Community service convenience level evaluation method, device, equipment and program product

Abstract

The application is suitable for the technical field of city management, and provides a community service convenience level evaluation method, device, equipment and program product. The method comprises the following steps: acquiring building information of a target community; respectively counting the number of different types of service facilities in the target community within a preset service range; calculating service facility related parameters of the target community according to the number of different types of service facilities in the target community, wherein the service facility related parameters comprise facility diversity parameters, per capita facility parameters, facility distribution density parameters and traffic coupling degree parameters; and evaluating the service convenience level of the target community according to target evaluation indexes determined according to the service facility related parameters of the target community. The method enriches the parameter design in the service convenience level evaluation method, and further improves the accuracy of the community evaluation result by fully considering the supply richness of the service facilities, thereby providing a reliable basis for optimizing the community life service facility construction.

Description

Technical Field

[0001] This application belongs to the field of urban management technology, and in particular relates to a method, device, equipment and program product for assessing the convenience level of community services. Background Technology

[0002] With the continuous advancement of urbanization, the level of service convenience in communities, as the basic unit of urban residents' lives, is a core element affecting residential comfort and quality of life, directly related to residents' quality of life. Community service convenience level assessment refers to the process of evaluating the degree of convenience a community provides to its residents through a comprehensive analysis of factors such as the quantity, distribution, and accessibility of various service facilities within the community.

[0003] The level of community convenience services should focus on meeting the actual needs of residents and pay attention to their real feelings and sense of gain. For example, whether the surrounding commercial facilities, leisure and entertainment facilities, public service facilities, medical and educational facilities are complete, their distance from the community, and the convenience of transportation are all directly related to the residents' service experience. Improved service facilities can significantly enhance the convenience of residents' lives.

[0004] However, existing methods for assessing the convenience of community services use relatively simple parameter designs and fail to fully consider the richness of service facilities, resulting in limited accuracy of the final assessment results. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and program product for assessing the convenience level of community services. These methods can enrich the parameter design in the assessment of service convenience levels, improve the accuracy of community assessment results, and provide a reliable basis for optimizing the construction of community life service facilities.

[0006] In a first aspect, embodiments of this application provide a method for evaluating the convenience level of community services, including:

[0007] Obtain the building information of the target community, whereby the building information is used to determine the center coordinates of the target community;

[0008] Within the preset service area, count the number of different types of service facilities in the target community. The preset service area is the range formed by taking the center coordinates as the starting point and the set duration corresponding to each type as the service radius.

[0009] Based on the number of different types of service facilities in the target community, calculate the relevant parameters of the service facilities in the target community. The relevant parameters of the service facilities include facility diversity parameters, per capita facility parameters, facility distribution density parameters, and traffic coupling parameters.

[0010] The service convenience level of the target community is assessed based on the target evaluation indicators determined according to the relevant parameters of the service facilities in the target community.

[0011] In one possible implementation of the first aspect, the different types include multiple primary types and multiple secondary types corresponding to each primary type. Within a preset service scope, the number of service facilities of different types in the target community is counted, including:

[0012] For each primary type, within the preset service area, count the number of service facilities of different secondary types corresponding to each primary type in the target community;

[0013] For each primary type, the total number of service facilities corresponding to each primary type in the target community is calculated based on the number of service facilities of different secondary types corresponding to each primary type in the target community.

[0014] In one possible implementation of the first aspect, service facility-related parameters of the target community are calculated based on the number of different types of service facilities in the target community, including:

[0015] For each primary type, the diversity parameter corresponding to each primary type in the target community is calculated based on the number of service facilities of different secondary types corresponding to each primary type in the target community;

[0016] For each primary type, based on the total number of service facilities corresponding to each primary type in the target community and the number of permanent residents in the target community, calculate the per capita facility parameters for each primary type in the target community;

[0017] For each primary type, based on the total number of service facilities corresponding to each primary type in the target community and the area of ​​the target community, calculate the facility distribution density parameter corresponding to each primary type in the target community;

[0018] Calculate the traffic coupling parameters corresponding to the target community based on the passenger flow at different traffic nodes and time intervals.

[0019] In one possible implementation of the first aspect, based on the passenger flow of the target community at different traffic nodes and in different time intervals, the traffic coupling degree parameter corresponding to the target community is calculated, including:

[0020] Based on the passenger flow of the target community at each bus stop and each time interval, and the passenger flow of the target community at each rail transit stop and each time interval, the first coupling degree parameter corresponding to the target community is calculated. The first coupling degree parameter is used to characterize the coupling degree between regular buses and rail transit within the target community.

[0021] Based on the passenger flow of the target community at each bus stop and in each time interval, as well as the passenger flow of the target community at each taxi hotspot and in each time interval, the second coupling parameter corresponding to the target community is calculated. The second coupling parameter is used to characterize the coupling degree between regular buses and taxis in the target community.

[0022] Based on the passenger flow of the target community at each rail transit station and in each time interval, as well as the passenger flow of the target community at each taxi hotspot and in each time interval, the third coupling parameter corresponding to the target community is calculated. The third coupling parameter is used to characterize the coupling degree between rail transit and taxis within the target community.

[0023] The traffic coupling parameters corresponding to the target community are determined based on the first coupling parameter, the second coupling parameter, and the third coupling parameter.

[0024] In one possible implementation of the first aspect, before assessing the service accessibility level of the target community based on target evaluation indicators determined according to relevant parameters of the service facilities in the target community, the method further includes:

[0025] Based on diversity parameters, per capita facility parameters, facility distribution density parameters, and traffic coupling parameters, calculate the original evaluation indicators corresponding to each primary type in the target community;

[0026] Based on the original evaluation indicators corresponding to each primary type in the target community, and the weight coefficient of each primary type, the target evaluation indicators of the target community are calculated.

[0027] In one possible implementation of the first aspect, the target evaluation index for the target community is calculated based on the original evaluation index corresponding to each primary type in the target community and the weight coefficient of each primary type, including:

[0028] Obtain the original evaluation indicators for each primary type in multiple communities, including the target community;

[0029] For each primary type, based on the original evaluation indicators corresponding to each primary type in multiple communities, determine the maximum and minimum evaluation indicators corresponding to each primary type in multiple communities;

[0030] For each primary type, the original evaluation indicators corresponding to each primary type in the target community are normalized based on the maximum and minimum evaluation indicators corresponding to each primary type, so as to obtain the normalized indicators corresponding to each primary type in the target community.

[0031] Based on the normalized index corresponding to each primary type in the target community, and the weight coefficient of each primary type, the target evaluation index of the target community is calculated.

[0032] In one possible implementation of the first aspect, the building information includes the centroid coordinates, outline area, and resident population data of each building in the target community. After obtaining the building information of the target community, the method further includes:

[0033] The weighting coefficient for each building is calculated based on its outline area and resident population data.

[0034] The center coordinates of the target community are obtained by calculating a weighted average of the weight coefficient and centroid coordinates of each building.

[0035] Secondly, embodiments of this application provide a device for assessing the convenience level of community services, including:

[0036] The acquisition module is used to obtain building information of the target community;

[0037] The statistics module is used to count the number of different types of service facilities in the target community within a preset service area. The preset service area is the range formed by taking the center coordinates as the starting point and the set duration corresponding to each type as the service radius.

[0038] The first calculation module is used to calculate the service facility-related parameters of the target community based on the number of different types of service facilities in the target community. The service facility-related parameters include facility diversity parameters, per capita facility parameters, facility distribution density parameters, and traffic coupling parameters.

[0039] The assessment module is used to evaluate the service convenience level of the target community based on the target assessment indicators determined by the relevant parameters of the service facilities in the target community.

[0040] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any of the first aspects.

[0041] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any of the first aspects.

[0042] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute any of the methods described in the first aspect above.

[0043] This application provides a method, apparatus, equipment, and program product for assessing the convenience level of community services. The method includes: acquiring building information of a target community, wherein the building information is used to determine the center coordinates of the target community; counting the number of different types of service facilities in the target community within a preset service range, wherein the preset service range is the range formed by taking the center coordinates as the starting point and the set duration corresponding to each type as the service radius; calculating service facility-related parameters of the target community based on the number of different types of service facilities in the target community, including facility diversity parameters, per capita facility parameters, facility distribution density parameters, and traffic coupling parameters; and assessing the service convenience level of the target community based on the target assessment indicators determined by the service facility-related parameters. Using the above technical solution, by calculating service facility-related parameters of the target community based on the number of different types of service facilities in the target community, including facility diversity parameters, per capita facility parameters, facility distribution density parameters, and traffic coupling parameters, the parameter design in the service convenience level assessment method is enriched. Furthermore, by fully considering the richness of service facility supply, the accuracy of community assessment results is improved, providing a reliable basis for optimizing the construction of community living service facilities. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art 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.

[0045] Figure 1 This is a flowchart illustrating a method for assessing the convenience level of community services provided in an embodiment of this application;

[0046] Figure 2 This is a flowchart illustrating a method for assessing the convenience level of community services provided in another embodiment of this application;

[0047] Figure 3 This is a schematic diagram of the architecture of a method for evaluating the convenience level of community services provided in an embodiment of this application;

[0048] Figure 4 This is a structural block diagram of a community service convenience level assessment device provided in one embodiment of this application;

[0049] Figure 5 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0050] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0051] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0052] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0053] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0054] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0055] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0056] It should be noted that the information collection process involved in this application (such as the building information collection process, the service facility quantity collection process, etc.) was carried out with the knowledge and permission of the users and the community. That is, the data privacy involved in the information collection process complies with the requirements of laws and regulations and does not constitute an act that harms the public interest.

[0057] It can be argued that while there are currently various methods for assessing the convenience level of community services, the following problems and shortcomings still exist:

[0058] First, existing assessment methods are mostly tailored to specific regions, lacking universality and systematicity, making it difficult to conduct horizontal comparisons between different regions. For example, existing methods often design assessment indicators based on the characteristics of a specific region, which makes it difficult to effectively compare assessment results across different regions.

[0059] Secondly, the existing method of delineating service areas based on the location of community service stations (or neighborhood committees) does not fully consider the irregularity of built-up areas in communities. For example, current assessment methods usually use community service stations or neighborhood committees as the center point to determine the service area, but in reality, the shape and distribution of communities are often irregular. This simplistic approach can lead to discrepancies between the assessment results and the actual situation.

[0060] Third, existing assessment methods have shortcomings in data utilization, with overly simplified parameter designs that fail to fully consider the richness of service supply. For example, many methods only consider the quantity and distance of service facilities, while ignoring important factors such as the diversity of service facilities and the proportion of facilities per capita, resulting in limited assessment accuracy.

[0061] Fourth, the weighting in existing evaluation methods largely relies on surveys or expert scoring, lacking objective basis. This highly subjective approach to weighting can affect the stability and reliability of the evaluation results.

[0062] Finally, existing assessment methods do not take traffic factors into account in a comprehensive manner, especially lacking analysis of the coupling relationship between different modes of transportation, and thus cannot fully reflect the impact of community transportation convenience on the overall level of service convenience.

[0063] Therefore, the aforementioned shortcomings make existing assessment methods lack systematic analysis and research, and difficult to adapt to different regions. Thus, there is an urgent need for an objective, comprehensive, and systematic assessment method for the level of community service convenience, in order to accurately assess the level of community convenience services, provide a basis for optimizing the construction of community life service facilities, thereby better guiding community planning and construction and the allocation of service facilities, improving the quality of life of residents, and enhancing the service capacity of the community service circle.

[0064] Based on this, the embodiments of this application provide a method for evaluating the convenience level of community services. By combining easily accessible multi-data, a scientific and comprehensive evaluation system is constructed using information theory expertise, thereby improving the accuracy and applicability of the evaluation.

[0065] Figure 1 This is a flowchart illustrating a method for assessing the convenience level of community services according to an embodiment of this application. It is intended as an example and not a limitation; the method can be applied to terminal devices, including but not limited to mobile phones, tablets, and laptops. Figure 1 As shown, the method includes:

[0066] S101. Obtain building information for the target community.

[0067] Building information is used to determine the center coordinates of the target community. Building information can refer to information related to buildings within the target community, such as building distribution maps, building area, building height, and building use. It can also include population data for each building in the target community, including demographic information such as the number of permanent residents, age distribution, and occupational distribution, providing a rich data foundation for subsequent calculations. Alternatively, building information can be configured according to actual calculation needs. The center coordinates can be the geometric center of the building complex in the target community or the center point after population density weighting.

[0068] This step involves obtaining building information for the target community through urban planning databases, geographic information systems, or field surveys to determine its center coordinates. The specific method for determining the center coordinates is not limited. For example, it can be based on a computational model, where the building information of the target community is input into the model to directly output its center coordinates. The computational model can be a pre-trained neural network model used to calculate the center coordinates of different communities. Alternatively, the center coordinates of the target community can be obtained by performing a series of calculations on different data points within the building information.

[0069] As a feasible implementation method, the building information includes the centroid coordinates, outline area, and resident population data of each building in the target community. After obtaining the building information of the target community, the method further includes: calculating the weight coefficient of each building based on the outline area and resident population data of each building; and performing a weighted average calculation on the weight coefficient and centroid coordinates of each building to obtain the center coordinates of the target community.

[0070] In actual building environments, considering that the geometric center of a community is often not representative of the actual situation, this embodiment calculates the center point (i.e., center coordinates) of the built-up area of ​​the target community based on Geographic Information System (GIS) and urban building outline data in urban planning, to ensure that the calculation of the center coordinates is more consistent with the actual spatial distribution.

[0071] In a specific implementation, the building information includes the centroid coordinates, outline area, and resident population data for each building in the target community. The centroid coordinates represent the center point of each building, the outline area represents the size of each building, and the resident population data represents the population activity intensity of each building. This data can rely on building outline data (such as polygon data), typically sourced from GIS databases or remote sensing imagery. For example, the outline area and centroid coordinates of each building can be calculated using its polygon data.

[0072] Furthermore, after obtaining the building information of the target community, the weight of each building can be determined based on population density, such as by assigning weight to each building. contour area and resident population data Calculate the weighting coefficient for each building. Then, assign weight coefficients and centroid coordinates to each building. The center coordinates of the target community are obtained by performing a weighted average calculation. ,in, Based on this, the center coordinates of the target community can be made closer to the actual spatial distribution of the community, avoiding the simple use of the geometric center or the location of the community workstation (community residents' committee) as the center point, ensuring a more scientific selection of the starting point and improving the accuracy of community center point matching.

[0073] In a preferred embodiment, the weighting coefficients can be further adjusted according to the functional type of the building. For example, higher weights can be assigned to commercial buildings and public service buildings to more accurately reflect community activity centers.

[0074] S102. Within the preset service area, count the number of different types of service facilities in the target community.

[0075] The preset service range is the area formed by taking the center coordinates as the starting point and the set duration corresponding to each type as the service radius.

[0076] In this embodiment, the types of service facilities are diverse, and may include one or more of the following: catering facilities, shopping facilities, medical facilities, educational facilities, leisure and entertainment facilities, and financial service facilities. The specific type can be determined based on the actual situation of the target community or different standards for facility classification. For example, different levels and regions have different perspectives on facility classification and evaluation. At some broad levels, the types of supporting service facilities in residential areas are clearly defined, including public management and public service facilities, commercial service facilities, and transportation facilities. At some narrower levels, the above-mentioned facility types can be expanded to 12 major categories, constructing a system of comprehensive services, basic education, medical and health care, social welfare, cultural facilities, sports facilities, convenient commerce, logistics services, transportation, parks and green spaces, and public safety. Alternatively, the above-mentioned service facilities of the same type can be further divided into different subcategories to achieve refined management of service facilities.

[0077] The preset service area can be understood as a service area calculated using isochronous walking circles. For example, an isochronous walking circle refers to the area covered by the distance that can be reached by walking from a certain point within a specific time period using Point of Interest (POI) data on a commercial map; it is a measure of accessibility. In this embodiment, the preset service area is the range formed by taking the center coordinates as the starting point and using the set duration corresponding to each type as the service radius. The set duration for each type can be the same or different. POI data can be data units used in a geographic information system to mark specific location points, representing entities such as buildings, shops, and bus stops.

[0078] For example, different types of service facilities often have significantly different service ranges due to differences in their service functions. Therefore, this embodiment can configure different time settings for different types of service facilities according to their functions. For example, community service and physical commercial service facilities often meet the daily needs of residents, and residents tend to visit these service facilities more frequently. Therefore, the service range of these service facilities is relatively small, so a smaller time setting can be set. On the other hand, cultural, sports, science and education, and medical service facilities have relatively low visitation rates from residents, so a larger time setting can be set.

[0079] Specifically, this step can use the center coordinates calculated in the previous step as the initial starting point, and count the number of service facilities within the coverage area of ​​different types of walking isochronous circles according to the principle of shortest time distance, so as to subsequently assess the supply of relevant service facilities in the target community within walking range.

[0080] Optionally, this embodiment can convert the set time into actual spatial distance based on the road network and traffic conditions, thereby determining the service radius of each type of service facility.

[0081] Optionally, this embodiment may take into account traffic conditions at different times and dynamically adjust the service radius corresponding to each first type.

[0082] S103. Calculate the relevant parameters of the service facilities in the target community based on the number of different types of service facilities in the target community.

[0083] It should be noted that the level of convenience services in a community depends on the richness of its service offerings. This step can calculate parameters such as diversity, facility density, and traffic coupling based on the number of different types of service facilities in the target community. For example, service facility-related parameters include facility diversity, per capita facility, facility distribution density, and traffic coupling. The facility diversity parameter can characterize the richness of service facility types in the target community. For instance, it can be calculated by comparing the number of service facility types in the community with the predefined total number of all service facility types. A higher diversity parameter indicates a richer variety and more balanced distribution of service facilities. The per capita facility parameter reflects the number of service facilities available to each person in the target community, which can be calculated using the total number of all types of service facilities. Dividing by the community's permanent resident population, this parameter reflects the accessibility of service facilities for community residents; a higher value indicates more service facilities per capita. The facility distribution density parameter reflects the spatial distribution of service facilities within the target community, determined by calculating the number of service facilities per unit area. This parameter reflects the spatial density of service facilities; a higher value indicates a denser distribution. The traffic coupling parameter reflects the degree of synergy between service facilities and transportation facilities, assessing the traffic convenience of the target community. It is determined by calculating the coverage of transportation facilities within a certain range around the service facilities. Transportation facilities can include bus stops, subway stations, taxi hotspots, and other types of transportation facilities such as shared vehicle stations, depending on actual needs. Based on this, by fully considering the richness of service facility supply, a rich data foundation is provided for subsequent evaluations.

[0084] S104. Evaluate the service convenience level of the target community based on the target evaluation indicators determined according to the relevant parameters of the service facilities in the target community.

[0085] The target evaluation indicators can be a weighted combination of the above four parameters, or an evaluation model built based on these parameters, which can be used to evaluate the service convenience level of the target community.

[0086] This embodiment does not limit the specific evaluation process. For example, the evaluation process can adopt a multi-level evaluation method, first standardizing each parameter to make it comparable; then determining the weight coefficients based on the importance of each parameter; finally calculating the comprehensive score (i.e., the target evaluation index), and measuring the service convenience level of the target community based on the comprehensive score. Alternatively, this embodiment can also combine relevant data from other communities to comprehensively determine the target evaluation index of the target community, and so on.

[0087] Furthermore, the level of service convenience can be divided into several levels, such as highly convenient, relatively convenient, generally convenient, less convenient, and inconvenient, providing a reference for community planning and improvement.

[0088] In a preferred embodiment, the evaluation results can be compared with those of other communities in the same area to form a relative evaluation, which more objectively reflects the position of the target community's service convenience level within the region.

[0089] This embodiment provides a method for assessing the convenience level of community services. Based on the number of different types of service facilities in the target community, it calculates relevant parameters of the service facilities in the target community. These parameters include facility diversity parameters, per capita facility parameters, facility distribution density parameters, and traffic coupling parameters. This enriches the parameter design in the service convenience level assessment method and improves the accuracy of community assessment results by fully considering the richness of service facility supply. It provides a reliable basis for optimizing the construction of community life service facilities.

[0090] Figure 2 This is a flowchart illustrating a method for assessing the convenience level of community services according to another embodiment of this application. In this embodiment, different types include multiple primary types and multiple secondary types corresponding to each primary type. The method of counting the number of service facilities of different types in the target community within a preset service range is further optimized as follows: For each primary type, the number of service facilities of different secondary types corresponding to each primary type in the target community is counted within the preset service range; for each primary type, the total number of service facilities corresponding to each primary type in the target community is calculated based on the number of service facilities of different secondary types corresponding to each primary type in the target community. For example... Figure 2 As shown, the method includes:

[0091] S201. Obtain building information for the target community.

[0092] S202. For each primary type, within the preset service scope, count the number of service facilities of different secondary types corresponding to each primary type in the target community.

[0093] S203. For each primary type, calculate the total number of service facilities corresponding to each primary type in the target community based on the number of service facilities of different secondary types corresponding to each primary type in the target community.

[0094] In this embodiment, different types may include multiple primary types and multiple secondary types corresponding to each primary type.

[0095] Taking the classification of service facilities according to 7 primary categories and their corresponding secondary categories as an example, the primary categories can include public services, cultural and sports services, science and education services, medical services, public utility services, physical commercial services, and transportation services.

[0096] Furthermore, the secondary categories for convenience services can include dry cleaners, beauty salons, moving companies, etc., with a service radius of 10 minutes' walk; the secondary categories for cultural and sports services can include stadiums, theaters, art centers, etc., with a service radius of 15 minutes' walk; the secondary categories for science and education services can include schools, museums, etc., with a service radius of 15 minutes' walk; the secondary categories for medical services can include hospitals, disease control centers, community hospitals, pharmacies, etc., with a service radius of 15 minutes' walk; the secondary categories for public facilities services can include public toilets, parking lots, parks, etc., with a service radius of 15 minutes' walk; the secondary categories for physical commercial services can include shopping malls, restaurants, supermarkets, farmers' markets, etc., with a service radius of 10 minutes' walk; transportation services can be calculated using special methods, such as counting the number of bus stops, subway stations, and taxi pick-up / drop-off points within the entire target community.

[0097] Specifically, this embodiment can utilize the open API interface of commercial maps to collect data on points of interest within a certain walking time radius, starting from the center coordinates of the target community, and summarize the number of service facilities in different secondary categories, thereby calculating the total number of service facilities corresponding to each primary type in the target community.

[0098] S204. Calculate the relevant parameters of the service facilities in the target community based on the number of different types of service facilities in the target community.

[0099] In some embodiments, service facility-related parameters of the target community are calculated based on the number of different types of service facilities in the target community, including:

[0100] For each primary type, the diversity parameter corresponding to each primary type in the target community is calculated based on the number of service facilities of different secondary types corresponding to each primary type in the target community;

[0101] For each primary type, based on the total number of service facilities corresponding to each primary type in the target community and the number of permanent residents in the target community, calculate the per capita facility parameters for each primary type in the target community;

[0102] For each primary type, based on the total number of service facilities corresponding to each primary type in the target community and the area of ​​the target community, calculate the facility distribution density parameter corresponding to each primary type in the target community;

[0103] Calculate the traffic coupling parameters corresponding to the target community based on the passenger flow at different traffic nodes and time intervals.

[0104] Specifically, for each primary type k, the percentage of the number of service facilities of different secondary types g in the total number of service facilities of the corresponding primary type k can be used as a basis. To calculate the diversity parameters corresponding to the first-level type in the target community. For example, for convenience services, the secondary indicator is the proportion of dry cleaning shops among convenience service facilities = number of dry cleaning shops / total number of service facilities corresponding to all secondary indicators of convenience services; per capita facility parameter =N / Number of permanent residents in the target community, where N is the total number of service facilities corresponding to type k in the target community; facility distribution density parameter. =N / Target community area. Based on this, by introducing information entropy theory and drawing on the Shannon diversity index to measure the diversity of community services, the method comprehensively considers the quantity and distribution characteristics of service facilities, effectively reducing computational complexity while ensuring the comprehensiveness of the assessment.

[0105] In calculating traffic coupling parameters, due to the limitations in the diversity of traffic service indicators, which generally only cover a limited number of categories such as regular buses, rail transit, and taxis, this embodiment can use public transportation coupling to assess the convenience of community transportation. Specifically, the traffic coupling parameter corresponding to the target community can be calculated based on the synergy between different modes of transportation. For example, the convenience of transportation can be quantified by analyzing the passenger flow coupling relationship among regular buses, rail transit, and taxis within the target community. The passenger flow coupling relationship reflects the strength of interaction between transportation systems; the higher the coupling degree, the stronger the system synergy, and the correspondingly improved the convenience of transportation.

[0106] As a feasible implementation method, based on the passenger flow of the target community at different traffic nodes and in different time intervals, the traffic coupling degree parameters corresponding to the target community are calculated, including:

[0107] Based on the passenger flow of the target community at each bus stop and each time interval, and the passenger flow of the target community at each rail transit stop and each time interval, the first coupling degree parameter corresponding to the target community is calculated. The first coupling degree parameter is used to characterize the coupling degree between regular buses and rail transit within the target community.

[0108] Based on the passenger flow of the target community at each bus stop and in each time interval, as well as the passenger flow of the target community at each taxi hotspot and in each time interval, the second coupling parameter corresponding to the target community is calculated. The second coupling parameter is used to characterize the coupling degree between regular buses and taxis in the target community.

[0109] Based on the passenger flow of the target community at each rail transit station and in each time interval, as well as the passenger flow of the target community at each taxi hotspot and in each time interval, the third coupling parameter corresponding to the target community is calculated. The third coupling parameter is used to characterize the coupling degree between rail transit and taxis within the target community.

[0110] The traffic coupling parameters corresponding to the target community are determined based on the first coupling parameter, the second coupling parameter, and the third coupling parameter.

[0111] For example, the first coupling parameter between conventional public transport and rail transit within the target community. The second coupling parameter between regular buses and taxis The third coupling parameter between rail transit and taxis .

[0112] Where r represents the station, such as the r-th bus stop, rail transit station, or taxi pick-up / drop-off hotspot (in particular, taxi pick-up / drop-off hotspots are identified through GPS data clustering); t represents the time interval, such as different time periods (morning and evening rush hours), or a set time interval.

[0113] To calculate the passenger flow of the public transportation system at a specific stop r and time t within the target community, similarly... , These represent the passenger flow of the rail transit system and the taxi system at a specific station r and time t within the target community.

[0114] This represents the magnitude of the total traffic flow vector of the public transportation system in the target community, i.e. Similarly, , These are the magnitudes of the total traffic vectors for the rail transit system and the taxi system in the target community, respectively.

[0115] Furthermore, the traffic coupling parameter corresponding to the target community can be... .

[0116] Alternatively, a weighted average method can be used to calculate the traffic coupling parameter corresponding to the target community, that is: traffic coupling parameter = h1 × first coupling parameter + h2 × second coupling parameter + h3 × third coupling parameter, where h1, h2, and h3 are traffic weights, and h1 + h2 + h3 = 1. The traffic weights can be adjusted according to the importance of each mode of transportation in the community.

[0117] S205. Evaluate the service convenience level of the target community based on the target evaluation indicators determined according to the relevant parameters of the service facilities in the target community.

[0118] This embodiment provides a method for assessing the convenience level of community services. For each primary type, within a preset service range, the number of service facilities of different secondary types corresponding to each primary type in the target community is counted, and the total number of service facilities corresponding to each primary type in the target community is calculated. This provides an accurate data foundation for subsequent calculation of service facility-related parameters of the target community, thereby further improving the accuracy of community assessment results.

[0119] In some embodiments, before assessing the service accessibility level of the target community based on target evaluation indicators determined according to service facility-related parameters of the target community, the method further includes:

[0120] Based on diversity parameters, per capita facility parameters, facility distribution density parameters, and traffic coupling parameters, calculate the original evaluation indicators corresponding to each primary type in the target community;

[0121] Based on the original evaluation indicators corresponding to each primary type in the target community, and the weight coefficient of each primary type, the target evaluation indicators of the target community are calculated.

[0122] The original evaluation indicators can be used to characterize the service convenience level corresponding to each primary type. For example, for the original evaluation indicators of different primary types (excluding transportation services), the calculation formula can be: The formula shows that communities with greater diversity of service facilities can receive a small bonus, where, It is a diversity adjustment coefficient that satisfies The specific values ​​can be configured according to the actual situation, such as Specifically, the calculation formula for the original evaluation indicators corresponding to transportation services can be... .

[0123] Furthermore, the weight coefficient of each primary category can be used to characterize the importance of each primary category. For example, the entropy weighting method in information theory can be used to calculate the weight coefficient of each primary category based on the degree of dispersion between the indicators. On this basis, the weight of each indicator is objectively determined based on information entropy, eliminating subjective interference, making the algorithm simple, efficient, and highly operable, and enhancing the scientific nature and efficiency of indicator calculation.

[0124] Table 1. Relevant information for each primary type.

[0125]

[0126] Table 1 lists the relevant information for each primary type, including the specific content of each primary type, its service radius, the multiple corresponding secondary types, and the calculation parameters (i.e., the original evaluation indicators). For example, for convenience services, if the service radius is a 10-minute walk, it can be further divided into secondary types such as dry cleaners, beauty salons, and moving companies. The original evaluation indicators for convenience services can be based on diversity parameters. per capita facility parameters and facility distribution density parameters The calculation is performed, and the specific calculation formula is as follows: Specific data sources may include open maps, community resident population, community GIS maps, etc.

[0127] After calculating the original evaluation indicators and weight coefficients for each primary type in the target community, the original evaluation indicators for each primary type can be directly weighted using the weight coefficients to obtain the target evaluation indicators for the target community. Alternatively, the original evaluation indicators for each primary type in the target community can be processed first, and then weighted using the weight coefficients to finally obtain the target evaluation indicators for the target community.

[0128] As a feasible implementation method, the target evaluation indicators for the target community are calculated based on the original evaluation indicators corresponding to each primary type in the target community, and the weight coefficients of each primary type, including:

[0129] Obtain the original evaluation indicators for each primary type in multiple communities, including the target community;

[0130] For each primary type, based on the original evaluation indicators corresponding to each primary type in multiple communities, determine the maximum and minimum evaluation indicators corresponding to each primary type in multiple communities;

[0131] For each primary type, the original evaluation indicators corresponding to each primary type in the target community are normalized based on the maximum and minimum evaluation indicators corresponding to each primary type, so as to obtain the normalized indicators corresponding to each primary type in the target community.

[0132] Based on the normalized index corresponding to each primary type in the target community, and the weight coefficient of each primary type, the target evaluation index of the target community is calculated.

[0133] In a specific implementation, the initial evaluation indicators for each primary type of multiple communities can be calculated first. These communities can be different communities within the same city or typical communities from different cities. For example, Table 2 below shows the initial evaluation indicators for 7 service types across M communities in all domains, where the initial evaluation indicator for each primary type in the m-th community is... , , .

[0134] Table 2 Original evaluation indicators for the 7 service types in M ​​communities

[0135]

[0136] Then, the original evaluation indicators corresponding to the 7 service types in the M communities can be normalized to obtain the normalized values. (As shown in Table 3), where, This represents the value of the k-th service convenience index (i.e., the first-level type) in the k-th service convenience index among all M communities in the domain. This represents the value of the k-th service convenience index of the community with the smallest value among the k-th service convenience index categories in all M communities within the domain.

[0137] Table 3 Normalized Indicators for 7 Service Types within M Communities

[0138]

[0139] Finally, the community convenience service level evaluation model can be built upon. To calculate the target evaluation indicators for the target community, among which, This represents the level of service convenience in a given community, m. This represents the weighting coefficient of the service convenience index k. The specific calculation method for the weighting coefficient includes, but is not limited to, the entropy weighting method in information theory.

[0140] This represents the normalized data of the service convenience index k for a specific community m. For example, when the target community is community 1, the target evaluation index would be... , The higher the value, the higher the level of service facilities configuration in Community 1.

[0141] Figure 3 This is a schematic diagram of the architecture of a method for evaluating the convenience level of community services provided in this application embodiment. First, the center point (i.e., center coordinates) of the built-up area of ​​the target community can be confirmed based on the urban building outline data in the geographic information system and urban planning, so that the confirmed center coordinates are more consistent with the actual spatial distribution. Then, based on the open API of the map service provider, the number of facilities of each convenience service indicator in the target community within different walking time circles can be obtained from the center point of the built-up area. For example, the number of POIs of convenient services within 10 minutes of walking, the number of POIs of cultural and sports services within 15 minutes of walking, the number of POIs of science and education services within 15 minutes of walking, the number of POIs of medical services within 15 minutes of walking, the number of POIs of public facilities services within 15 minutes of walking, the number of POIs of physical commercial services within 10 minutes of walking, and the number of POIs of transportation services within the entire community can be obtained.

[0142] Subsequently, based on different convenience service indicator parameter models, the values ​​of different types of service convenience indicators can be calculated. For example, different types of diversity parameters can be calculated first. Facility density parameters (i.e.) Traffic coupling parameters Then, for different primary types (excluding transportation services), calculate the corresponding service convenience index based on the parameters. The calculation formula for the original evaluation indicators corresponding to transportation services can be... .

[0143] Finally, the service convenience level of the target community can be calculated. For example, the original evaluation indicators corresponding to each primary type in multiple communities can be statistically analyzed, and the original evaluation indicators corresponding to each primary type in the target community can be normalized to obtain normalized values. Then, the target evaluation indicators of the target community can be calculated based on the normalized values ​​and weight coefficients corresponding to each primary type. Thus, the service convenience level of the target community can be evaluated based on the calculated target evaluation indicators.

[0144] As can be seen from the above description, the community service convenience level assessment method provided in this embodiment achieves a balance between data availability and richness by integrating community planning data, Internet data, and geographic information data, taking into account both comprehensive dimensions and computational feasibility, thereby enhancing the usability and mining potential of data and deepening the understanding of community characteristics.

[0145] Meanwhile, this embodiment also constructs a scientific evaluation index system. For example, by referring to the seven primary indicators and corresponding secondary classifications set by the standard, the classification method is actually adjusted to make the classification method more flexible. Based on the relationship between residents' travel frequency and walking distance, the facility functions and service radius are considered differently. Diverse parameters are designed for the characteristics of the indicators. In particular, traffic coupling analysis is introduced in the aspect of traffic services, which significantly improves the adaptability of the indicators, so that the overall indicator design is both practical and accurate.

[0146] Therefore, this embodiment comprehensively evaluates the configuration level of community service facilities by considering the diversity, availability, spatial distribution, and synergy with the transportation system of service facilities. It can objectively reflect the level of service convenience in the community, providing a scientific basis for urban planning, service facility optimization, and residents' choice of place of residence, and promoting the rational allocation of urban service resources and the improvement of community livability.

[0147] Corresponding to the community service convenience level assessment method in the above embodiments, Figure 4 This is a structural block diagram of a community service convenience level assessment device provided in one embodiment of this application. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0148] Reference Figure 4 The device includes:

[0149] Module 301 is used to acquire building information of the target community;

[0150] The statistics module 302 is used to count the number of different types of service facilities in the target community within a preset service range. The preset service range is the range formed by taking the center coordinate as the starting point and the set duration corresponding to each type as the service radius.

[0151] The first calculation module 303 is used to calculate the service facility-related parameters of the target community based on the number of different types of service facilities in the target community. The service facility-related parameters include facility diversity parameters, per capita facility parameters, facility distribution density parameters, and traffic coupling parameters.

[0152] The assessment module 304 is used to assess the service convenience level of the target community based on the target assessment indicators determined by the relevant parameters of the service facilities in the target community.

[0153] This embodiment provides a device for assessing the convenience level of community services. It acquires building information of a target community through an acquisition module; it then uses a statistics module to count the number of different types of service facilities within a preset service area, where the preset service area is defined by a service radius centered on a central coordinate and corresponding to a set duration for each type; a first calculation module calculates relevant parameters of the target community's service facilities based on the number of different types of facilities, including facility diversity, per capita facilities, facility distribution density, and traffic coupling; and an assessment module evaluates the convenience level of the target community based on target assessment indicators determined by these parameters. This device, by calculating relevant parameters based on the number of different types of service facilities in the target community (including facility diversity, per capita facilities, facility distribution density, and traffic coupling), enriches the parameter design in the service convenience level assessment method. Furthermore, by fully considering the richness of service facility supply, it improves the accuracy of community assessment results and provides a reliable basis for optimizing the construction of community living service facilities.

[0154] Optionally, different types include multiple primary types, and each primary type corresponds to multiple secondary types. The statistics module is specifically used for:

[0155] For each primary type, within the preset service area, count the number of service facilities of different secondary types corresponding to each primary type in the target community;

[0156] For each primary type, the total number of service facilities corresponding to each primary type in the target community is calculated based on the number of service facilities of different secondary types corresponding to each primary type in the target community.

[0157] Optionally, the first computing module includes:

[0158] The first calculation unit is used to calculate the diversity parameters corresponding to each primary type in the target community based on the number of sub-service facilities of different secondary types corresponding to each primary type in the target community.

[0159] The second calculation unit is used to calculate the per capita facility parameters for each primary type in the target community based on the total number of service facilities corresponding to each primary type in the target community and the number of permanent residents in the target community.

[0160] The third calculation unit is used to calculate the facility distribution density parameter for each primary type in the target community based on the total number of service facilities corresponding to each primary type in the target community and the area of ​​the target community.

[0161] The fourth calculation unit is used to calculate the traffic coupling degree parameter of the target community based on the passenger flow at different traffic nodes and at different time intervals.

[0162] Optionally, the fourth computing unit is specifically used for:

[0163] Based on the passenger flow of the target community at each bus stop and each time interval, and the passenger flow of the target community at each rail transit stop and each time interval, the first coupling degree parameter corresponding to the target community is calculated. The first coupling degree parameter is used to characterize the coupling degree between regular buses and rail transit within the target community.

[0164] Based on the passenger flow of the target community at each bus stop and in each time interval, as well as the passenger flow of the target community at each taxi hotspot and in each time interval, the second coupling parameter corresponding to the target community is calculated. The second coupling parameter is used to characterize the coupling degree between regular buses and taxis in the target community.

[0165] Based on the passenger flow of the target community at each rail transit station and in each time interval, as well as the passenger flow of the target community at each taxi hotspot and in each time interval, the third coupling parameter corresponding to the target community is calculated. The third coupling parameter is used to characterize the coupling degree between rail transit and taxis within the target community.

[0166] The traffic coupling parameters corresponding to the target community are determined based on the first coupling parameter, the second coupling parameter, and the third coupling parameter.

[0167] Optionally, the community service convenience level assessment device provided in this embodiment further includes:

[0168] The second calculation module is used to calculate the original evaluation indicators corresponding to each primary type in the target community before evaluating the service convenience level of the target community based on the target evaluation indicators determined according to the relevant parameters of the service facilities in the target community.

[0169] The third calculation module is used to calculate the target evaluation index of the target community before evaluating the service convenience level of the target community based on the target evaluation index determined according to the relevant parameters of the service facilities of the target community. It is based on the original evaluation index corresponding to each primary type in the target community and the weight coefficient of each primary type.

[0170] Optionally, the third calculation module is specifically used for:

[0171] Obtain the original evaluation indicators for each primary type in multiple communities, including the target community;

[0172] For each primary type, based on the original evaluation indicators corresponding to each primary type in multiple communities, determine the maximum and minimum evaluation indicators corresponding to each primary type in multiple communities;

[0173] For each primary type, the original evaluation indicators corresponding to each primary type in the target community are normalized based on the maximum and minimum evaluation indicators corresponding to each primary type, so as to obtain the normalized indicators corresponding to each primary type in the target community.

[0174] Based on the normalized index corresponding to each primary type in the target community, and the weight coefficient of each primary type, the target evaluation index of the target community is calculated.

[0175] Optionally, the building information includes the center-of-gravity coordinates, outline area, and resident population data of each building in the target community. The community service convenience level assessment device provided in this embodiment also includes:

[0176] The fourth calculation module is used to calculate the weight coefficient of each building based on the outline area and resident population data of each building after obtaining the building information of the target community.

[0177] The fifth calculation module is used to calculate the weighted average of the weight coefficient and centroid coordinates of each building to obtain the center coordinates of the target community.

[0178] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0179] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0180] Figure 5 This is a schematic diagram of the structure of a terminal device provided in one embodiment of this application, as shown below. Figure 5 As shown, the terminal device 500 of this embodiment includes: at least one processor 502 ( Figure 5 (Only one is shown) a processor, a memory 501, and a computer program 503 stored in the memory 501 and executable on at least one processor 502. When the processor 502 executes the computer program 503, it implements the steps in the control method embodiments of any of the above-described application programs.

[0181] Terminal device 500 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This terminal device may include, but is not limited to, a processor 502 and a memory 501. Those skilled in the art will understand that... Figure 5 This is merely an example of terminal device 500 and does not constitute a limitation on terminal device 500. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0182] The processor 502 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0183] In some embodiments, memory 501 may be an internal storage unit of terminal device 500, such as a hard disk or memory of terminal device 500. In other embodiments, memory 501 may be an external storage device of terminal device 500, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on terminal device 500. Furthermore, memory 501 may include both internal and external storage units of terminal device 500. Memory 501 is used to store operating system, applications, boot loader, data, and other programs, such as program code for computer programs. Memory 501 can also be used to temporarily store data that has been output or will be output.

[0184] This application also provides a computer-readable storage medium storing a computer program, which, when executed by processor 502, can implement the steps in the above-described method embodiments.

[0185] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments.

[0186] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 502, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0187] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0188] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0189] In the embodiments provided in this application, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0190] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0191] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for assessing the convenience level of community services, characterized in that, include: Obtain building information of the target community, wherein the building information is used to determine the center coordinates of the target community; Within a preset service area, the number of different types of service facilities in the target community is counted. The preset service area is the range formed by taking the center coordinates as the starting point and the set duration corresponding to each type as the service radius. Based on the number of different types of service facilities in the target community, calculate the service facility-related parameters of the target community. The service facility-related parameters include facility diversity parameters, per capita facility parameters, facility distribution density parameters, and traffic coupling parameters. The different types include multiple primary types and multiple secondary types corresponding to each primary type. The service convenience level of the target community is evaluated based on the target evaluation indicators determined according to the relevant parameters of the service facilities in the target community. The step of calculating service facility-related parameters of the target community based on the number of different types of service facilities in the target community includes: For each primary type, based on the number of service facilities of different secondary types corresponding to each primary type in the target community, the diversity parameter corresponding to each primary type in the target community is calculated; For each of the primary types, based on the total number of service facilities corresponding to each primary type in the target community and the number of permanent residents in the target community, calculate the per capita facility parameter corresponding to each primary type in the target community; For each of the primary types, based on the total number of service facilities corresponding to each primary type in the target community and the area of ​​the target community, calculate the facility distribution density parameter corresponding to each primary type in the target community; Based on the passenger flow of the target community at different traffic nodes and at different time intervals, calculate the traffic coupling degree parameter corresponding to the target community; The step of calculating the traffic coupling degree parameter corresponding to the target community based on the passenger flow at different traffic nodes and in different time intervals includes: Based on the passenger flow of the target community at each bus stop and each time interval, and the passenger flow of the target community at each rail transit stop and each time interval, a first coupling degree parameter corresponding to the target community is calculated. The first coupling degree parameter is used to characterize the coupling degree between conventional public transportation and rail transit within the target community. Based on the passenger flow of the target community at each bus stop and in each time interval, and the passenger flow of the target community at each taxi hotspot and in each time interval, a second coupling parameter corresponding to the target community is calculated. The second coupling parameter is used to characterize the coupling degree between regular buses and taxis in the target community. Based on the passenger flow of the target community at each rail transit station and in each time interval, and the passenger flow of the target community at each taxi hotspot and in each time interval, a third coupling parameter corresponding to the target community is calculated. The third coupling parameter is used to characterize the coupling degree between rail transit and taxis within the target community. The traffic coupling parameter corresponding to the target community is determined based on the first coupling parameter, the second coupling parameter, and the third coupling parameter; The first coupling parameter ; The second coupling parameter ; The third coupling parameter ; Where r represents the station and t represents the time interval; To determine the passenger flow of the public transportation system at a specific stop (r) and time (t) within the target community, For the passenger flow of the rail transit system at a station r and time t within the target community, The passenger flow of the taxi system at a station r and time t within the target community; , , These are the magnitudes of the total traffic flow vectors of the public transportation system, the rail transit system, and the taxi system in the target community, respectively.

2. The method for assessing the convenience level of community services as described in claim 1, characterized in that, The step of counting the number of different types of service facilities in the target community within the preset service area includes: For each primary type, the number of service facilities of different secondary types corresponding to each primary type in the target community is counted within the preset service range; For each primary type, the total number of service facilities corresponding to each primary type in the target community is calculated based on the number of service facilities of different secondary types corresponding to each primary type in the target community.

3. The method for assessing the convenience level of community services as described in claim 1, characterized in that, Before assessing the service convenience level of the target community based on the target evaluation indicators determined according to the service facility-related parameters of the target community, the method further includes: Based on the diversity parameter, the per capita facility parameter, the facility distribution density parameter, and the traffic coupling parameter, calculate the original evaluation index corresponding to each primary type in the target community; The target evaluation index of the target community is calculated based on the original evaluation index corresponding to each primary type in the target community and the weight coefficient of each primary type.

4. The method for assessing the convenience level of community services as described in claim 3, characterized in that, The step of calculating the target evaluation index for the target community based on the original evaluation index corresponding to each primary type in the target community and the weight coefficient of each primary type includes: Obtain the original evaluation indicators corresponding to each primary type in multiple communities, including the target community; For each of the primary types, the maximum and minimum evaluation indicators for each primary type in the plurality of communities are determined based on the original evaluation indicators corresponding to each of the primary types in the plurality of communities; For each of the first-level types, the original evaluation indicators corresponding to each of the first-level types in the target community are normalized according to the maximum evaluation indicator and the minimum evaluation indicator corresponding to each of the first-level types, so as to obtain the normalized indicators corresponding to each of the first-level types in the target community. Based on the normalized index corresponding to each of the first-level types in the target community, and the weight coefficient of each of the first-level types, the target evaluation index of the target community is calculated.

5. The method for assessing the convenience level of community services as described in claim 1, characterized in that, The building information includes the center-of-gravity coordinates, outline area, and resident population data of each building in the target community. After obtaining the building information of the target community, the method further includes: The weighting coefficient of each building is calculated based on the outline area of ​​each building and the resident population data; The center coordinates of the target community are obtained by performing a weighted average calculation on the weight coefficient and the centroid coordinates of each building.

6. A device for assessing the convenience level of community services, characterized in that, include: An acquisition module is used to acquire building information of a target community, wherein the building information is used to determine the center coordinates of the target community; The statistics module is used to count the number of different types of service facilities in the target community within a preset service range, wherein the preset service range is the range formed by taking the center coordinates as the starting point and the set duration corresponding to each type as the service radius; The first calculation module is used to calculate the service facility-related parameters of the target community based on the number of different types of service facilities in the target community. The service facility-related parameters include facility diversity parameters, per capita facility parameters, facility distribution density parameters, and traffic coupling parameters. The different types include multiple primary types and multiple secondary types corresponding to each primary type. The evaluation module is used to evaluate the service convenience level of the target community based on the target evaluation indicators determined by the relevant parameters of the service facilities of the target community. The first computing module includes: The first calculation unit is used to calculate the diversity parameters corresponding to each primary type in the target community based on the number of sub-service facilities of different secondary types corresponding to each primary type in the target community. The second calculation unit is used to calculate the per capita facility parameters for each primary type in the target community based on the total number of service facilities corresponding to each primary type in the target community and the number of permanent residents in the target community. The third calculation unit is used to calculate the facility distribution density parameter for each primary type in the target community based on the total number of service facilities corresponding to each primary type in the target community and the area of ​​the target community. The fourth calculation unit is used to calculate the traffic coupling degree parameter of the target community based on the passenger flow at different traffic nodes and at different time intervals. The fourth calculation unit is specifically used for: Based on the passenger flow of the target community at each bus stop and each time interval, and the passenger flow of the target community at each rail transit stop and each time interval, the first coupling degree parameter corresponding to the target community is calculated. The first coupling degree parameter is used to characterize the coupling degree between regular buses and rail transit within the target community. Based on the passenger flow of the target community at each bus stop and in each time interval, as well as the passenger flow of the target community at each taxi hotspot and in each time interval, the second coupling parameter corresponding to the target community is calculated. The second coupling parameter is used to characterize the coupling degree between regular buses and taxis in the target community. Based on the passenger flow of the target community at each rail transit station and in each time interval, as well as the passenger flow of the target community at each taxi hotspot and in each time interval, the third coupling parameter corresponding to the target community is calculated. The third coupling parameter is used to characterize the coupling degree between rail transit and taxis within the target community. The traffic coupling parameters corresponding to the target community are determined based on the first coupling parameter, the second coupling parameter, and the third coupling parameter. The first coupling parameter ; The second coupling parameter ; The third coupling parameter ; Where r represents the station and t represents the time interval; To determine the passenger flow of the public transportation system at a specific stop (r) and time (t) within the target community, For the passenger flow of the rail transit system at a station r and time t within the target community, The passenger flow of the taxi system at a station r and time t within the target community; , , These are the magnitudes of the total traffic flow vectors of the public transportation system, the rail transit system, and the taxi system in the target community, respectively.

7. A terminal device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the terminal device to implement the method as described in any one of claims 1-5.

8. A computer program product, characterized in that, When the computer program product is run on a terminal device, it causes the terminal device to perform the method as described in any one of claims 1-5.

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