A community life circle facility perfection degree evaluation method and system

Abstract

The application provides a community life circle facility perfection degree evaluation method and system, belonging to the technical field of urban planning and community governance. The method collects resident location data and facility location data, constructs a spatial accessibility matrix, calculates potential service load, obtains overflow transmission path parameters for overloaded facility points through spatial interpolation analysis, determines overflow priority, and realizes demand transfer feasibility evaluation and capacity adjustment based on comparison of residual capacity data and capacity change of adjacent facility points. Compared with the traditional evaluation method relying on straight-line distance coverage or facility type quantity statistics, the application forms a closed loop of evaluation, identification, dredging, re-evaluation and optimization through secondary difference comparison, can continuously track and solve the problem of inaccurate supply and demand matching caused by evaluation dimension fragmentation, improve the accuracy and adaptability of community life circle facility perfection degree evaluation, and meet the actual application needs of urban fine governance and community life circle planning.

Description

Technical Field

[0001] This application belongs to the field of urban planning and community governance technology, specifically a method and system for evaluating the completeness of community living circle facilities. Background Technology

[0002] Community living circles are the spatial carriers of urban residents' daily lives. The configuration level of public service facilities directly affects the quality of life of residents and the efficiency of urban space resource allocation. How to scientifically assess the completeness of facilities within the living circle has become a fundamental task for refined urban governance. At present, a relatively complete technical route has been formed around the configuration of public service facilities in community living circles. However, the existing assessment of the completeness of facilities still has problems such as relatively fragmented assessment dimensions and difficulty in quantifying the service capacity of facilities. Existing technologies struggle to fully reflect the true match between facility supply and residents' needs. Some assessment methods measure the straight-line distance or buffer zone coverage between residential areas and facilities to determine residents' accessibility, ignoring the constraints of actual road network conditions on residents' travel routes. Others evaluate completeness by statistically analyzing the quantity and types of educational, medical, and commercial facilities within the community. While this reflects facility diversity, it fails to determine whether the actual service capacity of these facilities is sufficient to meet the real needs of surrounding residents. Each approach has its own focus but fails to provide a definitive judgment on whether supply and demand truly match. Furthermore, structural imbalances can exist between supply capacity and demand scale, which are difficult to accurately identify and quantify. When a community health service center's service radius covers three surrounding residential communities, the accessibility of facilities in all three communities may be rated as satisfactory based solely on spatial distance. However, if the community health service center's daily capacity is only 200 visits, while the average daily potential medical demand in the three communities reaches 500 visits, then in actual medical treatment, residents will face the dilemma of long queues or inability to receive timely medical care. Therefore, there is an urgent need for a facility completeness assessment method that can simultaneously integrate three dimensions—spatial accessibility, facility service capacity, and the intensity of residents' demand—in the assessment process. Based on the accurate identification of overloaded facilities, big data analysis can be used to achieve supply and demand matching assessment, providing technical support for the refined allocation of public service facilities in community living circles.

[0003] The information disclosed in the background section is only for enhancing the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] In view of this, this application provides a method and system for assessing the completeness of community living circle facilities. It incorporates the spatial accessibility of facilities, actual service capacity and the intensity of residents' demand into a unified quantitative assessment framework, constructs a demand spillover transmission analysis and capacity adjustment mechanism under overload conditions, reduces the risk of supply and demand imbalance of facility services, improves the accuracy of assessment results and system robustness, and provides technical support for the refined allocation of public service facilities in community living circles.

[0005] In a first aspect, embodiments of this application provide a method and system for assessing the completeness of community living circle facilities. The method includes: collecting location data of residential points and facility points in a target area; constructing a spatial accessibility matrix based on the location data of residential points and facility points; obtaining facility type parameters and capacity parameters corresponding to the facility point location data, as well as resident demand data associated with the residential point location data, based on the spatial accessibility matrix; matching the resident demand data to corresponding facility points, and calculating the potential service load corresponding to each facility point in conjunction with the facility type parameters; calculating the difference between the capacity parameters and the potential service load to obtain a difference result; comparing the difference result with a preset matching degree grading standard to obtain a supply-demand matching degree value corresponding to each facility point; when the supply-demand matching degree value is lower than the critical value in the preset matching degree grading standard, based on the resident demand... The system calculates the excess demand exceeding the capacity parameters and marks the facility as overloaded. It performs spatial interpolation analysis on the excess demand corresponding to the overloaded state to obtain overflow transmission path parameters, extracts demand time period characteristics from the residential location data, and determines overflow priority based on these characteristics. The excess demand is allocated to neighboring facility points according to the overflow transmission path parameters and overflow priority, resulting in demand allocation for each neighboring facility point. Demand transfer feasibility data is obtained based on the demand allocation. When the demand transfer feasibility data meets preset reception conditions, the excess demand is superimposed on the corresponding capacity parameters of the neighboring facility points according to the corresponding demand allocation, resulting in updated capacity parameters for each facility point. A secondary interpolation comparison is performed between the updated capacity parameters and the potential service load to obtain a secondary interpolation comparison result, and evaluation data is output based on the secondary interpolation comparison result.

[0006] Secondly, embodiments of this application provide a community living circle facility perfection assessment system, which may include a spatial accessibility matrix construction module, a potential service load calculation module, a supply and demand matching degree assessment module, an overload status identification module, an overflow analysis module, a demand transfer assessment module, a capacity update module, and an assessment data output module. The system includes the following modules: a spatial accessibility matrix construction module, used to acquire residential location data and facility location data for the target area, and construct a spatial accessibility matrix based on the residential location data and facility location data; a potential service load calculation module, used to acquire facility type parameters and capacity parameters corresponding to the facility location data and resident demand data associated with the residential location data based on the spatial accessibility matrix, match the resident demand data to the corresponding facility, and calculate the potential service load corresponding to each facility based on the facility type parameters; a supply-demand matching degree evaluation module, used to calculate the difference between the capacity parameter and the potential service load, and compare the difference result with a preset matching degree grading standard to obtain the supply-demand matching degree value corresponding to each facility; and an overload status identification module, used to calculate the overload exceeding the capacity parameter based on the resident demand data when the supply-demand matching degree value is lower than the critical value in the preset matching degree grading standard. The system comprises the following modules: a demand transfer assessment module, a demand shift evaluation module, and a capacity update module. The capacity update module is used to add the excess demand to the capacity parameters of the corresponding neighboring facilities when the demand shift feasibility data meets preset reception conditions. The update capacity parameters are then compared with the potential service load using a secondary interpolation comparison to obtain the secondary interpolation comparison result, and the evaluation data is output based on the secondary interpolation comparison result.

[0007] This application provides a method and system for assessing the completeness of community living circle facilities. By constructing a spatial accessibility matrix under road network constraints, the actual road distance from residents to facilities is included in the accessibility assessment. Weighted calculations are performed using facility type parameters, capacity parameters, and resident demand data to obtain the potential service load corresponding to each facility, achieving a quantitative assessment of the supply-demand matching degree of facilities. For overloaded facilities identified in the assessment, spatial interpolation analysis is used to obtain the spatial distribution and transmission path of overflow demand. Overflow priority is determined by combining the characteristics of residents' demand periods, and demand transfer feasibility data is calculated to achieve intelligent redistribution and capacity adjustment of excess demand. Compared with traditional assessment methods that rely on straight-line distance coverage or statistical analysis of facility types, this application forms a complete closed loop of assessment, identification, diversion, and reassessment through secondary difference comparison. It can continuously track changes in the supply-demand status of facilities and output load status labels and spatial coverage optimization suggestions, reducing the risk of imbalance between facility service supply and demand. This meets the practical application needs of refined urban governance and community living circle planning, improving the accuracy and adaptability of public service facility configuration in community living circles. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a method for assessing the completeness of community living area facilities provided in an exemplary embodiment of this application.

[0010] Figure 2 This is a flowchart illustrating a method for assessing the completeness of community living area facilities, provided in another exemplary embodiment of this application.

[0011] Figure 3 This is a flowchart illustrating a method for assessing the completeness of community living area facilities, provided in another exemplary embodiment of this application.

[0012] Figure 4 This is a flowchart illustrating a method for assessing the completeness of community living area facilities, provided in another exemplary embodiment of this application.

[0013] Figure 5 This is a flowchart illustrating a method for assessing the completeness of community living area facilities, provided in another exemplary embodiment of this application.

[0014] Figure 6This is a flowchart illustrating a method for assessing the completeness of community living area facilities, provided in another exemplary embodiment of this application.

[0015] Figure 7 This is a flowchart illustrating a method for assessing the completeness of community living area facilities, provided in another exemplary embodiment of this application.

[0016] Figure 8 This is a flowchart illustrating a method for assessing the completeness of community living area facilities, provided in another exemplary embodiment of this application. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this application will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this application.

[0018] The terms “a,” “one,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended inclusion and that other elements / components / etc. may exist in addition to those listed. The terms “first” and “second” are used only as markers and are not a limitation on the number of objects.

[0019] It should be noted that existing methods for assessing the completeness of community living circle facilities generally suffer from fragmented assessment dimensions. Some existing methods judge facility accessibility solely based on straight-line distance or buffer zone coverage, ignoring the traffic constraints of the actual road network. Other existing methods focus on the richness of facility types, failing to incorporate the actual service capacity of facilities and the intensity of residents' real needs into a unified assessment framework. When the supply capacity of facilities cannot meet the demand, there is a lack of quantitative analysis methods for the direction and transmission path of demand overflow under overload conditions. There is also a lack of a closed-loop processing mechanism from overload identification and demand diversion to dynamic capacity adjustment. This results in a disconnect between assessment results and the actual service status of facilities, making it difficult to provide effective support for the precise allocation of public service facilities in community living circles.

[0020] Therefore, to address the issues of fragmented facility assessment dimensions and lack of a closed-loop diversion mechanism after overload, this application, through steps S110 to S190, first collects residential location data and facility location data in the target area to construct a spatial accessibility matrix, calculates the potential service load of each facility and determines the supply-demand matching degree, identifies overloaded facilities and calculates excess demand, performs spatial interpolation analysis on the excess demand to obtain overflow transmission path parameters, and determines overflow priority based on the characteristics of residents' demand periods, allocates excess demand to neighboring facility points according to the overflow transmission path parameters and overflow priority, and assesses the feasibility of demand transfer, and when the feasibility meets the preset receiving conditions, adds excess demand to the capacity parameters of neighboring facility points to update the capacity values ​​of each facility point, performs a secondary difference comparison, and outputs assessment data, providing technical support for supply-demand matching assessment, overload risk identification, and spatial layout optimization of community living circle facilities.

[0021] This application provides a method and system for assessing the completeness of community living area facilities, such as... Figure 1 This paper presents a method and system for assessing the completeness of community living area facilities. The method may include the following steps: Step S110: Collect residential location data and facility location data of the target area, and construct a spatial accessibility matrix based on the residential location data and the facility location data; Step S120: Based on the spatial accessibility matrix, obtain the facility type parameters and capacity parameters corresponding to the facility location data, as well as the resident demand data associated with the residential location data; Step S130: Match the resident demand data to the corresponding facility locations, and calculate the potential service load corresponding to each facility location in combination with the facility type parameters; Step S140: Calculate the difference between the capacity parameter and the potential service load to obtain the difference result. Compare the difference result with the preset matching degree classification standard to obtain the supply and demand matching degree value corresponding to each facility point. Step S150: When the supply-demand matching degree value is lower than the critical value in the preset matching degree grading standard, calculate the excess demand exceeding the capacity parameter based on the residents' demand data, and mark the facility as overloaded. Step S160: Perform spatial interpolation analysis on the excess demand corresponding to the overload state to obtain overflow transmission path parameters, extract demand time period characteristics from the residential location data, and determine overflow priority based on the demand time period characteristics; Step S170: Allocate the excess demand to neighboring facility points according to the overflow transmission path parameters and overflow priority to obtain the demand allocation amount for each neighboring facility point, and obtain demand transfer feasibility data based on the demand allocation amount; Step S180: When the demand transfer feasibility data meets the preset receiving conditions, the excess demand is added to the capacity parameters of the corresponding neighboring facility points according to the corresponding demand allocation amount to obtain the updated capacity parameters of each facility point. Step S190: Perform a second difference comparison between the updated capacity parameters and the potential service load to obtain the second difference comparison result, and output the evaluation data based on the second difference comparison result.

[0022] In the aforementioned method, by collecting residential location data and facility location data and constructing a spatial accessibility matrix, the potential service load is calculated. Spatial interpolation analysis is used to obtain overflow transmission path parameters for overloaded facilities and determine overflow priorities. Based on the comparison of remaining capacity data and capacity changes of neighboring facilities, the feasibility of demand transfer and capacity adjustment are achieved. Compared with traditional assessment methods that rely on straight-line distance coverage or statistical statistics of facility types, this application forms a closed loop of assessment, identification, diversion, reassessment, and optimization through secondary interpolation comparison. This can continuously track and solve the problem of inaccurate supply and demand matching judgments caused by fragmented assessment dimensions, improve the accuracy and adaptability of community living circle facility perfection assessment, and meet the practical application needs of refined urban governance and community living circle planning.

[0023] The following is a detailed description of the steps in the community living circle facility improvement assessment method and system provided in the embodiments of this application: In the embodiments of this application, step S110, which involves collecting residential location data and facility location data of the target area and constructing a spatial accessibility matrix based on the residential location data and the facility location data, further includes the following steps: Figure 2 As shown, the specific content is as follows: Step S210: Obtain the coordinate values ​​of the residential points and the coordinate values ​​of the facilities in the target area based on the residential point location data and the facility point location data, and construct an initial spatial distribution layer based on the residential point coordinate values ​​and the facility point coordinate values; Step S220: Obtain road network data based on the initial spatial distribution layer, and calculate the shortest path from each residential point to each facility point in the initial spatial distribution layer based on the road network data using the shortest path algorithm to obtain the shortest travel time; Step S230: Determine the service coverage of each residential point at each facility point based on the shortest travel time, and obtain the target service matching pair based on the service coverage; Step S240: Obtain the time decay resistance value based on the target service matching pair, smooth the time decay resistance value, and determine the smooth decay value of each target service matching pair; Step S250: Construct road network constraint terms based on the smoothing attenuation value and the shortest travel time, and perform matrix arrangement of the target service matching pairs through the road network constraint terms to obtain the spatial reachability matrix.

[0024] Specifically, by calling the public service interface for urban geographic information, the location data of 150 residential communities and the location data of 20 community medical service centers within the target area are captured. The latitude and longitude coordinates of the centroid of the 150 residential communities and the latitude and longitude coordinates of the 20 community medical service centers are used as the coordinate values ​​of the residential community and the facility, respectively. An initial spatial distribution layer is constructed based on the coordinate values ​​of the residential community and the facility. Road intersections are extracted as nodes and road segments as edges to construct a directed topology graph containing 3000 nodes and 4500 edges as road network data. The weight of each edge is assigned as the theoretical travel time obtained by dividing the road segment length by the legal speed limit of the road segment. In this embodiment, Dijkstra's shortest path algorithm is used to traverse and calculate the travel time of the shortest path from 150 residential points to 20 facility points. For example, the shortest travel time between the community node corresponding to residential point A and the hospital node corresponding to facility point B is calculated to be 12.5 minutes. According to the standards of the medical and health industry, a facility service radius threshold is set, such as 15.0 minutes. Based on the comparison between the shortest path and the facility service radius threshold, the residential points and facility points with shortest paths less than or equal to the service radius threshold are identified as target service matching pairs. The time decay resistance value is obtained based on the target service matching pairs, and a Gaussian decay function is introduced to decay the travel time. The time decay distance parameter t0 is set to 0.8. The calculation method of the Gaussian decay function is as follows:

[0025] in, This is the time-decay resistance value. t0 represents the shortest travel time from settlement i to facility j, and t0 represents the time decay distance parameter. The time decay resistance value is smoothed by substituting the time decay resistance value of each target service matching pair into the Gaussian decay function for calculation, and the smoothed decay value of each target service matching pair is determined. Road network constraints are constructed based on the smoothing attenuation value and the shortest travel time. The road network constraints are comprehensive weight coefficients obtained by combining the smoothing attenuation value and the shortest travel time. The target service matching pairs are arranged in a matrix using the road network constraints. The road network constraints between each residential point and each facility point are used as matrix elements to generate a 150-row, 20-column spatial accessibility matrix. For example, the element 0.75 in the 5th row and 8th column of the spatial accessibility matrix represents that the spatial accessibility weight from the 5th residential point to the 8th facility point under the road network resistance constraint is 0.75.

[0026] Using the methods described above, residential location data and facility location data can be extracted from the basic geographic information data of the target area. This incorporates the constraints of the actual road network on residents' travel into the accessibility assessment system, providing a quantitative basis for subsequent facility supply and demand matching analysis. This spatial accessibility data covers dimensions such as the shortest travel time between residential areas and facilities, time decay resistance values, and smoothing decay values, which are prerequisites for accurate assessment of facility completeness.

[0027] In an embodiment of this application, in step S120, based on the spatial accessibility matrix, the facility type parameters and capacity parameters corresponding to the facility location data, as well as the resident demand data associated with the residential location data, are obtained. The specific details are as follows: Specifically, based on the spatial accessibility matrix, the correspondence between the location data of each facility point and the location data of residential points is located. For example, for a facility point with coordinates of longitude 116.3° and latitude 39.9°, by querying the facility attribute database, the facility point is identified as a large general hospital, and the facility type parameters such as a weight factor of 1.5 are matched and obtained. At the same time, the capacity parameters, such as the maximum number of daily outpatient visits of 5,000 people, are read. Taking a residential area within a 2.0 square kilometer radius of the facility as an example, the service population data (e.g., a service population density of 12,000 people per square kilometer) from the census database is retrieved. Combined with the aging population ratio (e.g., 20%) within the 2.0 square kilometer radius of the facility, and according to general criteria for assessing healthcare service demand, the healthcare service demand intensity of the elderly population is typically 1.5 to 2.0 times the average of the entire population, this embodiment uses the demand intensity coefficient of the entire population as a baseline of 1.0, with an aging population ratio of 20%, and performs weighted adjustments using a correction factor. The calculation formula is as follows:

[0028] in, Here, 'a' is the demand intensity coefficient, 'b' is the aging population ratio, and 'c' is the incremental demand weight of the elderly population. 'c' is set to 1.0, and the calculated demand intensity coefficient for this residential area is 1.2. The system iterates through all residential location data associated with the facility in the spatial accessibility matrix, extracting the service population data and demand intensity coefficient for each residential location to obtain the corresponding resident demand data. For example, if five residential locations are found within a 2.0 square kilometer radius of the facility, the service population density value for each location is obtained, which is 12,000 people / km². 2 8500 people / km 2 9600 people / km 2 11,000 people / km 2 7200 people / km 2 The corresponding demand intensity coefficients are used to obtain a complete set of resident demand data. The same operation is repeated on the location data of the remaining 19 facility points in the spatial accessibility matrix to obtain the facility type parameters and capacity parameters corresponding to each facility point, as well as the resident demand data of the resident point location data associated with each facility point.

[0029] Using the above methods, all the key parameters required for facility completeness assessment can be extracted from the spatial accessibility matrix and associated geographic information databases and population census databases. This provides a structured data foundation for subsequent calculation of potential service load and supply-demand matching analysis, covering dimensions such as facility type parameters, capacity parameters, service population data, and demand intensity coefficients of residential areas. This is a prerequisite for achieving quantitative assessment of supply-demand matching of community living circle facilities.

[0030] In the embodiments of this application, step S130, which involves matching the resident demand data to corresponding facility locations and calculating the potential service load corresponding to each facility location based on the facility type parameters, further includes the following steps: Figure 3 As shown, the specific content is as follows: Step S310: Calculate the location data of the facility point and the location data of the residential point using the Euclidean distance calculation method to obtain the initial spatial distance value; Step S320: Calculate the initial spatial distance value using the Gaussian decay function to obtain the spatial decay value corresponding to each facility point; Step S330: Extract the facility type parameters and capacity parameters corresponding to the facility location data, and perform a weighted summation of the facility type parameters and the spatial attenuation value to obtain the initial service load corresponding to each facility point; Step S340: Use a regression algorithm to calculate the initial service load and the residents' demand data to obtain the potential service load corresponding to each facility.

[0031] Specifically, this embodiment takes a facility point with longitude of 116.3° and latitude of 39.9° as an example. The spatial correspondence between the facility point location data and the location data of each residential point is extracted from the spatial accessibility matrix. The facility point and the five residential points within the radiation range are processed one by one by Euclidean distance calculation to obtain the initial spatial distance values ​​corresponding to each residential point, which are 0.8km, 1.2km, 1.5km, 1.8km and 2.0km respectively. The Gaussian decay function is used to calculate and process each initial spatial distance value. The calculation method of the Gaussian decay function is as follows:

[0032] in, This is the spatial attenuation value. This represents the initial spatial distance between the i-th settlement and the j-th facility. The attenuation scale parameter is set to 1.5, and the calculated spatial attenuation values ​​for the five settlements are 0.862, 0.726, 0.607, 0.487 and 0.368, respectively. Extract the facility type parameters (e.g., weighting factor 1.5) and capacity parameters (e.g., maximum daily patient volume of 5000) corresponding to each facility location. Then, calculate the initial service load corresponding to each facility location by weighted summation of the spatial attenuation value and the facility type parameters. The weighted summation calculation method is as follows:

[0033] in, This is the initial service load. For the service population data of the i-th settlement, Let W be the spatial attenuation value corresponding to the i-th settlement, and W be the weighting factor. Taking the first settlement as an example, the service population density is 12,000 people / km². 2 Multiplied by the radiation area of ​​2.0 km 2 The service population data is 24,000 people. Multiplying it by the spatial attenuation value of 0.862 and then dividing it by the facility type weight factor of 1.5, the directional load generated by the residential point to the facility point is 13,792 person-times. The directional load of the other 4 residential points is calculated in turn, which are 11,616 person-times, 9,712 person-times, 7,792 person-times and 5,888 person-times respectively. After summing them up, the initial service load corresponding to the facility point is 48,800 person-times. Demand data for residents in five residential areas associated with the facility were retrieved, including demand intensity coefficients for each residential area (1.2, 1.0, 0.9, 1.1, and 0.8). The initial service load, demand intensity coefficient, and service population data for each residential area were used as input features and fed into a pre-trained random forest regression model. This model was trained based on historical facility operation data. During training, a large amount of sample data was extracted from the historical facility operation database. Each sample data point included the facility's initial service load, demand intensity coefficients for each residential area within its coverage area, and service population data as input features. The actual service load is used as the output label. Multiple different training subsets are generated by Bootstrap with replacement resampling and multiple decision trees are constructed. After each decision tree grows independently, they together form a preset random forest regression model. When predicting, the initial service load, demand intensity coefficient and service population data of the current facility point are input into the preset random forest regression model. Each decision tree outputs a predicted value. The preset random forest regression model takes the average of all predicted values ​​as the final output. In this embodiment, after processing the input initial service load, demand intensity coefficient and service population data, the output potential service load corresponding to the facility point is 34,560 person-times. Repeat the steps for the location data of the remaining 19 facility points in the spatial accessibility matrix, and sequentially complete the matching of resident demand data corresponding to each facility point. Then, calculate the initial service load by combining the facility type parameters and spatial attenuation values ​​of each facility point, and finally obtain the potential service load corresponding to each facility point.

[0034] Using the above method, we can start from the spatial matching relationship between resident demand data and facility location data, and incorporate facility type parameters, capacity parameters and resident demand data into a unified load quantification calculation framework. This provides a quantitative basis for the potential service load of each facility location for subsequent supply and demand matching assessment. It covers dimensions such as the directional load of resident locations within the radiation range of each facility location, the initial service load, and the potential service load obtained after correction. This is a prerequisite for achieving accurate assessment of facility supply and demand matching.

[0035] In the embodiments of this application, step S140 involves calculating the difference between the capacity parameter and the potential service load to obtain a difference result, comparing the difference result with a preset matching degree grading standard to obtain the supply-demand matching degree value corresponding to each facility point, and further includes the following steps, such as... Figure 4 As shown, the specific content is as follows: Step S410: Calculate the difference between the capacity parameter and the potential service load to obtain the difference result; Step S420: Perform a linear transformation on the difference result to obtain the first mapping value; Step S430: Extract the corresponding threshold data through the preset matching degree grading standard. When the first mapping value is greater than the threshold data, it is determined that the mapping value meets the high load condition, and the first matching degree is obtained. Step S440: Obtain the location points of the facility points corresponding to the first matching degree, perform spatial aggregation and division on the location points, and determine the proximity index corresponding to the location points; Step S450: Based on the proximity index, perform a weighted summation process in combination with the first matching degree to obtain the first index value; Step S460: When the first indicator value meets the preset conditions, perform a rejection operation on the first indicator value to obtain the supply and demand matching degree value.

[0036] Specifically, this embodiment takes a facility at longitude 116.3° and latitude 39.9° as an example. The facility's capacity parameter is a maximum daily patient volume of 5,000 and a potential service load of 34,560. Subtracting the facility's capacity parameter from its potential service load yields a difference of -29,560, indicating that the facility is currently in a state where demand far exceeds capacity. To eliminate the impact of differences in facility size on the assessment results, a capacity utilization rate interval is defined to perform a linear transformation on the difference results, mapping the difference results to a standardized interval. The linear transformation method for the difference results is as follows:

[0037] in, For mapping values, The result is the difference. This represents the lower limit of the capacity utilization range, with a value of -30000. The upper limit of the capacity utilization rate range is represented by a value of 10000. The difference result is linearly mapped to the standardized score range of 0 to 100 using the capacity utilization rate range. After calculation, the first mapped value of the difference result -29560 person-times is 1.1. Based on urban planning facility configuration standards and expert experience, a preset matching degree grading standard is set. The preset matching degree grading standard divides the mapping value into several intervals, such as a mapping value greater than 80 as extremely abundant, 60 to 79 as good matching, 40 to 59 as basic matching, 20 to 39 as slight mismatch, and less than 20 as severe mismatch. In the preset matching degree grading standard, the interval with a mapping value less than 20 corresponds to the most severe level of supply and demand mismatch. The critical value is set to 20, and each matching degree level is assigned a quantitative score to facilitate subsequent weighted calculation. For example, extremely abundant is assigned a value of 0, good matching is assigned a value of 0.2, basic matching is assigned a value of 0.4, slight mismatch is assigned a value of 0.6, and severe mismatch is assigned a value of 1.0. The corresponding threshold data is extracted through the preset matching degree grading standard. For example, if the critical threshold of the grading standard is 20, the first mapping value of 1.1 is compared with the threshold data. If the first mapping value is less than the critical threshold of 20, the first matching degree is determined to be severe mismatch and is assigned a value of 1.0. For facilities with severe mismatches, their coordinates (e.g., longitude 116.3°, latitude 39.9°) are obtained and spatially clustered using a hierarchical clustering algorithm along with the coordinates of the remaining 19 facilities in the spatial reachability matrix. The Euclidean distances between each pair of the 20 facilities are calculated, forming a 20×20 spatial distance matrix. Initially, each facility is treated as an independent cluster. The process iteratively searches for and merges the two closest Euclidean clusters, using the average distance method to calculate inter-cluster distances. The spatial distance matrix is ​​updated after each merge, repeating until all facilities are merged into a single independent cluster. This iterative process forms a clustering tree. In the clustering tree, the lower the level at which two facilities are first merged, the higher their spatial proximity. A proximity index is calculated based on the normalized level position at the time of the first merge; the earlier the facility is merged, the closer its proximity index is to 1, and the later the merge... The closer the proximity index is to 0, the better. Taking severely mismatched facilities as an example, the inter-cluster distance value when the severely mismatched facility is first merged with the other 19 facilities is extracted in the clustering tree. The inter-cluster distance values ​​are then sorted from closest to furthest. The proximity index is calculated by normalizing the inter-cluster distance value at the time of the first merge. The facility with the smallest inter-cluster distance value is mapped to a proximity index of 1.0, the facility with the largest inter-cluster distance value is mapped to 0, and the intermediate values ​​are mapped linearly proportionally. For example, if the inter-cluster distance value when the severely mismatched facility is first merged with another facility is 0.8 km, and the maximum inter-cluster distance value of all facility pairs in the clustering tree is 4.0 km, then the proximity index of the facility pair is 1 - 0.8 / 4.0 = 0.8. If the inter-cluster distance value when the severely mismatched facility is first merged with another facility is 3.2 km, then the proximity index is 1 - 3.2 / 4.0 = 0.2. This determines the proximity index between each pair of facilities. For the cluster where the current facility point is located, the first matching degree with neighboring facility points with a proximity index greater than 0.5 is weighted and summed using the proximity index as the weight to obtain the first index value of each facility point in the cluster. For example, if the first matching degree of the current facility point is severely mismatched and assigned a value of 1.0, it is weighted and summed with neighboring facility points with a proximity index of 0.8 and a first matching degree of slightly mismatched and assigned a value of 0.6, resulting in a first index value of 1.0 + 0.8 × 0.6 = 1.48. Based on the statistical distribution characteristics of the first indicator value in historical facility operation data and the experience of domain experts, a preset indicator classification rule is set. This preset indicator classification rule is used as the preset condition in this embodiment. The preset condition is: when the first indicator value is less than 2.0, the facility needs to be given special attention. The first indicator value of the current facility is determined, and a removal operation should be performed. When the first indicator value is too low, it indicates that the supply and demand matching degree is seriously mismatched and the spatial correlation with the surrounding facility points is weak. It belongs to an isolated abnormal facility point. After judging according to the preset condition, the abnormal facility point is removed to avoid the interference of abnormal facility points on the overall supply and demand matching degree assessment result. Therefore, the first indicator value is 1.48, which meets the preset condition. The removal operation is performed, and it is marked as an abnormal facility point that needs special attention. The first matching degree corresponding to the remaining facility point after removal is used as the final supply and demand matching degree value of the facility point.

[0038] By standardizing and mapping the difference results using the above method, and comparing them with the preset matching degree classification standard, the supply and demand matching status of each facility point is determined. Then, combined with the proximity index and index classification rules, a comprehensive evaluation and anomaly removal are carried out, providing accurate supply and demand matching degree values ​​for subsequent overload status identification. This covers dimensions such as the difference results, first mapping value, first matching degree, proximity index, and first index value of each facility point, which is a key link in realizing the quantitative evaluation of facility supply and demand matching.

[0039] In an embodiment of this application, step S150, when the supply-demand matching degree value is lower than the critical value in the preset matching degree grading standard, calculates the excess demand exceeding the capacity parameter based on the resident demand data and marks the facility as overloaded, further includes the following steps: Figure 5 As shown, the specific content is as follows: Step S510: When the supply and demand matching degree value is lower than the critical value in the preset matching degree grading standard, obtain the service population data in the resident demand data; Step S520: Calculate the actual demand based on the service population data and the service coverage area; Step S530: Calculate the excess demand based on the actual demand and the capacity parameters, and mark the facility as overloaded based on the excess demand.

[0040] Specifically, this embodiment takes a community health service station with longitude of 116.4° and latitude of 39.8° as an example. The capacity parameter is a maximum daily number of 500 patients. The supply and demand matching degree calculated in step S140 is 15.5, which is lower than the critical value of 20.0 in the preset matching degree grading standard, thus triggering the overload identification process. Obtain the service population data from the resident demand data of the community health service station, such as a service coverage area of ​​2.0 square kilometers, which includes three residential areas, residential point X, residential point Y, and residential point Z, and extract the service population density value within the service coverage area from the population census database, such as 15,000 people / square kilometer. The actual demand is calculated based on the service population data and the service coverage area, using the following formula:

[0041] in, This represents the actual demand. Indicates the population density value served, and A represents the service coverage area. This represents the average daily probability of medical visits per person, which can be taken as 0.025. Substituting this value into the calculation, the actual daily demand of the community health service station is 15000 × 2.0 × 0.025 = 750 visits. The excess demand is calculated based on the actual demand and capacity parameters. The excess demand is calculated by subtracting the capacity parameters from the actual demand.

[0042] in, The excess demand is represented by C, which indicates the capacity parameter of the facility. Substituting this parameter into the calculation, the excess demand is 250 people per day. It is determined that the actual demand of the facility has exceeded its service capacity, and the location data of the facility corresponding to the community health service station is marked as overloaded.

[0043] Using the above method, when the supply and demand matching degree of a facility is lower than a preset threshold, the actual demand can be quantitatively calculated based on the service population data and service coverage. Overloaded facilities whose actual demand exceeds the capacity parameters can be identified and their overload status can be marked. This provides a data foundation for subsequent operations, covering dimensions such as the service population data, actual demand, excess demand, and overload status marking of overloaded facilities. This is a prerequisite for the identification and control of overload risks in community living circles.

[0044] In an embodiment of this application, step S160 involves performing spatial interpolation analysis on the excess demand corresponding to the overload state to obtain overflow transmission path parameters, extracting demand time period characteristics from the residential location data, and determining overflow priority based on the demand time period characteristics. The step also includes the following steps: Figure 6 As shown, the specific content is as follows: Step S610: Perform spatial interpolation analysis on the excess demand to obtain overflow direction distribution data; Step S620: Extract the conduction path parameters for each overflow direction based on the overflow direction distribution data to obtain the overflow conduction path parameters; Step S630: Extract peak demand data for each residential location from the location data of the residential locations, and perform time-series decomposition on the peak demand data to obtain decomposed data; Step S640: Determine the overflow priority of each settlement based on the decomposed data.

[0045] Specifically, taking the community health service station marked as overloaded in step S150 as the origin, and the direction from the overloaded community health service station to each residential point as the overflow direction, the demand intensity of each residential point is weighted and calculated. The closer the residential point is, the higher the overflow weight is assigned. For example, the 360-degree space around the overloaded facility point is divided into 8 directional sectors at 45 degrees each. The excess demand of the residential points in each directional sector is counted, and the demand overflow intensity is allocated according to the relationship that the weight is inversely proportional to the square of the distance, satisfying the following relationship:

[0046] in Let i be the overflow weight of the i-th settlement. Given the initial spatial distance from the i-th residential point to the overloaded facility point, the overflow weights of residential points X, Y, and Z are calculated to be 1.563, 0.444, and 0.207, respectively. After normalizing the overflow weights of the three overflow directions, the overflow direction distribution data of the three residential points in the northeast direction are obtained. The overflow propagation path parameters for each overflow direction are obtained based on the distance attenuation function and the quantization of overflow direction distribution data. The distance attenuation function adopts an inverse proportional function form, and the calculation method is as follows:

[0047] in, The overflow conduction path parameters are α, which is the distance attenuation coefficient and can be set to 1.0. For example, the overflow conduction path parameters for residential point X, which is 0.8km away from the overloaded facility, the overflow conduction path parameters for residential point Y, which is 1.5km away from the overloaded facility, and the overflow conduction path parameters for residential point Z, which is 2.2km away from the overloaded facility, are 0.56, 0.28, and 0.16, respectively. It is determined that the overflow conduction path parameters in the three overflow directions are all greater than the preset threshold, such as 0.15. The preset threshold is set based on the statistical distribution of conduction path parameters in historical facility operation data, and all of them are determined as target conduction path parameters. The demand time period characteristics of three residential areas are extracted from the location data of the residential areas. The historical medical demand records of the past 30 days are retrieved from the location data of these three residential areas. For example, a seasonal trend decomposition algorithm is used to decompose the peak demand time period data of each residential area into time series, and random fluctuation components are separated to obtain decomposed data. The peak demand time period characteristics of each residential area are extracted. For example, in the decomposed data, there is a concentrated peak demand for medical treatment in residential area X from 18:00 to 22:00 in the evening, with a peak demand of 120 people per day; the demand in residential area Y is relatively concentrated from 8:00 to 10:00 in the morning, with a peak demand of 60 people per day; the demand time period of residential area Z is relatively evenly distributed, without obvious concentrated peaks. Based on the decomposed data, the overflow priority of each residential point is determined. The highest overflow transmission path parameter, such as 0.56 for residential point X, is multiplied by the corresponding residential point's demand peak characteristics, such as a demand peak coefficient of 1.8 and a daily average basic demand coefficient of 1.0.

[0048] in, For overflow priority, Let be the overflow propagation path parameters for the i-th settlement. Let B be the peak demand coefficient for the i-th residential point, which is the ratio of the peak demand during the peak period to the average daily basic demand coefficient. Let B be the adjustment coefficient, which can be 1.0. The comprehensive score of residential point X is calculated to be 0.56×1.8×1.0=1.008, the comprehensive score of residential point Y is 0.28×1.2×1.0=0.336, and the comprehensive score of residential point Z is 0.16×1.0×1.0=0.160. Based on the comprehensive scores, residents are ranked from highest to lowest to determine the overflow priority of each settlement. For example, settlement X is the first priority, settlement Y is the second priority, and settlement Z is the third priority. Each settlement is also labeled with a corresponding overflow demand type, such as settlement X being concentrated at night, settlement Y being concentrated during the day, and settlement Z being balanced.

[0049] Using the above methods, spatial interpolation analysis can be performed on the excess demand of overloaded facilities to obtain transmission path parameters. Combined with the characteristics of residents' demand periods, the overflow priority can be determined, providing a priority basis for subsequent intelligent demand diversion and capacity adjustment. This includes dimensions such as transmission path parameters of overflow direction, decomposed data, comprehensive scoring, and overflow priority of each residential point, which is a prerequisite for the orderly diversion of overloaded demand in community living circle facilities.

[0050] In an embodiment of this application, step S170, which involves allocating the excess demand to neighboring facilities based on the overflow transmission path parameters and overflow priority to obtain the demand allocation for each neighboring facility, and obtaining demand transfer feasibility data based on the demand allocation, further includes the following steps: Figure 7 As shown, the specific content is as follows: Step S710: Filter nearby facility points according to the overflow conduction path parameters to obtain an initial set of facility points; Step S720: Obtain the capacity parameters and current load data of each neighboring facility in the initial facility set, and calculate the remaining capacity data of each neighboring facility based on the capacity parameters and the current load data; Step S730: According to the overflow priority, the excess demand of each residential point is allocated to each neighboring facility point in the initial facility point set in sequence to obtain the demand allocation of each neighboring facility point; Step S740: Based on the demand allocation of each neighboring facility and the remaining capacity data, calculate the capacity change of each neighboring facility after receiving the demand allocation; Step S750: Compare the capacity change with a preset capacity threshold to obtain a comparison result, and obtain demand transfer feasibility data based on the comparison result.

[0051] Specifically, this embodiment takes a community health service station marked as overloaded and three associated residential points, such as residential point X, residential point Y, and residential point Z, as an example. The excess demand of the community health service station marked as overloaded as the source facility point is 250 people per day. The excess demand corresponding to residential point X, which has the first overflow priority, is 120 people per day. The excess demand corresponding to residential point Y, which has the second overflow priority, is 80 people per day. The excess demand corresponding to residential point Z, which has the third overflow priority, is 50 people per day. Neighboring facilities with overflow transmission path parameters greater than a preset threshold of 0.15 are included in the candidate range. Within a 2.5-kilometer radius of an overloaded facility, such as a community health service station marked as overloaded, two eligible neighboring facilities are identified, such as facility A, which is 0.8 km away from the overloaded facility and has an overflow transmission path parameter of 0.50, and facility B, which is 1.5 km away from the overloaded facility and has an overflow transmission path parameter of 0.30. Facility A and facility B are used as the initial set of facilities. Obtain the capacity parameters and current load data of each neighboring facility in the initial facility set. For example, facility A's capacity parameter is a maximum daily patient capacity of 800, and its current load data is 300 patients received per day; facility B's capacity parameter is a maximum daily patient capacity of 800, and its current load data is 200 patients received per day. Calculate the remaining capacity data of each neighboring facility based on the capacity parameters and current load data. The calculation formula is:

[0052] in, For remaining capacity data, This indicates the capacity parameters of the facility site. The current load data of the facilities is shown. The remaining capacity data of facility A is calculated to be 500 people / day, and the remaining capacity data of facility B is 400 people / day. Based on overflow priority, the excess demand of each residential area is allocated to the neighboring facility points in the initial facility point set in sequence, with the allocation ratio being the proportion of the overflow transmission path parameters of each neighboring facility point in the total transmission path parameters:

[0053] in, For the allocation ratio, Let m represent the overflow propagation path parameter of the j-th neighboring facility point, m represent the total number of neighboring facility points in the initial facility point set, the allocation ratio of facility point A is 0.50 / (0.50+0.30)=0.625, and the allocation ratio of facility point B is 0.30 / (0.50+0.30)=0.375; According to the overflow priority from high to low, the excess demand of residential point X is 120 person-times / day, which is allocated proportionally to facility point A (120 × 0.625 = 75 person-times / day) and facility point B (120 × 0.375 = 45 person-times / day); the excess demand of residential point Y is 80 person-times / day, which is allocated to facility point A (80 × 0.625 = 50 person-times / day) and facility point B (80 × 0.375 = 30 person-times / day); the excess demand of residential point Z is 50 person-times / day, which is allocated to facility point A (50 × 0.625 = 31.25 person-times / day) and facility point B (50 × 0.375 = 18.75 person-times / day). The total excess demand is 156.25 person-times / day for facility point A and 93.75 person-times / day for facility point B. Based on the demand allocation and remaining capacity data of each neighboring facility, the capacity change of each neighboring facility after receiving the demand allocation is calculated. The calculation method for capacity change is as follows:

[0054] in, This represents the change in capacity. This represents the remaining capacity data of the j-th neighboring facility. Let J represent the demand allocation of the j-th adjacent facility. The capacity change of facility A is 500-156.25=343.75 person-times / day, and the capacity change of facility B is 400-93.75=306.25 person-times / day. Based on the basic requirements for safe facility operation, a preset capacity threshold is set to ensure that the capacity change after receiving overflow demand is not less than zero, meaning the facility will not exceed its maximum carrying capacity due to receiving overflow demand. The preset capacity threshold is set to 0 person-times / day. The capacity change is compared with the preset capacity threshold. The capacity change of facility point A is 343.75 person-times / day, which is greater than the preset capacity threshold; the capacity change of facility point B is 306.25 person-times / day, which is also greater than the preset capacity threshold. The capacity change of both adjacent facility points is greater than 0, indicating that there will be no overload after receiving overflow demand. The comparison results show that both adjacent facility points have the conditions to accept demand. Based on the comparison results, demand transfer feasibility data is obtained. The feasible value is marked as 1.0, and the infeasible value is marked as 0. If facility point A is feasible, facility point B is also feasible. The demand transfer feasibility data of facility point A and facility point B are both 1.0.

[0055] Using the above method, neighboring facilities can be screened based on overflow transmission path parameters and their remaining capacity data can be obtained. The excess demand of each residential area can be allocated to neighboring facilities in order of overflow priority. The feasibility of demand transfer can be judged by comparing the capacity change with the preset capacity threshold, providing a basis for feasibility judgment for subsequent capacity adjustment. It covers dimensions such as the remaining capacity data of each neighboring facility, demand allocation, capacity change, comparison results and demand transfer feasibility data, which is a prerequisite for realizing intelligent guidance and capacity optimization of overloaded demand in community living circle facilities.

[0056] In an embodiment of this application, in step S180, when the demand transfer feasibility data meets the preset receiving conditions, the excess demand is added to the capacity parameters of the corresponding neighboring facility points according to the corresponding demand allocation amount to obtain the updated capacity parameters of each facility point. The specific content is as follows: Specifically, this embodiment uses facility point A and facility point B as examples. The demand transfer feasibility data of facility point A and facility point B are both 1.0. The preset receiving condition is that the demand transfer feasibility data must be greater than a preset receiving threshold, such as 0.5. The preset receiving threshold is based on the statistical distribution of feasibility scores in historical demand transfer cases and the setting of security strategies. It is determined that the demand transfer feasibility data of facility point A and facility point B both meet the preset receiving condition. According to the allocation results in step S170, the demand allocation for facility A is 156.25 visits / day, and the demand allocation for facility B is 93.75 visits / day. The capacity parameter for facility A is a maximum daily capacity of 800 visits, and the capacity parameter for facility B is a maximum daily capacity of 600 visits. Based on the corresponding demand allocation, the excess demand is sequentially added to the capacity parameters of the corresponding neighboring facilities to obtain the updated capacity parameters for each neighboring facility, as shown in the following formula:

[0057] in, To update capacity parameters, Let j be the capacity parameter of the j-th neighboring facility point. Let represent the demand allocation of the j-th neighboring facility. The calculated update capacity data for facility A is 956.25 person-times / day, and the update capacity data for facility B is 693.75 person-times / day. The remaining excess demand at overloaded facilities after demand transfer is updated to 0 person-days, while the capacity parameters remain unchanged. However, the current load data is updated from 500 person-days to 500 person-days, meaning that all excess demand has been allocated to nearby facilities.

[0058] Using the above method, when the feasibility data for demand transfer meets the preset receiving conditions, the excess demand of each residential point can be superimposed on the capacity parameters of adjacent facilities according to the corresponding demand allocation amount, so as to obtain the updated capacity parameters of each facility. This provides a data foundation for subsequent secondary difference comparison and evaluation data output, covering dimensions such as the demand allocation amount, updated capacity data and updated capacity parameters of each adjacent facility. It is a key link in realizing the adjustment of facility capacity and optimization of supply and demand balance in the community living circle.

[0059] In the embodiments of this application, step S190, which involves performing a secondary interpolation comparison between the updated capacity parameters and the potential service load to obtain a secondary interpolation comparison result, and outputting evaluation data based on the secondary interpolation comparison result, further includes the following steps: Figure 8 As shown, the specific content is as follows: Step S810: Calculate the difference between the updated capacity parameter and the potential service load to obtain a secondary difference comparison result; Step S820: Based on the results of the second difference comparison, calculate the capacity utilization rate and supply-demand balance deviation of each facility point; Step S830: When the supply-demand balance deviation is greater than the preset deviation threshold, determine the load status label of each facility point according to the capacity utilization rate; Step S840: Filter out overloaded facility points according to the load status label, and obtain the excess demand that the overloaded facility points have not been met; Step S850: Obtain surrounding candidate facility points based on the location of the overloaded facility point, and allocate the unmet excess demand to the surrounding candidate facility points to obtain the overflow demand redistribution result. Step S860: Obtain spatial coverage optimization suggestions based on the overflow demand redistribution results, and output the load status label, overflow demand redistribution results, and spatial coverage optimization suggestions as evaluation data.

[0060] Specifically, a secondary interpolation comparison is performed on the source facility point, facility point A, and facility point B. The difference between the updated capacity parameters and the potential service load of each facility point is calculated. The potential service load of each facility point is 750 people / day for the source facility point, 600 people / day for facility point A, and 500 people / day for facility point B. The secondary interpolation comparison result for the source facility point is 500-750=-250 people / day; the secondary interpolation comparison result for facility point A is 956.25-600=356.25 people / day; and the secondary interpolation comparison result for facility point B is 693.75-500=193.75 people / day. Based on the results of the second interpolation comparison, the capacity utilization rate and the deviation from the supply-demand balance of each facility point are calculated. The capacity utilization rate is calculated by dividing the potential service load by the updated capacity parameter. The calculated capacity utilization rate for facility point A is... The capacity utilization rate of facility point B is... The capacity utilization rate of the source facility is ; Based on the capacity utilization rate, the load status labels of each facility point were determined. The capacity utilization rate of facility point A was 62.75%, and the load status label was slightly overloaded; the capacity utilization rate of the source facility point was 150%, and the load status label was severely overloaded; the capacity utilization rate of facility point B was 72.07%, and the load status label was normal. The calculation method for the deviation between supply and demand balance is as follows:

[0061] in, The deviation from the supply-demand balance, This indicates the potential service load of the facility. The parameter representing the renewal capacity of facility point A is used to calculate the supply-demand imbalance deviation of facility point A. The supply and demand imbalance deviation at facility point B is: The supply and demand imbalance deviation of the source facility points is ; Based on the criteria for determining the load status of facilities in the facility configuration standards in the field of urban planning and the experience of facility operation, a preset deviation threshold is set, such as 30%. The supply and demand balance deviation of facility point A is 37.25%, which is greater than the preset deviation threshold; the supply and demand balance deviation of facility point B is 27.93%, which is less than the preset deviation threshold; and the supply and demand balance deviation of the source facility point is 50%, which is greater than the preset deviation threshold. Overloaded facility points are identified based on load status labels, resulting in the source facility point and facility point A. The unmet excess demand at these overloaded facility points is then determined. For example, after demand transfer, the excess demand of 250 person-days at the source facility point has been fully allocated to facility points A and B; therefore, the unmet excess demand at the source facility point is 0 person-days. After facility point A receives an allocated demand of 156.25 person-days, the difference between the potential service load of 600 person-days and the updated capacity parameter of 956.25 person-days is 356.25 person-days. This means facility point A still has a remaining capacity of 356.25 person-days, and the unmet excess demand is 0 person-days. Therefore, the unmet excess demand at facility point A is recorded as 0 person-days. Based on the location of the overloaded facility point, nearby candidate facility points are obtained. For example, within a 1.5-kilometer radius of facility point A, there are candidate facility points C and D. The remaining capacity data of candidate facility point C is 200 people / day, and the remaining capacity data of candidate facility point D is 150 people / day. The unmet excess demand of 0 people / day from the source facility point is allocated to the surrounding candidate facility points to obtain the overflow demand redistribution result. For example, if candidate facility point C receives 0 people / day and candidate facility point D receives 0 people / day, no allocation is required. Based on the location information of each candidate facility point in the overflow demand redistribution results, spatial coverage is calculated. For example, the location information of candidate facility point C (longitude 116.35°, latitude 39.85°), candidate facility point D (longitude 116.32°, latitude 39.82°), and the source facility point, facility point A, and facility point B are integrated to calculate the union of the service coverage of each facility point. Areas still outside the service coverage are identified, revealing an uncovered residential area of ​​approximately 0.3 square kilometers in the region between longitude 116.28° and 116.34° and latitude 39.79° and 39.83°. Spatial coverage optimization suggestions are generated based on this uncovered area, such as suggesting the construction of a new micro-clinic with a design capacity of no less than 200 people per day at longitude 116.30° and latitude 39.81°. This is expected to effectively fill the current service coverage blind spot and reduce queuing time at surrounding facilities. The load status labels of each facility, the results of overflow demand redistribution, and the suggestions for spatial coverage optimization are used as evaluation data outputs.

[0062] Using the above methods, after the capacity update is completed, a secondary difference comparison can be performed between the updated capacity parameters and the potential service load to quantitatively assess the capacity utilization rate and supply-demand balance deviation of each facility, determine the load status label of the facility, and generate overflow demand redistribution results and spatial coverage optimization suggestions based on the identification of overloaded facility points. This forms a closed loop of assessment, identification, diversion, reassessment, and optimization, providing quantitative data support for the supply-demand matching assessment, overload risk identification, and spatial layout optimization of public service facilities in community living circles.

[0063] In the above method, all overload identification, overflow transmission path analysis, demand allocation, and capacity adjustment operations are executed by the system program, avoiding biases caused by manual assessment and experience-based judgment. The updated facility capacity data and load status labels can be used in real time to generate spatial coverage optimization suggestions and facility configuration decisions, forming a closed loop of assessment, identification, mitigation, reassessment, and optimization. This provides a reliable data foundation for subsequent public service facility configuration, refined urban planning governance, and community living circle monitoring through big data analysis, improving the accuracy and adaptability of community living circle facility supply and demand matching assessment.

[0064] This application also provides a community living circle facility perfection assessment system, which may include a spatial accessibility matrix construction module, a potential service load calculation module, a supply and demand matching assessment module, an overload status identification module, an overflow analysis module, a demand transfer assessment module, a capacity update module, and an assessment data output module. The system includes the following modules: a spatial accessibility matrix construction module, used to acquire residential location data and facility location data for the target area, and construct a spatial accessibility matrix based on the residential location data and facility location data; a potential service load calculation module, used to acquire facility type parameters and capacity parameters corresponding to the facility location data and resident demand data associated with the residential location data based on the spatial accessibility matrix, match the resident demand data to the corresponding facility, and calculate the potential service load corresponding to each facility based on the facility type parameters; a supply-demand matching degree evaluation module, used to calculate the difference between the capacity parameter and the potential service load, and compare the difference result with a preset matching degree grading standard to obtain the supply-demand matching degree value corresponding to each facility; and an overload status identification module, used to calculate the overload exceeding the capacity parameter based on the resident demand data when the supply-demand matching degree value is lower than the critical value in the preset matching degree grading standard. The system comprises the following modules: a demand transfer assessment module, a demand shift evaluation module, and a capacity update module. The capacity update module is used to add the excess demand to the capacity parameters of the corresponding neighboring facilities when the demand shift feasibility data meets preset reception conditions. The update capacity parameters are then compared with the potential service load using a secondary interpolation comparison to obtain the secondary interpolation comparison result, and the evaluation data is output based on the secondary interpolation comparison result.

[0065] It should be noted that the embodiment of the community living circle facility improvement assessment system provided in this application can be used to execute the processing flow of the embodiment of the community living circle facility improvement assessment method and system described above. Its functions will not be repeated here, but can be referred to the detailed description of the above method embodiment.

[0066] As described above, the community living circle facility completeness assessment system provided in this application collects residential location data and facility location data to construct a spatial accessibility matrix, calculates potential service load, obtains overflow transmission path parameters for overloaded facilities through spatial interpolation analysis, determines overflow priority, and assesses the feasibility of demand transfer and adjusts capacity based on the comparison of remaining capacity data and capacity changes of neighboring facilities. Compared with traditional assessment methods that rely on straight-line distance coverage or statistical statistics of facility types, this application forms a closed loop of assessment, identification, diversion, reassessment, and optimization through secondary interpolation comparison. This continuously tracks and solves the problem of inaccurate supply and demand matching judgment caused by fragmented assessment dimensions, improves the accuracy and adaptability of community living circle facility completeness assessment, and meets the practical application needs of refined urban governance and community living circle planning.

[0067] This application also provides an electronic device, which includes one or more processors and memory resources, represented by a memory, for storing instructions executable by the processor, such as application programs. The application programs stored in the memory may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor is configured to execute instructions to perform the aforementioned method and system for assessing the completeness of community living area facilities.

[0068] The electronic device may also include a power supply component configured to perform power management of the electronic device, a wired or wireless network interface configured to connect the electronic device to a network, and an input / output (I / O) interface. The electronic device can be operated based on operating devices stored in memory, such as Windows Server™, MacOSX™, Unix™, Linux™, FreeBSD™, or similar.

[0069] In one embodiment, a computer device, which may be a server, is also provided. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device stores data. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method and system for assessing the completeness of community living area facilities.

[0070] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method and system for assessing the completeness of community living area facilities. The display unit of the computer device is used to form a visually visible image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0071] This application also provides a non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by the processor of the aforementioned electronic device, the electronic device is able to execute a method and system for evaluating the completeness of community living circle facilities, including: collecting residential location data and facility location data of a target area; constructing a spatial accessibility matrix based on the residential location data and the facility location data; obtaining facility type parameters and capacity parameters corresponding to the facility location data, as well as resident demand data associated with the residential location data, based on the spatial accessibility matrix; matching the resident demand data to the corresponding facility points, and calculating the potential service load corresponding to each facility point in combination with the facility type parameters; calculating the difference between the capacity parameters and the potential service load to obtain a difference result; comparing the difference result with a preset matching degree grading standard to obtain a supply-demand matching degree value corresponding to each facility point; when the supply-demand matching degree value is lower than the preset... When the critical value in the matching degree grading standard is reached, the excess demand exceeding the capacity parameter is calculated based on the resident demand data, and the facility is marked as overloaded. Spatial interpolation analysis is performed on the excess demand corresponding to the overloaded state to obtain overflow transmission path parameters, and demand time period characteristics are extracted from the resident location data to determine overflow priority. The excess demand is allocated to neighboring facility points according to the overflow transmission path parameters and overflow priority to obtain the demand allocation amount for each neighboring facility point, and demand transfer feasibility data is obtained based on the demand allocation amount. When the demand transfer feasibility data meets the preset reception conditions, the excess demand is superimposed on the capacity parameters of the corresponding neighboring facility points according to the corresponding demand allocation amount to obtain the updated capacity parameters of each facility point. A secondary difference comparison is performed between the updated capacity parameters and the potential service load to obtain the secondary difference comparison result, and evaluation data is output based on the secondary difference comparison result.

[0072] This application may take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0073] It should be noted that although the steps of the community living circle facility improvement assessment method and system of this application are described in a specific order in the accompanying drawings, this does not require or imply that these steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps, such as omitting certain steps, combining multiple steps into one step, and / or breaking down one step into multiple steps, should all be considered part of this application.

[0074] It should be understood that this application is not limited to the detailed structure and layout of the modules of the community living circle facility perfection assessment system proposed in this specification. This application can have other implementations and can be implemented and executed in various ways. The foregoing variations and modifications fall within the scope of this application. It should be understood that the disclosure and definition of this application extend to all alternative combinations of two or more individual features mentioned or apparent in the text and / or drawings. All these different combinations constitute multiple alternative aspects of this application. The embodiments described in this specification illustrate the best known mode for implementing this application and will enable those skilled in the art to utilize this application.

Claims

1. A method for evaluating the completeness of community living circle facilities, characterized in that, The method includes: Collect location data of residential areas and facility locations in the target area, and construct a spatial accessibility matrix based on the residential area location data and facility location data; Based on the spatial accessibility matrix, obtain the facility type parameters and capacity parameters corresponding to the facility location data, as well as the resident demand data associated with the residential location data; The resident demand data is matched to the corresponding facility locations, and the potential service load of each facility location is calculated by combining the facility type parameters. The difference between the capacity parameter and the potential service load is calculated to obtain the difference result. The difference result is then compared with the preset matching degree classification standard to obtain the supply and demand matching degree value corresponding to each facility point. When the supply-demand matching degree value is lower than the critical value in the preset matching degree grading standard, the excess demand exceeding the capacity parameter is calculated based on the residents' demand data, and the facility is marked as overloaded. Spatial interpolation analysis is performed on the excess demand corresponding to the overload state to obtain overflow transmission path parameters, and demand time period characteristics are extracted from the residential location data to determine overflow priority based on the demand time period characteristics. Based on the overflow transmission path parameters and overflow priority, the excess demand is allocated to nearby facility points to obtain the demand allocation amount for each nearby facility point, and demand transfer feasibility data is obtained based on the demand allocation amount. When the demand transfer feasibility data meets the preset receiving conditions, the excess demand is added to the capacity parameters of the corresponding neighboring facility points according to the corresponding demand allocation amount to obtain the updated capacity parameters of each facility point. The updated capacity parameters and the potential service load are compared using a second difference to obtain a second difference comparison result. Evaluation data is then output based on the second difference comparison result.

2. The method for evaluating the completeness of community living circle facilities as described in claim 1, characterized in that, A spatial accessibility matrix is ​​constructed based on the residential location data and the facility location data, including: Based on the residential location data and the facility location data, obtain the coordinate values ​​of residential points and facility points in the target area, and construct an initial spatial distribution layer based on the residential point coordinate values ​​and facility point coordinate values; Road network data is obtained based on the initial spatial distribution layer, and the shortest path algorithm is used to calculate the shortest path from each residential point to each facility point in the initial spatial distribution layer based on the road network data, so as to obtain the shortest travel time. The service coverage of each residential area at each facility is determined based on the shortest travel time, and the target service matching pair is obtained based on the service coverage. The time decay resistance value is obtained based on the target service matching pair, and the time decay resistance value is smoothed to determine the smooth decay value of each target service matching pair. Based on the smooth attenuation value and the shortest travel time, a road network constraint term is constructed. The target service matching pairs are then arranged in a matrix using the road network constraint term to obtain a spatial reachability matrix.

3. The method for evaluating the completeness of community living circle facilities as described in claim 1, characterized in that, The resident demand data is matched to the corresponding facility locations, and the potential service load corresponding to each facility location is calculated based on the facility type parameters, including: The initial spatial distance value is obtained by calculating the location data of the facility and the location data of the residential area using the Euclidean distance calculation method. The initial spatial distance value is calculated using the Gaussian decay function to obtain the spatial decay value corresponding to each facility point; Extract the facility type parameters and capacity parameters corresponding to the facility location data, and perform a weighted summation of the facility type parameters and the spatial attenuation value to obtain the initial service load corresponding to each facility point. A regression algorithm is used to calculate the initial service load and the residents' demand data to obtain the potential service load for each facility.

4. The method for evaluating the completeness of community living circle facilities as described in claim 1, characterized in that, The difference between the capacity parameter and the potential service load is calculated to obtain the difference result. The difference result is then compared with a preset matching degree grading standard to obtain the supply-demand matching degree value for each facility point, including: Calculate the difference between the capacity parameter and the potential service load to obtain the difference result; Perform a linear transformation on the difference result to obtain the first mapping value; The corresponding threshold data is extracted by a preset matching degree classification standard. When the first mapping value is greater than the threshold data, the mapping value is determined to meet the high load condition, and the first matching degree is obtained. Obtain the location points of the facility points corresponding to the first matching degree, perform spatial aggregation and division on the location points, and determine the proximity index corresponding to the location points; Based on the proximity index, a weighted summation is performed in combination with the first matching degree to obtain the first index value; When the first indicator value meets the preset conditions, the first indicator value is removed to obtain the supply and demand matching degree value.

5. The method for evaluating the completeness of community living circle facilities as described in claim 1, characterized in that, When the supply-demand matching degree value is lower than the critical value in the preset matching degree grading standard, the excess demand exceeding the capacity parameter is calculated based on the residents' demand data, and the facility is marked as overloaded, including: When the supply-demand matching degree value is lower than the critical value in the preset matching degree grading standard, the service population data in the resident demand data is obtained. The actual demand is calculated based on the service population data and the service coverage area. The excess demand is calculated based on the actual demand and the capacity parameters, and the facility is marked as overloaded based on the excess demand.

6. The method for evaluating the completeness of community living circle facilities as described in claim 1, characterized in that, Spatial interpolation analysis is performed on the excess demand corresponding to the overload state to obtain overflow transmission path parameters. Demand time period characteristics are extracted from the residential location data, and overflow priority is determined based on these characteristics, including: Spatial interpolation analysis was performed on the excess demand to obtain the overflow direction distribution data; Based on the overflow direction distribution data, the conduction path parameters for each overflow direction are extracted to obtain the overflow conduction path parameters; Extract peak demand data for each residential location from the residential location data, and perform time-series decomposition on the peak demand data to obtain decomposed data; The overflow priority of each settlement is determined based on the decomposed data.

7. The method for evaluating the completeness of community living circle facilities as described in claim 1, characterized in that, Based on the overflow transmission path parameters and overflow priority, the excess demand is allocated to neighboring facilities to obtain the demand allocation for each neighboring facility. Demand transfer feasibility data is then obtained based on the demand allocation, including: An initial set of facility points is obtained by filtering nearby facility points based on the overflow conduction path parameters; Obtain the capacity parameters and current load data of each neighboring facility in the initial facility set, and calculate the remaining capacity data of each neighboring facility based on the capacity parameters and current load data; According to the overflow priority, the excess demand of each residential point is allocated to each neighboring facility point in the initial facility point set in sequence to obtain the demand allocation of each neighboring facility point. Based on the demand allocation of each neighboring facility and the remaining capacity data, calculate the capacity change of each neighboring facility after receiving the demand allocation; The capacity change is compared with a preset capacity threshold to obtain a comparison result, and demand transfer feasibility data is obtained based on the comparison result.

8. The method for evaluating the completeness of community living circle facilities as described in claim 1, characterized in that, A secondary interpolation comparison is performed between the updated capacity parameters and the potential service load to obtain a secondary interpolation comparison result. Evaluation data is then output based on the secondary interpolation comparison result, including: The difference between the updated capacity parameter and the potential service load is calculated to obtain a secondary difference comparison result. Based on the results of the second difference comparison, the capacity utilization rate and the deviation from the supply-demand balance of each facility point are calculated. When the supply-demand balance deviation exceeds a preset deviation threshold, the load status label of each facility is determined based on the capacity utilization rate. Based on the load status labels, overloaded facility points are selected, and the excess demand that is not met by the overloaded facility points is obtained. Based on the location of the overloaded facility point, surrounding candidate facility points are obtained, and the unmet excess demand is allocated to the surrounding candidate facility points to obtain the overflow demand redistribution result. Based on the overflow demand redistribution results, spatial coverage optimization suggestions are obtained, and the load status label, overflow demand redistribution results, and spatial coverage optimization suggestions are output as evaluation data.

9. The method for evaluating the completeness of community living circle facilities as described in claim 8, characterized in that, Based on the overflow demand reallocation results, spatial coverage optimization suggestions are obtained, including: Obtain the location information of each candidate facility point in the overflow demand redistribution result; The service coverage area of ​​each candidate facility is determined based on the location information. Identify uncovered areas based on the service coverage range, and generate spatial coverage optimization suggestions based on the uncovered areas.

10. A community living circle facility perfection assessment system, using the method described in any one of claims 1-9, characterized in that, include: The spatial accessibility matrix construction module is used to acquire residential location data and facility location data of the target area, and construct a spatial accessibility matrix based on the residential location data and facility location data; The potential service load calculation module is used to obtain the facility type parameters and capacity parameters corresponding to the facility location data and the resident demand data associated with the residential location data according to the spatial accessibility matrix, match the resident demand data to the corresponding facility point, and calculate the potential service load corresponding to each facility point in combination with the facility type parameters. The supply-demand matching degree assessment module is used to calculate the difference between the capacity parameter and the potential service load to obtain the difference result, and compare the difference result with the preset matching degree classification standard to obtain the supply-demand matching degree value corresponding to each facility point. The overload status identification module is used to calculate the excess demand exceeding the capacity parameter based on the residents' demand data when the supply-demand matching degree value is lower than the critical value in the preset matching degree grading standard, and to mark the facility as overloaded. The overflow analysis module is used to perform spatial interpolation analysis on the excess demand corresponding to the overload state, obtain overflow transmission path parameters, extract demand time period characteristics from the residential location data, and determine overflow priority based on the demand time period characteristics. The demand transfer assessment module is used to allocate the excess demand to nearby facilities based on the overflow transmission path parameters and overflow priority, to obtain the demand allocation amount for each nearby facility, and to obtain demand transfer feasibility data based on the demand allocation amount. The capacity update module is used to add the excess demand to the capacity parameters of the corresponding neighboring facility points according to the corresponding demand allocation amount when the demand transfer feasibility data meets the preset reception conditions, so as to obtain the updated capacity parameters of each facility point. The evaluation data output module is used to perform a secondary difference comparison between the updated capacity parameters and the potential service load to obtain a secondary difference comparison result, and output evaluation data based on the secondary difference comparison result.

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