Future community construction method and system based on government big data

Abstract

The invention relates to the technical field of smart communities, in particular to a future community construction method and system based on government big data, and the method comprises the following steps: collecting power use data, water resource consumption data and public transport utilization rate data in a community, synchronizing the data, and carrying out the standardization processing, and generating a community resource use data set. According to the method, through a time sequence analysis method, the resource demand of a community in a future week can be effectively predicted, a resource flow simulation model is established, prediction data is input in real time, model parameters are adjusted, influences of policies are continuously simulated and evaluated, optimal allocation of community resources is achieved, and the community resource allocation efficiency is improved by constructing a resource allocation diagram and determining an optimal path. The method achieves the high efficiency and fairness of resource distribution, continuously tracks the distribution effect and resident feedback, further guarantees the adaptability and continuity of resource distribution, not only optimizes the community resource management, but also improves the welfare of residents, and enhances the sustainable development of communities.

Description

Technical Field

[0001] The present invention relates to the technical field of smart communities, and in particular to a future community construction method and system based on government big data. Background Art

[0002] The field of smart community technology mainly involves the use of modern information technology and big data analysis to enhance the services and management of urban communities, including the optimization of energy management, traffic flow, security monitoring and resident services through Internet of Things (IoT) devices, cloud computing, artificial intelligence (AI) and big data analysis. The development trend of future communities is mainly focused on creating a more intelligent and sustainable environment. The development direction of future communities is smart communities, which use high technology and data integration to improve the quality of life of residents while promoting the sustainable development of the environment. In smart communities, data collection and analysis are crucial to predicting and solving urban problems, which can improve the quality of life and efficiency of residents. It also supports remote monitoring and management of community resources, making community management more transparent and efficient.

[0003] Among them, the future community construction method of government big data refers to the use of large amounts of data resources collected by the government to plan and realize the intelligent upgrading and construction of the community. The purpose is to optimize urban planning, infrastructure layout and social services through data-driven decision-making processes, enhance the sustainability of the community and the well-being of residents. By analyzing residents' behavior patterns, resource consumption and service needs, the government and developers can more accurately predict future needs and provide targeted services and resources to ensure the long-term healthy development of the community.

[0004] Existing smart community technologies have significant deficiencies in actual operation, especially in terms of the accuracy and timeliness of resource flow and demand forecasting. Although modern information technology and big data are used, dynamic monitoring and immediate adjustment of resource use have not been fully realized. The lack of effective forecasting tools and comprehensive data sets has resulted in resource allocation that is not responsive enough to accurately match the actual needs of residents. For example, during peak power hours or when water resources are scarce, existing technologies make it difficult to adjust resource supply in real time, resulting in resource waste or shortages. These deficiencies not only affect the daily quality of life of residents, but also exacerbate inequalities within the community, posing a threat to the long-term healthy development of the community. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a future community construction method and system based on government big data.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution, a method for building a future community based on government big data, comprising the following steps:

[0007] S1: Collect electricity usage data, water consumption data, and public transportation utilization data within the community, synchronize and standardize the data, and generate a community resource usage data set;

[0008] S2: Based on the community resource usage dataset, use time series analysis of government big data to predict and calculate the predicted values ​​of the community's electricity, water resources and public transportation demand within a week to obtain resource demand forecast results;

[0009] S3: using the resource demand forecast results, constructing a resource flow simulation model, inputting forecast data in real time and adjusting model parameters, evaluating the impact of differentiated policies through continuous simulation, and generating resource management policy simulation results;

[0010] S4: Through the simulation results of the resource management policy, the location data and service needs of community residents are collected, a resource allocation map is constructed, the route for community resources to reach the demand point is determined, and the optimal resource allocation path result is obtained;

[0011] S5: Based on the optimal resource allocation path result, priority sorting and demand comparison are performed, the flow of community resources is adjusted, the resource allocation effect and residents' feedback are continuously tracked, and the resource allocation fairness analysis result is generated;

[0012] S6: Based on the results of the resource allocation fairness analysis, formulate a community resource management strategy, verify the sustainability of community services based on the service needs of community residents, and obtain community construction indicators.

[0013] As a further solution of the present invention, the community resource usage data set includes hourly records of electricity usage for each household, daily water consumption and statistics on the number of passengers on each public transportation trip; the resource demand forecast results include peak, trough and average electricity demand for each day in the next week, total water consumption per day, and peak and non-peak passenger forecast data for public transportation; the resource management policy simulation results include resource consumption change data under differentiated policy scenarios, water restriction measures, peak electricity usage period restrictions and public transportation preferential measures effect simulation results; the optimal resource allocation path results include a power supply priority roadmap, key water distribution points and public transportation adjustment route plans; the resource allocation fairness analysis results include resident satisfaction survey analysis, timeliness of resource allocation, coverage data and resource reallocation plans reflecting fairness; the community resource management strategy includes resource priority allocation rules, emergency resource response measures and long-term resource guarantee plans.

[0014] As a further solution of the present invention, the steps of collecting power usage data, water resource consumption data and public transportation utilization rate data in the community, synchronizing and standardizing the data, and generating a community resource usage data set are as follows:

[0015] S101: Collect electricity usage data, water consumption data, and public transportation utilization data in the community, aggregate the data into a unified data table, including timestamps and data source identifiers, and obtain an initialization data set;

[0016] S102: Based on the initialized data set, remove records with timestamp errors and uncertain data sources, interpolate missing data points, convert numerical data into floating-point format, and generate a processed data set;

[0017] S103: According to the processed data set, a standardization method is applied to the resource data, the value is scaled to between 0 and 1, and the verification data is compared on a similar scale to obtain a community resource usage data set.

[0018] As a further solution of the present invention, based on the community resource usage data set, time series analysis of government big data is used to predict and calculate the predicted values ​​of the community's electricity, water resources and public transportation demand within a week, and the steps of obtaining the resource demand prediction results are specifically as follows:

[0019] S201: Based on the community resource usage data set, select the usage data of community electricity, water resources and public transportation, serialize the usage data of each resource, and arrange them in date and time order to obtain serialized resource data;

[0020] S202: using the serialized resource data, applying a sliding window technique for each resource, calculating the average daily usage over the past seven days, assigning a time window to each data point, and obtaining windowed resource data;

[0021] S203: Based on the windowed resource data, a linear regression model is used to predict the community's electricity, water resources and public transportation needs for a week, and daily demand forecast values ​​are calculated based on the output of the regression model to obtain resource demand forecast results.

[0022] As a further solution of the present invention, the resource demand forecast results are used to construct a resource flow simulation model, the forecast data is input in real time and the model parameters are adjusted, and the impact of the differentiated policy is evaluated through continuous simulation to generate the resource management policy simulation results. Specifically, the steps are as follows:

[0023] S301: using the resource demand forecast results, constructing a resource flow simulation model for electricity, water resources and public transportation, initializing model parameters, including resource output, consumption rate and distribution efficiency, and obtaining an initialized model;

[0024] S302: Inputting the resource demand forecast results into the initialization model in real time, dynamically adjusting the model parameters according to the daily forecast demand, including adjusting the community's power grid load, water supply and bus operation frequency, performing simulation operation, and obtaining a parameter adjustment model;

[0025] S303: Through the parameter adjustment model, through continuous simulation, evaluate the differentiated policies, including the impact of demand management and pricing strategy changes on resource flow, record the resource allocation efficiency and consumption response during the simulation process, and obtain the resource management policy simulation results.

[0026] As a further solution of the present invention, the steps of collecting the location data and service needs of community residents through the resource management policy simulation results, constructing a resource allocation map, determining the route for community resources to reach the demand point, and obtaining the optimal resource allocation path result are specifically as follows:

[0027] S401: Based on the resource management policy simulation results, collect location data of community residents, including home addresses, workplaces, and geographic coordinates of service facilities, and combine with residents' service demand records to obtain a resident demand data set;

[0028] S402: Using the resident demand dataset and a geographic information system, construct a resource allocation map, mark the locations of demand points and resource supply centers, analyze resource flows and consumption hotspots, and obtain a resource flow map;

[0029] S403: Determine the optimal distribution path for electricity, water resources and public transportation based on the resource flow diagram, dynamically adjust the route to verify the speed and efficiency of resources reaching the demand point with reference to route congestion and construction factors, and obtain the optimal resource allocation path result.

[0030] As a further solution of the present invention, based on the optimal resource allocation path result, priority sorting and demand comparison are performed, the flow of community resources is adjusted, and the resource allocation effect and resident feedback are continuously tracked to generate the resource allocation fairness analysis result. Specifically, the steps are as follows:

[0031] S501: Based on the optimal resource allocation path result, priority sorting of resource demand points is performed, referring to the priority demands of emergency medical facilities, schools and key public facilities, combined with consumption data and residents' feedback, to obtain a priority sorting result;

[0032] S502: According to the priority sorting result, a linear programming algorithm is used to reconfigure the flow of community electricity, water resources and public transportation, calculate the optimal solution for resource configuration, and obtain the optimal resource flow value;

[0033] S503: Through the optimal resource flow value, continuously track the allocation effect of the adjusted resources, collect residents' feedback on the fairness and efficiency of resource allocation, conduct data analysis and evaluation, regularly update the resource allocation strategy, and obtain the resource allocation fairness analysis results.

[0034] As a further solution of the present invention, the formula of the linear programming algorithm is as follows:

[0035]

[0036] Among them, f(x) is the utility function of resource allocation, x1 represents the demand for electricity resources, x2 represents the demand for water resources, c1 represents the weight coefficient of electricity resources, c2 represents the weight coefficient of water resources, and a1 represents the adjustment coefficient of public transportation.

[0037] As a further solution of the present invention, according to the resource allocation fairness analysis results, a community resource management strategy is formulated, and according to the service needs of community residents, the sustainability of community services is verified, and the steps of obtaining community construction indicators are specifically as follows:

[0038] S601: Based on the resource allocation fairness analysis result, evaluate the satisfaction degree of service requirements and the fairness of resource allocation, identify service coverage areas and resource waste hot spots, and obtain an initialization community resource management strategy;

[0039] S602: According to the initialization community resource management strategy, adjust resource allocation, optimize the resource supply of medical, educational, sports, management and commercial facilities, avoid waste of resources in non-priority areas, implement adjustments and monitor the effects in the short term, and obtain adjusted resource allocation;

[0040] S603: Continuously monitor the adjusted resource allocation, combine the feedback from community residents, verify the impact of resource allocation on the sustainability of community services, determine whether long-term development goals are met through data analysis, and obtain community construction indicators.

[0041] A future community construction system based on government big data, the future community construction system based on government big data is used to execute the above-mentioned future community construction method based on government big data, the system includes:

[0042] The data collection and standardization module collects data on electricity usage, water consumption, and public transportation utilization from the community, synchronizes the data, and performs standardization processing to generate a community resource data set;

[0043] The demand analysis module uses the community resource dataset to perform time series analysis on future demand for electricity, water resources and public transportation to obtain resource demand forecast results;

[0044] The resource flow simulation module constructs a resource flow model according to the resource demand forecast results, continuously inputs forecast data, adjusts model parameters, and generates policy simulation results by simulating and evaluating the impact of differentiated policies on resource management;

[0045] The resource optimization module uses the policy simulation results to collect resident location and service demand data, construct a resource allocation map, determine the optimal route for resources to reach the demand point, and obtain the optimal resource allocation path result;

[0046] Based on the optimal resource allocation path results, the resource management strategy module prioritizes resource flows and compares demand, tracks resource allocation effects and resident feedback, and develops targeted community resource management strategies to obtain community construction indicators.

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

[0048] In the present invention, the transparency and efficiency of resource management are significantly enhanced by fine-tuning the analysis and adjustment of resource flows and allocations within the community. The use data of electricity, water resources and public transportation are collected and synchronized, and standardized, which not only provides an accurate basis for resource demand forecasting, but also can effectively predict the resource demand of the community within the next week through time series analysis. The accuracy of this prediction allows the establishment of a resource flow simulation model, real-time input of forecast data and adjustment of model parameters, continuous simulation and evaluation of the impact of policies, and the optimal allocation of community resources. By constructing a resource allocation map and determining the optimal path, the efficiency and fairness of resource allocation are achieved, and the continuous tracking of allocation effects and residents' feedback further ensures the adaptability and sustainability of resource allocation, which not only optimizes community resource management, but also improves the well-being of residents and enhances the sustainable development of the community. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] 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.

[0058] 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.

[0059] See also Figure 1 The present invention provides a technical solution, a method for building a future community based on government big data, comprising the following steps:

[0060] S1: Collect electricity usage data, water consumption data, and public transportation utilization data within the community, synchronize and standardize the data, and generate a community resource usage data set;

[0061] S2: Based on the community resource usage dataset, time series analysis of government big data is used to predict future demand, and the predicted values ​​of electricity, water resources and public transportation demand in the next week are calculated to obtain resource demand forecast results;

[0062] S3: Use resource demand forecast results to build a resource flow simulation model, input forecast data in real time and adjust model parameters to match real-time consumption patterns, evaluate the impact of differentiated policies through continuous simulation, and generate resource management policy simulation results;

[0063] S4: Through the simulation results of resource management policies, collect the location data and service needs of community residents, build a demand-based resource allocation map, determine the shortest route for resources to reach each demand point, and obtain the optimal resource allocation path results;

[0064] S5: Based on the results of the optimal resource allocation path, prioritize and compare the demand, adjust the flow of community resources, continuously track the resource allocation effect and residents' feedback, and generate resource allocation fairness analysis results;

[0065] S6: Based on the results of resource allocation fairness analysis, formulate community resource management strategies, integrate resource allocation priorities with the service needs of community residents, verify the sustainability of community services, and obtain community construction indicators.

[0066] The community resource usage dataset includes hourly records of electricity usage for each household, daily water consumption, and statistics on the number of passengers on each public transportation trip. The resource demand forecast results include the peak, trough, and average electricity demand for each day in the next week, the total water consumption per day, and the predicted number of passengers during peak and non-peak periods of public transportation. The resource management policy simulation results include resource consumption change data under differentiated policy scenarios, water restriction measures, peak electricity usage restrictions, and public transportation preferential measures. The results of the optimal resource allocation path include the priority roadmap for electricity supply, key water distribution points, and adjusted route plans for public transportation. The results of the resource allocation fairness analysis include resident satisfaction survey analysis, the timeliness and coverage data of resource allocation, and resource reallocation plans that reflect fairness. The community resource management strategy includes resource priority allocation rules, emergency resource response measures, and long-term resource guarantee plans.

[0067] See also Figure 2 , collect electricity usage data, water consumption data and public transportation utilization data in the community, synchronize and standardize the data, and generate the community resource usage data set in the following steps:

[0068] S101: Collect electricity usage data, water resource consumption data and public transportation utilization data in the community, aggregate the data into a unified data table, including timestamps and data source identifiers, and obtain the execution process of the initialization data set as follows;

[0069] Collect data on electricity usage, water consumption, and public transportation utilization within the community, including Regional bus and For community bus services, each data source is classified and sorted according to the statistical information of different areas and time periods in the community, and the collection time and validity of the source of each data point are distinguished. The data is initially screened, such as the peak time period of electricity usage, the difference in water consumption in the dry season and the rainy season, and the number of passengers during peak and non-peak bus periods. Each data point is timestamped and identified by the source to ensure the traceability and transparency of the data, and aggregated into a unified data table to form an initialized data set.

[0070] S102: Based on the initialization data set, remove the records with timestamp errors and uncertain data sources, interpolate the missing data points, convert the numerical data into floating point format, and generate the processed data set. The execution flow is as follows;

[0071] Interpolate the missing data points according to the formula Calculate the interpolated data points. In the formula, x prev represents the previous known data point, x next represents the next known data point, n represents the number of missing data points between the two known data points, and i represents the position index of the currently calculated missing point.

[0072] Formula explanation and formula calculation derivation process: Assume that the values ​​of the time series data at time points t1 and t3 are 100 and 130 respectively, but there is missing data at t2. At this time, x prev =100,x next =130, n=1, i=1. According to the formula, the data point at time t2 should be calculated as This interpolation method ensures the continuity and smoothness of the data, which facilitates subsequent analysis and processing.

[0073] S103: Based on the processed data set, a standardization method is applied to the resource data, the values ​​are scaled to between 0 and 1, and the verification data is compared on the same scale to obtain the community resource usage data set. The execution process is as follows;

[0074] Based on the processed data set, the community's electricity usage, water consumption, public transportation utilization and other data are standardized, and all data are converted to a ratio between 0 and 1 through a formula. By calculating the position of each data point relative to the maximum and minimum values, the data can be fairly compared between different dimensions. This not only eliminates the impact of data units and magnitudes, but also facilitates subsequent data analysis and resource allocation optimization, ensuring the comparability of data on the same scale, and obtaining a community resource usage data set.

[0075] See also Figure 3 Based on the community resource usage dataset, the time series analysis of government big data is used to predict and calculate the predicted values ​​of the community's electricity, water resources and public transportation demand within a week. The specific steps to obtain the resource demand prediction results are as follows:

[0076] S201: Based on the community resource usage data set, select the usage data of community electricity, water resources and public transportation, serialize the usage data of each resource, and arrange them in date and time order. The execution process of obtaining serialized resource data is as follows;

[0077] Based on the information in the community resource usage dataset, the serialization of the dataset involves cleaning and integrating the electricity, water resources and public transportation usage data in the community database, identifying and repairing missing or outliers in the data, and arranging them in order of date and time to ensure data integrity and sequence consistency. This is achieved through advanced data processing scripts. The scripts are written in Python or SQL, and the functions involved include data sorting, formatting, and timestamp conversion to obtain serialized resource data.

[0078] S202: Using serialized resource data, applying sliding window technology for each resource, calculating the average daily usage over the past seven days, assigning a time window to each data point, and obtaining the execution flow of windowed resource data is as follows;

[0079] Apply the sliding window technique to calculate the average daily usage over the past seven days, according to the formula Calculate the windowed resource data for each data point. Where, Represents the average daily usage in the window, n a Represents the window size, x a Represents the resource usage on day a in the window.

[0080] Detailed explanation of the formula and the process of formula calculation: Consider a specific example, setting the window size n a = 7 days, assuming that the daily electricity usage in the past week is 10, 12, 13, 11, 10, 14, and 9 kWh respectively, then the average daily electricity usage is calculated as: The results show that the average daily electricity usage in the past week was about 11.29 kWh, providing a basis for predicting future electricity demand.

[0082] S203: Based on the windowed resource data, a linear regression model is used to predict the power, water resources and public transportation demand of the community for one week, and the daily demand forecast value is calculated based on the output of the regression model. The execution process of obtaining the resource demand forecast result is as follows;

[0083] Based on the windowed resource data, the application of the linear regression model involves taking the resource usage data of the past seven days as the independent variable and the future resource demand as the dependent variable. The model parameters are estimated by the least squares method to ensure that the model is trained with actual data to improve the accuracy of the prediction. The model expression used is y=ax+b, where x is the input variable, that is, the average daily usage of the past seven days, y is the prediction result, a and b are model parameters. The model is trained and verified through a data-driven approach to obtain the resource demand prediction results.

[0084] See also Figure 4, using the resource demand forecast results, building a resource flow simulation model, inputting forecast data in real time and adjusting model parameters, evaluating the impact of differentiated policies through continuous simulation, and generating resource management policy simulation results in the following steps:

[0085] S301: Using the resource demand forecast results, construct resource flow simulation models for electricity, water resources and public transportation, initialize model parameters, including resource output, consumption rate and distribution efficiency, and obtain the execution process of the initialized model as follows;

[0086] The resource demand forecast results are adopted, including resource output, consumption rate and distribution efficiency. The parameters are crucial for the resource flow simulation model of electricity, water resources and public transportation. Resource output reflects the total supply of resources, consumption rate indicates the speed of resource use, and distribution efficiency describes the effective transmission capacity of resources from supply point to consumption point. The distribution efficiency of public transportation refers to the operating efficiency of the community's transportation tools on the route, including how to effectively transfer community personnel and services from one location to another. High distribution efficiency means that transportation services are quickly and accurately allocated to demand points. How to reduce delays and improve vehicle operating efficiency are all considerations of indicators. In order to ensure the accuracy of the simulation model, it is necessary to set parameters according to actual monitoring data. By collecting historical data, the daily consumption pattern of each resource can be established. By analyzing the data, the estimated value of resource output, the average level of consumption rate and the reasonable range of distribution efficiency can be obtained. The setting of parameters will directly affect the authenticity and practicality of the simulation, and the initialization model is obtained.

[0087] S302: Input the resource demand forecast results into the initialization model in real time, and dynamically adjust the model parameters according to the daily forecast demand, including adjusting the community's power grid load, water supply and bus operation frequency, and perform simulation operation to obtain the execution process of the parameter adjustment model as follows;

[0088] The resource demand forecast results are input into the initialization model in real time, according to the formula P = P0 + k ×

[0089] (D t -D0), calculate the grid load adjustment. In the formula, P represents the adjusted grid load, P0 represents the initial grid load, k represents the load adjustment coefficient, and D t represents the demand of the day, and D0 represents the baseline demand.

[0090] Formula explanation and formula calculation process: Assume that the initial grid load P0 is 500MW, the baseline demand D0 is 600MW, and the demand on the day D tThe load adjustment factor k is 650MW and 0.75, so the grid load adjustment calculation process is: P = 500 + 0.75 × (650-600) = 537.5MW. The results show that considering the increase in resource demand on that day, the grid needs to increase the load appropriately to meet the increased demand.

[0091] S303: Through the parameter adjustment model, through continuous simulation, evaluate the differentiated policies, including the impact of demand management and pricing strategy changes on resource flow, record the resource allocation efficiency and consumption response during the simulation process, and obtain the resource management policy simulation results. The execution process is as follows;

[0092] Through continuous simulation, we evaluate the impact of differentiated policies, such as demand-side management and pricing strategy changes on resource flow. By continuously adjusting model parameters, such as load adjustment of community power grids, water supply, and route configuration and operating frequency of community smart short-distance buses, we can accurately simulate resource allocation efficiency and consumer response. The optimization of resource allocation efficiency is achieved by comparing simulation results under different policies to find the most effective resource allocation plan. Consumer response is evaluated by observing user usage under different pricing strategies. This can help decision makers understand the specific impact of various policies on resource flow, formulate more effective resource management strategies, and obtain resource management policy simulation results.

[0093] See also Figure 5 ,Through the simulation results of resource management policies, the location data and service needs of community residents are collected, a resource allocation map is constructed, and the routes for community resources to reach the demand points are determined. The specific steps to obtain the optimal resource allocation path are:

[0094] S401: Based on the resource management policy simulation results, the location data of community residents are collected, including the geographical coordinates of home addresses, workplaces, and service facilities, and combined with the service demand records of residents, the execution process of obtaining the resident demand data set is as follows;

[0095] Based on the results of resource management policy simulation, the location data of community residents are collected. The process involves the geographic coordinates of home addresses, workplaces, and service facilities. The data integrates the service demand records of residents. To ensure the accuracy and completeness of the data, the collection of location data requires precise positioning through a geographic information system. The service demand records are collected and organized through community service centers. The movement paths, frequencies, and types and quantities of service needs of residents between different locations are recorded. This ensures that when allocating and optimizing resources, the specific needs of each location can be accurately reflected, and a resident demand data set can be obtained.

[0096] S402: Using the resident demand data set and the geographic information system, a resource allocation map is constructed, the locations of demand points and resource supply centers are marked, resource flows and consumption hotspots are analyzed, and the execution process of obtaining a resource flow map is as follows;

[0097] Use geographic information system to build resource allocation map according to formula Calculate resource flow. In the formula, F represents the total efficiency of resource flow, w i represents the weight of the i-th demand point, x i and i Represent the coordinates of the demand point and the resource supply center, respectively, and d is the distance function. Detailed explanation of the formula and the process of formula calculation: There are three demand points, with weights of 1.5, 2.0, and 1.0 respectively. The coordinates of the demand points are (2, 3), (4, 6), and (5, 1) respectively. The coordinates of the resource supply center are (3, 4). Then the total efficiency calculation process of the resource flow is:

[0098] F=1.5×d((2,3),(3,4))+2.0×d((4,6),(3,4))+1.0×d((5,1),(3,4))

[0099] The distance function uses Euclidean distance, which is calculated as follows:

[0100]

[0101] F≈1.5×1.41+2.0×2.24+1.0×4.24=2.12+4.48+4.24=10.84

[0102] The results show that the total efficiency of resource supply is 10.84 when considering the weight and distance of each demand point.

[0103] S403: According to the resource flow diagram, the optimal distribution path of electricity, water resources and public transportation is determined. Referring to the route congestion and construction factors, the route is dynamically adjusted to verify the speed and efficiency of resources reaching the demand point. The execution process of obtaining the optimal resource allocation path result is as follows;

[0104] According to the resource flow diagram, the optimal distribution route for electricity, water resources and public transportation is determined. During the process, route congestion and construction factors need to be considered to dynamically adjust the route to ensure the speed and efficiency of resources reaching the demand point. By analyzing the specific routes of various resources from the supply center to the demand point, resource managers can evaluate the routes due to traffic congestion or construction obstructions, and then choose alternative routes. At the same time, using real-time traffic data and construction information, resource distribution routes can be updated in real time. Optimizing route settings not only improves the efficiency of resource allocation, but also reduces transportation costs, and obtains the optimal resource allocation path results.

[0105] See also Figure 6 Based on the results of the optimal resource allocation path, priority sorting and demand comparison are carried out, the flow of community resources is adjusted, and the resource allocation effect and residents' feedback are continuously tracked. The specific steps for generating the results of resource allocation fairness analysis are as follows:

[0106] S501: Based on the optimal resource allocation path result, the resource demand points are prioritized, referring to the priority needs of emergency medical facilities, schools and key public facilities, combined with consumption data and residents’ feedback, and the execution process of obtaining the priority ranking result is as follows;

[0107] Prioritize resource demand points according to the formula Calculate the priority. In the formula, P represents the total priority score, s b represents the urgency score of the bth demand point, c b Represents the consumption data coefficient.

[0108] Detailed explanation of the formula and formula calculation derivation process: There are three resource demand points, with urgency scores of 10, 8, and 5, and consumption data coefficients of 1.2, 0.8, and 0.5, respectively. The priority score calculation process is: P = 10×1.2+8×0.8+5×0.5=12+6.4+2.5=20.9. The results show that considering the urgency score and consumption data coefficient, the total priority score is 20.9, which helps to determine that the demand points should be given priority in resource allocation.

[0109] S502: Based on the priority sorting results, a linear programming algorithm is used to reconfigure the flow of community electricity, water resources and public transportation, and the optimal solution for resource configuration is calculated. The execution process for obtaining the optimal resource flow value is as follows;

[0110] The formula for the linear programming algorithm is as follows:

[0111]

[0112] Among them, f(x) is the utility function of resource allocation, x1 represents the demand for electricity resources, x2 represents the demand for water resources, c1 represents the weight coefficient of electricity resources, c2 represents the weight coefficient of water resources, and a1 represents the adjustment coefficient of public transportation.

[0113] Detailed explanation of the formula and the process of formula calculation and derivation:

[0114] The formula is used to calculate the optimal solution for community resource allocation, including electricity, water resources and dependence on public transportation. The formula includes the weighted sum of electricity and water resource demands and the impact of the adjustment coefficient of public transportation on the overall allocation.

[0115] The following are the parameter settings and calculation derivations:

[0116] x1 is the electricity demand, which is the total electricity consumption data obtained through real-time monitoring from the municipal infrastructure database. For example, the monitored value of electricity demand in a community is 3,000 megawatts.

[0117] x2 is the water resource demand, which is obtained through the data interface of the community water system and is the daily water consumption statistics. For example, the water resource demand on a certain day is 20 million cubic meters.

[0118] c1 is the power resource weight coefficient, which is determined according to policy orientation, taking into account the importance of electricity in maintaining the basic operation of the community. In this case, it is set to 0.6.

[0119] c2 is the water resource weight coefficient, which reflects the impact of water resources on community life. It is set based on regional climate and population density. In this example, it is set to 0.4.

[0120] a1 is the adjustment coefficient of public transportation, which assesses the proportion of the public transportation system in the demand for electricity and water resources. The coefficient is determined through the monthly report of the Transportation Administration Bureau and is set to 50.

[0121] Calculation process:

[0122] 1. Calculate the weighted sum of electricity and water resources:

[0123] c1x1+c2x2=0.6×3000+0.4×2000=1800+800=2600

[0124] 2. Calculate the regulatory impact of public transportation:

[0125]

[0126] 3. Combine the above two parts to get the value of the resource allocation utility function:

[0127] f(x)=2600+0.01=2600.01

[0128] The results show that, considering the influence of public transportation resource dependence, the total demand for electricity and water resources obtained by the linear programming algorithm is a weighted sum and adjustment value of 2600.01. It reflects the resource utility maximization index of community operation under the current resource allocation state, provides a quantitative basis for decision makers to optimize resource allocation, and helps to optimize the resource dependence of public transportation while ensuring power supply and water supply in key areas.

[0129] S503: Through the optimal resource flow value, continuously track the allocation effect of adjusted resources, collect residents' feedback on the fairness and efficiency of resource allocation, conduct data analysis and evaluation, regularly update the resource allocation strategy, and obtain the execution process of the resource allocation fairness analysis result as follows;

[0130] Through the optimal resource flow value, the allocation effect of adjusted resources is continuously tracked, including collecting residents' feedback on the fairness and efficiency of resource allocation. Through regular data analysis and evaluation, the effectiveness of the resource allocation strategy can be determined. The fairness evaluation of resource allocation relies on the subjective feedback of residents. The feedback is collected through questionnaires, community meetings and online platforms. Analyzing the data helps to identify deviations or deficiencies in resource allocation. Regular updating of resource allocation strategies is based on the analysis results. When adjusting strategies, managers need to take into account the specific needs and resource allocation data of different communities to ensure that resource allocation is fair and efficient and obtain the fairness analysis results of resource allocation.

[0131] See also Figure 7 ,According to the results of resource allocation fairness analysis, a community resource management strategy is formulated, and the sustainability of community services is verified according to the service needs of community residents. The specific steps to obtain community construction indicators are as follows:

[0132] S601: Based on the resource allocation fairness analysis results, the satisfaction degree of service needs and the fairness of resource allocation are evaluated, the service coverage area and resource waste hot spots are identified, and the execution process of the initialization community resource management strategy is obtained as follows;

[0133] Based on the results of resource allocation fairness analysis, the degree of satisfaction of service needs and the fairness of resource allocation are evaluated. During the evaluation process, service coverage areas and resource waste hotspots are identified. Information is the basis for formulating target optimization strategies. Service coverage area analysis focuses on determining whether resources can widely cover all demand points. Resource waste hotspots help identify inefficient resource utilization in the region, which can clarify the deficiencies in resource allocation and potential directions for improvement. The target optimization strategy will focus on improving the breadth and depth of service coverage while reducing resource waste to improve the overall fairness and efficiency of resource allocation and obtain the initialization community resource management strategy.

[0134] S602: According to the initialization community resource management strategy, adjust resource allocation, optimize the resource supply of medical, educational, sports, management and commercial facilities, avoid the waste of resources in non-priority areas, implement adjustments and monitor the effects in the short term, and obtain the execution process of the adjusted resource allocation as follows;

[0135] According to the guidance of the initial community resource management strategy, the priorities of medical, educational, sports, administrative and commercial facilities are determined, and the distribution and utilization efficiency of existing resources are evaluated. Through meticulous data analysis, a preliminary plan for resource reallocation is formulated to optimize resource distribution and reduce waste. For example, medical resources are transferred from resource-rich but underutilized areas to areas in urgent need. At the same time, support for education and sports facilities is strengthened to ensure that the reasonable allocation of each resource can meet the actual needs of community residents. By adjusting the resource input of commercial facilities, the development of the local economy is promoted, the efficiency of resource utilization has been significantly improved, the quality of community services has also been improved, and the adjusted resource allocation has been obtained.

[0136] S603: Continuously monitor the adjusted resource allocation, combine the feedback from community residents, verify the impact of resource allocation on the sustainability of community services, and determine whether the long-term development goals are met through data analysis. The execution process of obtaining community construction indicators is as follows;

[0137] Continuously monitor the adjusted resource allocation and combine it with feedback from community residents. Monitoring and feedback collection are important parts of evaluating the impact of resource allocation on the sustainability of community services. By regularly collecting residents' satisfaction and specific feedback on resource allocation, the actual effectiveness of the resource allocation strategy can be evaluated. The sustainability of community services is evaluated by analyzing residents' satisfaction with service quality, the appropriateness of resource allocation, and the penetration rate of services. Data helps decision makers understand the long-term impact of resource allocation and determine whether it meets long-term development goals. Through continuous monitoring and evaluation, it can be ensured that various indicators of community construction meet the expected goals and obtain community construction indicators.

[0138] See also Figure 8 , a future community construction system based on government big data, the future community construction system based on government big data is used to execute the above-mentioned future community construction method based on government big data, the system includes:

[0139] The data collection and standardization module collects data on electricity usage, water consumption, and public transportation utilization from the community, synchronizes the data, and performs standardization processing to generate a community resource data set;

[0140] The demand analysis module uses the community resource dataset to perform time series analysis on the future demand for electricity, water resources, and public transportation to obtain resource demand forecast results;

[0141] The resource flow simulation module builds a resource flow model based on the resource demand forecast results, continuously inputs forecast data, adjusts model parameters, and generates policy simulation results by evaluating the impact of differentiated policies on resource management through simulation;

[0142] The resource optimization module uses policy simulation results to collect resident location and service demand data, builds a resource allocation map, determines the optimal route for resources to reach demand points, and obtains the optimal resource allocation path result;

[0143] The resource management strategy module prioritizes resource flows and compares demand based on the results of the optimal resource allocation path, tracks resource allocation effects and resident feedback, and develops targeted community resource management strategies to obtain community construction indicators.

[0144] 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. A method for building a future community based on government big data, characterized in that: The following steps are involved: Collect electricity usage data, water consumption data, and public transportation utilization data within the community, synchronize and standardize the data, and generate a community resource usage data set; Based on the community resource usage dataset, the government big data is analyzed using time series to predict and calculate the predicted values ​​of the community's electricity, water resources and public transportation demand within a week, and the resource demand forecast results are obtained; Using the resource demand forecast results, constructing a resource flow simulation model, inputting forecast data in real time and adjusting model parameters, evaluating the impact of differentiated policies through continuous simulation, and generating resource management policy simulation results; Through the simulation results of the resource management policy, the location data and service needs of community residents are collected, a resource allocation map is constructed, the route for community resources to reach the demand point is determined, and the optimal resource allocation path result is obtained; Based on the optimal resource allocation path results, priority sorting and demand comparison are performed to adjust the flow of community resources, continuously track resource allocation effects and resident feedback, and generate resource allocation fairness analysis results; According to the results of the resource allocation fairness analysis, a community resource management strategy is formulated, and according to the service needs of community residents, the sustainability of community services is verified to obtain community construction indicators.

2. The future community construction method based on government big data according to claim 1 is characterized in that: The community resource usage dataset includes hourly records of electricity usage for each household, daily water consumption and statistics on the number of passengers on each public transportation trip; the resource demand forecast results include the peak, trough and average electricity demand for each day in the next week, the total water consumption per day, and the predicted number of passengers during peak and non-peak periods of public transportation; the resource management policy simulation results include resource consumption change data under differentiated policy scenarios, water restriction measures, peak electricity usage period restrictions and public transportation preferential measures effect simulation results; the optimal resource allocation path results include a priority route map for electricity supply, key distribution points for water resources and a plan for adjusting the route of public transportation; the resource allocation fairness analysis results include a resident satisfaction survey analysis, the timeliness and coverage data of resource allocation and a resource reallocation plan that reflects fairness; the community resource management strategy includes resource priority allocation rules, emergency resource response measures and long-term resource guarantee plans.

3. The future community construction method based on government big data according to claim 1 is characterized in that: Collect the electricity usage data, water consumption data and public transportation utilization data in the community, synchronize and standardize the data, and generate the community resource usage data set in the following steps: Collect electricity usage data, water consumption data, and public transportation utilization data in the community, aggregate the data into a unified data table, including timestamps and data source identifiers, and obtain an initialization data set; Based on the initialized data set, remove records with timestamp errors and uncertain data sources, interpolate missing data points, convert numerical data into floating-point format, and generate a processed data set; Based on the processed dataset, a standardization method is applied to the resource data to scale the values ​​between 0 and 1, and the validation data are compared on a similar scale to obtain a community resource use dataset.

4. The future community construction method based on government big data according to claim 1 is characterized in that: Based on the community resource usage dataset, the government big data is analyzed using time series to predict and calculate the predicted values ​​of the community's electricity, water resources and public transportation demand within a week. The steps to obtain the resource demand prediction results are as follows: Based on the community resource usage data set, select the usage data of community electricity, water resources and public transportation, serialize the usage data of each resource, and arrange them in date and time order to obtain serialized resource data; Using the serialized resource data, applying sliding window technology for each resource, calculating the average daily usage over the past seven days, assigning a time window to each data point, and obtaining windowed resource data; Based on the windowed resource data, a linear regression model is used to predict the community's electricity, water resources and public transportation needs for a week. The daily demand forecast value is calculated based on the output of the regression model to obtain the resource demand forecast result.

5. The future community construction method based on government big data according to claim 1 is characterized in that: The steps of using the resource demand forecast results to construct a resource flow simulation model, inputting forecast data in real time and adjusting model parameters, and evaluating the impact of differentiated policies through continuous simulation to generate resource management policy simulation results are as follows: Using the resource demand forecast results, construct resource flow simulation models for electricity, water resources and public transportation, initialize model parameters, including resource output, consumption rate and distribution efficiency, and obtain an initialized model; Input the resource demand forecast results into the initialization model in real time, dynamically adjust the model parameters according to the daily forecast demand, including adjusting the community's power grid load, water supply and bus operation frequency, perform simulation operation, and obtain a parameter adjustment model; Through the parameter adjustment model, through continuous simulation, the differentiated policies, including demand management, the impact of changes in pricing strategies on resource flows, are evaluated, the resource allocation efficiency and consumption response during the simulation process are recorded, and the simulation results of resource management policies are obtained.

6. The future community construction method based on government big data according to claim 1 is characterized in that: Through the simulation results of the resource management policy, the location data and service needs of community residents are collected, a resource allocation map is constructed, and the route for community resources to reach the demand point is determined. The specific steps for obtaining the optimal resource allocation path result are as follows: Based on the simulation results of the resource management policy, the location data of community residents are collected, including the geographical coordinates of home addresses, workplaces and service facilities, and combined with the service demand records of residents to obtain a resident demand data set; Adopting the resident demand data set and using the geographic information system, constructing a resource allocation map, marking the locations of demand points and resource supply centers, analyzing resource flows and consumption hotspots, and obtaining a resource flow map; According to the resource flow diagram, the optimal distribution path for electricity, water resources and public transportation is determined. With reference to route congestion and construction factors, the route is dynamically adjusted to verify the speed and efficiency of resources reaching the demand point, and the optimal resource allocation path result is obtained.

7. The method for building a future community based on government big data according to claim 1 is characterized in that: Based on the optimal resource allocation path results, priority sorting and demand comparison are performed, the flow of community resources is adjusted, and the resource allocation effect and resident feedback are continuously tracked to generate the resource allocation fairness analysis results. The specific steps are as follows: Based on the optimal resource allocation path results, prioritize resource demand points, refer to the priority needs of emergency medical facilities, schools and key public facilities, and combine consumption data and resident feedback to obtain priority ranking results; According to the priority ranking results, a linear programming algorithm is used to reconfigure the flow of community electricity, water resources and public transportation, calculate the optimal solution for resource allocation, and obtain the optimal resource flow value; Through the optimal resource flow value, the allocation effect of adjusted resources is continuously tracked, residents' feedback on the fairness and efficiency of resource allocation is collected, data analysis and evaluation are performed, resource allocation strategies are regularly updated, and resource allocation fairness analysis results are obtained.

8. The method for building a future community based on government big data according to claim 7 is characterized in that: The formula of the linear programming algorithm is as follows: Among them, f(x) is the utility function of resource allocation, x1 represents the demand for electricity resources, x2 represents the demand for water resources, c1 represents the weight coefficient of electricity resources, c2 represents the weight coefficient of water resources, and a1 represents the adjustment coefficient of public transportation.

9. The future community construction method based on government big data according to claim 1 is characterized in that: According to the results of the resource allocation fairness analysis, a community resource management strategy is formulated, and the sustainability of community services is verified according to the service needs of community residents. The specific steps to obtain community construction indicators are as follows: Based on the resource allocation fairness analysis results, the satisfaction degree of service needs and the fairness of resource allocation are evaluated, the service coverage area and resource waste hot spots are identified, and the initialization community resource management strategy is obtained; According to the initial community resource management strategy, adjust resource allocation, optimize the supply of medical, educational, sports, management and commercial facilities, avoid wasting resources in non-priority areas, implement adjustments and monitor the effects in the short term to obtain adjusted resource allocation; Continue to monitor the adjusted resource allocation, combine it with feedback from community residents, verify the impact of resource allocation on the sustainability of community services, determine whether long-term development goals are met through data analysis, and obtain community construction indicators.

10. A future community construction system based on government big data, characterized in that: According to any one of claims 1 to 9, the method for building a future community based on government big data comprises: The data collection and standardization module collects data on electricity usage, water consumption, and public transportation utilization from the community, synchronizes the data, and performs standardization processing to generate a community resource data set; The demand analysis module uses the community resource dataset to perform time series analysis on future demand for electricity, water resources and public transportation to obtain resource demand forecast results; The resource flow simulation module constructs a resource flow model according to the resource demand forecast results, continuously inputs forecast data, adjusts model parameters, and generates policy simulation results by simulating and evaluating the impact of differentiated policies on resource management; The resource optimization module uses the policy simulation results to collect resident location and service demand data, construct a resource allocation map, determine the optimal route for resources to reach the demand point, and obtain the optimal resource allocation path result; Based on the optimal resource allocation path results, the resource management strategy module prioritizes resource flows and compares demand, tracks resource allocation effects and resident feedback, and develops targeted community resource management strategies to obtain community construction indicators.

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