Method and System for Constructing a Livelihood Facilities Database Based on Spatial Geocoding Technology

Abstract

The present invention discloses a method and system for constructing a livelihood facilities database based on spatial geocoding technology, which relates to the technical field of facility database construction. The method includes: obtaining livelihood facility information; setting up livelihood facility distribution grids in accordance with the service scope and service object attributes in the livelihood facility information; conducting accessibility assessment and optimizing the grid density of the livelihood facility distribution grids; at the same time, conducting collaborative optimization to generate a radar chart of time-segmented accessibility; conducting multi-layer superposition to determine public service gaps, constructing a closed-loop optimization system, and verifying and updating the livelihood equipment database. The present invention solves the technical problems in the prior art that the positioning of the service short board of livelihood facilities is inaccurate, the layout is difficult to be dynamically adjusted according to actual needs, and the update of the livelihood equipment database is not timely, and achieves the technical effects of accurately positioning the service short board of livelihood facilities, dynamically optimizing the layout of livelihood facilities, continuously updating the livelihood equipment database, and improving the urban livelihood service level.

Description

Technical Field

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

[0002] With the acceleration of the urbanization process, the urban population scale is constantly expanding, and the residents' demand for people's livelihood services is becoming increasingly diversified and refined. However, the existing planning and management of people's livelihood facilities face many difficulties. On the one hand, the traditional layout of people's livelihood facilities is often based on static data and empirical judgments, lacking sufficient consideration of urban dynamic change factors such as traffic flow, population density fluctuations, and real-time changes in public service demands, resulting in a serious imbalance between the supply and demand of people's livelihood facilities in some areas, with situations of insufficient service coverage or idle waste of resources. On the other hand, in the positioning of the short board of people's livelihood facility services, due to the lack of accurate analysis means and data integration capabilities, it is difficult to quickly and accurately determine the type, location, and influence range of public service gaps. At the same time, the update mechanism of the people's livelihood equipment database lags behind and cannot timely reflect the actual usage and changes of facilities, making it difficult for urban management departments to effectively optimize and adjust people's livelihood facilities according to the actual situation, seriously restricting the improvement of the urban people's livelihood service level.

[0003] The existing technology has technical problems such as inaccurate positioning of the short board of people's livelihood facility services, difficult dynamic adjustment of the layout according to actual needs, and untimely update of the people's livelihood equipment database. Summary of the Invention

[0004] The present application provides a method and system for constructing a people's livelihood facility database based on spatial geocoding technology, which is used to solve the technical problems in the existing technology such as inaccurate positioning of the short board of people's livelihood facility services, difficult dynamic adjustment of the layout according to actual needs, and untimely update of the people's livelihood equipment database.

[0005] In view of the above problems, the present application provides a method and system for constructing a people's livelihood facility database based on spatial geocoding technology.

[0006] In the first aspect of the present application, a method for constructing a people's livelihood facility database based on spatial geocoding technology is provided, and the method includes:

[0007] Obtain the livelihood facility information of the target area. The livelihood facility information includes facility type, service scope, and service object attributes. Both the service scope and service object attributes have location aggregation marks. Set the livelihood facility distribution grid according to the spatial geographic coding technology and the service scope and service object attributes in the livelihood facility information. The livelihood facility distribution grid includes traffic intersections, population-dense intersections, and public service intersections. Conduct an accessibility assessment of the traffic intersections in the livelihood facility distribution grid, and optimize the grid density of the livelihood facility distribution grid based on the accessibility differences in different time periods. At the same time, combine the distribution of population-dense intersections and public service intersections for collaborative optimization to generate a time-segmented accessibility radar chart. The time-segmented accessibility radar chart takes traffic intersections as the center and shows the correlation between population density hotspots and traffic intersections, and the correlation between public service connection points and traffic intersections. Based on the multi-layer superposition of the time-segmented accessibility radar chart, determine the public service gap, connect it to the urban digital twin model, construct a closed-loop optimization system, and verify and update the livelihood facility database.

[0008] In the second aspect of the present application, a livelihood facility database construction system based on spatial geographic coding technology is provided. The system includes:

[0009] A livelihood facility information acquisition module for obtaining the livelihood facility information of the target area. The livelihood facility information includes facility type, service scope, and service object attributes. Both the service scope and service object attributes have location aggregation marks. A livelihood facility distribution grid setting module for setting the livelihood facility distribution grid according to the spatial geographic coding technology and the service scope and service object attributes in the livelihood facility information. The livelihood facility distribution grid includes traffic intersections, population-dense intersections, and public service intersections. A grid density optimization module for conducting an accessibility assessment of the traffic intersections in the livelihood facility distribution grid and optimizing the grid density of the livelihood facility distribution grid based on the accessibility differences in different time periods. A time-segmented accessibility radar chart generation module for, at the same time, combining the distribution of population-dense intersections and public service intersections for collaborative optimization to generate a time-segmented accessibility radar chart. The time-segmented accessibility radar chart takes traffic intersections as the center and shows 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 for, based on the multi-layer superposition of the time-segmented accessibility radar chart, determining the public service gap, connecting it to the urban digital twin model, constructing a closed-loop optimization system, and verifying and updating the livelihood facility database.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] Obtain the information of people's livelihood facilities in the target area; set the distribution grid of people's livelihood facilities according to the spatial geographic coding technology and the service scope and service object attributes in the information of people's livelihood facilities; evaluate the accessibility of the traffic intersections of the distribution grid of people's livelihood facilities and the accessibility differences in different time periods, and optimize the grid density of the distribution grid of people's livelihood facilities; at the same time, combine the distribution of population-intensive intersections and public service intersections for collaborative optimization to generate a radar chart of accessibility by time period; based on the radar chart of accessibility by time period, perform multi-layer superposition to determine the public service gap, and access it into the urban digital twin model to construct a closed-loop optimization system to verify and update the people's livelihood equipment database. It achieves the technical effects of accurately positioning the service shortboards of people's livelihood facilities, dynamically optimizing the layout of people's livelihood facilities, continuously updating the people's livelihood equipment database, and improving the level of urban people's livelihood services. Brief Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0013] Figure 1 Schematic flowchart of the method for constructing a people's livelihood facility database based on spatial geographic coding technology provided by the embodiment of the present application;

[0014] Figure 2 Schematic structural diagram of the system for constructing a people's livelihood facility database based on spatial geographic coding technology provided by the embodiment of the present application.

[0015] Explanation of reference numerals: People's livelihood facility information acquisition module 10, people's livelihood facility distribution grid setting module 20, grid density optimization module 30, radar chart generation module 40 for accessibility by time period, closed-loop optimization system construction module 50. Detailed Description of the Embodiments

[0016] The present application provides a method and system for constructing a people's livelihood facility database based on spatial geographic coding technology, which is used to solve the technical problems in the prior art that the positioning of the service shortboards of people's livelihood facilities is inaccurate, the layout is difficult to dynamically adjust according to actual needs, and the update of the people's livelihood equipment database is not timely.

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present application belong to the protection scope of the present application.

[0018] Example 1, as Figure 1 shown, this application provides a method for constructing a people's livelihood facility database based on spatial geographic coding technology, and the method includes:

[0019] Step S100: Obtain the people's livelihood facility information of the target area, where the people's 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.

[0020] Specifically, by collaborating with departments such as urban planning, housing construction, and civil affairs, and leveraging their rich information resources, various types of people's livelihood facility information are collected. For the facility type, various categories are accurately distinguished, such as educational facilities (schools, training institutions, etc.), medical facilities (hospitals, clinics, etc.), commercial facilities (shopping malls, supermarkets, etc.), cultural and sports facilities (libraries, stadiums, etc.), and public transportation facilities (bus stops, subway stations, etc.). In terms of the service scope, using geographic information system (GIS) technology, combined with satellite map and electronic map data, the coverage area of each people's livelihood facility is accurately delimited, and a location aggregation mark is added to it, so that the boundary and coverage area of the service scope can be clearly presented, facilitating subsequent spatial analysis. For the service object attributes, information such as age, occupation, income level, and consumption habits is collected through methods such as questionnaire surveys, community visits, and population census data matching. At the same time, these attributes are also marked with location aggregation marks, for example, classified and marked according to geographical units such as communities and streets. Clearly defining the basic situation of various people's livelihood facilities can also establish a close connection between the facilities and the surrounding population based on the location aggregation marks, providing a solid data foundation for subsequent in-depth analysis and layout optimization using spatial geographic coding technology.

[0021] Step S200: Set up a people's livelihood facility distribution grid according to the spatial geographic coding technology and the service scope and service object attributes in the people's livelihood facility information, where the people's livelihood facility distribution grid includes traffic intersections, population-intensive intersections, and public service intersections.

[0022] Specifically, with the help of spatial geocoding technology, the service scope and service object attributes in the collected people's livelihood facility 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 based on the location aggregation markers of the service object attributes, the population distribution is spatially expressed. On this basis, the distribution grid of people's livelihood facilities is set up. First, traffic intersections are determined. Through traffic big data and map information, key locations such as the intersections of main roads, bus transfer hubs, subway stations, etc. are screened out. These traffic intersections, as important nodes for people's travel, play a key role in the accessibility of people's livelihood facilities. Then, according to the population density data, densely populated areas are found, such as large community centers, concentrated office building areas, etc. The core intersections in these areas are marked as densely populated intersections, which represent the main demand areas for people's 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 like hospitals, schools, libraries, or the key intersections around them. By integrating these three types of intersections, the distribution grid of people's livelihood facilities is constructed, providing an important spatial framework for subsequent layout analysis, accessibility assessment, and optimization adjustment of people's livelihood facilities.

[0023] Step S300: Evaluate the accessibility of the traffic intersections in the distribution grid of people's livelihood facilities, and optimize the grid density of the distribution grid of people's livelihood facilities according to the accessibility differences in different time periods.

[0024] Specifically, the accessibility of traffic intersections in the grid is evaluated, and the grid density is optimized based on the accessibility differences in different time periods. First, a variety of data and technical means are comprehensively used to conduct the accessibility evaluation. With the help of the traffic big data platform, real-time and historical data such as traffic flow and vehicle speed of each traffic intersection in the target area at different times are obtained; using Geographic Information System (GIS) technology, combined with information such as the road network topology and bus line planning, the travel time and distance from each traffic intersection to surrounding important livelihood facilities (such as hospitals, schools, shopping malls, etc.) are calculated. At the same time, considering the characteristics of different travel modes (such as driving, taking the bus, walking, etc.), comprehensive and accurate accessibility indicators are obtained. Analyzing the accessibility differences in different time periods, it will be found that during the morning and evening rush hours, traffic congestion is severe, and the travel time from some traffic intersections to livelihood facilities increases significantly, and the accessibility decreases significantly; while during non-peak hours, traffic is smooth and the accessibility is significantly improved. According to these differences, the grid density of the grid where the livelihood facilities are distributed is optimized. For areas with poor accessibility during peak hours but with dense surrounding population or important public service needs, the grid density is appropriately increased to more finely analyze and plan the traffic and livelihood facility layout in this area; for areas with good and relatively stable accessibility, the grid density can be appropriately reduced to improve the analysis efficiency. In this way, the grid where the livelihood facilities are distributed 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.

[0025] Step S400: At the same time, in combination with the distribution of population-dense intersections and public service intersections, collaborative optimization is carried out to generate a time-segmented accessibility radar chart, which takes the traffic intersection as the center and shows the correlation between the population density hotspots and the traffic intersection, and the correlation between the public service connection points and the traffic intersection.

[0026] Specifically, using big data analysis technology, extract the spatio-temporal distribution characteristics of population-dense intersections from census data, social media check-in data, and mobile operator signaling data, and obtain the precise location information of public service intersections from urban planning databases and government open data. Import these data into a Geographic Information System (GIS) platform and construct a comprehensive analysis model in combination with traffic network data. For different time periods, such as the morning rush hour on weekdays, the off-peak period on weekdays, weekends, etc., quantify the degree of association between population density hotspots and traffic intersections, and between public service connection points and traffic intersections by calculating the shortest travel time from traffic intersections to population-dense intersections and the traffic flow from public service intersections to traffic intersections. Use data visualization tools. With traffic intersections as the center, set multiple coordinate axes to represent the quantification indicators of the two degrees of association under different time periods respectively, and draw a time-segmented accessibility radar chart according to the calculation results. In the radar chart, use different colors to fill the areas to represent the high and low degrees of association, and accurately mark the values with coordinate scales, which is convenient for intuitively viewing the association differences of each intersection at different time periods, providing a strong basis for the subsequent optimization of the layout of people's livelihood facilities and traffic planning.

[0027] Step S500: Based on the time-segmented accessibility radar chart, perform multi-layer superposition to determine the public service gap, and connect it to the urban digital twin model to construct a closed-loop optimization system to check and update the people's livelihood equipment database.

[0028] Specifically, using the layer superposition analysis function of the Geographic Information System (GIS), orderly perform multi-layer superposition on the generated time-segmented accessibility radar chart according to different time periods. During the superposition process, in combination with the basic geographic information layer, the current land use layer, and the existing distribution layer of people's livelihood facilities in the city, use spatial analysis algorithms to identify the areas with insufficient public service coverage during different time periods, so as to determine the specific location, scope, and type of the public service gap. Connect the relevant information of the determined public service gap to the urban digital twin model. The urban digital twin model is a digital mapping of the real city. After accessing the gap information, use the model's simulation function to simulate and analyze different public service facility optimization plans, and predict the impacts on the surrounding areas after the implementation of these plans, such as changes in traffic flow, changes in population distribution, and the collaborative impacts on other people's livelihood facilities. According to the simulation results, screen out the optimal optimization plan, and through the information interaction platform with the urban management department and relevant construction units, feedback the optimization plan to the actual management and construction departments.

[0029] Meanwhile, a closed-loop optimization system is constructed. During the implementation of the optimization plan, the sensor network and Internet of Things devices are used to collect the usage data of people's livelihood facilities, the surrounding environment data, and the public feedback information in real time. These data are transmitted back to the urban digital twin model and the people's livelihood equipment database, compared and analyzed with the original data to determine whether the optimization measures have achieved the expected results. If the expected results are not achieved or new problems occur, the optimization plan is readjusted, and simulation, implementation, and monitoring are carried out again, continuously cycling this process. Through this closed-loop optimization system, the people's livelihood equipment database is dynamically inspected and updated, and the information about the location, service scope, usage status, etc. of people's livelihood facilities in the database is corrected in a timely manner to ensure that the database always reflects the real and accurate situation of people's livelihood facilities, providing reliable data support for the sustainable development of the city and the optimization of people's livelihood services.

[0030] In a possible implementation manner, step S500 further includes:

[0031] Step S510: Multilayer overlay the time-segmented accessibility radar map, the basic geographic information layer, and the basic geographic planning layer to determine the public service gap.

[0032] Step S520: Define the gap types of the public service gap, where the gap types include spatial coverage type, time-segmented service type, and capacity overloading type.

[0033] Step S530: Based on the gap types and combined with the gap influence scope, draw up a hierarchical repair list.

[0034] Specifically, with the help of the layer processing function of a professional geographic information system (GIS), the time-segmented accessibility radar map, the basic geographic information layer, and the basic geographic planning layer are accurately multilayer overlaid. The time-segmented accessibility radar map reflects the degree of association between traffic intersections and population density hotspots and public service connection points during different time periods; the basic geographic information layer contains natural geographic elements such as terrain, landform, and water system, as well as human geographic elements such as roads and buildings, providing a basic geographic framework for analysis; the basic geographic planning layer covers the future development planning information of the city. By overlaying these three layers and using spatial analysis tools, it is possible to intuitively and accurately discover the areas where public services are not covered or under-covered, so as to determine the specific location and scope of the public service gap.

[0035] 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.

[0036] 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.

[0037] In a possible implementation, step S300 further includes:

[0038] 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.

[0039] 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.

[0040] 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.

[0041] Specifically, multi-source data collection and analysis methods need to be comprehensively used to obtain the dynamic weight parameters of the target area. In terms of the traffic flow change coefficient, with the help of inductive traffic flow monitoring devices installed on the road, such as geomagnetic sensors, microwave radars, etc., the traffic flow data of each section 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 rush hours on weekdays, flat peaks, weekends, etc.), and then the traffic flow change coefficient is calculated. For the population density fluctuation coefficient, mobile phone signaling data is used to obtain the real-time population distribution in different regions through base station positioning; at the same time, combined with population census data, community occupancy rate survey data, etc., the dynamic changes of the population in different regions over time are analyzed, such as the population flow differences in different regions on weekdays and weekends, so as to obtain the population density fluctuation coefficient. The determination of the public service demand priority coefficient is achieved by constructing a resident demand survey platform, conducting online and offline questionnaires to understand the demand levels of residents 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 the usage frequency data of public service facilities, such as the number of patients in hospitals, the borrowing volume of libraries, etc., and determining the public service demand priority coefficient by synthesizing this information.

[0042] Using time series analysis algorithms, combined with historical traffic flow, population density, and public service demand data, trend predictions are made for the traffic flow change coefficient, population density fluctuation coefficient, and public service demand priority coefficient. For example, the ARIMA model is used to perform time series modeling on the traffic flow change coefficient to predict the fluctuations in traffic flow at different future time periods; regression algorithms in machine learning, such as support vector regression (SVR), are adopted to predict the change trend of the population density fluctuation coefficient based on historical data of population density and related influencing factors; for the public service demand priority coefficient, by combining expert experience with historical demand data, the analytic hierarchy process (AHP) is used to adjust the weights of various factors, and then its future changes are predicted. Then, the predicted dynamic weight parameters for each future time period are input into the traffic intersection weight calculation model, which calculates the weights of each traffic intersection according to the traffic flow change coefficient, population density fluctuation coefficient, and public service demand priority coefficient, and obtains the weight values of each traffic intersection at different future time periods. For example, for traffic intersections with high traffic flow, high population density, and high priority of surrounding public service demand, higher weights are assigned; otherwise, the weights are lower. Finally, sparse grid clusters and dense grid clusters are configured based on the calculated traffic intersection weight values. A weight threshold is set, and traffic intersections with weight values higher than the threshold are classified into dense grid clusters. These areas are often busy, densely populated, and have high public service demand, requiring more detailed analysis and management, so higher-density grids are configured; while traffic intersections with weight values lower than the threshold are classified into sparse grid clusters. These areas have less traffic pressure, relatively dispersed populations, or lower public service demands, and correspondingly lower-density grids are configured to achieve differential management and optimization of different regions.

[0043] Monitor the conditions of each traffic intersection in the sparse grid cluster and the dense grid cluster in real time. When it is detected that there are signs of traffic congestion at a certain traffic intersection in the dense grid cluster, such as the traffic flow exceeding the preset threshold, resulting in a significant decrease in the average driving speed of vehicles, an optimization instruction will be automatically sent to the traffic signal control system of the urban management center. After receiving the instruction, the traffic signal control system will quickly adjust the signal timing plan according to the real-time traffic conditions in this area, increase the green light duration in the congested direction, and reduce the green light time in the non-congested direction to relieve traffic pressure and ensure smooth vehicle passage. At the same time, if an emergency occurs in the sparse or dense grid cluster, such as a traffic accident, natural disaster, etc., an alarm message and the detailed coordinates of the incident location will be immediately sent to the emergency command system. Based on the accurate location information provided by the grid cluster and combined with the surrounding traffic conditions, the emergency command system will quickly plan the optimal rescue route and coordinate relevant emergency rescue forces such as fire, medical, and public security to rush to the scene for rescue in a timely manner. In addition, the emergency command system can also allocate rescue resources in advance according to the regional characteristics reflected by the grid cluster. For example, in the densely populated dense grid cluster area, more medical emergency vehicles and rescue personnel will be arranged in advance to ensure that rescue work can be carried out quickly and effectively in case of an emergency, minimizing the impact of emergencies on urban operation and residents' lives.

[0044] In a possible implementation manner, step S500 further includes:

[0045] Step S540: Access the citizen feedback platform and collect multiple usage experience feedbacks of people's livelihood facilities.

[0046] Step S550: Structurally process the multiple usage experience feedbacks, match them with the gap types, and combine the dynamic weight parameters to add additional impact factors to each usage experience feedback.

[0047] Step S560: Use the multiple usage experience feedbacks with additional impact factors as reverse supplementary data to perform synchronous verification on the people's livelihood equipment database.

[0048] Specifically, by establishing data docking with the existing citizen feedback platform, comprehensively collect the usage experience feedback of people's livelihood facilities. These feedbacks come from a wide range of sources, covering opinions submitted by citizens through various channels such as mobile phone apps, government website message boards, and offline questionnaires, including evaluations and suggestions on various people's livelihood facilities such as the convenience of bus stops, the hospital registration process, and community park facilities, thus converging into a large amount of rich and real usage experience data.

[0049] Structurally process the massive amount of collected usage experience feedback. Using natural language processing technology, convert the unstructured text feedback into structured data, and extract key information, such as the type of people's livelihood facilities involved in the feedback, the specific location, the problem description, etc. Subsequently, match these structured feedbacks with the previously defined types of public service gaps (spatial coverage type, time period service type, capacity overloading type). For example, if the feedback mentions that a certain type of people's livelihood facility has been lacking in a remote area for a long time, it can be matched with the spatial coverage type gap. At the same time, combined with dynamic weight parameters (traffic flow change coefficient, population density fluctuation coefficient, public service demand priority coefficient), comprehensively consider factors such as the traffic conditions, population density of the area involved in the feedback, and the demand priority of this people's livelihood facility, and add additional influencing factors to each piece of usage experience feedback. For feedback in areas with high traffic flow, dense population, and high public service demand priority, assign a higher additional influencing factor, and vice versa for those with lower values.

[0050] Take multiple pieces of usage experience feedback with additional influencing factors as reverse supplementary data and connect them to the verification process of the people's livelihood equipment database. The database management system will compare and analyze these feedback data with the existing people's livelihood facility information in the database. For example, check whether the location information of the facility accurately reflects the actual service coverage, whether the operating hours of the facility match the time period service type feedback, and whether the capacity data of the facility is consistent with the capacity overloading type feedback. Based on the comparison results, synchronously verify and update the people's livelihood equipment database, correct inaccurate or outdated information, supplement newly discovered problems and related data, and ensure that the people's livelihood equipment database can continuously and accurately reflect the actual usage experience and demand status of citizens for people's livelihood facilities, providing reliable data support for the optimization of urban people's livelihood services.

[0051] In a possible implementation manner, step S330 further includes:

[0052] Step S331: In the people's livelihood facility distribution grid, merge the sparse grid clusters.

[0053] Step S332: At the same time, for the dense grid clusters, perform elastic expansion, and the elastic expansion is used to adapt to the differences in urban day and night service demands to obtain an elastic expansion strategy.

[0054] Specifically, a merging operation is carried out on the sparse grid clusters. By analyzing the dynamic weight parameters of each traffic intersection in the sparse grid clusters, as well as data such as the surrounding population density and traffic flow, it is found that the traffic pressure in some grid areas is relatively low for a long time, the population distribution is relatively scattered, and the demand for public services is relatively stable. In view of the similar characteristics of these areas, adjacent sparse grids with similar functions and demands are merged. For example, some grids located in the far suburbs of the city with low traffic flow and sparse distribution of residential areas are integrated into a larger grid unit, reducing unnecessary grid divisions, improving management efficiency, and avoiding wasting resources on overly detailed grid analysis and maintenance.

[0055] Deeply study the functional characteristics and service demand change rules of different areas in the city during the day and at night. For example, in the central business district of the city, during the day, the office and shopping crowds are dense, and the demand for services such as transportation and catering is strong; while at night, it turns into mainly leisure and entertainment, and the demand for services such as entertainment venues and late-night catering increases. Based on this, through big data analysis, historical data statistics, and real-time monitoring information, a flexible expansion strategy is formulated. During the day, according to the prediction of traffic flow and population density, grids around traffic hubs and near office areas in dense grid clusters are expanded, and resource allocations such as traffic guidance and the placement of public service facilities are increased; at night, corresponding adjustments are made for grids around entertainment gathering areas and residential areas, such as increasing night bus lines and extending the opening hours of some public service facilities, so as to flexibly adjust grid resources and better meet the different service demands of the city day and night.

[0056] In a possible implementation manner, step S332 further includes:

[0057] Step S3321: Based on the dynamic weight parameters, calculate the service resilience indexes of the traffic intersections, population-dense intersections, and public service intersections in the dense grid clusters, and mark the intersections with service resilience indexes higher than the first threshold as key support points.

[0058] Step S3322: Based on the dynamic weight parameters, calculate the node influence values of the traffic intersections, population-dense intersections, and public service intersections in the dense grid clusters in the gap propagation chain.

[0059] Step S3323: Mark the intersections with node influence values higher than the second threshold and service resilience indexes lower than the first threshold as to-be-strengthened supporting points.

[0060] Step S3324: Perform flexible expansion according to the key support points and the to-be-strengthened supporting points.

[0061] Specifically, calculate the service resilience indexes of various intersections based on the dynamic weight parameters. For traffic intersections, first calculate the standard deviation of the traffic flow per unit time according to the traffic flow change coefficient , to measure the fluctuation degree of traffic flow. Meanwhile, the average distance from this intersection to the surrounding emergency facilities (such as fire stations, hospital emergency departments) is obtained through the Geographic Information System (GIS). , and the average emergency response time is obtained by combining historical data. . Set the weight of traffic flow stability and the weight of emergency response ability , where the weight of traffic flow stability and the weight of emergency response ability range from 0 to 1, and , through the formula , calculate the service resilience index of the traffic intersection , where is the average traffic flow. For densely populated intersections, according to the population density fluctuation coefficient, calculate the change rate of population density per unit time . With the help of community research data and public service facility registration information, count the number of various public service facilities (such as schools, supermarkets, parks) within a certain range around this intersection and the service coverage area , and then obtain the public service coverage index . Set the weight of population stability and the weight of public service coverage , where the weight of population stability and the weight of public service coverage range from 0 to 1, and , through the formula

[0062] calculate the service resilience index of the densely populated intersection . For public service intersections, combined with the public service demand priority coefficient, analyze the priority score of the public services borne by this intersection , where the priority score is scored according to factors such as the importance and scarcity of the service to residents' lives, and can be comprehensively determined by relevant departments formulating standards or through resident questionnaires, and count the number of facility failures and the repair duration , the number of failures and the repair duration are obtained by data statistics from the facility operation and maintenance management system. Set the demand priority weight and the facility reliability weight , where the demand priority weight and the facility reliability weight range from 0 to 1, and , calculate the service resilience index of the public service intersection through the formula Finally, set an appropriate first threshold, and mark the intersections with service resilience index higher than this threshold among traffic intersections, population-dense intersections, and public service intersections as key support points. Deeply analyze the dynamic weight parameters. For traffic intersections, based on the traffic flow change coefficient and combined with the traffic network topology, analyze the impact degree of the intersection on the redistribution of vehicle flow on surrounding roads when gaps such as traffic congestion occur. For example, if a traffic intersection is located at the intersection of multiple main roads and the traffic flow change coefficient shows that its traffic flow fluctuates greatly during peak hours, once a congestion gap appears here, due to its key position, it will quickly spread the gap to the connected sections by affecting the traffic efficiency of surrounding roads, thereby quantifying its influence in the spread of traffic flow gaps. For population-dense intersections, with the help of the population density fluctuation coefficient, consider the relationship between the direction of population flow and the demand for public services. When there is a service gap in public service facilities around a population-dense intersection in a certain area, such as a shortage of hospital beds, due to the large population density and frequent population flow at this intersection, the gap information will be quickly spread to the surrounding areas, attracting more people to seek alternative services, and the scope of influence will expand. According to factors such as the scale and frequency of population flow and the scope of the affected area, calculate its influence value in the spread of population-related service gaps. For public service intersections, combined with the public service demand priority coefficient, analyze that when there is a gap in a certain type of public service, such as a shortage of school places, due to the importance and demand priority of the public services carried by this 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 affected people, and the degree of resource allocation triggered, determine its node influence value in the public service gap propagation chain, so as to comprehensively evaluate the role of each intersection in the gap propagation chain.

[0063] Compare the previously calculated node influence values with the pre-set second threshold, and at the same time compare the service resilience index with the first threshold. Those intersections with node influence values higher than the second threshold mean that they have strong influence in the gap propagation chain. Once problems occur to themselves, it is 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 is poor in the face of various fluctuations or emergencies. Based on this, mark the intersections that simultaneously meet the conditions of having a node influence value higher than the second threshold and a service resilience index lower than the first threshold as the support points to be strengthened, so as to implement strengthening measures for these key but weak nodes subsequently, and improve the ability of the entire dense grid cluster to respond to service gaps.

[0064]

[0065] ​Perform elastic expansion based on the already marked key support points and the support points to be strengthened. For the key support points, it is possible to further optimize the surrounding transportation facilities and increase the reserve of public service resources to enhance their service resilience and carrying capacity, ensuring that they can stably provide services to the surrounding areas under any circumstances. For the support points to be strengthened, targeted strengthening measures are taken, such as improving traffic connections, increasing 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 more flexibly and efficiently adapt to the differences in urban day and night service demands.

[0066] In a possible implementation manner, step S3324 further includes:

[0067] Step S33241: Perform protective expansion according to the key support points. The protective expansion formula is as follows:

[0068] , where is the expansion capacity of the key support point, is used to represent the traffic flow change coefficient, is used to represent the population density fluctuation coefficient, is used to represent the public service demand priority coefficient, is the service resilience index corresponding to the key support point, is the first adjustment factor for balancing the expansion capacity and service resilience, is the lower limit of the basic capacity;

[0069] Perform complementary expansion according to the support points to be strengthened. The complementary expansion formula is as follows:

[0070] , where is the expansion capacity of the support point to be strengthened, is used to represent the node influence value corresponding to the support point to be strengthened, is used to represent the service resilience index corresponding to the support point to be strengthened, is used to represent the maximum service resilience index within the same dense grid cluster to which the support point to be strengthened belongs, is the second adjustment factor for balancing the expansion capacity and node influence, is the upper limit of the basic capacity.

[0071] Specifically, elastic expansion is implemented for the key support points and the support points to be strengthened within the dense grid cluster to enhance the overall service capacity of the dense grid cluster. For the 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

[0072] Calculate its expansion capacity. In the formula, , , respectively represent the traffic flow change coefficient, the population density fluctuation coefficient, and the public service demand priority coefficient. These coefficients comprehensively reflect the dynamic weight parameters of this area and will change according to the changes in traffic, population, and public service demands. is the service resilience index corresponding to the key support point, which reflects the ability of the key support point to maintain stable services. The higher the index, the stronger the stability. As the first adjustment factor, it plays a key role in balancing the expansion capacity and service resilience. By reasonably adjusting the value, it can ensure that, on the premise of ensuring the stable services of the key support point, the expansion capacity is scientifically and reasonably determined, avoiding over-expansion or under-expansion. is the lower limit of the basic capacity, which sets the minimum standard for the expansion capacity to ensure that the expansion capacity of the key support point can meet the basic needs.

[0073] For the to-be-strengthened bearing point, since its node influence value is higher than the second threshold but the service resilience index is lower than the first threshold, it means that although it has a greater influence in the gap propagation chain, its own service ability has shortcomings and needs complementary expansion. Use the formula

[0074] to calculate the expansion capacity. Among them, represents the node influence value corresponding to the to-be-strengthened bearing point, which reflects the importance of this point in the gap propagation chain. The higher the influence value, the greater the impact on the surrounding area during gap propagation. is the service resilience index corresponding to the to-be-strengthened bearing point, showing the strength of its current service ability. represents the maximum service resilience index within the same dense grid cluster to which the to-be-strengthened bearing point belongs, and is used to compare and evaluate the service resilience level of this point. As the second adjustment factor, it is mainly used to balance the expansion capacity and node influence. By adjusting its value, it can, while improving the service ability of the to-be-strengthened bearing point, avoid over-concentration or waste of resources due to too high a node influence value, and ensure that the expansion capacity is coordinated with the overall regional resource allocation. is the upper limit of the basic capacity, which limits the maximum value of the expansion capacity to prevent over-expansion. Through the targeted expansion of the key support point and the to-be-strengthened bearing point, the elastic expansion of the dense grid cluster is realized, the ability of the entire area to cope with public service gaps is improved, the layout of people's livelihood facilities is optimized, and the urban public service level is enhanced.

[0075] Example 2, based on the same inventive concept as the method for constructing a people's livelihood facility database based on spatial geocoding technology in the foregoing example, such as Figure 2As shown in the figure, the present application provides a construction system for a people's livelihood facility database based on spatial geocoding technology. The system and method embodiments in the present application are based on the same inventive concept. Among them, the system includes:

[0076] A people's livelihood facility information acquisition module 10, configured to acquire people's livelihood facility information of a target area. The people's 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.

[0077] A people's livelihood facility distribution grid setting module 20, configured to set a people's livelihood facility distribution grid according to spatial geocoding technology and the service scope and service object attributes in the people's livelihood facility information. The people's livelihood facility distribution grid includes traffic intersections, densely populated intersections, and public service intersections.

[0078] A grid density optimization module 30, configured to perform reachability evaluation on the traffic intersections of the people's livelihood facility distribution grid, and optimize the grid density of the people's livelihood facility distribution grid according to the reachability differences in different time periods.

[0079] A sub-period reachability radar chart generation module 40, configured to simultaneously, in combination with the distribution of densely populated intersections and public service intersections, perform collaborative optimization to generate a sub-period reachability radar chart. The sub-period reachability radar chart takes traffic intersections as the center and shows the correlation between population density hotspots and traffic intersections, and the correlation between public service connection points and traffic intersections.

[0080] A closed-loop optimization system construction module 50, configured to perform multi-layer superposition based on the sub-period reachability radar chart, determine public service gaps, and access them into the urban digital twin model to construct a closed-loop optimization system to verify and update the people's livelihood equipment database.

[0081] Furthermore, the system is also used to implement the following functions:

[0082] Perform multi-layer superposition of the sub-period reachability radar chart, the basic geographic information layer, and the basic geographic planning layer to determine public service gaps; define the gap types of the public service gaps, and the gap types include spatial coverage type, time period service type, and capacity overloading type; based on the gap types, combined with the gap influence range, draw up a hierarchical repair list.

[0083] Furthermore, the system is also used to implement the following functions:

[0084] Obtain the dynamic weight parameters of the target area, where the dynamic weight parameters include a traffic flow change 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 time period, and configure sparse grid clusters and dense grid clusters; according to the sparse grid clusters and dense grid clusters, link with the urban management center and automatically trigger an optimization instruction.

[0085] Further, the system is also used to implement the following functions:

[0086] Access the citizen feedback platform to collect multiple usage experience feedbacks of people's livelihood facilities; perform structured processing on the multiple usage experience feedbacks, match them with the gap types, and combine the dynamic weight parameters to add additional influencing factors to each usage experience feedback; use the multiple usage experience feedbacks with additional influencing factors as reverse supplementary data to perform synchronous verification on the people's livelihood equipment database.

[0087] Further, the system is also used to implement the following functions:

[0088] In the people's livelihood facility distribution grid, merge the sparse grid clusters; at the same time, for the dense grid clusters, perform elastic expansion, where the elastic expansion is used to adapt to the difference in urban day and night service demands and obtain an elastic expansion strategy.

[0089] Further, the system is also used to implement the following functions:

[0090] Based on the dynamic weight parameters, calculate the service resilience indices of the traffic intersections, population-dense intersections, and public service intersections within the dense grid clusters, and mark the intersections with service resilience indices higher than the first threshold as key support points; based on the dynamic weight parameters, calculate the node influence values of the traffic intersections, population-dense intersections, and public service intersections within the dense grid clusters in the gap propagation chain; mark the intersections with node influence values higher than the second threshold and service resilience indices lower than the first threshold as to-be-strengthened support points; perform elastic expansion according to the key support points and the to-be-strengthened support points.

[0091] Further, the system is also used to implement the following functions:

[0092] Perform protective expansion according to the key support points, and the protective expansion formula is as follows: , where is the expansion capacity of the key support point, is used to represent the traffic flow change coefficient, is used to represent the population density fluctuation coefficient, is used to represent the public service demand priority coefficient, is the service resilience index corresponding to the key support point, is the first adjustment factor, used to balance the expanded capacity and service resilience, is the lower limit of the basic capacity; make a compensatory expansion according to the to-be-strengthened bearing point, and the compensatory expansion formula is as follows: , where is the expanded capacity of the to-be-strengthened bearing point, is used to represent the node influence value corresponding to the to-be-strengthened bearing point, is used to represent the service resilience index corresponding to the to-be-strengthened bearing point, is used to represent the maximum service resilience index within the same dense grid cluster to which the to-be-strengthened bearing point belongs, is the second adjustment factor, used to balance the expanded capacity and node influence, is the upper limit of the basic capacity.

[0093] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the advantages or disadvantages of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0094] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0095] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications 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 equivalent technologies, the present application is intended to include these changes and modifications.

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 space 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.

Images