Livelihood facility database construction method and system based on spatial geocoding technology

Abstract

The invention discloses a livelihood facility database construction method and system based on a spatial geocoding technology, and relates to the technical field of facility database construction, and the method comprises the steps: obtaining livelihood facility information; setting a livelihood facility distribution grid according to a service range and a service object attribute in the livelihood facility information; carrying out reachability evaluation, and carrying out grid density optimization on the livelihood facility distribution grids; meanwhile, collaborative optimization is carried out, and a time-phased reachability radar map is generated; and carrying out multi-layer superposition, determining a public service gap, constructing a closed-loop optimization system, and checking and updating a livelihood equipment database. The technical problems that in the prior art, livelihood facility service short boards are inaccurate in positioning, layout is difficult to dynamically adjust according to actual needs, and livelihood facility databases are not updated in time are solved, and the purposes of accurately positioning the livelihood facility service short boards, dynamically optimizing the livelihood facility layout and continuously updating the livelihood facility databases are achieved. And the urban livelihood service level is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of facility database construction, and in particular to a method and system for constructing a livelihood facility database based on spatial geocoding technology. Background Art

[0002] With the acceleration of urbanization, the size of urban population continues to expand, and residents' demand for livelihood services is becoming increasingly diversified and refined. However, the existing planning and management of livelihood facilities face many difficulties. On the one hand, the traditional layout of livelihood facilities is often based on static data and empirical judgments, lacking sufficient consideration of dynamic urban factors such as traffic flow, population density fluctuations, and real-time changes in public service demand, resulting in a serious imbalance between the supply and demand of livelihood facilities in some areas, insufficient service coverage or idle resources. On the other hand, in terms of positioning the shortcomings of livelihood facilities services, due to the lack of precise analysis methods and data integration capabilities, it is difficult to quickly and accurately determine the type, location and scope of public service gaps. At the same time, the update mechanism of the livelihood equipment database is lagging behind, and it is unable to timely reflect the actual use and changes of the facilities, making it difficult for urban management departments to effectively optimize and adjust livelihood facilities according to actual conditions, which seriously restricts the improvement of urban livelihood service levels.

[0003] The existing technology has technical problems such as inaccurate positioning of the shortcomings of public welfare facilities services, difficulty in dynamically adjusting the layout according to actual needs, and untimely updating of the public welfare equipment database. Summary of the invention

[0004] The present application provides a method and system for constructing a livelihood facilities database based on spatial geocoding technology, which is used to solve the technical problems in the prior art of inaccurate positioning of livelihood facilities service shortcomings, difficulty in dynamically adjusting the layout according to actual needs, and untimely updating of the livelihood equipment database.

[0005] In view of the above problems, the present application provides a method and system for constructing a public facilities database based on spatial geocoding technology.

[0006] In a first aspect of the present application, a method for constructing a people's livelihood facilities database based on spatial geocoding technology is provided, the method comprising: The livelihood facilities information of the target area is obtained, and the livelihood facilities information includes facility type, service scope, and service object attributes, and the service scope and service object attributes all have location aggregation marks; according to the spatial geocoding technology and the service scope and service object attributes in the livelihood facilities information, a livelihood facilities distribution grid is set, and the livelihood facilities distribution grid includes traffic intersections, densely populated intersections, and public service intersections; accessibility assessment is performed on the traffic intersections of the livelihood facilities distribution grid, and the grid density of the livelihood facilities distribution grid is optimized based on the accessibility differences in different time periods; at the same time, combined with the distribution of densely populated intersections and public service intersections, collaborative optimization is performed to generate a time-divided accessibility radar map, and the time-divided accessibility radar map is centered on the traffic intersection, showing the correlation between population density hotspots and traffic intersections, and the correlation between public service connection points and traffic intersections; based on the time-divided accessibility radar map, multi-layer overlay is performed to determine the public service gap, and it is connected to the urban digital twin model to build a closed-loop optimization system to check and update the livelihood equipment database.

[0007] The second aspect of the present application provides a system for constructing a people's livelihood facilities database based on spatial geocoding technology, the system comprising: The livelihood facilities information acquisition module is used to obtain the livelihood facilities information of the target area, and the livelihood facilities information includes the facility type, service scope, and service object attributes, and the service scope and service object attributes both have location aggregation marks; the livelihood facilities distribution grid setting module is used to set the livelihood facilities distribution grid according to the spatial geocoding technology and the service scope and service object attributes in the livelihood facilities information, and the livelihood facilities distribution grid includes traffic intersections, densely populated intersections, and public service intersections; the grid density optimization module is used to evaluate the accessibility of the traffic intersections of the livelihood facilities distribution grid, and to calculate the accessibility differences in different time periods. , optimize the grid density of the distribution grid of the people's livelihood facilities; a time-divided accessibility radar map generation module is used to simultaneously combine the distribution of densely populated intersections and public service intersections to perform collaborative optimization and generate a time-divided accessibility radar map. The time-divided accessibility radar map is centered on traffic intersections and displays the correlation between population density hotspots and traffic intersections, and the correlation between public service connection points and traffic intersections; a closed-loop optimization system construction module is used to perform multi-layer overlay based on the time-divided accessibility radar map, determine the public service gap, and connect it to the city digital twin model to build a closed-loop optimization system to test and update the people's livelihood equipment database.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: Acquire the livelihood facilities information of the target area; set up the livelihood facilities distribution grid according to the spatial geocoding technology and the service scope and service object attributes in the livelihood facilities information; evaluate the accessibility of the traffic intersections of the livelihood facilities distribution grid, as well as the differences in accessibility in different time periods, and optimize the grid density of the livelihood facilities distribution grid; at the same time, combine the distribution of densely populated intersections and public service intersections to perform collaborative optimization and generate a time-divided accessibility radar map; perform multi-layer superposition based on the time-divided accessibility radar map to determine the public service gap, and connect it to the city digital twin model to build a closed-loop optimization system and test and update the livelihood equipment database. The technical effect of accurately locating the shortcomings of livelihood facilities services, dynamically optimizing the layout of livelihood facilities, and continuously updating the livelihood equipment database has been achieved to improve the level of urban livelihood services. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a method for constructing a livelihood facilities database based on spatial geocoding technology provided in an embodiment of the present application; Figure 2 A schematic diagram of the system structure for constructing a people's livelihood facilities database based on spatial geocoding technology provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: livelihood facilities information acquisition module 10, livelihood facilities distribution grid setting module 20, grid density optimization module 30, time period accessibility radar map generation module 40, closed-loop optimization system construction module 50. DETAILED DESCRIPTION

[0012] This application provides a method and system for constructing a livelihood facilities database based on spatial geocoding technology, which is used to solve the technical problems in the prior art of inaccurate positioning of livelihood facilities service shortcomings, difficulty in dynamically adjusting the layout according to actual needs, and untimely updating of the livelihood equipment database.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a method for constructing a livelihood facilities database based on spatial geocoding technology, the method comprising: Step S100: Acquire the livelihood facilities information of the target area, wherein the livelihood facilities information includes facility type, service scope, and service object attributes, and both the service scope and service object attributes have location aggregation marks.

[0015] Specifically, by collaborating with urban planning, housing and construction, civil affairs and other departments, with the help of their rich information resources, we collect information on various livelihood facilities. According to the types of facilities, we accurately distinguish between educational facilities (schools, training institutions, etc.), medical facilities (hospitals, clinics, etc.), commercial facilities (shopping malls, supermarkets, etc.), cultural and sports facilities (libraries, gymnasiums, etc.) and public transportation facilities (bus stops, subway stations, etc.). In terms of service scope, we use geographic information system (GIS) technology, combined with satellite maps and electronic map data, to accurately define the coverage area of ​​each livelihood facility, and add location aggregation marks to it, so that the boundaries and coverage areas of the service scope can be clearly presented, which is convenient for subsequent spatial analysis. For the attributes of service objects, we collect information such as age, occupation, income level, consumption habits, etc. through questionnaire surveys, community visits, and census data matching. At the same time, we also mark these attributes with location aggregation, such as classifying and labeling them by geographical units such as communities and streets. Clarifying the basic situation of various livelihood facilities can also establish a close connection between the facilities and the surrounding population based on location aggregation marks, providing a solid data foundation for subsequent in-depth analysis and layout optimization using spatial geocoding technology.

[0016] Step S200: According to the spatial geocoding technology and the service scope and service object attributes in the livelihood facilities information, a livelihood facilities distribution grid is set, and the livelihood facilities distribution grid includes traffic intersections, densely populated intersections, and public service intersections.

[0017] Specifically, with the help of spatial geocoding technology, the service scope and service object attributes in the collected livelihood facilities information are deeply analyzed and processed. Using professional geographic information system (GIS) software, the geographical location data of the service scope is converted into specific geographical coordinates, and the population distribution is spatially expressed based on the location aggregation mark of the service object attributes. On this basis, the distribution grid of livelihood facilities is set up. First, the traffic intersections are determined. Through traffic big data and map information, key locations such as the intersections of major roads, bus transfer hubs, and subway stations are screened out. These traffic intersections, as important nodes for people's travel, play a key role in the accessibility of livelihood facilities. Then, based on population density data, densely populated areas such as large community centers and concentrated office buildings are found, and the core intersections of these areas are marked as densely populated intersections, which represent the main demand areas for livelihood facilities. Finally, combined with the actual distribution of public service facilities, public service intersections are determined, such as the locations of public service facilities such as hospitals, schools, and libraries or key intersections around them. By integrating these three types of intersections, a distribution grid of livelihood facilities is constructed, which provides an important spatial framework for the subsequent layout analysis, accessibility evaluation, and optimization and adjustment of livelihood facilities.

[0018] Step S300: performing accessibility evaluation on the traffic intersections of the livelihood facilities distribution grid, and optimizing the grid density of the livelihood facilities distribution grid based on the accessibility differences in different time periods.

[0019] Specifically, the accessibility of traffic intersections in the grid is evaluated, so as to optimize the grid density according to the accessibility differences in different time periods. First, a variety of data and technical means are used to carry out accessibility evaluation. With the help of the traffic big data platform, real-time and historical data such as traffic flow and vehicle speed at each traffic intersection in the target area at different times are obtained; the geographic information system (GIS) technology is used, combined with information such as road network topology and bus route planning, to calculate the travel time and distance from each traffic intersection to the surrounding important livelihood facilities (such as hospitals, schools, shopping malls, etc.). At the same time, considering the characteristics of different travel modes (such as driving, bus, walking, etc.), a comprehensive and accurate accessibility index is obtained. Analyzing the accessibility differences in different time periods, it can be found that during peak hours in the morning and evening, traffic congestion is serious, the travel time from some traffic intersections to livelihood facilities has increased significantly, and accessibility has been significantly reduced; while during non-peak hours, traffic is smooth and accessibility is significantly improved. According to these differences, the grid density of the distribution grid of livelihood facilities is optimized. For areas with poor accessibility during peak hours but densely populated areas or important public service needs, the grid density can be appropriately increased to more finely analyze and plan the layout of transportation and livelihood facilities in the area; for areas with good accessibility and relative stability, the grid density can be appropriately reduced to improve analysis efficiency. In this way, the distribution grid of livelihood facilities can more accurately reflect the actual traffic conditions and livelihood needs in different time periods, providing a scientific basis for subsequent public service planning and resource allocation.

[0020] Step S400: At the same time, combined with the distribution of densely populated intersections and public service intersections, collaborative optimization is performed to generate a time-divided accessibility radar map. The time-divided accessibility radar map is centered on traffic intersections and displays the correlation between population density hotspots and traffic intersections, and the correlation between public service connection points and traffic intersections.

[0021] Specifically, big data analysis technology is used to extract the spatiotemporal distribution characteristics of densely populated intersections from census data, social media check-in data, and mobile operator signaling data, and to obtain the precise location information of public service intersections from urban planning databases and government public data. These data are imported into the geographic information system (GIS) platform, and a comprehensive analysis model is constructed in combination with traffic network data. For different time periods, such as weekday morning peak, weekday off-peak, and weekends, the shortest travel time from traffic intersections to densely populated intersections and the traffic flow from public service intersections to traffic intersections are calculated to quantify the degree of correlation between population density hotspots and traffic intersections, and between public service connection points and traffic intersections. Using data visualization tools, with traffic intersections as the center, multiple coordinate axes are set to represent the quantitative indicators of the two correlations in different time periods, and a radar map of accessibility in different time periods is drawn based on the calculation results. In the radar map, different colors are used to fill the area to indicate the degree of correlation, and the coordinate scale is used to accurately mark the value, so as to facilitate and intuitively view the correlation differences of each intersection in different time periods, and provide a strong basis for the subsequent optimization of the layout of people's livelihood facilities and traffic planning.

[0022] Step S500: Based on the time-divided accessibility radar map, multiple layers are superimposed to determine the public service gap, and the public service gap is connected to the city digital twin model to build a closed-loop optimization system and check and update the public welfare equipment database.

[0023] Specifically, the layer overlay analysis function of the geographic information system (GIS) is used to overlay the generated time-divided accessibility radar map in order according to different time periods. In the overlay process, the basic geographic information layer, land use status layer and existing livelihood facilities distribution layer of the city are combined, and the spatial analysis algorithm is used to identify the areas with insufficient public service coverage in different time periods, so as to determine the specific location, scope and type of the public service gap. The relevant information of the determined public service gap is connected to the city digital twin model. The city digital twin model is a digital mapping of the real city. After accessing the gap information, the simulation function of the model is used to simulate and analyze different public service facility optimization plans, and predict the impact of these plans on the surrounding areas after implementation, such as changes in traffic flow, changes in population distribution, and the synergistic impact on other livelihood facilities. According to the simulation results, the best optimization plan is selected, and the optimization plan is fed back to the actual management and construction departments through the information interaction platform with the city management department and relevant construction units.

[0024] At the same time, a closed-loop optimization system is constructed. During the implementation of the optimization plan, sensor networks and IoT devices are used to collect usage data of livelihood facilities, surrounding environment data, and public feedback information in real time. These data are transmitted back to the city's digital twin model and livelihood equipment database, and compared and analyzed with the original data to determine whether the optimization measures have achieved the expected results. If expectations are not met or new problems arise, the optimization plan is readjusted, and simulation, implementation and monitoring are carried out again, and this process is continuously repeated. Through this closed-loop optimization system, the livelihood equipment database is dynamically inspected and updated, and information on the location, service scope, and usage status of livelihood facilities in the database is corrected in a timely manner to ensure that the database always reflects the true and accurate situation of livelihood facilities, and provides reliable data support for the sustainable development of the city and the optimization of livelihood services.

[0025] In a possible implementation, step S500 further includes: Step S510: The time-divided accessibility radar map, the basic geographic information layer, and the basic geographic planning layer are superimposed in multiple layers to determine the public service gap.

[0026] Step S520: define the gap type of the public service gap, where the gap type includes space coverage type, time period service type, and capacity overload type.

[0027] Step S530: Based on the gap type and the impact range of the gap, a graded repair list is prepared.

[0028] Specifically, with the help of the layer processing function of the professional geographic information system (GIS), the time-divided accessibility radar map, basic geographic information layer and basic geographic planning layer are accurately superimposed in multiple layers. The time-divided accessibility radar map reflects the degree of correlation between traffic intersections and population density hotspots and public service connection points in different time periods; the basic geographic information layer contains natural geographical elements such as terrain, landforms, and water systems, as well as human geographical elements such as roads and buildings, providing a basic geographical framework for analysis; the basic geographic planning layer covers the city's future development planning information. By superimposing these three layers and using spatial analysis tools, it is possible to intuitively and accurately discover areas where public services are not covered or are insufficiently covered, thereby determining the specific location and scope of the public service gap.

[0029] After identifying the public service gap, it is carefully classified and defined. If a certain area is difficult to obtain adequate public service coverage in different periods of time over a long period of time, such as the long-term lack of medical institutions and educational facilities in remote areas, this type of gap is defined as spatial coverage. If public services cannot meet demand in certain specific periods of time, such as insufficient public transportation capacity during peak hours in the morning and evening, or seats in libraries, gymnasiums and other places are in short supply on weekends, this type of gap belongs to the time service type. When the number of service recipients accepted by public service facilities in a certain period of time or for a long period of time exceeds their design capacity, resulting in a decline in service quality, such as public toilets around some popular scenic spots are overcrowded during the peak tourist season. This situation is classified as capacity overload. By accurately dividing the types of gaps, more targeted solutions can be formulated.

[0030] According to the defined gap types and the size of the gap impact range, a hierarchical repair list is drawn up. For spatial coverage gaps, if the impact range involves multiple blocks or even the entire urban area and has a serious impact on residents' lives, it will be listed as a high-level repair item, and resources will be prioritized for construction or adjustment; if the impact range is small and only involves individual communities, it can be listed as a medium or low level and gradually resolved according to actual conditions. For time-of-day service gaps, if there is a lack of service in multiple important time periods and a large number of people are affected, it should be the focus of repair; if the problem only occurs in a relatively minor time period and the impact range is also narrow, the repair level can be lowered. For capacity overload gaps, if the overload is serious, it will cause safety hazards or seriously affect the quality of service, such as public facilities where overcrowding may cause safety accidents, it will be listed as an emergency repair level; if the overload is relatively light, it will be arranged for repair in subsequent batches. Through such hierarchical processing, resources can be reasonably allocated and the repair of public service gaps can be promoted in an orderly manner.

[0031] In a possible implementation, step S300 further includes: Step S310: Acquire dynamic weight parameters of the target area, wherein the dynamic weight parameters include a traffic flow variation coefficient, a population density fluctuation coefficient, and a public service demand priority coefficient.

[0032] Step S320: Based on the dynamic weight parameters, predict the weight change trend of each traffic intersection in the future period, and configure sparse grid clusters and dense grid clusters.

[0033] Step S330: According to the sparse grid cluster and the dense grid cluster, the optimization instruction is automatically triggered by cooperating with the city management center.

[0034] Specifically, it is necessary to comprehensively use multi-source data collection and analysis methods to obtain the dynamic weight parameters of the target area. In terms of traffic flow variation coefficient, with the help of inductive traffic flow monitoring equipment installed on the road, such as geomagnetic sensors, microwave radars, etc., the vehicle flow data of each section of the road is collected in real time, and the historical data is analyzed through traffic flow statistics software to obtain the flow change rules in different time periods (such as morning and evening peaks, flat peaks, weekends, etc. on weekdays), and then calculate the traffic flow variation coefficient. For the population density fluctuation coefficient, mobile phone signaling data is used to obtain the real-time distribution of population in different areas through base station positioning; at the same time, combined with population census data, community occupancy rate survey data, etc., the dynamic changes of population in different regions over time are analyzed, such as the difference in population flow in different regions on weekdays and weekends, so as to obtain the population density fluctuation coefficient. The public service demand priority coefficient is determined by building a resident demand survey platform and conducting online and offline questionnaire surveys to understand the residents' demand for various public services (such as medical care, education, culture and entertainment, etc.); referring to the government's public service planning documents to clarify the positioning and key construction directions of different public service facilities in urban development; collecting frequency data on the use of public service facilities, such as the number of patients in hospitals and the number of books borrowed from libraries, etc., and combining this information to determine the public service demand priority coefficient.

[0035] Using the time series analysis algorithm, combined with historical traffic flow, population density and public service demand data, the trend of traffic flow change coefficient, population density fluctuation coefficient and public service demand priority coefficient is predicted. For example, the ARIMA model is used to model the traffic flow change coefficient in time series to predict the fluctuation of traffic flow in different periods in the future; the regression algorithm in machine learning, such as support vector regression (SVR), is used to predict the change trend of population density fluctuation coefficient based on historical data of population density and related influencing factors; for the public service demand priority coefficient, the weight of each factor is adjusted by combining expert experience with historical demand data, and then its future changes are predicted by using the analytic hierarchy process (AHP). Then, the predicted dynamic weight parameters of each period in the future are input into the traffic intersection weight calculation model. The model performs weighted calculation on each traffic intersection according to the traffic flow change coefficient, population density fluctuation coefficient and public service demand priority coefficient, and obtains the weight value of each traffic intersection in different periods in the future. For example, a traffic intersection with large traffic flow, high population density and high priority of surrounding public service demand is given a higher weight; otherwise, the weight is lower. Finally, sparse grid clusters and dense grid clusters are configured according to the calculated traffic intersection weight values. A weight threshold is set, and traffic intersections with weight values ​​higher than the threshold are divided into dense grid clusters. These areas often have busy traffic, dense populations, and high public service demands, requiring more detailed analysis and management, so higher-density grids are configured; while traffic intersections with weight values ​​lower than the threshold are classified as sparse grid clusters. These areas have less traffic pressure, relatively dispersed populations, or lower public service demands, so lower-density grids are configured accordingly, so as to achieve differentiated management and optimization of different areas.

[0036] Real-time monitoring of the conditions of each traffic intersection in the sparse grid cluster and the dense grid cluster. When a traffic congestion sign is detected at a traffic intersection in the dense grid cluster, such as when the traffic volume exceeds the preset threshold, resulting in a significant decrease in the average vehicle speed, an optimization instruction will be automatically sent to the traffic signal control system of the city management center. After receiving the instruction, the traffic signal control system will quickly adjust the timing plan of the signal light according to the real-time traffic conditions in the area, increase the green light time in the congested direction, and reduce the green light time in the non-congested direction to relieve traffic pressure and ensure smooth traffic of vehicles. At the same time, if an emergency occurs in a sparse or dense grid cluster, such as a traffic accident or natural disaster, an alarm message and detailed coordinates of the location of the incident will be immediately sent to the emergency command system. Based on the precise location information provided by the grid cluster and the surrounding traffic conditions, the emergency command system quickly plans the optimal rescue route, coordinates relevant emergency rescue forces such as fire, medical, and public security, and rushes to the scene for rescue in a timely manner. In addition, the emergency command system can also deploy rescue resources in advance according to the regional characteristics reflected by the grid cluster. For example, in densely populated grid cluster areas, more medical emergency vehicles and rescue personnel can be pre-arranged to ensure that rescue work can be carried out quickly and effectively in emergency situations, minimizing the impact of emergencies on urban operations and residents' lives.

[0037] In a possible implementation, step S500 further includes: Step S540: Access the citizen feedback platform to collect multiple user experience feedbacks on public facilities.

[0038] Step S550: Structural processing is performed on the multiple pieces of usage experience feedback, matching them with the gap types, and adding additional influencing factors to each piece of usage experience feedback in combination with the dynamic weight parameter.

[0039] Step S560: using the plurality of usage experience feedbacks with additional influencing factors as reverse supplementary data to perform synchronization verification on the livelihood equipment database.

[0040] Specifically, by establishing data connection with the existing citizen feedback platform, we can comprehensively collect feedback on the user experience of livelihood facilities. These feedbacks come from a wide range of sources, including opinions submitted by citizens through mobile phone apps, government website message boards, offline questionnaires, and other channels, including evaluations and suggestions on various livelihood facilities such as the convenience of bus stops, hospital registration procedures, and community park facilities, thus gathering a large amount of rich and real user experience data.

[0041] The massive amount of user experience feedback collected is structured, and natural language processing technology is used to convert unstructured text feedback into structured data to extract key information, such as the type of livelihood facilities involved in the feedback, specific location, problem description, etc. Subsequently, these structured feedbacks are matched with the previously defined public service gap types (spatial coverage type, time period service type, capacity overload type). For example, if the feedback mentions that a certain type of livelihood facility has been lacking in a remote area for a long time, it can be matched with a spatial coverage gap. At the same time, combined with dynamic weight parameters (traffic flow change coefficient, population density fluctuation coefficient, public service demand priority coefficient), the traffic conditions, population density and demand priority of the livelihood facilities in the area involved in the feedback are comprehensively considered, and additional impact factors are added to each user experience feedback. For feedback in areas with large traffic flow, dense population and high priority of public service demand, a higher additional impact factor is given, and vice versa.

[0042] Multiple pieces of user experience feedback with additional influencing factors are used as reverse supplementary data and connected to the verification process of the livelihood equipment database. The database management system will compare and analyze these feedback data with the existing livelihood facility information in the database, such as checking whether the location information of the facility accurately reflects the actual service coverage, whether the operation time of the facility is consistent with the time period service feedback, and whether the capacity data of the facility is consistent with the capacity overload feedback. Based on the comparison results, the livelihood equipment database is synchronously verified and updated, inaccurate or outdated information is corrected, and newly discovered problems and related data are supplemented to ensure that the livelihood equipment database can continuously and accurately reflect the actual use experience and demand status of citizens for livelihood facilities, and provide reliable data support for the optimization of urban livelihood services.

[0043] In a possible implementation, step S330 further includes: Step S331: merging the sparse grid clusters in the public facilities distribution grid.

[0044] Step S332: At the same time, elastic expansion is performed for the dense grid cluster, and the elastic expansion is used to adapt to the difference in service demand during the day and night in the city and obtain an elastic expansion strategy.

[0045] Specifically, the sparse grid clusters are merged. By analyzing the dynamic weight parameters of each traffic intersection in the sparse grid cluster and the surrounding population density, traffic flow and other data, it is found that some grid areas have long-term low traffic pressure, relatively dispersed population distribution, and relatively stable public service demand. In view of the similar characteristics of these areas, adjacent sparse grids with similar functions and needs are merged. For example, some grids located in the far suburbs of the city with low traffic flow and sparsely distributed settlements are integrated into a larger grid unit to reduce unnecessary grid divisions, improve management efficiency, and avoid wasting resources on over-segmented grid analysis and maintenance.

[0046] In-depth research on the functional characteristics and service demand changes of different areas in the city during the day and at night. For example, in the central business district of the city, there are dense crowds of people working and shopping during the day, and there is a strong demand for services such as transportation and catering; while at night, it turns to leisure and entertainment, and the demand for entertainment venues, late-night snacks and catering services increases. Based on this, a flexible expansion strategy is formulated through big data analysis, historical data statistics and real-time monitoring information. During the day, according to traffic flow and population density forecasts, the grids around transportation hubs and office areas in the dense grid cluster are expanded, and resources such as traffic diversion and public service facilities are increased; at night, corresponding adjustments are made to the grids around entertainment gathering areas and residential areas, such as adding night bus routes and extending the opening hours of some public service facilities, so as to flexibly adjust grid resources to better meet the different service needs of the city during the day and night.

[0047] In a possible implementation, step S332 further includes: Step S3321: Based on the dynamic weight parameters, the service resilience index of the traffic intersections, densely populated intersections, and public service intersections within the dense grid cluster is calculated, and the intersections whose service resilience index is higher than the first threshold are marked as key support points.

[0048] Step S3322: Based on the dynamic weight parameters, calculate the node influence values ​​of the traffic intersections, densely populated intersections, and public service intersections within the dense grid cluster in the gap propagation chain.

[0049] Step S3323: Mark the intersection point where the node influence value is higher than the second threshold and the service resilience index is lower than the first threshold as a supporting point to be strengthened.

[0050] Step S3324: Perform elastic expansion according to the key supporting points and the supporting points to be strengthened.

[0051] Specifically, the service resilience index of various intersections is calculated based on the dynamic weight parameters. For traffic intersections, the standard deviation of traffic flow per unit time is first calculated based on the traffic flow variation coefficient. , to measure the degree of fluctuation of traffic flow. At the same time, the average distance from the intersection to the surrounding emergency facilities (such as fire stations and hospital emergency departments) is obtained through the geographic information system (GIS). , and combined with historical data to derive the average emergency response time . Set traffic flow stability weights , Emergency Response Capability Weight , where the traffic flow stability weight and emergency response capability weights The value range of is between 0 and 1, and , through the formula , calculate the service resilience index of traffic intersections ,in is the average traffic flow. For densely populated intersections, the rate of change of population density per unit time is calculated based on the population density fluctuation coefficient. With the help of community survey data and public service facility registration information, the number of various public service facilities (such as schools, supermarkets, and parks) within a certain range around the intersection is counted. and service coverage area , and then get the public service coverage index . Set population stability weights , Public service coverage weight , where the population stability weight and public service coverage weight The value range of is between 0 and 1, and , through the formula Calculating the service resilience index for densely populated intersections For public service intersections, the priority score of the public services carried by the intersection is analyzed in combination with the public service demand priority coefficient. , where the priority score Scoring is based on factors such as the importance of services to residents’ lives and scarcity. The standards can be set by relevant departments or determined comprehensively through resident questionnaires, and the number of failures of facilities in recent periods of time is counted. and maintenance time , number of failures and maintenance time Obtain data statistics from the facility operation and maintenance management system. Set demand priority weights , Facility reliability weight , where the demand priority weight and facility reliability weights The value range of is between 0 and 1, and , through the formula Calculating the service resilience index at the intersection of public services Finally, an appropriate first threshold is set, and traffic intersections, densely populated intersections, and public service intersections with service resilience indexes higher than the threshold are marked as key support points.

[0052] In-depth analysis of dynamic weight parameters. For traffic intersections, based on the traffic flow variation coefficient and combined with the topological structure of the traffic network, the influence of the intersection on the redistribution of traffic flow on surrounding roads when gaps such as traffic congestion occur is analyzed. For example, if a traffic intersection is located at the intersection of multiple main roads, and the traffic flow variation coefficient shows that its traffic flow fluctuates greatly during peak hours, once a congestion gap occurs here, it will rely on its key position to affect the traffic efficiency of surrounding roads and quickly spread the gap to the connected sections, thereby quantifying its influence in the spread of traffic flow gaps. For densely populated intersections, the relationship between the direction of population flow and public service demand is considered with the help of the population density fluctuation coefficient. When a service gap occurs in public service facilities around a densely populated intersection in a certain area, such as insufficient hospital beds, due to the high population density and frequent population flow at the intersection, the gap information will be quickly spread to the surrounding areas, attracting more people to seek other alternative services, and the scope of influence will be expanded. Based on factors such as the scale and frequency of population flow and the scope of the affected area, its influence value in the spread of population-related service gaps is calculated. For public service intersections, combined with the public service demand priority coefficient, it is analyzed that when there is a gap in a certain type of public service, such as insufficient school places, due to the importance and demand priority of the public service carried by the intersection, it will trigger a strong demand for related services in the surrounding areas, prompting a chain reaction of resource allocation. By measuring the radiation range of the public service to the surrounding areas, the number of people affected, and the degree of resource allocation caused, the node influence value in the public service gap propagation chain is determined, thereby comprehensively evaluating the role of each intersection in the gap propagation chain.

[0053] Compare the previously calculated node influence value with the pre-set second threshold, and compare the service resilience index with the first threshold. Those intersections whose node influence values ​​are higher than the second threshold mean that they have a strong influence in the gap propagation chain. Once problems occur themselves, they are very likely to trigger a large-scale chain reaction. However, if the service resilience index of these intersections is lower than the first threshold, it indicates that their ability to maintain stable services in the face of various fluctuations or emergencies is poor. Based on this, such intersections that meet the requirements of both a node influence value higher than the second threshold and a service resilience index lower than the first threshold are marked as support points to be strengthened, so that subsequent strengthening measures can be implemented for these critical but weak nodes to enhance the ability of the entire dense grid cluster to cope with service gaps.

[0054] Flexible expansion is carried out based on the marked key support points and support points to be strengthened. For key support points, further optimization of surrounding transportation facilities and increase of public service resource reserves can be considered to enhance service resilience and carrying capacity, ensuring that services can be provided to surrounding areas stably under any circumstances. For support points to be strengthened, targeted strengthening measures are taken, such as improving transportation connections, improving the quality and quantity of public service facilities, and enhancing their ability to cope with service gaps and risks, so that the entire dense grid cluster can adapt more flexibly and efficiently to the differences in urban day and night service needs.

[0055] In a possible implementation, step S3324 further includes: Step S33241: Perform protective expansion according to the key support points. The protective expansion formula is as follows: ,in, To expand the capacity of key support points, Used to characterize the traffic flow variation coefficient, Used to characterize the population density fluctuation coefficient, Used to characterize the priority coefficient of public service demand, is the service resilience index corresponding to the key support points, It is the first adjustment factor, used to balance the expansion capacity and service resilience. is the lower limit of basic capacity; Complementary expansion is performed according to the supporting points to be strengthened. The formula for complementary expansion is as follows: ,in, To expand the capacity of the supporting points to be strengthened, It is used to characterize the node influence value corresponding to the supporting point to be strengthened. It is used to characterize the service resilience index corresponding to the supporting point to be strengthened. It is used to characterize the maximum service resilience index within the same dense grid cluster to which the supporting point to be strengthened belongs. is the second adjustment factor, used to balance the expansion capacity and node impact. The upper limit of basic capacity.

[0056] Specifically, elastic expansion is implemented for key support points and support points to be strengthened within the dense grid cluster to improve the overall service capacity of the dense grid cluster. For key support points, since their service resilience index is higher than the first threshold and they play a significant role in maintaining the stability of urban services, a protective expansion strategy is adopted. Using the formula Calculate its expansion capacity, the formula , , They respectively represent the traffic flow variation coefficient, population density fluctuation coefficient, and public service demand priority coefficient. These coefficients comprehensively reflect the dynamic weight parameters of the area and will change according to changes in traffic, population and public service demands. It is the service resilience index corresponding to the key support point, which reflects the ability of the key support point to maintain stable service. The higher the index, the stronger the stability. As the first adjustment factor, it plays a key role in balancing capacity expansion and service resilience. The value of can ensure that the expansion capacity is determined scientifically and reasonably under the premise of ensuring stable service of key support points, avoiding over-expansion or under-expansion. It is the lower limit of basic capacity, which sets the minimum standard for expanding capacity and ensures that the expanded capacity of key support points can meet basic needs.

[0057] For the supporting points to be strengthened, since their node influence value is higher than the second threshold but their service resilience index is lower than the first threshold, it means that although they have a greater influence in the gap transmission chain, their own service capabilities have shortcomings and need to be supplemented and expanded. Calculate the expansion capacity, where Represents the node influence value corresponding to the supporting point to be strengthened, which reflects the importance of the point in the gap propagation chain. The higher the influence value, the greater the impact on the surrounding area when the gap propagates. It is the service resilience index corresponding to the supporting point to be strengthened, which shows the strength of its current service capability. It represents the maximum service resilience index within the same dense grid cluster to which the supporting point to be strengthened belongs, and is used to compare and evaluate the service resilience level of the point. As the second adjustment factor, it is mainly used to balance the expansion capacity and node influence. By adjusting its value, it can improve the service capacity of the supporting point to be strengthened while avoiding excessive concentration or waste of resources due to excessively high node influence value, ensuring that the expansion capacity is coordinated with the overall resource allocation of the region. It is the upper limit of basic capacity, which limits the maximum value of expansion capacity and prevents excessive expansion. Through targeted expansion of key support points and supporting points to be strengthened, the elastic expansion of dense grid clusters can be achieved, the ability of the entire region to cope with public service gaps can be improved, the layout of people's livelihood facilities can be optimized, and the level of urban public services can be improved.

[0058] Embodiment 2 is based on the same inventive concept as the method for constructing a livelihood facilities database based on spatial geocoding technology in the aforementioned embodiment. Figure 2 As shown, the present application provides a system for constructing a people's livelihood facilities database based on spatial geocoding technology. The system and method embodiments in the present application are based on the same inventive concept. The system includes: The livelihood facilities information acquisition module 10 is used to acquire the livelihood facilities information of the target area, wherein the livelihood facilities information includes facility type, service scope, and service object attributes, and both the service scope and service object attributes have location aggregation marks.

[0059] The livelihood facilities distribution grid setting module 20 is used to set the livelihood facilities distribution grid according to the spatial geocoding technology and the service scope and service object attributes in the livelihood facilities information. The livelihood facilities distribution grid includes traffic intersections, densely populated intersections, and public service intersections.

[0060] The grid density optimization module 30 is used to evaluate the accessibility of the traffic intersections of the livelihood facilities distribution grid, and optimize the grid density of the livelihood facilities distribution grid based on the accessibility differences in different time periods.

[0061] The time-divided accessibility radar map generation module 40 is used to simultaneously perform collaborative optimization based on the distribution of densely populated intersections and public service intersections to generate a time-divided accessibility radar map. The time-divided accessibility radar map is centered on traffic intersections and displays the correlation between population density hotspots and traffic intersections, and the correlation between public service connection points and traffic intersections.

[0062] The closed-loop optimization system construction module 50 is used to perform multi-layer superposition based on the time-divided accessibility radar map, determine the public service gap, and connect it to the city digital twin model to build a closed-loop optimization system and check and update the people's livelihood equipment database.

[0063] Furthermore, the system is also used to implement the following functions: The time-divided accessibility radar map, basic geographic information layer, and basic geographic planning layer are superimposed in multiple layers to determine the public service gap; the gap types of the public service gap are defined, and the gap types include spatial coverage type, time-division service type, and capacity overload type; based on the gap types and in combination with the impact scope of the gap, a graded repair list is drawn up.

[0064] Furthermore, the system is also used to implement the following functions: The dynamic weight parameters of the target area are obtained, wherein the dynamic weight parameters include a traffic flow variation coefficient, a population density fluctuation coefficient, and a public service demand priority coefficient; based on the dynamic weight parameters, the weight variation trend of each traffic intersection in a future period is predicted, and sparse grid clusters and dense grid clusters are configured; according to the sparse grid clusters and dense grid clusters, the optimization instructions are automatically triggered by cooperating with the city management center.

[0065] Furthermore, the system is also used to implement the following functions: Access the citizen feedback platform to collect multiple usage experience feedbacks of livelihood facilities; perform structured processing on the multiple usage experience feedbacks, match them with the gap type, and add additional impact factors to each usage experience feedback in combination with the dynamic weight parameter; use the multiple usage experience feedbacks with additional impact factors as reverse supplementary data to perform synchronization verification on the livelihood equipment database.

[0066] Furthermore, the system is also used to implement the following functions: In the livelihood facilities distribution grid, the sparse grid clusters are merged; at the same time, elastic expansion is performed on the dense grid clusters, and the elastic expansion is used to adapt to the difference in service demand during the day and night in the city and obtain an elastic expansion strategy.

[0067] Furthermore, the system is also used to implement the following functions: Based on the dynamic weight parameters, the service resilience index of the traffic intersections, densely populated intersections, and public service intersections in the dense grid cluster is calculated, and the intersections whose service resilience index is higher than the first threshold are marked as key support points; based on the dynamic weight parameters, the node influence values ​​of the traffic intersections, densely populated intersections, and public service intersections in the gap propagation chain are calculated in the dense grid cluster; the intersections whose node influence values ​​are higher than the second threshold and whose service resilience index is lower than the first threshold are marked as support points to be strengthened; elastic expansion is performed based on the key support points and the support points to be strengthened.

[0068] Furthermore, the system is also used to implement the following functions: According to the key support points, protective expansion is performed, and the protective expansion formula is as follows: ,in, To expand the capacity of key support points, Used to characterize the traffic flow variation coefficient, Used to characterize the population density fluctuation coefficient, Used to characterize the priority coefficient of public service demand, is the service resilience index corresponding to the key support points, It is the first adjustment factor, used to balance the expansion capacity and service resilience. is the lower limit of the foundation capacity; and the complementary expansion is carried out according to the supporting points to be strengthened. The complementary expansion formula is as follows: ,in, To expand the capacity of the supporting points to be strengthened, It is used to characterize the node influence value corresponding to the supporting point to be strengthened. It is used to characterize the service resilience index corresponding to the supporting point to be strengthened. It is used to characterize the maximum service resilience index within the same dense grid cluster to which the supporting point to be strengthened belongs. is the second adjustment factor, used to balance the expansion capacity and node impact. The upper limit of basic capacity.

[0069] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0071] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for constructing a livelihood facilities database based on spatial geocoding technology, characterized in that: The method comprises: Acquire the livelihood facilities information of the target area, wherein the livelihood facilities information includes the facility type, service scope, and service object attributes, and the service scope and service object attributes both have location aggregation marks; According to the spatial geocoding technology and the service scope and service object attributes in the livelihood facilities information, a livelihood facilities distribution grid is set, and the livelihood facilities distribution grid includes traffic intersections, densely populated intersections, and public service intersections; Conducting accessibility assessment on the traffic intersections of the livelihood facilities distribution grid, and optimizing the grid density of the livelihood facilities distribution grid based on the accessibility differences in different time periods; At the same time, combined with the distribution of densely populated intersections and public service intersections, collaborative optimization is performed to generate a time-divided accessibility radar map. The time-divided accessibility radar map is centered on traffic intersections and displays the correlation between population density hotspots and traffic intersections, and the correlation between public service connection points and traffic intersections. Based on the time-divided accessibility radar map, multiple layers are superimposed to determine the public service gap, and the map is connected to the city’s digital twin model to build a closed-loop optimization system and to inspect and update the public welfare equipment database.

2. The method for constructing a livelihood facilities database based on spatial geocoding technology according to claim 1, characterized in that: Based on the time-divided accessibility radar map, multiple layers are superimposed to determine the public service gap, and the method further includes: The time-divided accessibility radar map, basic geographic information layer, and basic geographic planning layer are superimposed in multiple layers to determine the public service gap; defining the gap type of the public service gap, wherein the gap type includes spatial coverage type, time period service type, and capacity overload type; Based on the types of gaps described and the scope of impact of the gaps, a graded repair list is drawn up.

3. The method for constructing a livelihood facilities database based on spatial geocoding technology according to claim 2, characterized in that: Optimizing the grid density of the distribution grid of the livelihood facilities according to the accessibility differences in different time periods, and the method further includes: Acquire dynamic weight parameters of the target area, wherein the dynamic weight parameters include a traffic flow variation coefficient, a population density fluctuation coefficient, and a public service demand priority coefficient; Based on the dynamic weight parameters, predict the weight change trend of each traffic intersection in the future period, and configure sparse grid clusters and dense grid clusters; According to the sparse grid cluster and the dense grid cluster, the city management center is linked to automatically trigger the optimization instruction.

4. The method for constructing a livelihood facilities database based on spatial geocoding technology according to claim 3, characterized in that: Connecting to the city digital twin model, building a closed-loop optimization system, and inspecting and updating the livelihood equipment database, the method includes: Access the citizen feedback platform to collect multiple user experience feedback on livelihood facilities; Structuring the plurality of usage experience feedbacks, matching them with the gap types, and adding an additional influencing factor to each usage experience feedback in combination with the dynamic weight parameter; A plurality of usage experience feedbacks with additional influencing factors are used as reverse supplementary data to perform synchronous verification on the livelihood equipment database.

5. The method for constructing a people's livelihood facilities database based on spatial geocoding technology according to claim 3, characterized in that: According to the sparse grid cluster and the dense grid cluster, the method is linked with the city management center, and further includes: In the livelihood facilities distribution grid, merging the sparse grid clusters; At the same time, elastic expansion is performed for the dense grid cluster, and the elastic expansion is used to adapt to the difference in service demand during the day and night in the city and obtain an elastic expansion strategy.

6. The method for constructing a livelihood facilities database based on spatial geocoding technology according to claim 5, characterized in that: The method further comprises: Based on the dynamic weight parameter, the service resilience index of the traffic intersection, the densely populated intersection, and the public service intersection in the dense grid cluster is calculated, and the intersection whose service resilience index is higher than the first threshold is marked as a key support point; Based on the dynamic weight parameters, the node influence values ​​of the traffic intersections, densely populated intersections, and public service intersections in the dense grid cluster in the gap propagation chain are calculated; Mark the intersection where the node influence value is higher than the second threshold and the service resilience index is lower than the first threshold as a supporting point to be strengthened; Flexible expansion is performed based on the key supporting points and the supporting points to be strengthened.

7. The method for constructing a livelihood facilities database based on spatial geocoding technology according to claim 6, characterized in that: According to the key supporting points and the supporting points to be strengthened, elastic expansion is performed, and the method further comprises: According to the key support points, protective expansion is performed, and the protective expansion formula is as follows: ,in, To expand the capacity of key support points, Used to characterize the traffic flow variation coefficient, Used to characterize the population density fluctuation coefficient, Used to characterize the priority coefficient of public service demand, is the service resilience index corresponding to the key support points, It is the first adjustment factor, used to balance the expansion capacity and service resilience. is the lower limit of basic capacity; Complementary expansion is performed according to the supporting points to be strengthened. The formula for complementary expansion is as follows: ,in, To expand the capacity of the supporting points to be strengthened, It is used to characterize the node influence value corresponding to the supporting point to be strengthened. It is used to characterize the service resilience index corresponding to the supporting point to be strengthened. It is used to characterize the maximum service resilience index within the same dense grid cluster to which the supporting point to be strengthened belongs. is the second adjustment factor, used to balance the expansion capacity and node impact. The upper limit of basic capacity.

8. A system for constructing a people's livelihood facilities database based on spatial geocoding technology, characterized in that: The system is used to implement the method for constructing a livelihood facilities database based on spatial geocoding technology according to any one of claims 1 to 7, and the system comprises: A livelihood facility information acquisition module is used to acquire livelihood facility information of a target area, wherein the livelihood facility information includes facility type, service scope, and service object attributes, and both the service scope and service object attributes have location aggregation marks; A livelihood facilities distribution grid setting module is used to set a livelihood facilities distribution grid according to the spatial geocoding technology and the service scope and service object attributes in the livelihood facilities information, wherein the livelihood facilities distribution grid includes traffic intersections, densely populated intersections, and public service intersections; A grid density optimization module is used to evaluate the accessibility of the traffic intersections of the livelihood facilities distribution grid, and optimize the grid density of the livelihood facilities distribution grid based on the accessibility differences in different time periods; A time-divided accessibility radar map generation module is used to simultaneously combine the distribution of densely populated intersections and public service intersections to perform collaborative optimization and generate a time-divided accessibility radar map. The time-divided accessibility radar map is centered on traffic intersections and displays the correlation between population density hotspots and traffic intersections, and the correlation between public service connection points and traffic intersections; The closed-loop optimization system construction module is used to perform multi-layer superposition based on the time-divided accessibility radar map, determine the public service gap, and connect it to the city's digital twin model to build a closed-loop optimization system and inspect and update the people's livelihood equipment database.

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