Matching analysis method and system between elderly living circle center and elderly care service facilities

Abstract

The present invention discloses a matching degree analysis method and system between the center of the elderly living circle and elderly care service facilities, and relates to the technical field of matching degree analysis. If there is no target facility in the elderly living circle, the corresponding sub-area is regarded as a low-density area, and the load data of the target facility when it is running is collected. The target facility is distinguished according to the load data, and the service concentration area and the service blank area of ​​the target facility are identified by a service area identification model; a matching degree index is generated by a service indicator data set, and if the obtained matching degree index is lower than expected, the corresponding sub-area is regarded as a low-matching area, and the low-matching area is optimized by an optimization scheme output by an optimization scheme library; an optimization degree is constructed according to the matching degree index, and if the optimization degree is lower than expected, an alarm instruction is issued. The load of the target facility in the low-matching area is reduced, the service coverage or accessibility is adjusted, etc., so as to achieve adjustment and improvement of the living conditions of the elderly in the sub-area.

Description

Technical Field

[0001] The present invention relates to the technical field of matching degree analysis, and in particular to a matching degree analysis method and system between a living circle center for the elderly and elderly care service facilities. Background Art

[0002] Public service facilities refer to facilities that are provided by the government directly or indirectly to residents with public services and products. These facilities are designed to ensure the basic living needs of the public and the normal conduct of social activities. From a broader perspective, public service facilities are the embodiment of the comprehensive carrying capacity of a city or community, are closely related to the daily lives of residents, and are an indispensable component. There are many types of public service facilities, covering many aspects.

[0003] The elderly living circle refers to the space in a specific geographical environment where the elderly carry out daily production and communication activities with the family space as the core. This spatial category is an important issue in social geography, transportation geography, and national space planning. As they age, the physical strength, movement, and physical agility of the elderly group gradually decline, and they are prone to fatigue when traveling. Therefore, their travel needs are mostly focused on basic activities such as socializing, shopping, and medical care, and they are particularly dependent on public service facilities such as elderly care, commerce, and entertainment. The elderly living circle can be divided according to different activity ranges, such as community living circle / extended living circle / opportunity living circle or divided by 5 / 15 / 60 minutes.

[0004] Among them, the matching of public facilities such as elderly care facilities with the living circle of the elderly aims to build a safe, convenient and warm living environment, ensuring that the elderly can integrate into society, participate in activities and maintain social interaction while enjoying professional care services. The ideal matching state is that elderly care facilities not only provide necessary basic services such as medical care and daily care, but also closely connect the living needs and emotional exchanges of the elderly through reasonably planned public activity spaces, convenient transportation connections, and diversified interest groups and cultural and entertainment activities, forming an all-round living circle integrating living, rehabilitation, entertainment and learning, so that the elderly can enjoy their old age in a comfortable environment and realize that they are well cared for, happy and useful in their old age.

[0005] In the Chinese invention patent with application publication number CN113935608A, a method for evaluating community elderly care service facilities is disclosed, including: analyzing the elderly care construction specification documents and the daily behavior trajectory data of the elderly group within the preset evaluation range, and constructing a comprehensive index system of community elderly care service facilities within the preset evaluation range; constructing a judgment matrix according to the basic principle of AHP and the Likert five-level scale method, and calculating the weight of each indicator in the comprehensive index system of community elderly care service facilities; combining the analysis results of the index system, the daily behavior trajectory characteristics of the elderly group, the basic data of community elderly care service facilities and the population data of the elderly group, and calculating the construction score, distance attenuation score, quality attenuation score and supply and demand matching score of the community elderly care service facilities within the preset evaluation range; calculating and determining the comprehensive level score of the elderly care facility construction within the preset evaluation range, and determining the construction level of the elderly care service facilities. The present invention can provide a theoretical basis for scientific and reasonable layout and construction planning of elderly care facilities.

[0006] Combined with the above application and the contents of the prior art:

[0007] When planning the space in the target area, it is necessary to consider the quality of life of the elderly in the target area, improve the construction level of the elderly living circle, fully meet the basic living needs of the elderly, and strive to create a comfortable, safe and convenient environment, especially improve the level of elderly-friendly services in places such as city squares and chess and card activity rooms, so as to provide the elderly with more abundant social and leisure activities opportunities;

[0008] When planning a community with a large number of elderly people, the focus of the planning is on the layout and adjustment of the location of elderly care facilities. Before that, in order to improve the effect of the layout of elderly care facilities, it is necessary to analyze the matching degree between the elderly living circle and the elderly care service facilities, and determine the focus of the planning process based on the results of the matching degree analysis;

[0009] Existing matching analysis methods usually start from the coverage rate of facilities. In a certain area, the ratio of the number of service facilities to the total population of the area is determined to evaluate whether the configuration of elderly care facilities meets the needs of the elderly and screen out areas that are difficult to meet. This matching analysis method is relatively direct, but considering that the distribution of elderly care facilities may be very uneven, the coverage rate of elderly care facilities is difficult to reflect the actual situation. In particular, when the density of elderly care facilities is low and the density of the elderly population is high, the richness of elderly care facilities in the planned area is reduced. Applying the existing matching analysis method to regional planning may not be conducive to determining the areas that need to be supplemented with elderly care facilities, and is not conducive to targeted supplementation and optimization of target facilities, and it is difficult to achieve the expected results.

[0010] To this end, the present invention provides a method and system for analyzing the matching degree between the elderly living circle center and elderly care service facilities. Summary of the invention

[0011] 1. Technical issues to be resolved

[0012] In view of the deficiencies of the prior art, the present invention provides a matching degree analysis method and system between the elderly living circle center and the elderly care service facilities. By taking the corresponding sub-area as a low-density area, the load data of the target facility during operation is collected, and the target facility is distinguished according to the load data; and the service area identification model identifies the service concentration area and the service blank area of ​​the target facility; the matching degree index is generated by the service indicator data set, and if the obtained matching degree index is lower than expected, the corresponding sub-area is taken as a low-matching area, and the low-matching area is optimized by the optimization scheme output by the optimization scheme library; the optimization degree is constructed according to the matching degree index, and if the optimization degree is lower than expected, an alarm instruction is issued. The load of the target facility in the low-matching area is reduced, the service coverage or accessibility is adjusted, etc., thereby solving the technical problems raised in the background technology.

[0013] Taking into account the need to match the main areas of daily travel activities of urban elderly groups with the layout of public service facilities, the relevant research contents include: in theoretical preparation, the relevant theories and research contexts of spatial centers and community life circles are sorted out, the configuration ideas and evaluation methods of public service facilities are summarized, and the novelty and rationality of the theoretical combination of macro spatial center structure and micro community life circle for studying the configuration of public service facilities are explained. The spatial center of the elderly living circle is identified at multiple walking scales. On the other hand, the correlation between the two is calculated based on multi-scale geographical weighted regression, and corresponding optimization strategies are proposed based on the results.

[0014] (II) Technical solution

[0015] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0016] The matching analysis method between the elderly living circle center and the elderly care service facilities includes:

[0017] The target area is divided into several sub-areas, and the density of the target facilities is evaluated based on the location data of the target facilities in the sub-areas. The density of the target facilities and the population density are analyzed. If the obtained correlation is Exceeding expectations, issuing data collection instructions to external parties;

[0018] The elderly living circle is defined based on the collected elderly travel data and the corresponding target location data. If the target facilities do not exist in the elderly living circle, the regional concentration is constructed. , if the regional concentration If the load exceeds expectations, the corresponding sub-area will be regarded as a low-density area, and a load analysis instruction will be issued to the outside;

[0019] Collect the load data of the target facility during operation, and generate the load degree of the target facility from the load data , according to the load Differentiate the target facilities; after identifying the service coverage and buffer zone of the target facilities, the service concentration area and service blank area of ​​the target facilities are identified by the service area identification model;

[0020] Collect the service index data of the target facility and generate the matching index from the service index data set , if the obtained matching index If the number is lower than expected, the corresponding sub-region is regarded as a low-matching region, and key factors are identified from various conditional factors in the low-matching region;

[0021] Taking key factors as input, the optimization solution output by the optimization solution library optimizes the low matching area; after optimization, the matching index in the low matching area is Changes in build optimization , if the optimization degree If the value is lower than the optimization threshold, an alarm command will be issued to the outside.

[0022] Furthermore, the area to be analyzed is determined as the target area and an electronic map covering the target area is constructed. Clustering analysis is performed using a clustering algorithm, and the target area is divided on the electronic map to obtain several sub-areas. Various types of elderly care service facilities and their geographical location information are collected in the sub-areas and used as target facilities.

[0023] Furthermore, the location information of the target facility is marked on the electronic map and the density of the target facility is calculated. ,in,

[0024] ;

[0025] Where: , weight coefficient, , ,and ; is the radius of the subregion; It is i The weighted value of the target facility, It is i to j The distance value to the target facility.

[0026] Furthermore, the elderly population data in each sub-area is collected, and the density of target facilities and population density are analyzed to obtain the corresponding correlation. , as follows:

[0027] ;

[0028] Where: It is i The ratio of the density of target facilities to population density in each sub-area. is the average value of the corresponding ratio; is the weight coefficient.

[0029] Further, after receiving the data collection instruction, the living locations of the elderly in the sub-area are statistically obtained, and the travel data of the elderly are statistically obtained; the daily activity places frequently visited by the elderly in the target area are sampled and statistically counted as target places;

[0030] Collect the geographical location and facility descriptions of relevant locations, determine the service coverage of the target locations based on the travel data of the elderly, and define the area covering all daily activity places as the living circle of the elderly.

[0031] Furthermore, if there is no target facility in the elderly's living circle, a reminder command is issued to the outside. Based on the location data of the reminder command, the regional concentration of the reminder command in the sub-region is analyzed under dimensionless conditions. , as follows:

[0032] ;

[0033] in, The number of reminder instructions issued. It is i Reminder command to j The location distance of the reminder instruction. is the average value of the distance, is the weight coefficient.

[0034] Furthermore, the actual operation time and customer reception volume of the target facility are monitored and obtained in the sub-period, and the obtained monitoring data are used as the operation load data; the load degree of the target facility is generated from the operation load data in the low-density area. ;

[0035] If the load degree of the target facility When it exceeds the upper load threshold, it is treated as a high-load facility; when it is between the upper and lower load thresholds, it is treated as a medium-load facility; and when it is below the lower load threshold, it is treated as a low-load facility.

[0036] Furthermore, the actual operation time and customer reception Perform linear normalization and map the corresponding data values ​​to the interval The load degree is generated as follows :

[0037] ;

[0038] Weight coefficient: , ; For the i The actual operation time in a sub-cycle, is the acceptable value of the actual operation time in the corresponding sub-period, is the average of the actual operation time; For the i The number of customers received in a sub-period, is the acceptable value of customer reception volume in the corresponding sub-period, is the average number of customers received.

[0039] Furthermore, the service coverage of the target facility is determined based on the location information of the elderly received by the target facility, and the extended part of the service coverage is used as the service coverage buffer zone of the target facility;

[0040] Mark the service coverage and corresponding buffer zone on the electronic map, and use spatial analysis tools to generate a spatial distribution map of the target facilities.

[0041] Further, the service index data set of the target facility is generated by summarizing and generating a matching index from the service index data set. , and collect various conditional factors in the low-matching area, perform multivariate regression analysis on the conditional factors and the matching index, and construct the corresponding linear regression equation. If the regression coefficient of the conditional factor exceeds expectations, it will be regarded as a key factor.

[0042] Furthermore, the load facility ratio , Service Coverage and accessibility coefficient Perform linear normalization and map the corresponding data values ​​to the interval Then, the matching index is calculated as follows: :

[0043] ;

[0044] Weight coefficient, , .

[0045] Further, the obtained optimization solutions are summarized to generate an optimization solution library; after receiving the optimization instruction, for the low matching area, the optimization solution library outputs the corresponding optimization solution with the key factors as input;

[0046] Obtain the matching index in the low matching area in several consecutive sub-periods , the matching index Sort by timeline and build optimization as follows :

[0047] ;

[0048] in, For the i The matching index within the sub-period, is the corresponding mean, is its qualified standard value; For the i The optimal intermediate value, is its mean value, , is the number of sub-periods, and the weight coefficient is: , ,and .

[0049] The matching analysis system between the elderly living circle center and the elderly care service facilities includes:

[0050] The correlation analysis unit divides the target area into several sub-areas, evaluates the density of the target facilities based on the location data of the target facilities in the sub-areas, and performs correlation analysis on the density of the target facilities and the population density. Exceeding expectations, issuing data collection instructions to external parties;

[0051] The aggregation evaluation unit defines the elderly living circle based on the collected elderly travel data and the corresponding target location data. If the target facility does not exist in the elderly living circle, the regional aggregation is constructed. , if the regional concentration If the load exceeds expectations, the corresponding sub-area will be regarded as a low-density area, and a load analysis instruction will be issued to the outside;

[0052] The area identification unit collects the load data of the target facility during operation and generates the load degree of the target facility from the load data. , according to the load Differentiate the target facilities; after identifying the service coverage and buffer zone of the target facilities, the service concentration area and service blank area of ​​the target facilities are identified by the service area identification model;

[0053] The matching analysis unit collects the service index data of the target facility and generates a matching index from the service index data set. , if the obtained matching index If the number is lower than expected, the corresponding sub-region is regarded as a low-matching region, and key factors are identified from various conditional factors in the low-matching region;

[0054] The regional optimization unit uses key factors as input and uses the optimization solution output by the optimization solution library to optimize the low-matching area. After optimization, the matching index in the low-matching area is Changes in build optimization , if the optimization degree If the value is lower than the optimization threshold, an alarm command will be issued to the outside.

[0055] (III) Beneficial effects

[0056] The present invention provides a matching degree analysis method and system for elderly living circle centers and elderly care service facilities, which has the following beneficial effects:

[0057] 1. Based on relevance The richness of the target facilities can be judged as a whole. If the richness of the target facilities is insufficient, it will help to optimize or supplement the target facilities. By preliminarily determining the activity range and living circle of the elderly, the location of the target facilities and other facilities can be adjusted based on the size of the elderly's living circle to improve the living comfort of the elderly.

[0058] 2. Based on regional concentration Starting from the daily life of the elderly, we can conduct targeted assessments on the degree of lack of target facilities in the sub-area, verify the matching degree between the target facilities and the living circle of the elderly, and increase the carrying capacity of the target facilities in the corresponding sub-area for the elderly by determining the load degree between different target facilities and adjusting the location or load capacity of the target facilities.

[0059] 3. The trained service area recognition model can be used to identify the service concentration areas and service blank areas of the target facilities, clarify the areas and locations where the target facilities are relatively abundant and relatively scarce, and supplement and optimize the target facilities in a targeted manner.

[0060] 4. Based on the obtained matching index On the whole, a targeted analysis is carried out on the matching degree between the target facilities in the sub-region and the living circle of the elderly to verify the matching degree between the two. Low-matching areas are screened out in several sub-regions, and target facilities are added in the service gap areas of the low-matching areas to adjust the matching index.

[0061] 5. Based on the selected key factors, targeted optimization plans are given for low-matching areas to reduce the load of target facilities in low-matching areas, adjust service coverage or accessibility, etc., so as to adjust and improve the living conditions of the elderly in the sub-areas.

[0062] 6. Based on the degree of optimization Evaluate the optimization effect to determine whether the current optimization of target facilities and related conditions in low-matching areas has met expectations, which will help improve the adjustment effect of low-matching areas and accelerate the improvement of the quality of life of the elderly in their activity circles. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of the matching degree analysis method of the present invention;

[0064] Figure 2 Schematic diagram of the matching degree analysis system of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] See also Figure 1 The present invention provides a matching degree analysis method between elderly living circle centers and elderly care service facilities, including:

[0067] Step 1: Divide the target area into several sub-areas, evaluate the density of target facilities based on the location data of the target facilities in the sub-areas, and conduct a correlation analysis between the density of target facilities and population density. Exceeding expectations, issuing data collection instructions to external parties;

[0068] The step 1 includes the following contents:

[0069] Step 101: After the facility matching analysis is required, the area to be analyzed is determined as the target area and an electronic map covering the target area is constructed. A clustering analysis is performed using a clustering algorithm, and the target area is divided into several sub-areas on the electronic map, and each sub-area is numbered respectively;

[0070] Step 102: collect various elderly care service facilities and their geographical location information in the sub-region, such as nursing homes, day care centers, community medical services, etc., and use them as target facilities. Mark the location information of the target facilities on the electronic map and calculate the density of the target facilities. ,in,

[0071] ;

[0072] Where: , weight coefficient, , ,and ; The value is 3.142;

[0073] is the radius of the subregion; It is i The weighted value of the target facility, whose value falls into , It is i to j The distance value of each target facility;

[0074] When used, after dividing the target area into several sub-areas, by processing the target areas separately, it helps to diversify the processing methods, and each can serve as a reference to ensure that the adjustment and optimization of elderly care facilities and other similar facilities can achieve the expected results;

[0075] Step 103: Collect data on the elderly population in each sub-area, conduct correlation analysis on the density of target facilities and population density, and obtain the corresponding correlation , as follows:

[0076] ;

[0077] Where: It is i The ratio of the density of target facilities to population density in each sub-area. is the average value of the corresponding ratio; is the weight coefficient, and its value falls into ;

[0078] If the obtained correlation If it exceeds expectations, it means that the elderly population is concentrated in the target area, but the service facilities are insufficient and need to be adjusted, supplemented or optimized. At this time, a data collection instruction is issued to the outside.

[0079] When using, combine the contents in steps 101 to 103:

[0080] Based on the obtained statistical data, the correlation between the density of target facilities and population density is obtained. Based on relevance The richness of the target facilities can be judged as a whole. If the richness of the target facilities is insufficient, it will help to optimize, adjust or supplement the target facilities.

[0081] In the existing matching analysis methods, it is usually based on the coverage rate of facilities. In a certain area, the ratio of the number of service facilities to the total population of the area is determined to evaluate whether the configuration of elderly care facilities meets the needs of the elderly and screen out areas that are difficult to meet. This matching analysis method is relatively direct, but considering that the distribution of elderly care facilities may be very uneven, the coverage rate of elderly care facilities is difficult to reflect the actual situation. In particular, when the density of elderly care facilities is low and the density of the elderly population is high, the richness of elderly care facilities in the planned area is reduced. Applying the existing matching analysis method to regional planning may not be conducive to determining the areas that need to be supplemented with elderly care facilities, and is not conducive to targeted supplementation and optimization of target facilities, and it is difficult to achieve the expected results.

[0082] Step 2: Define the elderly living circle based on the collected elderly travel data and the corresponding target location data. If there is no target facility in the elderly living circle, construct the regional concentration , if the regional concentration If the load exceeds expectations, the corresponding sub-area will be regarded as a low-density area, and a load analysis instruction will be issued to the outside;

[0083] The step 2 includes the following contents:

[0084] Step 201: after receiving the data collection instruction, the residential locations of the elderly in the sub-area are obtained, and the travel data of the elderly are counted, including travel modes, such as walking, public transportation, and means of transportation, as well as data on travel radius, travel habits and restrictions, etc. The above data are summarized as a set of elderly travel data;

[0085] Step 202: sampling and counting daily activity places frequented by the elderly in the target area, taking them as target places, such as supermarkets, parks, and community activity centers, collecting the geographical locations and facility descriptions of the relevant places, and determining the service coverage of the target places based on the elderly travel data, for example, the distance from the target places, and taking the area covering all daily activity places as the elderly living circle;

[0086] When in use, the daily travel data of the elderly is counted and recorded, and the activity range and living circle of the elderly are preliminarily determined through the daily travel data and the places they often visit. Based on the size of the living circle of the elderly, the location of the target facilities and other facilities can be adjusted to improve the living comfort of the elderly.

[0087] Step 203: If the target facility does not exist in the elderly's living circle, a reminder instruction is issued to the outside;

[0088] Based on the location data of the reminder instructions, the regional concentration of the reminder instructions in the sub-region is analyzed and obtained under dimensionless conditions. , as follows:

[0089] ;

[0090] in, The number of reminder instructions issued. It is i Reminder command to j The location distance of the reminder instruction. is the average value of the distance, is the weight coefficient, and its value is consistent with the previous value;

[0091] If the regional concentration If the preset concentration threshold is exceeded, the corresponding sub-area will be regarded as a low-density area, and a load analysis instruction will be issued to the outside;

[0092] When using, combine the contents in steps 201 to 203:

[0093] After determining the living area of ​​the elderly, the location information of the reminder instructions is statistically analyzed to generate the corresponding regional concentration , according to regional concentration Starting from the daily life of the elderly, we can conduct a targeted assessment of the lack of target facilities in the sub-area and verify the matching degree between the target facilities and the living circle of the elderly.

[0094] Step 3: Collect the load data of the target facility during operation, and generate the load degree of the target facility from the load data. , according to the load Differentiate the target facilities; after identifying the service coverage and buffer zone of the target facilities, the service concentration area and service blank area of ​​the target facilities are identified by the service area identification model;

[0095] The step three includes the following contents:

[0096] Step 301: Set an operation monitoring cycle including several sub-cycles, monitor and obtain the actual operation time and customer reception volume of the target facility in the sub-cycle, use the obtained monitoring data as operation load data, and summarize and generate an operation load data set of the target facility;

[0097] Step 302: Generate the load degree of the target facility from the operating load data in the low-density area , the load degree obtained is used to evaluate the service load of the target facility, among which the actual operation time is and customer reception Perform linear normalization and map the corresponding data values ​​to the interval In the following way:

[0098] ;

[0099] Weight coefficient: , , the weight coefficient can be obtained by referring to the hierarchical analysis method;

[0100] For the i The actual operation time within a sub-cycle, is the acceptable value of the actual operation time in the corresponding sub-period, is the average of the actual operation time; For the i The number of customers received in a sub-period, is the acceptable value of customer reception volume in the corresponding sub-period, is the mean of the number of customers received;

[0101] Based on historical data and load management of target facilities, high upper and lower load thresholds are pre-set;

[0102] If the load degree of the target facility If it exceeds the upper load threshold, it is treated as a high-load facility; if it is between the upper and lower load thresholds, it is treated as a medium-load facility; if it is below the lower load threshold, it is treated as a low-load facility;

[0103] When in use, when target facilities in low-density areas are in a state of shortage, some target facilities may have a large operating load. By determining the load degree between different target facilities and adjusting the location or load capacity of the target facilities, the carrying capacity of the target facilities in the corresponding sub-area for the elderly can be increased;

[0104] Step 303: Mark the target facilities with different loads on the electronic map, determine the service coverage of the target facility based on the location information of the elderly received by the target facility, and use the extended part of the service coverage as the service coverage buffer of the target facility; mark the service coverage and the corresponding buffer on the electronic map, and use the spatial analysis tool to generate a spatial distribution map of the target facility;

[0105] The neural convolution network is trained by the labeled sample data to generate a trained service area recognition model. The marking information on the electronic map is used as input, and the trained service area recognition model is used to identify the service concentration area and service blank area of ​​the target facility.

[0106] When used, combine the contents in steps 301 to 303:

[0107] Based on the travel data and location data of the elderly, the service coverage of each target facility can be determined. Based on the overlap and alternation between the service coverage areas, the service concentration areas and service gap areas of the target facilities can be identified through the trained service area recognition model. After clarifying the areas and locations where target facilities are more abundant and less abundant, the target facilities can be supplemented and optimized in a targeted manner.

[0108] Step 4: Collect service indicator data of the target facility and generate a matching index from the service indicator data set , if the obtained matching index If the number is lower than expected, the corresponding sub-region is regarded as a low-matching region, and key factors are identified from various conditional factors in the low-matching region;

[0109] The step 4 includes the following contents:

[0110] Step 401: Obtain statistics on the proportion of the elderly in the service concentration area and generate a service coverage rate, record the proportion of non-high-load facilities in the sub-area and generate a load-facility ratio; after calculating the shortest distance for the elderly in the service blank area to reach the target facility, take the average of several shortest distances as the accessibility coefficient of the elderly from their residence to the target facility; summarize the service coverage rate, load-facility ratio and accessibility coefficient in the sub-area to generate a service indicator data set for the target facility;

[0111] When used, the accessibility coefficient can be used to evaluate the difficulty of the elderly reaching the target facilities in the service gap area, and the necessity of adding target facilities in the corresponding sub-area can be determined based on the evaluation results;

[0112] Step 402: Generate a matching index from the service indicator data set , among which, the load facility ratio , Service Coverage and accessibility coefficient Perform linear normalization and map the corresponding data values ​​to the interval Then follow the steps below:

[0113] ;

[0114] Weight coefficient, , ;The weight coefficient can be obtained by referring to the hierarchical analysis method;

[0115] According to historical data and the expected distribution management of target facilities in the sub-area, the matching threshold is set; if the matching index obtained is If it is lower than the matching threshold, the corresponding sub-region is regarded as a low matching region;

[0116] When used, the matching index generated , based on the obtained matching index The matching degree between the target facilities in the sub-region and the living circle of the elderly can be analyzed in a targeted manner to verify the matching degree between the two. Low matching areas can be screened out in several sub-regions. At this time, target facilities can be added to the service blank areas of the low matching areas to adjust the matching index.

[0117] Step 403: After collecting various condition factors in the low matching area, such as traffic data, load capacity of target facilities and service content diversity data, a multivariate regression analysis is performed on the condition factors and the matching index, and a corresponding linear regression equation is constructed. If the regression coefficient of the condition factor exceeds the expectation, it is regarded as a key factor;

[0118] When used, combine the contents in steps 401 to 403:

[0119] After obtaining the conditional factors that may affect the matching degree in the low matching area, it is possible to screen the key factors, thereby increasing the pertinence and efficiency of the adjustment when the low matching area needs to be adjusted.

[0120] Step 5: Using key factors as input, the optimization solution output by the optimization solution library optimizes the low-matching area; after optimization, the matching index in the low-matching area is used Changes in build optimization , if the optimization degree If the value is lower than the optimization threshold, an alarm command will be issued to the outside;

[0121] The step five includes the following contents:

[0122] Step 501: Obtain several regional optimization plans through online retrieval or offline formulation, and generate an optimization plan library after summarizing the obtained optimization plans; after receiving the optimization instruction, for the low-matching area, take the key factors as input, and the optimization plan library outputs the corresponding optimization plan, for example, increase the number of target facilities, increase the functions of target facilities, improve the load capacity of target facilities, increase barrier-free bus routes, such as customized buses or community shuttle buses, etc.; optimize the low-matching area after executing the optimization plan;

[0123] When in use, after completing the matching analysis between the target facilities and the living circle of the elderly, the key factors can be screened out to provide targeted optimization solutions for low-matching areas, reduce the load of target facilities in low-matching areas, adjust service coverage or accessibility, etc., and adjust and improve the living conditions of the elderly in the sub-areas;

[0124] Step 502: After presetting the observation period including several sub-areas, obtain the matching index in the low matching area in several consecutive sub-periods. , according to the matching index The change of construct optimization degree, in which the matching index Sort by timeline, and then follow the following method:

[0125] ;

[0126] in, For the i The matching index within the sub-period, is the corresponding mean, is its qualified standard value; For the i The optimal intermediate value, is its mean value, , is the number of sub-periods, and the weight coefficient is: , ,and ;The weight coefficient remains consistent with the previous value;

[0127] Based on historical data and the expected optimization management of target facilities in low-matching areas, the optimization threshold is set in advance; if the optimization degree If it is lower than the optimization threshold, it means that the current optimization has not achieved the expected effect and needs further adjustment and optimization. At this time, an alarm command is issued to the outside.

[0128] When using, combine the contents in steps 501 and 502:

[0129] After adjusting and optimizing the target facilities in the low matching area, several matching indexes are obtained continuously. Build Optimization , evaluate the optimization effect according to the optimization degree, and determine whether the current optimization of the target facilities and their related conditions in the low-matching area has met the expectations, which will help improve the adjustment effect of the low-matching area and accelerate the improvement of the quality of life of the elderly in their activity circle.

[0130] The Analytic Hierarchy Process (AHP) is a decision-making method that decomposes decision-related elements into levels such as goals, criteria, and plans, and conducts qualitative and quantitative analysis on this basis. It is particularly suitable for dealing with target systems with hierarchical and staggered evaluation indicators, and when the target value is difficult to describe quantitatively, the AHP is an effective decision-making tool.

[0131] The core of the hierarchical analysis method is to decompose the decision problem into multiple levels to form a hierarchical structure, which usually includes the target level, the criterion level, the sub-criterion level and the solution level. By solving the eigenvector of the judgment matrix, the priority weight of each element of each level to an element of the previous level is obtained, and the final weight of each alternative solution to the total goal is recursively merged by the weighted sum method, so as to find the optimal solution.

[0132] See also Figure 2 The present invention provides a matching degree analysis system between elderly living circle centers and elderly care service facilities, including:

[0133] The correlation analysis unit divides the target area into several sub-areas, evaluates the density of the target facilities based on the location data of the target facilities in the sub-areas, and performs correlation analysis on the density of the target facilities and the population density. Exceeding expectations, issuing data collection instructions to external parties;

[0134] The aggregation evaluation unit defines the elderly living circle based on the collected elderly travel data and the corresponding target location data. If the target facility does not exist in the elderly living circle, the regional aggregation is constructed. , if the regional concentration If the load exceeds expectations, the corresponding sub-area will be regarded as a low-density area, and a load analysis instruction will be issued to the outside;

[0135] The area identification unit collects the load data of the target facility during operation and generates the load degree of the target facility from the load data. , according to the load Differentiate the target facilities; after identifying the service coverage and buffer zone of the target facilities, the service concentration area and service blank area of ​​the target facilities are identified by the service area identification model;

[0136] The matching analysis unit collects the service index data of the target facility and generates a matching index from the service index data set. , if the obtained matching index If the number is lower than expected, the corresponding sub-region is regarded as a low-matching region, and key factors are identified from various conditional factors in the low-matching region;

[0137] The regional optimization unit uses key factors as input and uses the optimization solution output by the optimization solution library to optimize the low-matching area. After optimization, the matching index in the low-matching area is Changes in build optimization , if the optimization degree If the value is lower than the optimization threshold, an alarm command will be issued to the outside.

[0138] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0139] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0140] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0141] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0144] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0145] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A matching analysis method between the elderly living circle center and elderly care service facilities, characterized by: include, The target area is divided into several sub-areas, and the density of the target facilities is evaluated based on the location data of the target facilities in the sub-areas. The density of the target facilities and the population density are analyzed. If the obtained correlation is Exceeding expectations, a data collection instruction was issued to the outside; among them, data on the elderly population in each sub-area was collected, and the density of the target facilities and population density were analyzed, and the corresponding correlation was obtained. , as follows: ； Where: It is The ratio of the density of target facilities to population density in each sub-area. is the average value of the corresponding ratio; is the weight coefficient; The elderly living circle is defined based on the collected elderly travel data and the corresponding target location data. If the target facilities do not exist in the elderly living circle, the regional concentration is constructed. , if the regional concentration If the target facility does not exist in the elderly living circle, a reminder command will be issued to the outside. Based on the location data of the reminder command, the regional concentration of the reminder command in the sub-region is obtained by dimensionless analysis. , as follows: ； in, The number of reminder instructions issued. It is Reminder command to The location distance of the reminder instruction. is the average value of the distance, is the weight coefficient; Collect the load data of the target facility during operation, and generate the load degree of the target facility from the load data , according to the load The target facilities are distinguished. After identifying the service coverage and buffer zone of the target facilities, the service concentration area and service blank area of ​​the target facilities are identified by the service area identification model. and customer reception Perform linear normalization and map the corresponding data values ​​to the interval The load degree is generated as follows : ； Where: For the The actual operation time within a sub-cycle, is the acceptable value of the actual operation time in the corresponding sub-period, is the average of the actual operation time; For the The number of customers received in a sub-period, is the acceptable value of customer reception volume in the corresponding sub-period, is the mean of the number of customers received, and the weight coefficient is: , ; Collect the service index data of the target facility, summarize the service coverage rate, load facility ratio and accessibility coefficient in the sub-area to generate the service index data set of the target facility, and generate the matching index from the service index data set , if the obtained matching index If the load facility ratio is lower than expected, the corresponding sub-region is regarded as a low-matching region, and key factors are identified from various conditional factors in the low-matching region; , Service Coverage and accessibility coefficient Perform linear normalization and map the corresponding data values ​​to the interval Then, the matching index is calculated as follows: : ； Weight coefficient, , ; Taking key factors as input, the optimization solution output by the optimization solution library optimizes the low matching area. After optimization, the matching index in the low matching area is calculated. Changes in build optimization , if the optimization degree If the value is lower than the optimization threshold, an alarm command will be issued to the outside; Among them, the matching index in the low matching area is obtained in several consecutive sub-periods. , the matching index Sort by timeline and build optimization as follows : ； in, For the The matching index within the sub-period, is the corresponding mean, is its qualified standard value; For the The optimal intermediate value, is its mean value, , is the number of sub-periods, and the weight coefficient is: , and .

2. The matching degree analysis method according to claim 1, characterized in that: The area to be analyzed is determined as the target area and an electronic map covering the target area is constructed. Clustering analysis is performed using a clustering algorithm. The target area is divided into several sub-areas on the electronic map. Various types of elderly care service facilities and their geographical location information are collected in the sub-areas and used as target facilities.

3. The matching degree analysis method according to claim 2, characterized in that: After marking the location information of the target facility on the electronic map, calculate the density of the target facility ,in, ； Where: , weight coefficient, , ,and ; is the radius of the subregion; No. The weighted value of the target facility, It is to The distance value of each target facility, Refers to the number of target facilities.

4. The matching degree analysis method according to claim 3, characterized in that: After receiving the data collection instruction, the residential locations of the elderly in the sub-area are obtained and the travel data of the elderly are counted. The daily activity places frequently visited by the elderly are sampled and counted in the target area and used as the target places. Collect the geographical location and facility descriptions of relevant locations, determine the service coverage of the target locations based on the travel data of the elderly, and define the area covering all daily activity places as the living circle of the elderly.

5. The matching degree analysis method according to claim 4, characterized in that: The actual operating hours and customer reception volume of the target facility are monitored in the sub-period, and the acquired monitoring data are used as the operating load data. The load degree of the target facility is generated from the operating load data in the low-density area. ; If the load degree of the target facility When it exceeds the upper load threshold, it is treated as a high-load facility; when it is between the upper and lower load thresholds, it is treated as a medium-load facility; and when it is below the lower load threshold, it is treated as a low-load facility.

6. The matching degree analysis method according to claim 5, characterized in that: Determine the service coverage of the target facility based on the location information of the elderly received by the target facility, and use the extended part of the service coverage as the service coverage buffer of the target facility; Mark the service coverage and corresponding buffer zone on the electronic map, and use spatial analysis tools to generate a spatial distribution map of the target facilities.

7. The matching degree analysis method according to claim 6, characterized in that: Summarize and generate a service indicator data set for the target facility, and generate a matching index from the service indicator data set , Collect various condition factors in the low-matching area, perform multivariate regression analysis on the condition factors and the matching index, and construct the corresponding linear regression equation. If the regression coefficient of the condition factor exceeds the expectation, it will be regarded as the key factor; The obtained optimization solutions are summarized to generate an optimization solution library. After receiving the optimization instruction, for the low matching area, the key factors are used as input, and the optimization solution library outputs the corresponding optimization solution.

8. A matching degree analysis system for elderly living circle centers and elderly care service facilities, using the matching degree analysis method described in any one of claims 1 to 7, characterized in that: include, The correlation analysis unit divides the target area into several sub-areas, evaluates the density of the target facilities based on the location data of the target facilities in the sub-areas, and performs correlation analysis on the density of the target facilities and the population density. Exceeding expectations, issuing data collection instructions to external parties; The aggregation evaluation unit defines the elderly living circle based on the collected elderly travel data and the corresponding target location data. If the target facility does not exist in the elderly living circle, the regional aggregation is constructed. , if the regional concentration If the load exceeds expectations, the corresponding sub-area will be regarded as a low-density area, and a load analysis instruction will be issued to the outside; The area identification unit collects the load data of the target facility during operation and generates the load degree of the target facility from the load data. , according to the load Differentiate the target facilities; after identifying the service coverage and buffer zone of the target facilities, the service concentration area and service blank area of ​​the target facilities are identified by the service area identification model; The matching analysis unit collects the service index data of the target facility and generates a matching index from the service index data set. , if the obtained matching index If the number is lower than expected, the corresponding sub-region is regarded as a low-matching region, and key factors are identified from various conditional factors in the low-matching region; The regional optimization unit uses key factors as input and uses the optimization solution output by the optimization solution library to optimize the low-matching area. After optimization, the matching index in the low-matching area is Changes in build optimization , if the optimization degree If the value is lower than the optimization threshold, an alarm command will be issued to the outside.

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