resource demands, but also enables the design of more effective sharing and allocation strategies for shortage areas. By simulating various allocation schemes, the response strategy can be optimized, the resource utilization efficiency can be enhanced, and at the same time, the operating cost can be reduced, ensuring the long-term benefits and sustainable development of the resource sharing economic system, improving cooperation and interaction among communities, and supporting environmental sustainability.

Description

Technical Field

[0001] The present invention relates to the technical field of resource sharing, and particularly to an entity resource sharing economic system and a method thereof. Background Art

[0002] The technical field of resource sharing involves the development and utilization of platforms for the effective allocation and use of idle physical and digital resources. It includes various systems and models such as sharing economy platforms, P2P networks, and integrated services, aiming to maximize the utilization efficiency and accessibility of resources. Technically, it mainly involves resource scheduling, data analysis, and user interaction optimization to achieve the efficient circulation of resources among users. The applications of this technology include the sharing of transportation vehicles, accommodation, office space, and supplies, as well as the sharing of digital resources such as storage space and computing power.

[0003] Among them, the entity resource sharing economic system refers to a specific resource sharing model for optimizing the allocation and use of physical assets. Such a system allows individuals or organizations to share physical resources such as tools, vehicles, or equipment through an online platform to manage the borrowing and return of resources. The purpose is to reduce the idle time of resources, improve the resource utilization rate, promote environmental sustainability, help reduce costs, and increase interaction and cooperation among communities.

[0004] The existing resource sharing technologies lack an in-depth understanding of resource flow and periodic demand changes, resulting in the inability of resource allocation to respond to market demand changes in real time, with low efficiency and utilization rate. The lack of comprehensive analysis of real-time data makes resource allocation often rely on empirical judgment, easily leading to problems of resource surplus or shortage, increasing operating costs and reducing user satisfaction. For example, the lack of effective demand forecasting tools leads to insufficient shared cars during peak demand or excessive idleness during low-demand periods. This not only affects the economic benefits of service providers but also reduces users' trust and dependence on the shared resource platform, further hindering the popularization and development of the sharing economy model. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose an entity resource sharing economic system and a method thereof.

[0006] To achieve the above purpose, the present invention adopts the following technical solution. An entity resource sharing economic system includes:

[0007] The resource monitoring module collects the usage and flow data of entity resources using geographical location and time tags, records the entity resource flows in different regions and at different times in real time, and generates a dynamic resource map;

[0008] The resource flow analysis module uses the dynamic resource map to analyze the regional entity resource flow and periodic changes, and obtains the resource flow trend analysis result by comparing the differences in the inflow and outflow of regional resources;

[0009] The demand response strategy module, based on the resource flow trend analysis result, identifies the peak usage and shortage areas of entity resources, simulates the response effects of entity resource sharing and allocation plans on demand, and obtains the optimized response strategy plan;

[0010] The cost evaluation module implements the optimized response strategy plan, conducts entity resource sharing and allocation simulations, evaluates the effects and cost-benefits of different plans, and obtains the resource allocation simulation result;

[0011] The periodic analysis module, through the resource allocation simulation result, determines the optimal frequency and scale of entity resource sharing and allocation, makes adjustments for different resource types, and generates periodic adjustment indicators;

[0012] The stability evaluation module uses the periodic adjustment indicators to analyze the entity resource sharing under different allocation strategies, and obtains the stability prediction result by long-term tracking and evaluating the resource allocation effect and stability.

[0013] As a further solution of the present invention, the dynamic resource map includes a resource usage frequency map, a resource flow velocity map, and a resource distribution hotspot map. The resource flow trend analysis result includes a periodic demand fluctuation map, a regional resource inflow and outflow comparison table, and a time series change map. The optimized response strategy plan includes a peak period resource allocation plan, an emergency response plan for shortage areas, and a long-term resource balance strategy. The resource allocation simulation result includes a cost-benefit ratio analysis chart, a comparison table of expected and real-time effects, and a resource utilization rate calculation result. The periodic adjustment indicators include resource allocation frequency settings, resource type differentiation processing parameters, and optimal scale estimation values. The stability prediction result includes a long-term resource allocation effect chart, a stability score table, and a resource supply and demand balance prediction map.

[0014] As a further solution of the present invention, the resource monitoring module includes:

[0015] The geographic tag collection sub-module uses geographic information system technology to capture and record the locations of entity resources, extracts geographic coordinates from the location data of entity resources, standardizes the geographic information, and integrates the geographic tags of entity resources to generate a geographic information summary;

[0016] The time tag analysis sub-module, based on the geographic information summary, collects the time tags of entity resources, identifies the usage frequency and duration through time series analysis, performs trend and pattern recognition on the time data, and obtains the time pattern analysis result;

[0017] Based on the time pattern analysis result, the resource flow mapping sub-module identifies the flow status of entity resources at different times and locations, and obtains a dynamic resource map by comparing the resource usage differences in different regions and time periods.

[0018] As a further solution of the present invention, the resource flow analysis module includes:

[0019] Based on the dynamic resource map, the resource trend calculation sub-module extracts the inflow and outflow data of entity resources in different regions, identifies the key resource flows by calculating the total flow volume of resources in each region, and constructs a resource flow baseline.

[0020] The seasonal change analysis sub-module performs periodic analysis on the data in the resource flow baseline, detects the seasonal and periodic fluctuations of resource flow, determines the regular changes of resource flow through periodic data, and obtains a periodic change map.

[0021] Using the periodic change map, the regional difference comparison sub-module compares the differences in the inflow and outflow of entity resources between different regions, records the trends and characteristics of the regional entity resource flow through data comparison and analysis, and obtains the resource flow trend analysis result.

[0022] As a further solution of the present invention, the demand response strategy includes:

[0023] Based on the resource flow trend analysis result, the usage analysis sub-module analyzes the usage of entity resources in different regions, identifies the peak and shortage regions of entity resource usage, and generates a resource demand analysis chart.

[0024] According to the resource demand analysis chart, the resource sharing simulation sub-module uses the genetic algorithm to optimize and iterate the sharing and allocation plan of entity resources, and simulates the execution effect of the plan in different scenarios to obtain the resource allocation simulation result.

[0025] Using the resource allocation simulation result, the strategy optimization sub-module evaluates the response effect of different allocation plans on regional resource demands, optimizes the resource usage efficiency and matches the regional demands by circularly adjusting the plan, determines the optimal resource sharing and allocation strategy, and obtains the response strategy optimization plan.

[0026] As a further solution of the present invention, the formula of the genetic algorithm is as follows:

[0027] ;

[0028] where R is the resource allocation efficiency, represents the total demand at the current time t, is the weight coefficient of the demand quantity, represents the supply quantity of resources, is the weight coefficient of the supply quantity, represents the number of nodes participating in resource allocation, is the adjustment coefficient of the number of nodes, represents the constraint violation amount of the current configuration.

[0029] As a further solution of the present invention, the cost evaluation module includes:

[0030] The solution simulation sub-module executes the entity resource sharing and allocation in the response strategy optimization solution, simulates the resource allocation process under different scenarios, evaluates the implementation feasibility of different solutions, identifies potential execution obstacles and allocation efficiency, and generates an implementation simulation result;

[0031] The effect quantification sub-module quantifies and evaluates the real-time effect of resource allocation based on the implementation simulation result, compares the matching degree of different solutions to resource requirements, identifies the advantages and limitations of multiple solutions, and obtains an allocation effect evaluation result;

[0032] The cost-benefit analysis sub-module calculates the cost-benefit ratio of different solutions through the allocation effect evaluation result, evaluates the economy of different solutions, and obtains a resource allocation simulation result.

[0033] As a further solution of the present invention, the periodic analysis module includes:

[0034] The frequency determination sub-module determines the optimal allocation frequency by trend analysis and effect comparison based on the resource allocation simulation result, referring to the effect and continuity of resource sharing and allocation at different time intervals, and generates an optimal frequency index;

[0035] The performance evaluation sub-module uses the optimal frequency index to analyze the resource sharing and allocation effects under different scales, evaluates the performance and feasibility of resource sharing, and obtains an allocation scale evaluation result through comparative analysis;

[0036] The type adjustment sub-module uses the allocation scale evaluation result to adjust the entity resources of different types, customizes allocation strategies for consumables and durable goods, and optimizes the resource utilization efficiency through simulation and adjustment cycles to obtain a periodic adjustment index.

[0037] As a further solution of the present invention, the stability evaluation module includes:

[0038] The differential strategy analysis sub-module analyzes the sharing effect of different entity resource types under the implementation of the allocation strategy based on the periodic adjustment index, evaluates the response speed and efficiency of the strategy, and generates a strategy adaptability analysis result;

[0039] The tracking and evaluation sub-module adopts the above-mentioned strategy adaptability analysis results to conduct long-term tracking of the entity resource configuration, collect continuous data on resource sharing and allocation, detect the continuous effect and change trend of the resource configuration through time series analysis, and obtain the long-term configuration effect evaluation result;

[0040] The risk prediction sub-module analyzes the stability of the resource configuration based on the long-term configuration effect evaluation result, predicts the sustainability and potential risks of the resource configuration within the next week, and obtains the stability prediction result.

[0041] An entity resource sharing economy method, the entity resource sharing economy method is executed based on the above-mentioned entity resource sharing economy system, and includes the following steps:

[0042] S1: Collect entity resource usage data in multiple regions and at different times, locate the peaks of resource flow and usage through geographical location and time tags, synchronize and organize the data, and generate a dynamic resource map;

[0043] 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 result;

[0044] S3: According to the resource flow trend analysis result, identify the peak periods and shortage regions of resource usage, simulate entity resource sharing and allocation plans, capture the impact of different plans on demand response, and generate a response strategy optimization plan;

[0045] S4: Implement the response strategy optimization plan, simulate the sharing and allocation of entity resources, and use the simulation results to evaluate the cost-benefit of different strategies to obtain the resource allocation simulation result;

[0046] S5: Utilize the resource allocation simulation result to analyze the implementation frequency and scale of multiple resource sharing and allocation plans, make periodic adjustments according to different resource types and regional demands, and generate a stability prediction result through long-term tracking analysis.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0048] In the present invention, by collecting and analyzing the usage and flow data of entity resources, the dynamic adaptability and accuracy of resource allocation are effectively improved. The data recorded in real time with geographical location and time tags can generate an accurate resource flow map to analyze the periodic changes and flow directions of resources. Such dynamic monitoring and analysis not only helps to predict peak resource demands, but also enables the design of more effective sharing and allocation strategies for shortage areas. By simulating various allocation scenarios, the response strategies can be optimized, the resource utilization efficiency can be enhanced, and the operating costs can be reduced at the same time. Periodic analysis and stability assessment further ensure the long-term benefits and sustainable development of the resource sharing economy system, improve cooperation and interaction among communities, and support environmental sustainability. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is the system flowchart of the present invention;

[0050] Figure 2 is the schematic diagram of the system framework of the present invention;

[0051] Figure 3 is the flowchart of the resource monitoring module of the present invention;

[0052] Figure 4 is the flowchart of the resource flow direction analysis module of the present invention;

[0053] Figure 5 is the flowchart of the demand response strategy module of the present invention;

[0054] Figure 6 is the flowchart of the cost assessment module of the present invention;

[0055] Figure 7 is the flowchart of the periodic analysis module of the present invention;

[0056] Figure 8 is the flowchart of the stability assessment module of the present invention;

[0057] Figure 9 is the schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0059] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is 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 should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0060] Please refer to Figures 1 to 2 , the present invention provides a technical solution. An entity resource sharing economic system includes:

[0061] The resource monitoring module collects the usage and flow data of entity resources by using geographical location and time tags, records the entity resource flow in different regions and at different times in real time, and generates a dynamic resource map;

[0062] The resource flow analysis module uses the dynamic resource map to analyze the flow direction and periodic changes of entity resources in different regions, and obtains the analysis result of resource flow trend by comparing the inflow and outflow differences of regional resources;

[0063] The demand response strategy module identifies the peak usage and shortage areas of entity resources in different regions according to the analysis result of resource flow trend, simulates the response effect of the demand for the sharing and allocation plan of different entity resources, and obtains the optimized response strategy plan;

[0064] The cost evaluation module implements the optimized response strategy plan, conducts the simulation of entity resource sharing and allocation, evaluates the effect and cost-benefit of different plans, and obtains the resource allocation simulation result;

[0065] The periodic analysis module determines the optimal frequency and scale of entity resource sharing and allocation through the resource allocation simulation result, performs periodic adjustments for different resource types, matches the long-term demand changes, optimizes the economic benefits, and generates periodic adjustment indicators;

[0066] The stability evaluation module uses the periodic adjustment indicators to analyze the entity resource sharing under different allocation strategies, and obtains the stability prediction result by long-term tracking and evaluating the resource allocation effect and stability.

[0067] The dynamic resource graph includes a resource usage frequency graph, a resource flow velocity graph, and a resource distribution hotspot graph. The resource flow trend analysis results include a periodic demand fluctuation graph, a regional resource inflow and outflow comparison table, and a time series change graph. The response strategy optimization plan includes a peak period 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 graph, a comparison table of expected and real-time effects, and a resource utilization rate 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 graph, a stability score table, and a resource supply and demand balance prediction graph.

[0068] Please refer to Figure 2 、 3 , the resource monitoring module includes:

[0069] The geographical tag collection sub-module uses geographic information system technology to capture and record the location of entity resources, extract geographical coordinates from the location data of entity resources, standardize geographical information, and integrate the geographical tags of entity resources. The execution process of generating the geographical information summary is as follows;

[0070] Use geographic information system technology to accurately record the location of entity resources, collect location data through specialized sensing devices, and generate geographical coordinates. The data standardization in the process involves converting geographical information in different formats into a unified format, and using a standardization algorithm to process the coordinate data for more accurate geographical information integration. The acquisition and standardization of geographical coordinates are the basis for realizing geographical information integration. Through the integration of geographical information, accurate geographical tags can be established for entity resources, providing an accurate geographical data basis for subsequent data applications and analysis, and generating a geographical information summary.

[0071] The time tag analysis sub-module collects the time tags of entity resources based on the geographical information summary, identifies the usage frequency and duration through time series analysis, and performs trend and pattern recognition on the time data. The execution process of obtaining the time pattern analysis results is as follows;

[0072] Identify the usage frequency and duration through time series analysis, and calculate the time pattern analysis results according to the formula . In the formula, f(t) represents the time series function, t represents time, and a, b, and c represent model parameters respectively. Detailed explanation of the formula and the derivation process of the formula calculation: There is a set of time series data , where Represents the data at the g-th time point. The parameters a, b, and c are obtained through data fitting, 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. Calculate f(t) for each g, and analyze the time pattern. This analysis result shows that the usage frequency increases exponentially over time.

[0073] Based on the time pattern analysis result, the resource flow mapping sub-module identifies the flow status of entity resources at different times and locations. By comparing the resource usage differences in different regions and time periods, the execution process of obtaining the dynamic resource map is as follows;

[0074] Based on the time pattern analysis result, determine the flow status of entity resources at different times and locations. The process involves collecting and comparing resource usage data in different time periods and regions, deeply analyzing the data, and using data comparison methods such as differential analysis and flow simulation to identify the patterns and trends of resource flow. The resource usage differences in different locations illustrate the non-uniformity of resource distribution. The analysis result is crucial for understanding the dynamic distribution of resources and optimizing resource allocation. In this way, the dynamic resource map is obtained.

[0075] Please refer to Figure 2 、 4 , the resource flow analysis module includes:

[0076] Based on the dynamic resource map, the resource trend calculation sub-module extracts the inflow and outflow data of entity resources in different regions. By calculating the total flow volume of resources in each region, the execution process of identifying the key resource flow directions is as follows;

[0077] The resource trend calculation sub-module calculates the total flow volume of resources in each region according to the formula , and calculates the key resource flow directions. In the formula, Q represents the total resource flow volume of the region, and represent the resource inflow and outflow volumes of the i-th region respectively, and n is the number of regions. Detailed explanation of the formula and the process of formula calculation and derivation: Set the resource inflow volumes of three regions as , , , and the resource outflow volumes are , , . According to the formula calculation, Q = (100 - 80) + (150 - 110) + (120 - 100) = 20 + 40 + 20 = 80. This result shows that the total resource flow volume of the whole region is positive, indicating that the overall resource is in a net inflow state.

[0078] The seasonal change analysis sub-module conducts periodic analysis on the data in the resource flow baseline, detects the seasonal and periodic fluctuations of resource flow, determines the regular changes in resource flow directions through periodic data, and the execution process for obtaining the periodic change map is as follows;

[0079] Use statistical methods to conduct periodic analysis on the resource flow baseline, calculate the statistical characteristics of the periodic fluctuations in resource flow directions, such as mean, standard deviation, and peak value, use time series analysis tools to detect and confirm the periodic fluctuations in resource flow. The results of these periodic analyses help determine the regular changes in resource flow directions, reveal the seasonal patterns of resource flow, are of great significance for formulating future resource allocation and management policies, provide a basis for predicting future resource flow directions, and obtain the periodic change map.

[0080] The regional difference comparison sub-module uses the periodic change map to compare the differences in the inflow and outflow of physical resources between different regions. Through data comparison and analysis, record the trends and characteristics of regional physical resource flow. The execution process for obtaining the resource flow trend analysis results is as follows;

[0081] Use the periodic change map as a basis to compare the resource inflow and outflow data between different regions. Through horizontal and vertical comparison and analysis of the data, comprehensively apply statistical analysis methods, such as variance analysis and correlation analysis, to clarify the characteristics and trends of resource flow in each region. Through comparative analysis, significant differences in resource flow between regions can be observed. These differences reflect the imbalance of regional economic activities. The results of regional difference comparison analysis provide data support for formulating resource management and optimization strategies and construct a resource flow trend map.

[0082] Please refer to Figure 2 、 5 , and the demand response strategies include:

[0083] The usage analysis sub-module, based on the resource flow trend analysis results, analyzes the usage of physical resources in different regions, identifies the peak and shortage regions of physical resource usage, and the execution process for generating a resource demand analysis map is as follows;

[0084] Starting from the resource flow trend analysis results, specifically analyze the usage of physical resources in different regions. During the process, identify the peak and shortage situations of resource usage in each region. Through statistical analysis methods, such as demand forecasting and demand gap analysis, conduct data clustering and classification, and conduct a deeper interpretation and analysis of the data in the peak and shortage regions. These analyses help depict a detailed map of resource usage, show the specific situation of resource utilization in each region, provide a basis for resource reallocation and optimization, provide data support and decision-making basis for the effective management and optimization of physical resources, and obtain a resource demand analysis map.

[0085] Based on the resource demand analysis diagram, the resource sharing simulation sub-module uses the genetic algorithm to optimize and iterate the sharing and allocation plan of entity resources, and simulates the execution effect of the plan under different scenarios. The execution process of obtaining the resource allocation simulation result is as follows;

[0086] The formula of the genetic algorithm is as follows:

[0087] ;

[0088] Among them, R is the resource allocation efficiency, represents the total demand at the current time t, is the weight coefficient of the demand quantity, represents the supply quantity of resources, is the weight coefficient of the supply quantity, represents the number of nodes participating in resource allocation, is the adjustment coefficient of the number of nodes, represents the constraint violation amount of the current configuration.

[0089] Each parameter has the following meanings and acquisition methods:

[0090] represents the total resource demand at time t. It is accumulated from the demand data monitored in real time by the resource management system and is set to 1200 units at a certain moment.

[0091] is the weight coefficient of the demand quantity, set to 1.5, and the value is obtained based on data analysis and is proportional to the importance of the demand quantity.

[0092] represents the resource supply quantity at time t, which is also obtained from the real-time monitoring data of the resource management system, and the current supply quantity is set to 800 units.

[0093] is the weight coefficient of the supply quantity, set to 0.8, and the value is obtained based on the analysis of resource circulation speed and supply stability.

[0094] is the number of nodes participating in resource allocation, which is the number of active nodes directly queried from the network resource database, and is set to 25 currently.

[0095] is the adjustment coefficient of the number of nodes, set to 0.3, and the coefficient is adjusted based on the statistical analysis of network scale and configuration efficiency.

[0096] 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 (a negative value indicates a violation situation).

[0097] Substitute the actual values into the formula for calculation to obtain:

[0098]

[0099] The results show that in the current resource allocation model, the allocation efficiency is 205.04. The value reflects the performance of the resource allocation plan optimized by the genetic algorithm under the given resource supply, demand, node participation, and violation conditions. The performance indicators provide a numerical basis for resource management decisions and demonstrate the effectiveness of the optimization algorithm in actual operations. Through further analysis of the values, the resource allocation strategy can be adjusted in a timely manner to cope with dynamic demand and supply situations.

[0100] The strategy optimization sub-module uses the resource allocation simulation results to evaluate the response effect of different allocation plans on regional resource demands, optimizes the resource utilization efficiency and matches the regional demands by cyclically adjusting the plans, determines the optimal resource sharing and allocation strategy, and the execution process of obtaining the response strategy optimization plan is as follows;

[0101] Optimize the resource utilization efficiency by cyclically adjusting the plan, according to the formula , determine the optimal resource sharing and allocation strategy. In the formula, E represents the resource utilization efficiency, represents the resource usage amount of the i-th region, represents the resource demand amount of the i-th region, and n is the number of regions. Explanation of the formula and the derivation process of the formula calculation: Set the resource usage amounts of three regions as , , , and the resource demand amounts are , , . Calculate according to the formula . This efficiency value indicates that the matching degree of resource usage is close to optimization. By further adjusting the resource allocation plan, the resource utilization efficiency can be further improved to ensure more accurate matching of resource supply and demand. The response strategy optimization plan ensures the effective allocation and utilization of resources in each region.

[0102] Please refer to Figure 2 , 6 , the cost assessment module includes:

[0103] The plan simulation sub-module executes the entity resource sharing and allocation in the response strategy optimization plan, simulates the resource allocation process under different scenarios, evaluates the implementation feasibility of different plans, identifies potential implementation obstacles and allocation efficiency, and the execution process of generating the implementation simulation results is as follows;

[0104] Responsible for implementing the sharing and allocation of entity resources in the response strategy optimization plan. During the process, simulate the resource allocation in different scenarios, dynamically display the flow process of resources from one area to another through the use of a simulation system, evaluate the implementation feasibility of the plan, which involves the allocation efficiency of resources and the obstacles encountered during the execution process, such as logistics delays or resource matching problems. In this way, reflect the operation details and potential problems of the plan in different scenarios, provide actual operation data and predictive analysis for the optimization of the allocation strategy, show the performance of different resource allocation plans in actual operation and potential execution difficulties, and generate implementation simulation results.

[0105] Based on the implementation simulation results, the effect quantification sub-module quantifies and evaluates the real-time effect of resource allocation, compares the matching degree of different schemes to resource requirements, identifies the advantages and limitations of multiple schemes, and the execution process of obtaining the allocation effect evaluation results is as follows;

[0106] By comparing the matching degree of different schemes to resource requirements, according to the formula , identify the advantages and limitations of multiple schemes. In the formula, M represents the matching degree, represents the amount of resources that meet the requirements of the z-th scheme, represents the total amount of resources of the z-th scheme, is the number of schemes. Detailed explanation of the formula and the derivation process of formula calculation: Set the amounts of resources that meet the requirements in three schemes as , , , the total amounts of resources are , , . Calculate according to the formula . This matching degree indicates that the scheme generally meets the requirements well, but there is still room for improvement. The allocation effect evaluation results provide a basis for scheme improvement and selection, ensuring that resource utilization reaches the optimal effect.

[0107] Through the allocation effect evaluation results, the cost-benefit analysis sub-module calculates the cost-benefit ratio of different schemes, evaluates the economy of different schemes, and the execution process of obtaining the resource allocation simulation results is as follows;

[0108] Use the allocation effect evaluation results to calculate the cost-benefit ratio. The process evaluates the economy of different schemes by analyzing in detail the economic data and resource usage data of each scheme, adopting cost-benefit analysis methods such as internal rate of return or net present value method, examines the ratio relationship between input and output, evaluates the economic feasibility of the scheme, provides an economic evaluation for decision-makers to implement different resource allocation schemes, and enables the resource allocation decision to be optimized and adjusted based on economic benefits, obtaining the resource allocation simulation results.

[0109] Please refer to Figure 2 and 7 . The periodic analysis module includes:

[0110] Based on the resource allocation simulation results, the frequency determination sub-module determines the optimal allocation frequency by trend analysis and effect comparison with reference to the effects and continuity of resource sharing and allocation under different time intervals, and the execution process of generating the optimal frequency index is as follows;

[0111] Determine the optimal allocation frequency by trend analysis and effect comparison, and generate the optimal frequency index according to the formula . In the formula, F represents the optimal frequency, represents the change in effect under the o-th time interval, represents the number of time intervals for analysis. Explanation of the formula and the derivation process of the formula calculation: Set the change in effect under four time intervals as , , , . Calculate according to the formula . The calculation shows that the allocation frequency should be adjusted to increase the effect by 0.15 per unit time interval to ensure the continuity and maximization of resource allocation. The optimal frequency index ensures the effective allocation and use of resources under different time intervals.

[0112] The efficiency evaluation sub-module uses the optimal frequency index to analyze the resource sharing and allocation effects under different scales, evaluate the efficiency and feasibility of resource sharing, and the execution process of obtaining the allocation scale evaluation result through comparative analysis is as follows;

[0113] Use the optimal frequency index to analyze the resource sharing and allocation effects under different scales. During the process, evaluate the efficiency and feasibility of resource sharing through statistical analysis and efficiency comparison. Use data analysis techniques such as variance analysis and correlation coefficient calculation. By comparing the resource allocation results under different scales, reflect the actual efficiency of resource sharing and allocation under different scales, provide a quantitative basis for the optimization and adjustment of resource allocation strategies, help identify the advantages and limitations of resource sharing under specific scales, provide strategic suggestions for resource management, and obtain the allocation scale evaluation result.

[0114] The type adjustment sub-module uses the allocation scale evaluation result to adjust the entity resources of different types, customize the allocation strategies for consumables and durable goods, and optimize the resource utilization efficiency through simulation and adjustment cycles. The execution process of obtaining the periodic adjustment index is as follows;

[0115] Adjust different types of entity resources using the deployment scale evaluation results, including customizing different deployment strategies for consumables and durable goods, optimizing resource utilization efficiency through data simulation and strategy adjustment cycles, conducting resource allocation simulations, comparing the usage efficiency and consumption of different types of resources, and adjusting resource deployment strategies based on these simulation results, providing a basis for adjusting the continuous and effective use of resources. Through continuous optimization cycles, ensure that the adjustment of resource types is more in line with actual needs, improve the overall resource utilization efficiency, and obtain periodic adjustment indicators.

[0116] Please refer to Figure 2 、 8 , the stability evaluation module includes:

[0117] The differential strategy analysis sub-module analyzes the sharing effect of different types of entity resources 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 result as follows;

[0118] Based on the periodic adjustment indicators, analyze the sharing effect of different types of entity resources under the implementation of the deployment strategy. The process involves the evaluation of the response speed and efficiency of the strategy. Through implementation simulation and effect tracking, using data analysis techniques such as regression analysis and efficiency testing, compare the effects of deployment strategies for different resource types, reveal the adaptability and effectiveness of the strategy for various types of resources, provide an improvement direction and decision support for resource sharing and deployment, show the response and adaptation of the strategy in actual operation, provide a basis for further optimization, and generate the strategy adaptability analysis result.

[0119] The tracking evaluation sub-module uses the strategy adaptability analysis result to conduct long-term tracking of entity resource allocation, collect continuous data on resource sharing and deployment, detect the continuous effect and change trend of resource allocation through time series analysis, and obtain the execution process of the long-term allocation effect evaluation result as follows;

[0120] Detect the continuous effect of resource allocation through time series analysis. According to the formula , obtain the long-term allocation effect evaluation result. In the formula, Z(t) represents the resource allocation effect at time t, Z(t - 1) represents the resource allocation effect at the previous moment, X(t) represents the new input data at time t, is the smoothing coefficient, and α is the weight coefficient of the demand. Formula details and formula calculation derivation process: Set , if the resource allocation effect at time t - 1 is Z(t - 1) = 50 and new input data is received at time t, the calculation according to the formula gives . The results indicate that the effectiveness of resource allocation is continuously improving, demonstrating the stability and adaptability of resource allocation in long-term tracking. The long-term allocation effectiveness evaluation results provide a basis for continuous monitoring and adjustment of resource allocation strategies.

[0121] Based on the long-term allocation effectiveness evaluation results, the risk prediction sub-module analyzes the stability of resource allocation, predicts the sustainability and potential risks of resource allocation within the next week, and the execution process for obtaining the stability prediction results is as follows;

[0122] Use the long-term allocation effectiveness evaluation results to conduct a stability analysis of resource allocation. During the process, predict the sustainability and potential risks of resource allocation within the next week. By using risk analysis methods and prediction models, such as probability models and risk assessment matrices, quantify and evaluate the potential risks of resource allocation, provide forward-looking risk information and early warnings for resource management, help decision-makers formulate preventive measures and optimization strategies, demonstrate the risk prediction and sustainability assessment of resource allocation in the future, and provide a scientific basis for the continuous optimization of resource allocation and risk management, thus obtaining the stability prediction results.

[0123] Please refer to Figure 9 , a method for the sharing economy of tangible resources. The method for the sharing economy of tangible resources is based on the above-mentioned system for the sharing economy of tangible resources and includes the following steps:

[0124] S1: Collect the usage data of tangible resources in multiple regions and at different times, locate the peaks of resource flow and usage through geographical location and time tags, synchronize and organize the data, and generate a dynamic resource map;

[0125] 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;

[0126] S3: According to the resource flow trend analysis results, identify the peak periods and shortage regions of resource usage, simulate the sharing and allocation plans of tangible resources, capture the impact of different plans on demand response, and generate an optimized response strategy plan;

[0127] S4: Implement the optimized response strategy plan, simulate the sharing and allocation of tangible resources, and use the simulation results to evaluate the cost-effectiveness of different strategies to obtain the resource allocation simulation results;

[0128] S5: Use the resource allocation simulation results to analyze the implementation frequency and scale of various resource sharing and allocation plans, make periodic adjustments according to different resource types and regional demands, and generate stability prediction results through long-term tracking analysis.

[0129] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention in any other form. Any person skilled in the relevant art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An entity resource sharing economy system, characterized in that The system includes: The resource monitoring module collects the usage and flow data of entity resources using geographical location and time tags, records the entity resource flow in different regions and times in real time, and generates a dynamic resource map; The resource flow analysis module uses the dynamic resource map to analyze the flow direction and periodic changes of entity resources in the region, and obtains the resource flow trend analysis result by comparing the inflow and outflow differences of regional resources; The demand response strategy module identifies the peak usage and shortage areas of entity resources according to the resource flow trend analysis result, simulates the response effect of the entity resource sharing and allocation plan on demand, and obtains the response strategy optimization plan; The cost evaluation module implements the response strategy optimization plan, conducts entity resource sharing and allocation simulation, evaluates the effects and cost-benefits of different plans, and obtains the resource allocation simulation result; The periodic analysis module determines the optimal frequency and scale of entity resource sharing and allocation through the resource allocation simulation result, adjusts according to different resource types, and generates periodic adjustment indicators; The stability evaluation module uses the periodic adjustment indicators to analyze the entity resource sharing under different allocation strategies, and obtains the stability prediction result by long-term tracking and evaluating the resource allocation effect and stability; 2. The entity resource sharing economy system according to claim 1, wherein The dynamic resource map includes a resource usage frequency map, a resource flow speed map, and a resource distribution hot spot map. The resource flow trend analysis result includes 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 period resource allocation plan, an emergency response plan for shortage areas, and a long-term resource balance strategy. The resource allocation simulation result includes a cost-benefit ratio analysis map, a comparison table of expected and real-time effects, and a resource utilization rate calculation result. The periodic adjustment indicators include resource allocation frequency settings, resource type differentiation processing parameters, and optimal scale estimation values. The stability prediction result includes a long-term resource allocation effect map, a stability score table, and a resource supply and demand balance prediction map.

3. The entity resource sharing economy system according to claim 1, characterized in that The resource monitoring module includes: The geographical tag collection sub-module uses geographic information system technology to capture and record the location of entity resources, extracts geographical coordinates from the location data of entity resources, standardizes geographical information, and integrates the geographical tags of entity resources to generate a geographical information summary; The time tag analysis sub-module collects the time tags of entity resources based on the geographical information summary, identifies the usage frequency and duration through time series analysis, and performs trend and pattern recognition on time data to obtain the time pattern analysis result; The resource flow mapping sub-module identifies the flow status of entity resources at different times and locations according to the time pattern analysis result, and obtains a dynamic resource map by comparing the resource usage differences in different regions and time periods; 4. The entity resource sharing economy system according to claim 1, wherein The resource flow analysis module includes: The resource trend calculation sub-module extracts the inflow and outflow data of entity resources in different regions based on the dynamic resource map, identifies the key resource flow directions by calculating the total flow of resources in each region, and constructs a resource flow baseline; The seasonal change analysis sub-module performs periodic analysis on the data in the resource flow baseline, detects the seasonal and periodic fluctuations of resource flow, determines the regular changes in resource flow direction through periodic data, and obtains a periodic change map; The regional difference comparison sub-module uses the periodic change map to compare the differences in the inflow and outflow of physical resources between different regions. Through data comparison and analysis, it records the trends and characteristics of regional physical resource flow and obtains the resource flow trend analysis result.

5. The entity resource sharing economy system according to claim 1, characterized in that The demand response strategy module includes: The usage analysis sub-module, based on the resource flow trend analysis result, analyzes the usage of physical resources in different regions, identifies the peak and shortage regions of physical resource usage, and generates a resource demand analysis map; The resource sharing simulation sub-module, according to the resource demand analysis map, uses the genetic algorithm to optimize and iterate the sharing and allocation plan of physical resources, and simulates the execution effect of the plan in different scenarios to obtain the resource allocation simulation result; The strategy optimization sub-module uses the resource allocation simulation result to evaluate the response effect of different allocation plans on regional resource demand, optimizes the resource usage efficiency and matches the regional demand by circularly adjusting the plan, determines the optimal resource sharing and allocation strategy, and obtains the response strategy optimization plan.

6. The entity resource sharing economy system according to claim 5, wherein 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 the demand quantity, represents the supply quantity of the resource, is the weight coefficient of the supply quantity, represents the number of nodes participating in resource allocation, is the adjustment coefficient of the number of nodes, represents the constraint violation amount of the current allocation.

7. The entity resource sharing economy system according to claim 1, characterized in that, The cost evaluation module includes: The plan simulation sub-module executes the sharing and allocation of physical resources in the response strategy optimization plan, simulates the resource allocation process in different scenarios, evaluates the implementation feasibility of different plans, identifies potential implementation obstacles and allocation efficiency, and generates an implementation simulation result; The effect quantification sub-module, based on the implementation simulation result, quantifies and evaluates the real-time effect of resource allocation, compares the matching degree of different plans to resource demand, and identifies the advantages and limitations of multiple plans to obtain the allocation effect evaluation result; The cost-benefit analysis sub-module calculates the cost-benefit ratio of different plans through the allocation effect evaluation result, evaluates the economy of different plans, and obtains the resource allocation simulation result.

8. The entity resource sharing economy system according to claim 1, wherein The periodic analysis module includes: The frequency determination sub-module, based on the resource allocation simulation result, refers to the effect and continuity of resource sharing and allocation at different time intervals, determines the optimal allocation frequency through trend analysis and effect comparison, and generates an optimal frequency index; The efficiency evaluation sub-module uses the optimal frequency index to analyze the resource sharing and allocation effect at different scales, evaluates the efficiency and feasibility of resource sharing, and obtains the allocation scale evaluation result through comparative analysis; The type adjustment sub-module uses the allocation scale evaluation result to adjust physical resources of different types, formulates allocation strategies for consumables and durable goods, and optimizes the resource utilization efficiency through simulation and adjustment cycles to obtain a periodic adjustment index.

9. The entity resource sharing economy system according to claim 1, wherein The stability evaluation module includes: The differential strategy analysis sub-module, based on the periodic adjustment index, analyzes the sharing effect of different types of physical resources under the implementation of the allocation strategy, evaluates the response speed and efficiency of the strategy, and generates a strategy adaptability analysis result; The tracking and evaluation sub-module adopts the results of the policy adaptability analysis to conduct long-term tracking of the entity resource configuration, collect continuous data on resource sharing and allocation, detect the continuous effect and change trend of the resource configuration through time series analysis, and obtain the long-term configuration effect evaluation results; The risk prediction sub-module analyzes the stability of the resource configuration according to the long-term configuration effect evaluation results, predicts the sustainability and potential risks of the resource configuration within the next week, and obtains the stability prediction results.

10. An entity resource sharing economy method, characterized in that, Execute according to the entity resource sharing economy system described in any one of claims 1-9, including the following steps: Collect entity resource usage data in multiple regions and at different times, locate the peaks of resource flow and usage through geographical location 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; According to the resource flow trend analysis results, identify the peak periods and shortage regions of resource usage, simulate entity resource sharing and allocation plans, capture the impact of different plans on demand response, and generate an optimized response strategy plan; Implement the optimized response strategy plan, simulate the sharing and allocation of entity resources, and use the simulation results to evaluate the cost-benefit of different strategies to obtain the resource allocation simulation results; Utilize the resource allocation simulation results, analyze the implementation frequency and scale of multiple resource sharing and allocation plans, make periodic adjustments according to different resource types and regional demands, and generate stability prediction results through long-term tracking analysis.

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