A site selection evaluation method, device and medium integrating POI data

Abstract

The embodiment of the present application discloses a method, device and medium for site selection evaluation that integrates POI data. It belongs to the field of data processing technology and solves the problem of large deviation in site selection results of traditional site selection methods. It includes the following steps: dividing the acquired POI data based on the constructed POI classification system; determining the site selection evaluation index according to the POI classification system, and determining the basic formula of the site selection evaluation index corresponding to the site selection evaluation index through a preset analysis algorithm; performing double-layer screening on the divided POI data based on the preset screening range conditions, and determining the site selection evaluation index value based on the screened POI data and the basic formula of the site selection evaluation index; optimizing the site selection evaluation index value through a particle swarm optimization algorithm to determine the standardized parameters for site selection evaluation; and realizing site selection evaluation based on the site selection evaluation index value, the standardized parameters for site selection evaluation and the comprehensive scoring function for site selection evaluation.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a site selection evaluation method, device, and medium integrating POI data. Background Art

[0002] With the accelerating pace of urbanization, site selection and planning, as a core component of urban construction and development, are becoming increasingly crucial. Site selection and planning are not simply a matter of geographical selection; rather, they require in-depth analysis and comprehensive consideration of a variety of complex factors, such as the superior location, convenient transportation, rational population distribution, and potential for economic development.

[0003] These key factors are often closely related to the spatial distribution and quantity of various facilities within a city. Commercial facilities should be located near bustling commercial centers to attract more consumers. The planning of public service facilities should consider the distribution of residential areas to ensure convenient access to various services. The positioning of industrial parks should also take into account the comprehensiveness of the surrounding industrial chain, the convenience of transportation and logistics, and the abundance of talent resources.

[0004] However, existing site selection and planning technologies rely on a single type of data, resulting in a low level of rationality in site assessment. Traditional site selection methods often rely on limited geographic data or statistical data, lacking comprehensiveness and accuracy. This single data source not only limits the depth and breadth of site selection analysis but also leads to significant deviations in site selection results, making it difficult to meet the needs of urban construction and development. Summary of the Invention

[0005] The embodiments of the present application provide a site selection evaluation method, device and medium that integrate POI data to solve the following technical problems: traditional site selection methods often rely only on limited geographic data or statistical data, lack comprehensiveness and accuracy, and result in large deviations in site selection results.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] The embodiment of the present application provides a method for site selection evaluation that integrates POI data. The method comprises the following steps: dividing the acquired POI data based on a constructed POI classification system; determining a site selection evaluation index based on the POI classification system, and determining a site selection evaluation index basic formula corresponding to the site selection evaluation index through a preset analysis algorithm; performing a double-layer screening on the divided POI data based on a preset screening range condition, and determining a site selection evaluation index value based on the screened POI data and the site selection evaluation index basic formula; optimizing the site selection evaluation index value through a particle swarm optimization algorithm to determine a site selection evaluation standardization parameter; and implementing a site selection evaluation based on the site selection evaluation index value, the site selection evaluation standardization parameter, and the site selection evaluation comprehensive scoring function.

[0008] The embodiments of the present application fully consider the impact of various related facilities on site selection evaluation based on the supply and demand relationship, enhance the diversity of site selection data, and improve the rationality of site selection evaluation. Secondly, the standardized parameters for site selection evaluation are obtained based on the adaptive optimization of POI data, which reduces the dependence on external data and avoids the irrationality of subjective settings. The site selection evaluation process in the embodiments of the present application adopts a two-layer screening mechanism for POI data, which can reduce complex calculation operations and shorten the overall time of the evaluation process.

[0009] In one implementation of the present application, the acquired POI data is divided based on the constructed POI classification system, specifically including: constructing a POI classification system according to the supply and demand relationship and the influence effect; wherein the POI classification system includes demand POI, active POI, supply POI and passive POI; based on preset request parameters, regularly calling relevant interfaces to obtain the required various types of POI data and storing them in a database; based on the POI classification system, dividing and storing the POI data in the database.

[0010] In one implementation of the present application, a basic formula of the site selection evaluation index corresponding to the site selection evaluation index is determined through a preset analysis algorithm, specifically including: based on kernel density analysis, determining the basic function of the indicator according to the attribute data corresponding to the indicator-associated POI and the distance between it and the site selection location; based on the basic function of the indicator, substituting the POI data related to the site selection evaluation index to obtain the corresponding basic formula of the site selection evaluation index; based on the hierarchical analysis algorithm, determining the weight of the site selection evaluation index; constructing a judgment matrix using the indicator pairwise comparison method; normalizing the column vectors of the judgment matrix, and determining the row sum of the matrix obtained by normalizing the column vectors, and normalizing again to obtain the weight of the site selection evaluation index; wherein, the site selection evaluation index is related to the POI classification system; determining the consistency ratio based on the weight, and performing a consistency test on the judgment matrix based on the consistency ratio, and if the test passes, performing an average calculation on the weights determined multiple times to obtain the final weight.

[0011] In one implementation of the present application, based on preset screening range conditions, the divided POI data is subjected to double-layer screening, specifically including: constructing a rough screening range of POI data based on the longitude, latitude, site selection radius, longitude difference corresponding to the preset distance unit, and latitude difference corresponding to the preset distance unit corresponding to the site selection; constructing a fine screening range of POI data based on the distance between the site selection and the actual representation of the POI in the rough screening range of POI data, and the site selection radius; based on the rough screening range of POI data, roughly screening the divided POI data; based on the fine screening range of POI data, fine screening the roughly screened POI data.

[0012] In one implementation of the present application, the site selection evaluation index value is optimized by a particle swarm optimization algorithm to determine the site selection evaluation standardization parameters, specifically including: determining multiple single-objective optimization problems and determining the objective function corresponding to each single-objective optimization problem according to the different basic formulas of the site selection evaluation index; determining a multi-objective optimization problem for determining the comprehensive standardization parameters; wherein the objective function corresponding to the multi-objective optimization problem is a linear weighted sum formula of the standard values ​​of each site selection evaluation index, and the maximum value of the objective function is used as the comprehensive standardization parameter; using the particle swarm optimization algorithm to solve multiple single-objective optimization problems and multi-objective optimization problems to determine the indicator standardization parameters and the comprehensive standardization parameters.

[0013] In one implementation of the present application, a particle swarm optimization algorithm is used to solve multiple single-objective optimization problems and multi-objective optimization problems to determine indicator standardization parameters and comprehensive standardization parameters, specifically including: using the position search range as the search space for feasible solutions to the single-objective optimization problem, and the speed limit range as the particle speed constraint range, and using the particle swarm optimization algorithm to solve the multiple single-objective optimization problems separately to obtain the optimal objective function values ​​corresponding to the multiple single-objective optimization problems; repeating the optimization solution process until the number of times reaches the preset value of the optimization number, and based on each single-objective optimization problem, taking the maximum value of the optimal objective function value as the corresponding indicator standardization parameter; using the position search range as the search space for feasible solutions to the multi-objective optimization problem, and the speed limit range as the particle speed constraint range, and using the particle swarm optimization algorithm to solve the multi-objective optimization problem to obtain the optimal objective function value; repeating the optimization solution process until the number of times reaches the preset value of the optimization number, and taking the maximum value of the optimal objective function value as the comprehensive standardization parameter.

[0014] In one implementation of the present application, the position search range is used as the search space for feasible solutions to the single-objective optimization problem, and the speed limit range is used as the particle speed constraint range, specifically including: the position search range is:

[0015] P={(plng ,p lat )|α min ≤p lng ≤α max ,β min ≤p lat ≤β max};

[0016] Among them, α min is the minimum longitude corresponding to the site selection area; α max is the maximum longitude corresponding to the site selection area; β min is the minimum latitude corresponding to the site selection area; β max is the maximum latitude corresponding to the site selection area; p lng is longitude; p lat is the latitude; P is the search range corresponding to the site selection area;

[0017] The speed limit ranges are:

[0018] V=(v lng ,v lat )|-α≤v lng ≤α,-β≤v lat ≤β};

[0019] Among them, α represents the longitude difference per kilometer of the site selection area; β represents the latitude difference per kilometer of the site selection area; v lng is the speed limit value in the longitude direction; v lat is the speed limit value in the latitude direction; V is the speed limit range.

[0020] In one implementation of the present application, site selection evaluation is implemented based on site selection evaluation index values, site selection evaluation standardized parameters and site selection evaluation comprehensive scoring function, specifically including: constructing a site selection evaluation comprehensive scoring function based on the site selection evaluation index values, index weights, comprehensive standardized parameters and index standardized parameters of the site selection location; obtaining the site selection evaluation comprehensive score of the site selection location through the site selection evaluation comprehensive scoring function to implement site selection evaluation.

[0021] An embodiment of the present application provides a site selection evaluation device that integrates POI data, including: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: divide the acquired POI data based on a constructed POI classification system; determine a site selection evaluation index according to the POI classification system, and determine a basic site selection evaluation index formula corresponding to the site selection evaluation index through a preset analysis algorithm; perform a double-layer screening on the divided POI data based on a preset screening range condition, and determine a site selection evaluation index value based on the screened POI data and the basic site selection evaluation index formula; optimize the site selection evaluation index value through a particle swarm optimization algorithm to determine a site selection evaluation standardization parameter; and implement a site selection evaluation based on the site selection evaluation index value, the site selection evaluation standardization parameter, and the site selection evaluation comprehensive scoring function.

[0022] A non-volatile computer storage medium provided in an embodiment of the present application stores computer-executable instructions, wherein the computer-executable instructions are configured to: divide the acquired POI data based on a constructed POI classification system; determine a site selection evaluation index according to the POI classification system, and determine a site selection evaluation index basic formula corresponding to the site selection evaluation index through a preset analysis algorithm; perform a double-layer screening on the divided POI data based on a preset screening range condition, and determine a site selection evaluation index value based on the screened POI data and the site selection evaluation index basic formula; optimize the site selection evaluation index value through a particle swarm optimization algorithm to determine a site selection evaluation standardization parameter; and implement a site selection evaluation based on the site selection evaluation index value, the site selection evaluation standardization parameter, and the site selection evaluation comprehensive scoring function.

[0023] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: the embodiments of the present application fully consider the influence of various related facilities on the site selection evaluation on the basis of supply and demand, enhance the diversity of site selection data, and improve the rationality of site selection evaluation. Secondly, the standardized parameters for site selection evaluation are obtained based on the adaptive optimization of POI data, which reduces the dependence on external data and avoids the irrationality of human subjective settings. The site selection evaluation process in the embodiments of the present application adopts a two-layer screening mechanism for POI data, which can reduce complex calculation operations and shorten the overall time of the evaluation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present application. For those skilled in the art, other drawings can be obtained based on these drawings without inventive work. In the drawings:

[0025] Figure 1 A flow chart of a site selection evaluation method integrating POI data provided in an embodiment of the present application;

[0026] Figure 2 An example diagram of a two-tier screening mechanism for POI data provided in an embodiment of the present application;

[0027] Figure 3 A structural diagram of a site selection evaluation device provided in an embodiment of the present application;

[0028] Figure 4 A flow chart of a data collection and management module provided in an embodiment of the present application;

[0029] Figure 5 A flow chart of a parameter calculation and update module provided in an embodiment of the present application;

[0030] Figure 6 A flowchart of a site selection evaluation module provided in an embodiment of the present application;

[0031] Figure 7 A schematic diagram of the structure of a site selection evaluation device integrating POI data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The embodiments of the present application provide a site selection evaluation method, device, and medium that integrate POI data.

[0033] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0034] The technical solutions proposed in the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0035] Figure 1 A flowchart of a method for evaluating a site selection by integrating POI data is provided in an embodiment of the present application, as shown in FIG. Figure 1As shown in FIG, the site selection evaluation method integrating POI data includes the following steps:

[0036] S101. Based on the constructed POI classification system, the acquired POI data is divided.

[0037] In one embodiment of the present application, a POI classification system is constructed based on supply and demand relationships and their impact. The POI classification system includes demand POIs, active POIs, supply POIs, and passive POIs. Based on preset request parameters, the relevant interfaces are periodically called to obtain the required POI data and store it in a database. Based on the POI classification system, the POI data in the database is divided and stored.

[0038] Specifically, the embodiment of the present application reasonably classifies POI data that can represent relevant information of various types of facilities based on the supply and demand relationship and the impact on the site selection evaluation of the facility, and constructs a POI classification system as follows:

[0039] Demand POI: represents the POI of the residential facilities of the service recipients of the site selection facility;

[0040] Positive POI: refers to the POI of facilities that have a positive impact on the service provision process of the location facility, excluding demand POI;

[0041] Supply POI: indicates POIs where similar facilities or facilities providing similar services already exist;

[0042] Negative POI: refers to the POI of facilities that have a negative impact on the service provision process of the sited facility, excluding supply POI.

[0043] Furthermore, in the embodiments of the present application, based on preset request parameters, a script program in a relevant programming language is used to regularly call the relevant interface to obtain the required various types of POI data and store it in a designated database. Based on other relevant data in the database, a stored procedure built based on specific data processing logic is used to regularly supplement, improve, classify and aggregate the POI data collected by the interface to generate various types of POI data.

[0044] S102: Determine a site selection evaluation index based on the POI classification system, and determine a basic formula for the site selection evaluation index corresponding to the site selection evaluation index through a preset analysis algorithm.

[0045] The embodiment of the present application constructs site selection evaluation indicators based on the POI classification system, specifically including:

[0046] Service object scale: The number of service objects in the demand POI within the site selection radius, reflecting the demand generated by all service objects within the site selection radius, which is a positive indicator;

[0047] Active POI scale: The number of active POIs within the site selection radius, reflecting the spatial distribution of active POIs within the site selection radius, is a positive indicator;

[0048] Service supply scale: The average daily number of service visits provided to POIs within the location radius of the selected site, reflecting the supply situation of all service providers within the location radius. It is a negative indicator.

[0049] Negative POI scale: The number of negative POIs within the site selection radius, reflecting the spatial distribution of negative POIs within the site selection radius, is a negative indicator.

[0050] In one embodiment of the present application, based on kernel density analysis, the basic function of the indicator is determined according to the attribute data corresponding to the indicator-associated POI and the distance between it and the site. Based on the basic function of the indicator, the POI data related to the site selection evaluation indicator is substituted to obtain the corresponding basic formula of the site selection evaluation indicator; based on the hierarchical analysis algorithm, the weight of the site selection evaluation indicator is determined. The judgment matrix is ​​constructed using the indicator pairwise comparison method. The column vectors of the judgment matrix are normalized, and the row sum of the matrix obtained by normalizing the column vectors is determined, and the normalization is performed again to obtain the weight of the site selection evaluation indicator; wherein, the site selection evaluation indicator is related to the POI classification system. Based on the weight, the consistency ratio is determined, and the judgment matrix is ​​subjected to a consistency test based on the consistency ratio. If the test passes, the weights determined multiple times are averaged to obtain the final weight.

[0051] Specifically, the site selection evaluation index formula is determined by combining kernel density analysis. Combining the characteristics and principles of kernel density analysis, according to the attribute data corresponding to the index-associated POI and its distance from the site, the basic index function is determined as follows:

[0052]

[0053] Among them, n is the number of POIs associated with the indicator within the site selection radius of location p, n i The actual location of the POI associated with the indicator i Attribute data, d p,i The actual representation of the location p associated with the indicator POI i is the distance between the two nodes (in kilometers), h is the bandwidth, and K(·) is the Gaussian kernel function.

[0054] Based on the basic function of the indicator, substituting the POI data related to the site selection evaluation indicator, the corresponding basic formula of the site selection evaluation indicator can be obtained:

[0055] For the service object scale, the indicator-associated POI is the demand POI, and the attribute data corresponding to the indicator-associated POI is the number of service objects of the demand POI. The corresponding basic indicator formula in this case is the formula for the service object scale, which is recorded as s1(p);

[0056] For the active POI scale, the POI associated with the indicator is an active POI, and the attribute data corresponding to the indicator associated POI is a fixed value of 1. The corresponding basic formula for the indicator in this case is the formula for the active POI scale, which is recorded as s2(p);

[0057] For the scale of service supply, the indicator-associated POI is the supply POI, and the attribute data corresponding to the indicator-associated POI is the average daily number of service visits of the supply POI. The basic formula for the indicator in this case is the formula for the scale of service supply, denoted as s3(p);

[0058] For the negative POI scale, the indicator-associated POI is a negative POI, and the attribute data corresponding to the indicator-associated POI is a fixed value of 1. The corresponding basic indicator formula in this case is the formula for the negative POI scale, recorded as s4(p).

[0059] Furthermore, the analytic hierarchy process (AHP) was used to determine the weights of the site selection evaluation indicators. Combining professional knowledge, experience, and site selection requirements, and referring to the Saaty nine-level scale, a judgment matrix was constructed using the paired comparison method. The column vectors of the judgment matrix were normalized, and the row sums of the resulting matrix were calculated. This matrix was then normalized again to obtain the weights of the site selection evaluation indicators. A consistency check was performed on the judgment matrix based on the weighted consistency ratio. If the check failed, the judgment matrix needed to be adjusted and the weights recalculated. In practical applications, to make the indicator weights more reasonable, the average of the weights determined by multiple professionals could be used as the final weight of the site selection evaluation indicator.

[0060] S103 : Based on the preset screening range conditions, the divided POI data is subjected to double-layer screening, and the site selection evaluation index value is determined based on the screened POI data and the basic formula of the site selection evaluation index.

[0061] In one embodiment of the present application, a rough screening range for POI data is constructed based on the longitude, latitude, and site selection radius corresponding to the site selection location, the longitude difference corresponding to a preset distance unit, and the latitude difference corresponding to the preset distance unit. A fine screening range for POI data is constructed based on the distance between the site selection location and the actual location of the POI within the rough screening range for POI data, as well as the site selection radius. Based on the rough screening range for POI data, a rough screening is performed on the divided POI data. Based on the fine screening range for POI data, a fine screening is performed on the roughly screened POI data.

[0062] Specifically, to quickly calculate site selection evaluation metrics when the POI data is large, a two-tiered POI data screening mechanism is incorporated to implement a process of gradually filtering POI data based on relevant screening ranges. This reduces complex distance calculations after determining the target POI data. The POI data screening process depends on the screening range, and a coarse screening range and a fine screening range can be constructed based on the site selection radius.

[0063] The rough screening range of POI data for the site selection is:

[0064]

[0065] Among them, P(p) is the rough screening range of POI data of location p, p lng and p lat are the longitude and latitude of the site p, r is the site radius (in kilometers), α is the longitude difference per kilometer of the site area, and β is the latitude difference per kilometer of the site area. The specific formula is:

[0066]

[0067] in, represents the distance between two places (in kilometers), α min ,α max ,β min ,β max They are the minimum longitude, maximum longitude, minimum latitude and maximum latitude corresponding to the site selection area.

[0068] Furthermore, the formula for fine-tuning the POI data of the selected location is:

[0069]

[0070] Among them, F(p) is the fine screening range of POI data of location p, Indicates the actual location of the selected location p and the POI in the rough screening range P(p) is the distance between the sites (in kilometers), and r is the site radius (in kilometers).

[0071] Figure 2 This is an example diagram of a double-layer screening mechanism for POI data provided in an embodiment of the present application, such as Figure 2 As shown in the figure, based on the double-layer screening mechanism of POI data and the site selection evaluation index formula, the process of calculating the site selection evaluation index includes:

[0072] POI data rough screening process: for the selected location, determine the corresponding POI data rough screening range; based on the current screening range conditions, filter out various types of POI data that meet the conditions from all POI data to obtain the roughly screened POI data.

[0073] POI data fine screening and site selection evaluation index calculation process: For the site selection, determine the corresponding POI data fine screening range; based on the current screening range conditions, filter out various types of POI data within the site selection radius from the roughly screened POI data, and calculate the various site selection evaluation index values ​​of the site selection in combination with the site selection evaluation index formula.

[0074] S104. Optimize the site selection evaluation index value by using a particle swarm optimization algorithm to determine the site selection evaluation standardization parameter.

[0075] In one embodiment of the present application, based on the different basic formulas for site selection evaluation indicators, multiple single-objective optimization problems are determined, as well as the objective function corresponding to each single-objective optimization problem. A multi-objective optimization problem is determined for determining a comprehensive normalization parameter; wherein the objective function corresponding to the multi-objective optimization problem is a linear weighted summation of the standard values ​​of each site selection evaluation indicator, and the maximum value of the objective function is used as the comprehensive normalization parameter. A particle swarm optimization algorithm is used to solve the multiple single-objective optimization problems and the multi-objective problem to determine the indicator normalization parameter and the comprehensive normalization parameter.

[0076] Specifically, the properties, dimensions, magnitudes, and other characteristics of the various indicators in the site selection evaluation index system of the embodiment of the present application usually have certain differences. In order to eliminate the influence of dimensions, unify the evaluation calculation standards, and ensure the consistency of the indicator properties and the reliability of the evaluation results, it is necessary to standardize the site selection evaluation index values ​​during the site selection evaluation process. The standardization process depends on the site selection evaluation standardization parameters, which can be divided into indicator standardization parameters and comprehensive standardization parameters according to different usage methods. The indicator standardization parameters are used for the standardization of site selection evaluation indicator data, and the comprehensive standardization parameters are used for the standardization of the weighted comprehensive data of the site selection evaluation indicators. The two will be determined by solving specific optimization problems.

[0077] Furthermore, based on the site selection evaluation index formula, a single-objective optimization problem for determining the index standardization parameter is constructed. The objective function is a single evaluation index formula, and the maximum value of the objective function is used as the index standardization parameter of the corresponding site selection evaluation index. According to the different site selection evaluation index formulas, the following optimization problems are determined: The objective function of the first single-objective optimization problem is the formula of the service object scale, that is, the optimization problem is: max p∈P {s1(p); The objective function of the second single-objective optimization problem is the formula for the active POI scale, that is, the optimization problem is: max p∈P{s2(p); The objective function of the third single-objective optimization problem is the formula for the service supply scale, that is, the optimization problem is: max p∈P {s3(p); The objective function of the fourth single-objective optimization problem is the formula of negative POI scale, that is, the optimization problem is: max p∈P {s4(p).

[0078] Furthermore, based on the index standardization parameters, a multi-objective optimization problem is constructed to determine the comprehensive standardization parameters. The objective function is the linear weighted summation of the standard values ​​of each site selection evaluation index, and the maximum value of the objective function is used as the comprehensive standardization parameter. The multi-objective optimization problem is:

[0079]

[0080] Among them, s i (p) is the index value of the site selection evaluation index, w i For s i The indicator weight of (p), For s i (p) Corresponding indicator standardization parameters.

[0081] In one embodiment of the present application, the position search range is used as the search space for feasible solutions to the single-objective optimization problem, the speed limit range is used as the particle speed constraint range, and the particle swarm optimization algorithm is used to solve the multiple single-objective optimization problems respectively to obtain the optimal objective function values ​​corresponding to the multiple single-objective optimization problems. The optimization solution process is repeated until the number of times reaches the preset value of the optimization number, and based on each of the single-objective optimization problems, the maximum value of the optimal objective function value is used as the corresponding index standardization parameter. The position search range is used as the search space for feasible solutions to the multi-objective optimization problem, the speed limit range is used as the particle speed constraint range, and the particle swarm optimization algorithm is used to solve the multi-objective optimization problem to obtain the optimal objective function value. The optimization solution process is repeated until the number of times reaches the preset value of the optimization number, and the maximum value of the optimal objective function value is used as the comprehensive standardization parameter.

[0082] Specifically, the location search range is:

[0083] P={(p lng ,p lat )|α min ≤p lng ≤α max ,β min ≤p lat ≤β max};

[0084] Among them, α min is the minimum longitude corresponding to the site selection area; α maxis the maximum longitude corresponding to the site selection area; β min is the minimum latitude corresponding to the site selection area; β max is the maximum latitude corresponding to the site selection area; p lng is longitude; p lat is the latitude; P is the search range corresponding to the site selection area;

[0085] The speed limit range is:

[0086] V={(v lng ,v lat )|-α≤v lng ≤α,-β≤v lat ≤β};

[0087] Among them, α represents the longitude difference per kilometer of the site selection area; β represents the latitude difference per kilometer of the site selection area; v lng is the speed limit value in the longitude direction; v lat is the speed limit value in the latitude direction; V is the speed limit range.

[0088] Furthermore, in conjunction with the particle swarm optimization algorithm, the process of calculating the standardized parameters for site selection evaluation includes the following steps: Indicator standardized parameter calculation process: In conjunction with the site selection evaluation indicator calculation process, the location search range is used as the search space for feasible solutions to the optimization problem, and the speed limit range is used as the particle speed constraint range. The particle swarm optimization algorithm is used to solve the first single-objective optimization problem, the second single-objective optimization problem, the third single-objective optimization problem, and the fourth single-objective optimization problem, respectively, to obtain the optimal objective function value of the corresponding optimization problem. The optimization solution process is repeated until the number of optimizations reaches a preset number. For each optimization problem, the maximum value of the optimal objective function value obtained from multiple optimization solution processes is used as the corresponding indicator standardized parameter.

[0089] Furthermore, the comprehensive standardized parameter calculation process combines the site selection evaluation index calculation process and the index standardized parameters. Using the location search range as the search space for feasible solutions to the optimization problem and the speed limit range as the particle speed constraint range, the particle swarm optimization algorithm is used to solve the multi-objective optimization problem and obtain the optimal objective function value. The optimization process is repeated until the number of optimizations reaches the preset number. The maximum value of the optimal objective function obtained from these multiple optimization processes is used as the comprehensive standardized parameter.

[0090] Important parameters of the PSO algorithm include the initial position and velocity of the particle swarm, the maximum number of iterations, the inertia weight, and the learning factor. The values ​​of these parameters and the update strategy during the optimization process affect the effectiveness of the algorithm's optimization solution. Therefore, it is necessary to select the PSO algorithm's parameter update strategy and continuously adjust the parameter values ​​based on the POI data and the optimization problem to be solved.

[0091] The updating strategy of important parameters of the particle swarm optimization algorithm is as follows:

[0092] For particle swarm initialization, the initial position and initial velocity of the particle swarm are initialized using Circle mapping according to the position search range and velocity limit range;

[0093] For the inertia weight, a nonlinear decreasing update strategy is used to update the inertia weight, namely:

[0094]

[0095] Among them, w(k) is the inertia weight of the kth iteration process, w s and w e are the initial and final values ​​of the weight, respectively, and T is the maximum number of iterations;

[0096] For the learning factor, the asynchronous linear decreasing update strategy is used to update the learning factor, that is:

[0097]

[0098] Among them, c1(k) and c2(k) are the individual learning factor and group learning factor of the kth iteration process respectively. and are the initial and final values ​​of the individual factors, and are the initial and final values ​​of the population factor, and T is the maximum number of iterations.

[0099] According to the POI data and the optimization problem to be solved, for the site selection evaluation standardized parameter calculation process, the particle swarm optimization algorithm based on the above parameter updating strategy is implemented, and the value of the maximum number of iterations, the value range of the inertia weight and the learning factor are continuously adjusted to determine the parameters that can achieve better optimization results. These parameters are used as the parameters of the particle swarm optimization algorithm for solving the indicator standardized parameter calculation process and the comprehensive standardized parameter calculation process.

[0100] In addition, affected by the complexity of the problem and the characteristics of the algorithm itself, a single optimization solution process may converge to the local extreme value of the objective function. Therefore, in the process of calculating the index standardization parameters and the comprehensive standardization parameters, the optimization solution process is executed multiple times to determine the final calculation result.

[0101] S105: Implementing site selection evaluation based on the site selection evaluation index value, the site selection evaluation standardization parameter, and the evaluation comprehensive scoring function.

[0102] In one embodiment of the present application, a comprehensive scoring function for site selection evaluation is constructed based on the site selection evaluation index values, index weights, comprehensive normalized parameters, and index normalized parameters of the site selection site. The comprehensive scoring function is used to obtain a comprehensive site selection evaluation score for the site selection site, thereby implementing site selection evaluation.

[0103] Specifically, the comprehensive score of site selection evaluation is the ratio of the weighted average of the standard values ​​of each site selection evaluation indicator and the comprehensive standardized parameter. It is used to quantify the attractiveness of all POI data within the site selection radius to the site selection facilities, thereby reflecting the advantages and disadvantages of the site selection.

[0104] Based on the site selection evaluation index values ​​and site selection evaluation standardization parameters, the process of calculating the comprehensive site selection evaluation score is as follows: for the site selection site, obtain the values ​​of each site selection evaluation index calculated by the site selection evaluation index calculation process, and then combine the index standardization parameters and comprehensive standardization parameters, according to the site selection evaluation comprehensive score formula, calculate the comprehensive site selection evaluation score of the site selection site.

[0105] Based on the site selection evaluation index values ​​and the site selection evaluation standardized parameters, the site selection evaluation comprehensive scoring formula is constructed as follows:

[0106]

[0107] Among them, s(p) is the comprehensive site selection evaluation score of site p, s i (p) is the site selection evaluation index value of site p, w i For s i (p) indicator weight, s max For comprehensive standardization parameters, For s i (p) is the corresponding index normalization parameter, and sgn·) is the sign function.

[0108] Figure 3 This is a structural diagram of a site selection evaluation device provided in an embodiment of the present application, such as Figure 3 As shown, the site selection assessment device includes:

[0109] Data collection and management module: This module is used to collect POI data in a time-triggered manner, mainly including the interface data collection process and POI data management process;

[0110] Parameter calculation and update module: This module is used to update the calculated site selection evaluation standardization parameters in a time-triggered manner based on POI data. It mainly includes the site selection evaluation index calculation process, the index standardization parameter calculation process, and the comprehensive standardization parameter calculation process;

[0111] Site selection evaluation module: This module is used to evaluate the site selection based on POI data and standardized site selection evaluation parameters. It mainly includes the site selection evaluation index calculation process and the site selection evaluation comprehensive score calculation process. Among them, the site selection evaluation index calculation process includes the POI data rough screening process, POI data fine screening and site selection evaluation index calculation process.

[0112] Figure 4 A data collection and management module flow chart provided in the embodiment of this application is as follows: Figure 4 As shown in the figure, based on preset request parameters, the relevant interfaces are regularly called to obtain the required POI data of various types and stored in the database. Based on other relevant database data, a stored procedure built with specific data processing logic is used to regularly supplement, improve, classify and aggregate the POI data collected by the interfaces, generating various types of POI data. These types of POI data include demand POI data, active POI data, supply POI data, and passive POI data.

[0113] Figure 5 A parameter calculation and update module flow chart provided in an embodiment of the present application is as follows: Figure 5 As shown, the acquired demand POI data, active POI data, supply POI data, and passive POI data are used to calculate the site selection evaluation index. The index standardization parameters are calculated based on the particle longitude and latitude coordinates and the site selection evaluation index value. The comprehensive standardization parameters are calculated based on the particle longitude and latitude coordinates, the site selection evaluation index value, and the index standardization parameters. Based on the obtained index standardization parameters and comprehensive standardization parameters, the site selection evaluation standardization parameters are obtained.

[0114] Figure 6 A flowchart of a site selection evaluation module provided in an embodiment of the present application is as follows: Figure 6 As shown, based on the longitude and latitude of the site and the acquired POI data, a rough screening of the POI data is performed, and the data after the rough screening is finely screened. The site selection evaluation index is calculated based on the finely screened data to obtain the site selection evaluation index value. Based on the acquired site selection evaluation index value and the index standardization parameters and comprehensive standardization parameters, a comprehensive site selection evaluation score is calculated to obtain the comprehensive site selection evaluation score.

[0115] Figure 7 This is a schematic diagram of the structure of a site selection evaluation device that integrates POI data provided in an embodiment of the present application. Figure 7As shown, a site selection evaluation device that integrates POI data includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: divide the acquired POI data based on the constructed POI classification system; determine the site selection evaluation index according to the POI classification system, and determine the site selection evaluation index basic formula corresponding to the site selection evaluation index through a preset analysis algorithm; perform double-layer screening on the divided POI data based on a preset screening range condition, and determine the site selection evaluation index value based on the screened POI data and the site selection evaluation index basic formula; optimize the site selection evaluation index value through a particle swarm optimization algorithm to determine the site selection evaluation standardization parameter; and realize site selection evaluation based on the site selection evaluation index value, the site selection evaluation standardization parameter and the site selection evaluation comprehensive scoring function.

[0116] A non-volatile computer storage medium provided in an embodiment of the present application stores computer-executable instructions, wherein the computer-executable instructions are configured to: divide the acquired POI data based on a constructed POI classification system; determine a site selection evaluation index according to the POI classification system, and determine a site selection evaluation index basic formula corresponding to the site selection evaluation index through a preset analysis algorithm; perform a double-layer screening on the divided POI data based on a preset screening range condition, and determine a site selection evaluation index value based on the screened POI data and the site selection evaluation index basic formula; optimize the site selection evaluation index value through a particle swarm optimization algorithm to determine a site selection evaluation standardization parameter; and implement a site selection evaluation based on the site selection evaluation index value, the site selection evaluation standardization parameter, and the site selection evaluation comprehensive scoring function.

[0117] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0118] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. It will be apparent to those skilled in the art that various modifications and variations may be made to the embodiments of the present application. However, such modifications or substitutions do not deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A site selection evaluation method integrating POI data, characterized in that: The method comprises: Based on the constructed POI classification system, the acquired POI data is divided, specifically, according to the supply and demand relationship and the influence effect, the POI classification system is constructed; wherein the POI classification system includes demand POI, positive POI, supply POI and negative POI; Determine a site selection evaluation index according to the POI classification system, and determine a site selection evaluation index basic formula corresponding to the site selection evaluation index through a preset analysis algorithm; Based on the preset screening range conditions, the divided POI data is subjected to double-layer screening, and the site selection evaluation index value is determined based on the screened POI data and the site selection evaluation index basic formula; Optimizing the site selection evaluation index value by a particle swarm optimization algorithm to determine the site selection evaluation standardization parameter; Implementing a site selection evaluation based on the site selection evaluation index value, the site selection evaluation standardization parameter, and the site selection evaluation comprehensive scoring function; The double-layer screening of the divided POI data based on the preset screening range conditions specifically includes: Based on the longitude, latitude, site radius, longitude difference and latitude difference of the preset distance unit corresponding to the site selection, a rough screening range of POI data is constructed; Constructing a fine screening range of POI data based on the distance between the site selection location and the actual location of the POI in the rough screening range of POI data, and the site selection radius; Based on the rough screening range of the POI data, roughly screening the divided POI data; Based on the fine screening range of the POI data, fine screening is performed on the roughly screened POI data; The optimization of the site selection evaluation index value by the particle swarm optimization algorithm to determine the site selection evaluation standardization parameter specifically includes: Determining a plurality of single-objective optimization problems and an objective function corresponding to each of the single-objective optimization problems according to the different basic formulas of the site selection evaluation indicators; Determining a multi-objective optimization problem for determining a comprehensive standardized parameter; wherein the objective function corresponding to the multi-objective optimization problem is a linear weighted summation formula of the standard values ​​of each site selection evaluation index, and the maximum value of the objective function is used as the comprehensive standardized parameter; Solving the plurality of single-objective optimization problems and the multi-objective optimization problems using a particle swarm optimization algorithm to determine an index standardization parameter and a comprehensive standardization parameter; The particle swarm optimization algorithm is used to solve the plurality of single-objective optimization problems and the multi-objective optimization problems to determine the index standardization parameters and the comprehensive standardization parameters, specifically including: Using the position search range as the search space for feasible solutions to the single-objective optimization problem and the speed limit range as the particle speed constraint range, a particle swarm optimization algorithm is used to solve the multiple single-objective optimization problems respectively to obtain the optimal objective function values ​​corresponding to the multiple single-objective optimization problems respectively; Repeat the optimization process until the number of optimization times reaches a preset number, and based on each of the single-objective optimization problems, use the maximum value of the optimal objective function as the corresponding indicator normalization parameter; The position search range is used as the search space for feasible solutions of the multi-objective optimization problem, the speed limit range is used as the particle speed constraint range, and the particle swarm optimization algorithm is used to solve the multi-objective optimization problem to obtain the optimal objective function value; Repeat the optimization process until the number of optimization times reaches a preset value, and use the maximum value of the optimal objective function as the comprehensive normalization parameter; The position search range is used as the search space for the feasible solution of the single-objective optimization problem, and the speed limit range is used as the particle speed constraint range, specifically including: The location search range is: ； in, is the minimum longitude corresponding to the site selection area; is the maximum longitude corresponding to the site selection area; Minimum latitude; is the maximum latitude corresponding to the site selection area; is the longitude; is latitude; P The search scope corresponding to the site selection area; The speed limit range is: ； in, It represents the longitude difference per kilometer in the site selection area; It represents the latitude difference per kilometer in the site selection area; is the speed limit value in the longitude direction; is the speed limit value in the latitude direction; The speed limit range; The implementing of the site selection evaluation based on the site selection evaluation index value, the site selection evaluation standardization parameter and the site selection evaluation comprehensive scoring function specifically includes: Constructing the site selection evaluation comprehensive scoring function based on the site selection evaluation index value, index weight, comprehensive standardized parameter and index standardized parameter of the site selection location; The comprehensive scoring formula for the site selection evaluation is: ； in, For site selection Comprehensive score of site selection assessment, For site selection Site selection evaluation index value, for The indicator weight, For comprehensive standardization parameters, for The corresponding indicator standardization parameters, is a symbolic function; the basic formula of the indicator corresponding to the demand POI is the formula of the service object scale, which is recorded as The basic formula for the indicator corresponding to the active POI is the formula for the scale of the active POI, which is recorded as The basic formula for the indicator corresponding to the supply POI is the formula for the scale of service supply, which is recorded as The basic formula for the negative POI indicator is the negative POI scale formula, which is expressed as ; The comprehensive scoring formula for site selection evaluation is used to obtain the comprehensive scoring formula for site selection evaluation, thereby achieving site selection evaluation.

2. A site selection evaluation method integrating POI data according to claim 1, characterized in that: The constructed POI classification system divides the acquired POI data into categories, and further includes: Based on the preset request parameters, the relevant interfaces are called regularly to obtain the required POI data and store them in the database; Based on the POI classification system, the POI data in the database is divided and stored.

3. The site selection evaluation method for integrating POI data according to claim 1, characterized in that: The preset analysis algorithm is used to determine the basic formula of the site selection evaluation index corresponding to the site selection evaluation index, specifically including: Based on kernel density analysis, the basic function of the indicator is determined according to the attribute data corresponding to the indicator-related POI and the distance between it and the site. Based on the basic function of the indicator, the POI data related to the site selection evaluation indicator is substituted to obtain the corresponding basic formula of the site selection evaluation indicator; Determining the weights of the site selection evaluation indicators based on a hierarchical analysis algorithm; Use the indicator pairwise comparison method to construct the judgment matrix; Normalizing the column vectors of the judgment matrix, determining the row sum of the matrix obtained by normalizing the column vectors, and performing normalization again to obtain the weight of the site selection evaluation index; wherein the site selection evaluation index is related to the POI classification system; A consistency ratio is determined based on the weights, and a consistency test is performed on the judgment matrix based on the consistency ratio. If the test passes, the weights determined multiple times are averaged to obtain a final weight.

4. A site selection evaluation device integrating POI data, characterized in that: The device comprises a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute the method according to any one of claims 1 to 3.

5. A non-volatile computer storage medium storing computer-executable instructions, characterized in that: The computer-executable instructions can execute the method according to any one of claims 1 to 3.

Images