Service station planning and layout method, device, computer readable medium, and program product

Abstract

The application relates to a service station planning and layout method and device, a computer readable medium and a program product, and applies to the technical field of computers. The method comprises the following steps: acquiring environment resource data and historical service information matched with a target area; analyzing the historical service information, predicting employee service data and business demand data matched with the target area, and consumption resource data and service income resource data corresponding to the employee service data; predicting a consumption resource return period matched with the consumption resource data based on the environment resource data, the consumption resource data and the service income resource data; and determining the number of service station layouts of the target area in the case of the shortest consumption resource return period based on a first constraint condition, a second constraint condition and a third constraint condition. The method can improve the accuracy of service station planning and layout.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a service station planning and layout method, apparatus, computer-readable medium, and program product. Background Technology

[0002] Auto service stations provide a range of services for vehicles, including charging, repair, maintenance, and inspection. As the number of cars continues to increase, the demand for these service stations is also growing. Taking electric vehicles as an example, auto service stations that can provide services for electric vehicles can address the various needs of electric vehicle owners during use, improving the user experience and convenience.

[0003] In order to provide better services, there is a need to plan and lay out new service stations. However, when planning and laying out new service stations, it is easy to cause a mismatch between the number of service stations determined and the overall situation of the planned area, resulting in low accuracy in service station planning. Summary of the Invention

[0004] Therefore, it is necessary to provide a service station planning and layout method, apparatus, computer-readable medium, and program product that can improve the accuracy of service station planning and layout in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a service station planning and layout method. The method includes: acquiring environmental resource data and historical service information matching a target area; analyzing the historical service information to predict employee service data and business demand data matching the target area, as well as resource consumption data and service-generated resource data corresponding to the employee service data; predicting a resource consumption return cycle matching the resource consumption data based on the environmental resource data, the resource consumption data, and the service-generated resource data; and determining the number of service stations to be laid out in the target area under the condition of the shortest resource consumption return cycle based on a first constraint, a second constraint, and a third constraint; wherein the first constraint represents the constraint corresponding to the employee service data, the second constraint represents the constraint corresponding to the resource consumption return cycle, and the third constraint represents the constraint between the business demand data, the employee service data, and the number of service stations to be laid out.

[0006] In the above embodiments, by combining historical service information prediction with target area matching data, the predicted data is matched with the actual construction of service stations in the target area. From the perspective of resource consumption return cycle, the feasibility of resource recovery of resources consumed when establishing service stations in the target area can be evaluated. Thus, when determining the number of service stations based on various constraints, the determined number of service stations matches the environmental conditions of the target area, the actual construction situation, and the employee service situation, which can improve the accuracy of service station planning and layout.

[0007] In one embodiment, analyzing the historical service information to predict employee service data matching the target area, as well as resource consumption data and service-obtained resource data corresponding to the employee service data, includes: analyzing the historical service information to obtain service type structure data and mileage data; predicting employee service data matching the target area based on the service type structure data, employee layout data and service station business data corresponding to the target area; and predicting resource consumption data and service-obtained resource data corresponding to the target area based on the employee service data, the mileage data, and the service type structure data.

[0008] In the above embodiments, by predicting resource consumption data and service revenue data from the perspective of service type structure data and mileage data, the planning accuracy can be improved when planning the layout of service stations based on the analysis of the service scope and maintenance status of service objects.

[0009] In one embodiment, the employee service data includes employee participation rate; the service type structure data includes repair ratio and average repair hours corresponding to different fault types; the employee layout data includes the number of service station employees, maximum employee working hours, and preset employee working days; and the service station business data includes the number of service station services. Predicting employee service data matching the target area based on the service type structure data, the employee layout data corresponding to the target area, and the service station business data includes: for each fault type, calculating a first product between the number of service station services, the repair ratio, and the average repair hours; calculating a second product between the number of service station services, the maximum employee working hours, and the preset employee working days; and determining the ratio between the first statistical result of the first product corresponding to different fault types and the second product as the predicted employee participation rate matching the target area.

[0010] In the above embodiments, by considering the fault type, the accuracy of the predicted employee participation rate can be improved when predicting the employee participation rate based on the statistical results between different fault types, and the accuracy of service station planning and layout can be further improved.

[0011] In one embodiment, the resource consumption data includes resources consumed during outings and resources consumed by employees; the service-earned resource data includes resources earned from repairs, resources earned from spare parts sales, and resources for outing service subsidies; the mileage data includes the average outing mileage corresponding to the regional level of the target area, mileage subsidy resources, energy consumption corresponding to the average outing mileage, and energy unit price; the outing-consumed resources include the product of the number of service station services, the average outing mileage, the energy consumption, the energy unit price, and the employee participation rate; the employee-consumed resources include the sum of a first reference value and a second reference value, the first reference value including the product of the number of service station employees and the preset resources earned by employees and a set value, and the second reference value including the... The first statistical result, the preset service hour resources, the preset service ratio, and the employee participation rate are multiplied together; the repair-derived resources include the product of the first statistical result, the preset service hour resources, and the employee participation rate; the spare parts sales-derived resources include the product of the second statistical result and the employee participation rate, wherein the second statistical result is obtained by calculating the third product corresponding to different fault types after calculating the third product of the number of service station business services, the repair ratio, the average repair hours, and the spare parts service resources corresponding to the fault type for each fault type; the off-site service subsidy resources include the product of the number of service station business services, the average off-site mileage, the mileage subsidy resources, and the employee participation rate.

[0012] In the above embodiments, by determining different types of consumed resources and obtained resources, the accuracy of the predicted resource return cycle can be improved when predicting the resource return cycle based on the determined consumed resources and obtained resources.

[0013] In one embodiment, the business demand data includes the required repair time, and the method further includes: predicting the total number of target batteries in the target area based on the historical retention of the target batteries and the future sales volume of the target batteries in the target area; and predicting the required repair time corresponding to the target area based on the total number of target batteries and the first statistical result.

[0014] In the above embodiments, the required maintenance time is predicted based on historical retention and total inventory. The predicted required maintenance time is compared with the maintenance situation in the target area, thus improving the accuracy of the predicted required maintenance time.

[0015] In one embodiment, the employee service data includes employee participation rate, and the business demand data includes demand repair duration; the first constraint includes: the employee participation rate is less than or equal to a participation rate threshold; the second constraint includes: the resource consumption return cycle is within a set cycle range; the third constraint includes: the product of the number of service station employees, preset working days, maximum employee working hours, number of service station layouts, and employee participation rate corresponding to the target area is greater than or equal to a first target value and less than or equal to a second target value; the first target value and the second target value are determined based on the demand repair duration.

[0016] In the above embodiments, by designing different constraints, the factors considered when determining the number of service stations based on the constraints are greater, thus improving the accuracy of the determined number of service stations.

[0017] In one embodiment, the method further includes: determining the location of a service station in the target area that matches the number of service station layouts, based on the number of service station layouts matching the target area and the locations of the already deployed service stations in the target area.

[0018] In the above embodiments, by determining the location of service stations in conjunction with the location of service stations in the target area, competition between service stations can be avoided and the risk of resource consumption can be reduced.

[0019] In one embodiment, determining the service station layout location in the target area that matches the number of service station layouts based on the number of service station layouts matching the target area and the locations of already deployed service stations in the target area includes: determining a service station layout scheme, the service station layout scheme including candidate layout locations that match the number of service station layouts; and determining the service station layout location in the target area that matches the number of service station layouts based on the number of service station layout schemes and the locations of already deployed service stations in the target area.

[0020] In the above embodiments, by determining the service station layout scheme, the service station layout location in the target area can be determined by different implementation methods based on the number of service station layout schemes, thereby improving the practicality of service station planning and layout.

[0021] In one embodiment, the number of service station layout schemes is one; determining the service station layout location in the target area that matches the number of service station layouts based on the number of service station layout schemes and the locations of already deployed service stations in the target area includes: for each candidate layout location in the service station layout schemes, taking the candidate layout location as the center, and provided that the distance between any two locations is within a preset range, updating the candidate layout location based on the clustering results of the service object locations in the target area to obtain a first target layout location; wherein, the distance between any two locations includes the distance between the candidate layout location and the already deployed service station locations, and the distance between different candidate layout locations; and determining the first target layout location corresponding to each candidate layout location as the service station layout location in the target area that matches the number of service station layouts.

[0022] In the above embodiments, when there is only one service station layout scheme, the efficiency of planning and layout can be improved by determining the service station layout location through clustering.

[0023] In one embodiment, the number of service station layout schemes is multiple. Determining the service station layout location in the target area that matches the number of service station layouts based on the number of service station layout schemes and the locations of already deployed service stations within the target area includes: for each candidate layout location in each service station layout scheme, taking the candidate layout location as the center, and provided that the distance between any two locations is within a preset range, updating the candidate layout location based on the clustering results of the service object locations in the target area to obtain a second target layout location; wherein, the distance between any two locations includes the distance between the candidate layout location and the already deployed service station locations, and the distance between different candidate layout locations; for each second target layout location, taking the second target layout location as the center, calculating the service object weights within the service radius corresponding to the second target layout location, and obtaining the target statistical results between the service object weights corresponding to each candidate layout location in each service station layout scheme; and determining the candidate layout location in the service station layout scheme corresponding to the service station layout with the largest target statistical result among the target statistical results of multiple service station layout schemes as the service station layout location in the target area that matches the number of service station layouts.

[0024] In the above embodiments, by determining the candidate layout locations in the service station layout scheme corresponding to the maximum target statistical result as the service station layout locations in the target area that are consistent with the number of service station layouts, when a service station is established at the service station layout location, the service station can provide business services to more service objects, thereby improving the accuracy of service station planning and layout.

[0025] Secondly, this application provides a service station planning and layout apparatus, the apparatus comprising: an acquisition module for acquiring environmental resource data and historical service information matching a target area; a first prediction module for analyzing the historical service information to predict employee service data and business demand data matching the target area, as well as resource consumption data and service-generated resource data corresponding to the employee service data; a second prediction module for predicting a resource consumption return cycle matching the resource consumption data based on the environmental resource data, the resource consumption data, and the service-generated resource data; and a determination module for determining the number of service stations to be laid out in the target area under the condition of the shortest resource consumption return cycle based on a first constraint, a second constraint, and a third constraint; wherein the first constraint represents the constraint corresponding to the employee service data, the second constraint represents the constraint corresponding to the resource consumption return cycle, and the third constraint represents the constraint between the business demand data, the employee service data, and the number of service stations to be laid out.

[0026] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program, performs the following steps: acquiring environmental resource data and historical service information matching a target area; analyzing the historical service information to predict employee service data and business demand data matching the target area, as well as resource consumption data and service-obtained resource data corresponding to the employee service data; predicting the resource consumption return cycle matching the resource consumption data based on the environmental resource data, the resource consumption data, and the service-obtained resource data; and determining the number of service stations to be deployed in the target area under the condition of the shortest resource consumption return cycle based on a first constraint, a second constraint, and a third constraint; wherein the first constraint represents the constraint corresponding to the employee service data, the second constraint represents the constraint corresponding to the resource consumption return cycle, and the third constraint represents the constraint between the business demand data, the employee service data, and the number of service stations.

[0027] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps: acquiring environmental resource data and historical service information matching a target area; analyzing the historical service information to predict employee service data and business demand data matching the target area, as well as resource consumption data and service-obtained resource data corresponding to the employee service data; predicting a resource consumption return cycle matching the resource consumption data based on the environmental resource data, the resource consumption data, and the service-obtained resource data; and determining the number of service stations to be deployed in the target area under the condition of the shortest resource consumption return cycle based on a first constraint, a second constraint, and a third constraint; wherein the first constraint represents the constraint corresponding to the employee service data, the second constraint represents the constraint corresponding to the resource consumption return cycle, and the third constraint represents the constraint between the business demand data, the employee service data, and the number of service stations.

[0028] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps: acquiring environmental resource data and historical service information matching a target area; analyzing the historical service information to predict employee service data and business demand data matching the target area, as well as resource consumption data and service-generated resource data corresponding to the employee service data; predicting a resource consumption return cycle matching the resource consumption data based on the environmental resource data, the resource consumption data, and the service-generated resource data; and determining the number of service stations to be deployed in the target area under the condition of the shortest resource consumption return cycle based on a first constraint, a second constraint, and a third constraint; wherein the first constraint represents the constraint corresponding to the employee service data, the second constraint represents the constraint corresponding to the resource consumption return cycle, and the third constraint represents the constraint between the business demand data, the employee service data, and the number of service stations.

[0029] The aforementioned service station planning and layout methods, devices, computer-readable media, and program products acquire environmental resource data and historical service information matching the target area, analyze the historical service information, and predict employee service data and business demand data matching the target area, as well as resource consumption data and service revenue data corresponding to the employee service data. By combining historical service information with the predicted data matching the target area, the predicted data matches the actual construction of service stations in the target area. Furthermore, based on environmental resource data, resource consumption data, and service revenue data, the resource consumption return cycle matching the resource consumption data is predicted. This allows for an assessment of the feasibility of resource recovery when establishing service stations in the target area from the perspective of the resource consumption return cycle. Finally, based on the constraints corresponding to employee service data and resource consumption return cycles, as well as the constraints between business demand data, employee service data, and the number of service stations, the number of service stations in the target area with the shortest resource consumption return cycle is determined. The determined number of service stations matches the environmental conditions, actual construction conditions, and employee service conditions of the target area, thus improving the accuracy of service station planning and layout. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is an application environment diagram of the service station planning and layout method in one embodiment;

[0032] Figure 2 This is an application environment diagram of the service station planning and layout method in one embodiment;

[0033] Figure 3 This is a flowchart illustrating a process for analyzing historical service information to predict employee service data matching a target area, as well as resource consumption data and service obtained resource data corresponding to the employee service data, in one embodiment.

[0034] Figure 4 This is a flowchart illustrating a process for predicting employee service data that matches a target area based on service type structure data, employee layout data and service station business data corresponding to the target area, in one embodiment.

[0035] Figure 5 This is a schematic diagram illustrating the relationship between the resource consumption return cycle and employee participation rate in one embodiment.

[0036] Figure 6 This is a flowchart illustrating the service station planning and layout method in another embodiment;

[0037] Figure 7 This is a flowchart illustrating a process for determining the location of a service station in a target area that matches the number of service stations in the target area, based on the number of service stations that match the target area and the locations of the already deployed service stations in the target area, in one embodiment.

[0038] Figure 8 This is a flowchart illustrating the process of determining the location of a service station in a target area that matches the number of service stations, based on the number of service station layout schemes and the locations of already deployed service stations in the target area, in one embodiment.

[0039] Figure 9 This is a flowchart illustrating the process of determining the location of a service station in a target area that matches the number of service station layouts, based on the number of service station layout schemes and the locations of already laid-out service stations within the target area, in another embodiment.

[0040] Figure 10 This is a flowchart illustrating the service station planning and layout method in another embodiment;

[0041] Figure 11 This is a structural block diagram of a service station planning and layout device in one embodiment;

[0042] Figure 12 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0045] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0046] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0047] Typically, when planning the layout of service stations in a certain area, taking a car service station that provides services for electric vehicles as an example, the area can be divided into multiple grid areas with a predetermined number of service stations. The charging and swapping needs of electric vehicles in each grid area are determined, and the grid areas are used as sample points. The objective function is determined based on the total distance from all sample points to the nearest service station. The constraints corresponding to the objective function are determined based on the shortest distance between different grid areas and whether the establishment of service stations in a grid area can cover other grid areas. Thus, under the constraints, when the objective function is minimized, the final site selection planning scheme can be determined.

[0048] However, the above-mentioned service station planning and layout scheme is based on the pre-set number of service stations. The number of service stations determined does not match the overall regional situation of the planned area, resulting in low accuracy in service station planning. There may be too many or too few service stations, which leads to low accuracy in service station planning and layout.

[0049] In light of this, research has shown that planning and layout can be carried out by considering the overall regional conditions of the planned area. For example, by taking into account the environmental and construction resources incurred in establishing service stations, the period for the resources incurred to recoup their costs after the service stations are established can be predicted, thereby determining the number of service stations to be established to match the planned area. For example, the resources incurred in establishing service stations can be reflected in aspects such as staff, land rent, and utility costs.

[0050] In view of this, this application provides a service station planning and layout method that can be applied to Figure 1The planning and layout system 102 shown communicates with the environmental resource management system 104 and the business management system 106 via a network. The planning and layout system 102 obtains environmental resource data matching the target area from the environmental resource management system 104 and historical service information matching the target area from the business management system 106. By analyzing the historical service information, it predicts employee service data and business demand data matching the target area, as well as resource consumption data and service-obtained resource data corresponding to the employee service data. Based on the environmental resource data, resource consumption data, and service-obtained resource data, it predicts the resource consumption return cycle matching the resource consumption data. Then, based on the first constraint, the second constraint, and the third constraint, it determines the number of service stations to be laid out in the target area with the shortest resource consumption return cycle. The first constraint represents the constraint corresponding to the employee service data, the second constraint represents the constraint corresponding to the resource consumption return cycle, and the third constraint represents the constraint between the business demand data, the employee service data, and the number of service stations to be laid out.

[0051] The environmental resource management system 104 can refer to a system that stores environmental resource data for various regions. For example, after collecting environmental resource data from various regions, the collected data can be stored in the environmental resource management system 104. The environmental resource management system may also include systems that provide environmental resource data for each region, so that the planning and layout system 102 can obtain the corresponding environmental resource data from the systems corresponding to different regions.

[0052] The business management system 106 can refer to a system that stores service information of existing service stations in different regions, or it can include a system that provides service information of existing service stations in each region. Thus, the planning and layout system 102 can obtain the corresponding service information from the system corresponding to different regions.

[0053] In one embodiment, such as Figure 2 As shown, this application provides a service station planning and layout method. Taking the application of this method to a service station planning and layout system 102 as an example, the method includes the following steps:

[0054] S202, Obtain environmental resource data and historical service information that match the target area.

[0055] The target area refers to the area where the service station to be built will be located, excluding areas where station construction is prohibited. In some cases, the target area may include one or more cities; in others, it may include one, more, or all areas of one or more cities; and still others, it may include at least one preset area from one or more cities.

[0056] Environmental resource data refers to data related to the geographical environment of the target area, including but not limited to: land rent, water and electricity costs, and environmental quality. Historical service information refers to information related to the staff assigned to the service station and the services they provide, including but not limited to: spare parts sales information and maintenance service information.

[0057] In some cases, historical service information can be obtained based on the business information corresponding to existing service stations in the target area. In other cases, for areas with the same regional level as the target area and adjacent to it, historical service information can be obtained based on the business information corresponding to existing service stations in that area.

[0058] The business service information can also be obtained through other means, and this embodiment does not limit it. The regional level can be determined based on the wage level within the region, or it can be determined through other means, and this embodiment does not limit it.

[0059] S204 analyzes historical service information to predict employee service data and business demand data that match the target area, as well as resource consumption data and service revenue data corresponding to the employee service data.

[0060] In this embodiment, employee service data refers to data related to the service status of employees configured in the service station when a service station is established in the target area, including but not limited to: average employee service capacity, average employee service hours, etc.

[0061] Among them, the average service capacity of employees is used to measure the overall level and efficiency of all employees in a single service station in providing services. The higher the average service capacity of employees, the more resources the service station will obtain when providing business services. The average service hours of employees are used to measure the average service time of all employees in a single service station within a preset period. The preset period may include a week, a month, or a year. In this embodiment, the preset period may be a year.

[0062] Business demand data refers to the demand data related to the business services provided by the service station when establishing a service station in the target area. It is used to characterize the business demands that the service station can meet, including but not limited to: maintenance demand data, spare parts sales demand data, etc.

[0063] Among them, maintenance demand data refers to the maintenance needs that the service station can meet, such as the total maintenance man-hours and the number of service recipients within a preset period, where service recipients represent customers. Spare parts sales demand data refers to the spare parts sales needs that the service station can meet, such as the number of spare parts sold and the total amount of spare parts sold within a preset period.

[0064] Resource consumption data refers to the resource data consumed when establishing a service station in the target area, including but not limited to: employee wage subsidies and employee wages, where employee wage subsidies and employee wages respectively represent the total wage subsidies and total wages of all employees within a preset period. Service-generated resource data refers to the resource data obtained when the service station provides business services in the target area, including but not limited to: spare parts sales revenue, fault inspection fees, repair consultation fees, charging fees, etc. The above fees are considered from the perspective of all employees and represent the service-generated resources within the preset period.

[0065] In some embodiments, employee service data may include average employee service hours, business demand data may include spare parts sales volume, resource consumption data may include employee salary subsidies, and service-generated resource data may include fees received from spare parts sales. Depending on whether service stations are already deployed in the target area, different methods can be used to predict employee service data and business demand data matching the target area, as well as the corresponding resource consumption and service-generated resource data.

[0066] In some cases, where service stations have already been deployed in the target area, the historical service information corresponding to the deployed service stations can be used to obtain the total service hours of employees, total sales volume of spare parts, total sales amount of spare parts, and total subsidies of employees for the deployed service stations within a preset period.

[0067] Furthermore, the product of the determined total service hours of employees and the time coefficient corresponding to the preset period is determined as the predicted average service hours of employees matching the target area.

[0068] The product of the determined total sales volume of spare parts and the sales quantity coefficient corresponding to the preset period is used to determine the predicted sales volume of spare parts matching the target area.

[0069] The product of the total sales amount of spare parts and the sales amount coefficient corresponding to the preset period is determined as the predicted spare parts sales revenue that matches the target area.

[0070] The product of the total employee subsidy and the subsidy coefficient corresponding to the preset period is determined as the predicted employee wage subsidy that matches the target area.

[0071] It should be understood that the service needs of existing service stations and service stations to be built in the same target area are roughly the same. Therefore, by analyzing the historical service information of existing service stations and using the analysis results as the prediction results for service stations to be built, the accuracy of service station planning and layout can be improved.

[0072] In some cases, where there are no established service stations in the target area, the preset average service hours per employee, number of spare parts sold, revenue from spare parts sales, and employee wage subsidies will be used. The determined average service hours per employee will be used as the predicted average service hours per employee, number of spare parts sold, revenue from spare parts sales, and employee wage subsidies that match the target area.

[0073] S206, based on environmental resource data, resource consumption data, and service-derived resource data, predicts the resource consumption return cycle that matches the resource consumption data.

[0074] Both environmental resource data and resource consumption data refer to the resources consumed when establishing a single service station in the target area. Both include the resources spent on establishing the service station. The resource consumption payback period refers to the time it takes for the resources spent on establishing a single service station in the target area to recoup their investment. For example, the resources spent may recoup their investment in 1-3 years. In other words, the resource consumption payback period can be determined based on the time it takes for the resources spent on station construction to equal the resources gained from the services provided.

[0075] In some embodiments, environmental resource data includes land rent and water and electricity costs, and resource consumption data includes employee resource consumption. Employee resource consumption refers to the employee-related resource consumption configured when establishing a service station, including but not limited to employee wages. Service-obtained resource data includes fault inspection fees and maintenance consultation fees.

[0076] Specifically, the sum of land rent, water and electricity costs, and employee wages is determined as the first predicted value; the sum of fault inspection costs and maintenance consultation costs, and the difference between the first predicted value and the second predicted value, is determined as the second predicted value; the ratio of the resources invested by the service stations corresponding to the target area to the second predicted value, and the sum of the second predicted value and the preset period adjustment value, is determined as the predicted resource consumption return period that matches the resource consumption data.

[0077] S208. Based on the first constraint, the second constraint, and the third constraint, determine the number of service stations to be deployed in the target area under the condition of the shortest resource consumption return cycle; wherein, the first constraint represents the constraint corresponding to the employee service data, the second constraint represents the constraint corresponding to the resource consumption return cycle, and the third constraint represents the constraint between the business demand data, the employee service data, and the number of service stations to be deployed.

[0078] The number of service stations refers to the number of service stations to be pre-established in the target area.

[0079] In some embodiments, employee service data may include average employee service hours, and business demand data may include spare parts sales volume. Then, the constraints corresponding to the employee service data can be satisfied as follows: average employee service hours are less than or equal to the maximum working hours of employees. The constraints between business demand data, employee service data, and the number of service stations include: the ratio between spare parts sales volume, average spare parts unit price, and resource consumption return cycle is less than or equal to the product of the number of service stations, the preset daily sales volume corresponding to the average employee service hours, the number of service station employees in the target area, and the maximum working hours of employees.

[0080] It is understandable that by taking the shortest resource consumption return cycle as the objective function, and using the constraints corresponding to employee service data and resource consumption return cycles, as well as the constraints between business demand data, employee service data, and the number of service station layouts as the overall constraints, the number of service station layouts to be established in the target area can be obtained.

[0081] based on Figure 2 The method described above acquires environmental resource data and historical service information matching the target area, analyzes the historical service information, and predicts employee service data and business demand data matching the target area, as well as resource consumption data and service revenue data corresponding to the employee service data. By combining historical service information with the predicted data matching the target area, the predicted data matches the actual establishment of service stations in the target area. Furthermore, based on environmental resource data, resource consumption data, and service revenue data, the method predicts the resource consumption return cycle matching the resource consumption data. This allows for an assessment of the feasibility of resource recovery when establishing service stations in the target area from the perspective of the resource consumption return cycle. Finally, based on the constraints corresponding to employee service data and resource consumption return cycles, as well as the constraints between business demand data, employee service data, and the number of service stations, the method determines the number of service stations in the target area with the shortest resource consumption return cycle. The determined number of service stations matches the environmental conditions, actual establishment, and employee service situation of the target area, thus improving the accuracy of service station planning and layout.

[0082] In one embodiment, the analysis of historical service information to predict employee service data matching the target area, as well as the corresponding resource consumption data and service revenue data (i.e., S204) can be implemented as follows: Figure 3 As shown, it includes the following steps:

[0083] S302 analyzes historical service information to obtain service type structure data and mileage data.

[0084] In some embodiments, historical service information can be input into the first analysis model to obtain service type structure data. This service type structure data refers to fault service data related to maintenance operations, including but not limited to: the maintenance ratio and average maintenance hours corresponding to different fault types, and the fault level corresponding to different fault types.

[0085] Fault types include, but are not limited to: engine faults, transformer faults, battery faults, etc. Among them, the fault level corresponding to different fault types can be determined based on the mapping relationship between fault types and fault levels.

[0086] The maintenance ratio refers to the proportion of maintenance times corresponding to a certain type of fault in the total number of maintenance times. In other words, for each fault type, the maintenance ratio can be determined by the ratio of the number of maintenance times of each employee to the total number of faults.

[0087] Maintenance man-hours refer to the total time required to complete the maintenance task corresponding to the fault type. In some cases, for each fault type, the average maintenance man-hours of multiple employees can be used to determine the average maintenance man-hours for each fault type.

[0088] In some embodiments, historical service information can be input into a second analysis model to obtain mileage data. Mileage data refers to data related to the mileage consumed during service trips, including but not limited to: average mileage per trip corresponding to the regional level of the target area, mileage subsidy resources, energy consumption corresponding to the average mileage per trip, energy unit price, and total mileage per trip.

[0089] The total mileage can refer to the total mileage of all employees during their service trips within a preset period, while the average mileage can refer to the average mileage of all employees during their service trips within the preset period. The average mileage is the ratio of the total mileage to the number of employees. Mileage subsidy resources and energy unit price can refer to the resources subsidized per kilometer of mileage and the corresponding energy unit price per kilometer of mileage, respectively.

[0090] S304, based on service type structure data, employee layout data and service station business data corresponding to the target area, predicts employee service data that matches the target area.

[0091] In this embodiment, employee layout data refers to employee-related data configured when establishing a service station in the target area, including but not limited to: the number of service station employees, their ages, and their length of service. Service station business data refers to service object-related data configured when establishing a service station in the target area, including but not limited to: the total number of service objects and the location of each service object.

[0092] In this context, "service recipients" refers to individuals or organizations receiving services from service stations. In some cases, the total number of service recipients can be calculated from the service recipients already served by service stations within the target area. In other cases, the total number of service recipients for the target area can be determined based on the mapping relationship between area level and the number of service recipients, with the number of service recipients corresponding to the area level of the target area being the total number of service recipients for that area.

[0093] In some embodiments, employee service data includes average employee service hours, service type structure data includes fault levels corresponding to different fault types, employee layout data includes the number of service station employees, and service station business data includes the total number of service objects. Therefore, based on the fault levels corresponding to different fault types, the number of service station employees, and the total number of service objects, the average employee service hours matching the target area can be predicted.

[0094] Specifically, from the fault levels corresponding to multiple fault types, the highest fault level is determined as the target fault level; the average service hours between the target fault level and the total number of service objects is determined; and the ratio of the average service hours to the number of service station employees is determined as the predicted average service hours per employee matching the target area.

[0095] Among them, the service hours corresponding to the target fault level can be obtained based on the mapping relationship between fault level and service hours, and the service hours corresponding to the total number of service objects can be obtained based on the mapping relationship between the total number of service objects and service hours.

[0096] S306, based on employee service data, mileage data, and service type structure data, predicts the resource consumption data and service revenue data corresponding to the target area.

[0097] In this embodiment, employee service data may include average employee service hours, mileage data may include total mileage, service type structure data may include fault levels and repair times corresponding to different fault types, resource consumption data may include employee wages and employee wage subsidies, and service-derived resource data may include fault inspection fees and repair consultation fees. Therefore, based on the average employee service hours and the fault levels corresponding to different fault types, the employee wages and employee wage subsidies, as well as fault inspection fees and repair consultation fees corresponding to the average employee service hours can be predicted.

[0098] In some embodiments, employee wages and wage subsidies corresponding to the target area can be predicted based on the fault level corresponding to different fault types, total travel mileage, and average service hours of employees.

[0099] Specifically, from the fault levels corresponding to multiple fault types, the highest fault level is determined as the target fault level; the product of the average of the employee wage subsidy corresponding to the target fault level and the employee wage subsidy corresponding to the average service hours of employees, and the subsidy coefficient corresponding to the number of employees at the service station, is determined as the predicted employee wage subsidy corresponding to the target area; the product of the average of the wage coefficient corresponding to the total mileage traveled and the wage coefficient corresponding to the target fault level, and the preset wage corresponding to the average service hours of employees, is determined as the predicted employee wage corresponding to the target area.

[0100] In some embodiments, the cost of fault inspection and maintenance consultation for the target area can be predicted based on the maintenance time, total mileage, and average service hours of employees for different fault types.

[0101] Specifically, from the repair times corresponding to multiple fault types, the longest repair time is determined as the target time; the sum of the fault inspection cost and repair consultation cost corresponding to the target time and the fault inspection cost and repair consultation cost corresponding to the average service hours of employees is determined; the product of the cost coefficient corresponding to the total mileage and the determined sum is determined as the predicted fault inspection cost and repair consultation cost corresponding to the target area.

[0102] based on Figure 3 The method shown predicts resource consumption and service revenue data from the perspectives of service type structure data and mileage data. Therefore, based on the analysis of the service scope and maintenance status of service objects, the planning and layout of service stations can be improved, thus improving the planning accuracy.

[0103] In one embodiment, employee service data may include employee participation rate, which refers to the participation rate of the service personnel configured at the service station in business services.

[0104] Service type structure data can include the repair ratio and average repair man-hours corresponding to different fault types, and employee layout data includes the number of service station employees, the maximum working hours of employees, and the preset number of working days for employees.

[0105] Service station business data can include the number of service station services, which refers to the total number of work orders configured for each service station. This number can be preset, determined based on the historical work order count of existing service stations within the target area, or set using other methods.

[0106] Furthermore, based on service type structure data, employee layout data corresponding to the target area, and service station business data, the method for predicting employee service data matching the target area (i.e., S304) can be as follows: Figure 4As shown, it includes the following steps:

[0107] S402, for each fault type, calculate the first product of the number of service station business services, the repair ratio, and the average repair man-hours.

[0108] S404, the second product of the number of business services provided by the statistical service station, the maximum working hours of employees, and the preset number of working days of employees.

[0109] S406, based on the first statistical result between the first products corresponding to different fault types and the ratio between the first product and the second product, the predicted employee participation rate matching the target area is determined.

[0110] for example, W represents employee participation rate, W represents the number of service station business services, and j represents the fault type. This indicates the repair ratio corresponding to the fault type. This represents the average repair man-hours corresponding to the fault type, and N represents the number of service station employees. Indicates the maximum working hours of an employee. If the number of workdays employees pre-set is allowed, then employee participation rate can be expressed as: .

[0111] In summary, based on Figure 4 The content shown demonstrates that by considering the types of faults, the accuracy of predicting employee participation rates based on statistical results between different fault types can be improved, which in turn can improve the accuracy of service station planning and layout.

[0112] In one embodiment, the resource consumption data may include resources consumed while traveling and resources consumed by employees. Resources consumed while traveling refer to the resources consumed by employees when they travel to provide business services, i.e., employee travel costs, such as transportation and accommodation expenses.

[0113] Employee resource consumption can include employee wages. Service-generated resource data includes resources earned from repairs, resources earned from spare parts sales, and resources for on-site service subsidies. Resources earned from repairs refer to repair revenue, resources earned from spare parts sales refer to spare parts sales revenue, and resources for on-site service subsidies refer to subsidies received by employees when providing business services on-site.

[0114] Mileage data can include average travel distance corresponding to the regional level of the target area, mileage subsidy resources, energy consumption corresponding to the average travel distance, and energy unit price.

[0115] Specifically, resources consumed during fieldwork include the product of the number of service station services provided, average fieldwork mileage, energy consumption, energy unit price, and employee participation rate. In some cases, this product can be determined as the resources consumed during fieldwork.

[0116] Specifically, the resources consumed by employees include the sum of the first reference value and the second reference value. The first reference value includes the product of the number of service station employees and the preset resources and set values ​​of employees. The second reference value includes the product of the first statistical result, the preset service hours resources, the preset service ratio, and the employee participation rate.

[0117] The pre-set resources for employees include their fixed salaries, pre-set service hour resources include their hourly wages, and pre-set service rates include the percentage of hourly wages charged. In some cases, the sum of the first and second reference values ​​can be determined as the resources consumed by the employee.

[0118] Specifically, the resources gained from maintenance include the product of the initial statistical results, the preset service man-hour resources, and the employee participation rate. In some cases, this product can be determined as the resources gained from maintenance.

[0119] Specifically, the resources obtained from spare parts sales include the product of the second statistical result and the employee participation rate. The second statistical result is obtained by calculating the third product corresponding to different fault types, based on the service station's service volume, repair ratio, average repair hours, and the spare parts service resources corresponding to each fault type. Spare parts service resources include spare parts prices. In some cases, the product of the second statistical result and the employee participation rate can be determined as the resources obtained from spare parts sales.

[0120] Specifically, the resources for providing subsidies for on-site services include the product of the number of services provided by the service station, the average mileage traveled, mileage subsidies, and employee participation rate. In some cases, this product can be used to determine the resources for providing subsidies for on-site services.

[0121] Furthermore, in one embodiment, environmental resource data may include land rent and water and electricity costs, resource consumption data may include resources consumed during travel (i.e., employee travel costs) and employee resources consumed (i.e., employee wages), and service-generated resource data may include resources generated from repairs (i.e., repair revenue), resources generated from spare parts sales (i.e., spare parts sales revenue), and resources for travel service subsidies (i.e., travel subsidy revenue).

[0122] Specifically, the sum of land rent, water and electricity costs, employee travel expenses, and employee wages is determined as the third predicted value; the sum of repair revenue, spare parts sales revenue, and travel allowance revenue is determined as the fourth predicted value; the difference between the fourth and third predicted values ​​is determined, and the ratio of the resources invested by the service stations in the target area to the difference is determined as the predicted resource return cycle that matches the resource consumption data. Here, the resources invested by the service stations refer to the pre-set resources invested when establishing the service stations.

[0123] For example, R represents repair revenue. This indicates revenue from spare parts sales. This indicates income from subsidies for working outside the home. This indicates the land rent fee. This indicates the cost of water and electricity. This indicates the cost of employees traveling. Let I represent employee wages, and 'I' represent the resources invested by the service station. Then, the return on investment period for these resources can be expressed as:

[0124] .

[0125] in, W represents the number of services provided by the service station. This indicates the repair ratio corresponding to fault type j. This represents the average maintenance man-hours corresponding to fault type j. This represents the preset service time resources corresponding to target region i. The preset service time resources corresponding to target regions of different region levels can be the same or different.

[0126] , This indicates the spare parts service resources (i.e., the spare parts sales price) corresponding to fault type j.

[0127] , This represents the average travel distance corresponding to target region i. This represents the mileage subsidy resources corresponding to target region i. The mileage subsidy resources corresponding to target regions of different regions can be the same or different.

[0128] F represents energy consumption, and E represents the unit price of energy.

[0129] N represents the number of service station employees, T represents the preset resources for employees (i.e., fixed employee salaries), M1 represents the set value, and H represents the preset service ratio. It is derived from a fixed salary. In some cases, M1 can be 13 or other values, but this embodiment does not limit it.

[0130] Based on the above, taking a city as the target area as an example, such as Figure 5 The diagram illustrates the relationship between resource consumption return cycle and employee participation rate. A1-A3 represent different city levels, with the regional level decreasing sequentially from A1 to A3. Figure 5In this study, as employee participation increases, the return on investment period for resources consumed in each city decreases. The return on investment period is minimized when the employee participation rate reaches 110%. In other words, for each city, considering the resources consumed, including service station investment, land rent, water and electricity costs, travel expenses, and employee wages, increasing employee participation can effectively reduce the return on investment period for that city, thereby reducing resource waste and lowering the risk of station construction.

[0131] In one embodiment, business requirement data may include required repair duration, such as... Figure 6 As shown, a service station planning and layout method is also provided. Taking the application of this method to a processor in a service station planning and layout system as an example, it may include the following steps:

[0132] S602 predicts the total number of batteries in the target area based on the historical retention of the target battery and the future sales volume of the target battery in the target area.

[0133] The target battery can refer to a battery manufactured by a specific company. Historical retention refers to the number of vehicles in the current market that have the target battery and meet the retention requirements. The retention requirements stipulate that the age of the target vehicle's equipment is less than a preset vehicle age. In this embodiment, the preset vehicle age can be 8, or other settings are possible; this embodiment does not impose specific limitations.

[0134] In some embodiments, for the vehicle where the target battery is located, the number of vehicles in each region at different vehicle ages is counted; for different vehicle ages, the product between the number of vehicles and the retention rate corresponding to the vehicle age is determined as the quantity statistics; the quantity statistics corresponding to different vehicle ages are counted to determine the historical retention of the target battery.

[0135] In some embodiments, the historical sales volume of all batteries within the target region can be input into a time-series model to predict the sales forecast; the sales forecast and the market share corresponding to the target battery are then used to determine the future sales volume of the target battery within the target region. The time-series model may include the Prophet time-series model, etc.

[0136] In some embodiments, the sum of historical retention and future sales can be determined as the total number of target batteries in the predicted target area.

[0137] S604, based on the total number of vehicles in operation and the first statistical result, predicts the required repair time for the target area.

[0138] In some embodiments, the required maintenance time matching the total number of vehicles can be determined as the first working hour based on the mapping relationship between the number of vehicles and the required maintenance time; the required maintenance time matching the first statistical result can be determined as the second working hour based on the mapping relationship between the statistical result and the required maintenance time; and the average of the first working hour and the second working hour can be determined as the predicted required maintenance time corresponding to the target area.

[0139] In some embodiments, the repair ratio corresponding to different fault types is affected by vehicle age. Therefore, considering vehicle age, the required repair time corresponding to the target area can be predicted based on the total number of vehicles in operation and the first statistical result.

[0140] Specifically, for different vehicle ages, the repair ratio corresponding to different fault types under different vehicle ages and the fourth product with the average repair time are statistically analyzed; the statistical value of the fourth product corresponding to different fault types and the fifth product with the total number of vehicles in operation are determined; the statistical value of the fifth product corresponding to different vehicle ages is determined as the predicted demand repair time corresponding to the target area.

[0141] for example, This indicates the required repair time for target area i. This represents the total inventory corresponding to target region i. This represents the repair ratio corresponding to fault type j for vehicle age s. Let j represent the average maintenance man-hours corresponding to fault type j. Then the required maintenance time can be expressed as: .

[0142] In one embodiment, employee service data may include employee participation rate, and business demand data may include required repair time. The methods for determining employee participation rate and required repair time can be found in the foregoing description and will not be repeated here.

[0143] Specifically, the constraints corresponding to employee service data, i.e., the first constraint, include: employee participation rate is less than or equal to the participation rate threshold. For example, This represents the employee participation rate corresponding to target region i. Indicates the participation rate threshold, then .

[0144] Specifically, the constraints corresponding to the resource consumption return cycle, i.e., the second constraint, include: the resource consumption return cycle is within a set cycle range; the difference between the upper and lower limits of the set cycle range can be less than or equal to the minimum set cycle; the minimum set cycle can be 2 years or other values. For example, This indicates the lower limit of the set period range. This indicates the upper limit of the set period range. .

[0145] Specifically, the constraints between business demand data, employee service data, and the number of service stations, namely the third constraint, include: the product of the number of service station employees, the preset number of working days, the maximum working hours of employees, the number of service stations, and the employee participation rate is greater than or equal to the first target value and less than or equal to the second target value; the first target value and the second target value are determined based on the required repair time.

[0146] The preset working days can refer to the number of statutory holidays within a preset period. The first target value can be the product of a first preset value and the required repair time, and the second target value can be the product of a second preset value and the required repair time. In this embodiment, the first preset value can be 0.9, and the second preset value can be 1.1. The first and second preset values ​​can also be set in other ways, and this embodiment does not limit them.

[0147] For example, taking a first preset value of 0.9 and a second preset value of 1.1 as an example, This represents employee participation rate, where N represents the number of employees at the service station. Indicates the maximum working hours of an employee. This indicates the number of service stations deployed in target region i. M1 represents the required repair time for target area i, and M2 represents the preset number of working days. Therefore, if the condition is met... .

[0148] In summary, based on Figure 6 The content shown predicts the required maintenance time based on historical retention and total inventory. The predicted required maintenance time is compared with the maintenance situation in the target area, thus improving the accuracy of the predicted required maintenance time.

[0149] Based on the above content, it can be seen that the above content describes the relevant content of determining the number of service stations in the target area. Furthermore, given the determined number of service stations, the location of the service stations in the target area can be determined to match the number of service stations, so as to realize the complete planning and layout process of service stations in the target area.

[0150] In one embodiment, the location of a service station in the target area that matches the number of service stations in the target area can be determined based on the number of service stations matched with the target area and the locations of existing service stations in the target area. The service station location can be determined using the following implementation method.

[0151] In one implementation, for a planned circle centered on the locations of existing service stations and with the distance between these locations and the locations of service stations to be built as its radius, provided that the distance between any two locations of service stations to be built is greater than or equal to a preset distance, the determined location of the service station to be built is selected from any location outside the planned circle within the target area and designated as the service station location in the target area with the same number of existing service stations. The distance between the locations of existing and planned service stations is greater than or equal to a distance threshold.

[0152] In another implementation, the following can be used: Figure 7 The method shown determines the locations of service station layouts within the target area that match the number of service station layouts, where:

[0153] S702, Determine the service station layout plan, which includes candidate layout locations consistent with the number of service stations.

[0154] S704, based on the number of service station layout schemes and the locations of existing service stations in the target area, determine the service station layout locations in the target area that are consistent with the number of service station layouts.

[0155] In some embodiments, there may be only one service station layout scheme. When there is only one service station layout scheme, different methods may be used to determine the service station layout locations in the target area that are consistent with the number of service stations.

[0156] In one implementation, at least two candidate regions can be identified from multiple reference regions that have the same regional level as the target region, are adjacent in location, and have a regional similarity greater than a preset similarity. From the at least two candidate regions, the candidate region with the highest regional similarity can be identified. For the multiple existing service station locations in the candidate region with the highest regional similarity and the existing service station locations in the target region, the service station layout locations in the target region that are consistent with the number of service station layouts can be identified.

[0157] Specifically, for multiple existing service station locations in the candidate area, if one of the existing service station locations is used as a reference location, a first positional relationship is determined between the first reference location and other existing service station locations; if the already deployed service station locations in the target area are used as reference locations, a second positional relationship is determined between the already deployed service station locations and the locations of service stations to be built based on the first positional relationship; the locations of service stations to be built in the second positional relationship are determined as service station locations in the target area that have the same number of service station locations.

[0158] In another implementation, when the number of service station layout schemes is one, the method for determining the service station layout locations in the target area that match the number of service station layouts, based on the number of service station layout schemes and the locations of already deployed service stations within the target area, can be as follows: Figure 8 As shown, specifically:

[0159] S802, for each candidate layout location in the service station layout scheme, taking the candidate layout location as the center, and provided that the distance between any two locations is within a preset range, the candidate layout location is updated based on the clustering results of the service object locations in the target area to obtain the first target layout location.

[0160] The distance between any two locations includes the distance between the candidate layout location and the already deployed service station location, as well as the distance between different candidate layout locations.

[0161] S804, determine the first target layout position corresponding to each candidate layout position as the service station layout position in the target area that is consistent with the number of service station layouts.

[0162] based on Figure 8 As shown, when there is only one service station layout plan, clustering can be used to determine the location of the service stations, which can improve the efficiency of planning and layout.

[0163] In some embodiments, there may be multiple service station layout schemes. When there are multiple service station layout schemes, different methods may be used to determine the service station layout locations in the target area that are consistent with the number of service stations.

[0164] In one implementation, for each service station layout scheme, a third positional relationship is determined among the candidate layout positions in each service station layout scheme; the positional similarity between the third positional relationship and the first positional relationship corresponding to each service station layout scheme is obtained; and the candidate layout positions in the service station layout schemes with a positional similarity greater than or equal to the similarity threshold are determined as the service station layout positions in the target area that have the same number of service station layouts. The method for obtaining the first positional relationship can be referred to the aforementioned description and will not be repeated here.

[0165] In another implementation, when there are multiple service station layout schemes, it can be based on Figure 9 The content shown indicates the location of service stations within the designated area that matches the number of service stations. Specifically:

[0166] S902, for each candidate layout location in each service station layout scheme, taking the candidate layout location as the center, and provided that the distance between any two locations is within a preset range, the candidate layout location is updated based on the clustering results of the service object locations in the target area to obtain the second target layout location.

[0167] The distance between any two locations includes the distance between the candidate layout location and the already deployed service station location, as well as the distance between different candidate layout locations.

[0168] Among them, the minimum distance L between the candidate layout location and the already deployed service station location is defined. In other words, This is the lower limit of the preset range. Service target location refers to the location of the customers served by the service station, including but not limited to the location of 4S stores and the home address of car owners.

[0169] Specifically, taking the candidate layout location as the center, and provided that the distance between the candidate layout location and the already laid-out service station location is within a preset range, clustering is performed on the service object locations in the target area. When the clustering iteration stops, a cluster with the same number of service station layouts can be obtained. Each cluster includes a candidate layout location. For each cluster, the centroid location in the cluster can be determined as the first target layout location after updating the candidate layout location.

[0170] In some embodiments, the k-means clustering algorithm can be used to cluster the locations of service objects in the target area, or other clustering methods can be used; this embodiment does not limit the methods.

[0171] S904, for each second target layout location, with the second target layout location as the center, calculate the weight of the service objects within the service radius corresponding to the second target layout location, and obtain the target statistical results between the weights of the service objects corresponding to each candidate layout location in each service station layout scheme.

[0172] In some embodiments, the maximum driving distance can be determined based on the service timeliness corresponding to the target area; based on the mapping relationship between driving distance and service radius, the service radius matching the maximum driving distance is determined as the service radius corresponding to the first target layout location. Here, service timeliness refers to the time from when a service request is submitted to when the request is resolved, which can be obtained by analyzing work order data from existing service stations in the target area.

[0173] Among them, the service object weight is used to measure the number of service objects and the number of vehicles that the service station can serve within the service radius corresponding to the first target layout location. The larger the service object weight, the more resources will be obtained after the service station is established at the first target layout location, and the shorter the return on investment period for the consumed resources.

[0174] In some embodiments, for each first target layout location, the service object weight corresponding to the target area can be determined based on the current inventory and historical work orders within the service radius corresponding to the first target layout location, and the distance between each service object location and the first target layout location. The current inventory can be based on... Figure 6 The content shown is for illustrative purposes only and will not be repeated here. The number of historical work orders can be obtained by statistically analyzing the work order data of existing service stations within the target area.

[0175] Specifically, the first weight is determined by summing the product of the current inventory and its corresponding inventory coefficient, and the product of the historical work order number and its corresponding work order coefficient. For each service object location within the service radius, a distance coefficient matching the distance between the service object location and the first target layout location is determined based on the mapping relationship between distance and distance coefficient. For each service object location, a sixth product is determined between the distance between the service object location and the first target layout location and its corresponding distance coefficient. The statistical value of the sixth product corresponding to each service object location within the target area is determined as the second weight. The sum of the first weight and the second weight is determined as the service object weight.

[0176] The current inventory level corresponds to the inventory level coefficient, which is obtained based on the mapping relationship between the inventory level and the inventory level coefficient. The historical work order number corresponds to the work order coefficient, which is obtained based on the mapping relationship between the work order number and the work order coefficient.

[0177] S906, among the target statistical results corresponding to the multiple service station layout schemes, the candidate layout position in the service station layout scheme corresponding to the largest target statistical result is determined as the service station layout position in the target area that is consistent with the number of service station layouts.

[0178] based on Figure 9 The content shown demonstrates that by identifying the candidate layout locations in the service station layout scheme corresponding to the maximum target statistical result as the service station layout locations in the target area that match the number of service station layouts, the service stations built at the service station layout locations can provide business services to more service recipients, thus improving the accuracy of service station planning and layout.

[0179] In conjunction with the above, in one embodiment, such as Figure 10As shown, a service station planning and layout method is provided. Taking the application of this method to a processor in a service station planning and layout system as an example, the method includes the following steps:

[0180] S1002, acquire historical service information matching the target area, and analyze the historical service information to obtain service type structure data and mileage data; the service type structure data includes the repair ratio and average repair man-hours corresponding to different fault types, and the mileage data includes the average outbound mileage, mileage subsidy resources, energy consumption and energy unit price corresponding to the regional level of the target area.

[0181] S1004, for each fault type, calculate the first product of the number of service station business services, the repair ratio, and the average repair man-hours.

[0182] S1006, the second product of the number of business services provided by the statistical service station, the maximum working hours of employees, and the preset number of working days of employees.

[0183] S1008, the ratio between the first statistical result of the first product corresponding to different fault types and the second product is determined as the predicted employee participation rate matching the target area.

[0184] S1010: For the vehicle where the target battery is located, count the number of vehicles in each region at different vehicle ages, and for each vehicle age, determine the quantity statistics by multiplying the number of vehicles by the retention rate corresponding to the vehicle age, and count the quantity statistics corresponding to each vehicle age to determine the historical retention of the target battery.

[0185] S1012, input the historical sales volume of all batteries in the target area into the time series model, predict the sales forecast, and determine the future sales volume of the target battery in the target area by combining the sales forecast with the market share corresponding to the target battery.

[0186] S1014, the sum of historical retention and future sales is determined as the total number of target batteries in the predicted target area.

[0187] S1016. For different vehicle ages, calculate the repair ratio corresponding to the fault type under different vehicle ages, the fourth product between the ratio and the average repair time, and determine the statistical value of the fourth product corresponding to different fault types and the fifth product with the total number of vehicles in operation. The statistical value of the fifth product corresponding to different vehicle ages is determined as the predicted demand repair time corresponding to the target area.

[0188] Among them, the required repair time satisfy: .

[0189] S1018. Based on the land rent, water and electricity costs, employee travel costs, employee wages, maintenance revenue, spare parts sales revenue, and travel allowance revenue that match the target area, determine the resource consumption return cycle corresponding to the target area.

[0190] in, R represents repair revenue. This indicates revenue from spare parts sales. This indicates income from subsidies for working outside the home. This indicates the land rent fee. This indicates the cost of water and electricity. This indicates the cost of employees traveling. "I" represents employee wages, and "I" represents resources invested by the service station.

[0191] Specifically, , , , , .

[0192] S1020, based on the constraints corresponding to employee participation rate and resource consumption return cycle, as well as the constraints between demand maintenance time, employee participation rate and the number of service stations, determine the number of service stations to be deployed in the target area under the condition of the shortest resource consumption return cycle.

[0193] The number of service stations can be determined based on genetic algorithms, including employee participation rate. The corresponding constraints, i.e., the first constraint, include: The constraints corresponding to the resource consumption return cycle, i.e., the second constraint, include: The constraints between demand repair time and employee participation rate and the number of service stations, i.e., the third constraint, include: N represents the number of employees at the service station. Indicates the maximum working hours of an employee. M1 represents the number of service stations corresponding to target region i, and M2 represents the preset number of working days.

[0194] S1022, determine multiple service station layout schemes, each service station layout scheme includes candidate layout locations consistent with the number of service station layouts.

[0195] S1024, for each candidate layout location in each service station layout scheme, taking the candidate layout location as the center, and provided that the distance between any two locations is within a preset range, the candidate layout location is updated based on the clustering results of the service object locations in the target area to obtain the second target layout location.

[0196] S1026, for each second target layout location, with the second target layout location as the center, calculate the weight of the service objects within the service radius corresponding to the second target layout location, and obtain the target statistical results between the weights of the service objects corresponding to each candidate layout location in each service station layout scheme.

[0197] S1028, among the target statistical results corresponding to the multiple service station layout schemes, the candidate layout position in the service station layout scheme corresponding to the largest target statistical result is determined as the service station layout position in the target area that is consistent with the number of service station layouts.

[0198] The specific content of S1002-S1028 can be found in the aforementioned description and will not be repeated here.

[0199] In S1016, a maintenance labor demand prediction model can be constructed based on the repair ratio, average maintenance man-hours, and total vehicle inventory corresponding to different fault types under different vehicle ages. By inputting relevant parameters matching the target area into the maintenance labor demand prediction model, the required maintenance man-hours matching the target area can be predicted. The maintenance labor demand prediction model satisfies the following: .

[0200] Specifically, in S1018, a single-store economic calculation model can be constructed based on repair revenue, spare parts sales revenue, travel allowance revenue, land rent, water and electricity costs, employee travel costs, employee wages, and service station resource investment. Therefore, when planning the layout of service stations in a target area, relevant parameters matching the target area can be input into the single-store economic calculation model to predict the resource consumption return cycle corresponding to the target area. The single-store economic calculation model satisfies the following: .

[0201] In S1020, the constraints corresponding to employee participation rate and resource consumption return cycle, as well as the constraints between demand maintenance time, employee participation rate and the number of service stations, can be used as the overall constraints. With minimizing the resource consumption return cycle as the objective function, a service network size prediction model can be constructed. Thus, by inputting relevant parameters matching the target area into the service network size prediction model, the number of service stations in the target area can be determined.

[0202] In S1022-S1028, the constraint that the distance between any two locations is within a preset range can be used. The objective function is to maximize the target statistical result between the service object weights corresponding to each candidate layout location in each service station layout scheme. This determines the service station layout locations in the target area that match the number of service station layouts. For example, Z represents the service object weight corresponding to each candidate layout location, provided that... In this case, determine the location of the service station.

[0203] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0204] Based on the same inventive concept, this application also provides a service station planning and layout apparatus for implementing the service station planning and layout method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more service station planning and layout apparatus embodiments provided below can be found in the limitations of the service station planning and layout method described above, and will not be repeated here.

[0205] In one embodiment, such as Figure 11 As shown, a service station planning and layout device is provided, including: an acquisition module 1102, a first prediction module 1104, a second prediction module 1106, and a determination module 1108, wherein: the acquisition module 1102 is used to acquire environmental resource data and historical service information matching a target area; the first prediction module 1104 is used to analyze the historical service information and predict employee service data and business demand data matching the target area, as well as resource consumption data and service-obtained resource data corresponding to the employee service data; the second prediction module 1106 predicts the resource consumption return cycle matching the resource consumption data based on the environmental resource data, resource consumption data, and service-obtained resource data; the determination module 1108 is used to determine the number of service stations to be laid out in the target area under the condition of the shortest resource consumption return cycle based on a first constraint, a second constraint, and a third constraint; wherein the first constraint represents the constraint corresponding to the employee service data, the second constraint represents the constraint corresponding to the resource consumption return cycle, and the third constraint represents the constraint between the business demand data, the employee service data, and the number of service stations to be laid out.

[0206] In one embodiment, the first prediction module 1104 is further configured to: analyze historical service information to obtain service type structure data and mileage data; predict employee service data matching the target area based on the service type structure data, employee layout data and service station business data corresponding to the target area; and predict resource consumption data and service obtained resource data corresponding to the target area based on employee service data, mileage data and service type structure data.

[0207] In one embodiment, employee service data includes employee participation rate, service type structure data includes repair ratio and average repair man-hours corresponding to different fault types, employee layout data includes the number of service station employees, maximum working hours of employees, and preset working days of employees, and service station business data includes the number of service station business services; the first prediction module 1104 is further configured to: for each fault type, calculate a first product between the number of service station business services, repair ratio, and average repair man-hours; calculate a second product between the number of service station business services, maximum working hours of employees, and preset working days of employees; and determine the ratio between the first statistical result of the first product corresponding to different fault types and the second product as the predicted employee participation rate matching the target area.

[0208] In one embodiment, resource consumption data includes resources consumed during outings and resources consumed by employees; service-earned resource data includes resources earned from repairs, resources earned from spare parts sales, and resources for outing service subsidies; mileage data includes average outing mileage corresponding to the regional level of the target area, mileage subsidy resources, energy consumption corresponding to the average outing mileage, and energy unit price; outing-related resource consumption includes the product of the number of service station services, average outing mileage, energy consumption, energy unit price, and employee participation rate; employee-consuming resources include the sum of a first reference value and a second reference value, the first reference value being the product of the number of service station employees and the preset resources earned by employees plus a set value, and the second reference value including... The first statistical result is the product of the preset service hours resources, the preset service ratio, and the employee participation rate; the resources obtained from repairs include the product of the first statistical result, the preset service hours resources, and the employee participation rate; the resources obtained from spare parts sales include the product of the second statistical result and the employee participation rate. The second statistical result is obtained by calculating the third product corresponding to different fault types, based on the third product of the number of service station business services, the repair ratio, the average repair hours, and the spare parts service resources corresponding to each fault type for each fault type; the resources for off-site service subsidies include the product of the number of service station business services, the average off-site mileage, the mileage subsidy resources, and the employee participation rate.

[0209] In one embodiment, the business demand data includes the demand repair duration. The first prediction module 1104 is further configured to: predict the total number of target batteries in the target area based on the historical retention of the target batteries and the future sales volume of the target batteries in the target area; and predict the demand repair duration corresponding to the target area based on the total number of target batteries and the first statistical result.

[0210] In one embodiment, employee service data includes employee participation rate, and business demand data includes demand repair duration; the first constraint includes: the employee participation rate is less than or equal to the participation rate threshold; the second constraint includes: the resource consumption return cycle is within a set cycle range; the third constraint includes: the product of the number of service station employees, preset working days, maximum employee working hours, the number of service station layouts, and employee participation rate corresponding to the target area is greater than or equal to the first target value, and less than or equal to the second target value; the first target value and the second target value are determined based on the demand repair duration.

[0211] In one embodiment, the determining module 1108 is further configured to: determine the service station layout location in the target area that matches the number of service station layouts, based on the number of service station layouts matching the target area and the locations of the already laid-out service stations in the target area.

[0212] In one embodiment, the determining module 1108 is further configured to: determine a service station layout scheme, the service station layout scheme including candidate layout locations consistent with the number of service station layouts; and determine service station layout locations in the target area consistent with the number of service station layouts based on the number of service station layout schemes and the locations of already laid-out service stations in the target area.

[0213] In one embodiment, the number of service station layout schemes is one. The determining module 1108 is further configured to: for each candidate layout location in the service station layout scheme, taking the candidate layout location as the center, and provided that the distance between any two locations is within a preset range, update the candidate layout location based on the clustering results of the service object locations in the target area to obtain a first target layout location; wherein, the distance between any two locations includes the distance between the candidate layout location and the already laid-out service station location, as well as the distance between different candidate layout locations; and determine the first target layout location corresponding to each candidate layout location as the service station layout location in the target area that is consistent with the number of service station layouts.

[0214] In one embodiment, the number of service station layout schemes is multiple. The determining module 1108 is further configured to: for each candidate layout location in each service station layout scheme, with the candidate layout location as the center, and provided that the distance between any two locations is within a preset range, update the candidate layout location based on the clustering results of the service object locations in the target area to obtain a second target layout location; wherein, the distance between any two locations includes the distance between the candidate layout location and the already laid-out service station location, and the distance between different candidate layout locations; for each second target layout location, with the second target layout location as the center, calculate the service object weights within the service radius corresponding to the second target layout location, and obtain the target statistical results between the service object weights corresponding to each candidate layout location in each service station layout scheme; and determine the candidate layout location in the service station layout scheme corresponding to the service station layout scheme with the largest target statistical result among the target statistical results corresponding to the multiple service station layout schemes as the service station layout location in the target area that is consistent with the number of service station layouts.

[0215] Each module in the aforementioned service station layout device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0216] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 12 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores historical service information and other data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a service station planning and layout method.

[0217] Those skilled in the art will understand that Figure 12The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0218] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0219] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0220] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0221] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0222] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0223] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for planning and laying out service stations, characterized in that, The method includes: Obtain environmental resource data and historical service information that match the target area; The historical service information is analyzed to predict employee service data and business demand data that match the target area, as well as resource consumption data and service-obtained resource data corresponding to the employee service data. Based on the environmental resource data, the resource consumption data, and the resource acquisition data from the service, predict the resource consumption return cycle that matches the resource consumption data; Based on the first constraint, the second constraint, and the third constraint, determine the number of service stations to be deployed in the target area under the condition of the shortest resource consumption payback period. Wherein, the first constraint condition represents the constraint condition corresponding to the employee service data, the second constraint condition represents the constraint condition corresponding to the resource consumption return cycle, and the third constraint condition represents the constraint condition between the business demand data, the employee service data, and the number of service stations.

2. The method according to claim 1, characterized in that, Analyzing the historical service information to predict employee service data matching the target area, and corresponding resource consumption and service revenue data, including: The historical service information is analyzed to obtain service type structure data and mileage data; Based on the service type structure data, the employee layout data and service station business data corresponding to the target area, predict the employee service data that matches the target area; Based on the employee service data, the mileage data, and the service type structure data, predict the resource consumption data and service revenue data corresponding to the target area.

3. The method according to claim 2, characterized in that, The employee service data includes employee participation rate; the service type structure data includes the repair ratio and average repair man-hours corresponding to different fault types; the employee layout data includes the number of service station employees, the maximum working hours of employees, and the preset number of working days for employees; and the service station business data includes the number of service station business services. The step of predicting employee service data matching the target area based on the service type structure data, the employee layout data and service station business data corresponding to the target area includes: For each fault type, calculate the first product of the number of service station services, the repair ratio, and the average repair man-hours; The second product of the number of services provided by the service station, the maximum working hours of the employee, and the preset number of working days of the employee is calculated. The ratio between the first statistical result of the first product corresponding to different fault types and the second product is determined as the predicted employee participation rate matching the target area.

4. The method according to claim 3, characterized in that, The resource consumption data includes resources consumed during outings and resources consumed by employees; the service revenue data includes resources earned from repairs, resources earned from spare parts sales, and resources for outing service subsidies; the mileage data includes the average outing mileage corresponding to the regional level of the target area, mileage subsidy resources, energy consumption corresponding to the average outing mileage, and energy unit price; The resources consumed during outings include the product of the number of services provided by the service station, the average mileage traveled, the energy consumption, the energy unit price, and the employee participation rate. The employee resource consumption includes the sum of a first reference value and a second reference value. The first reference value includes the product of the number of service station employees and the preset resources and set values ​​of the employees. The second reference value includes the product of the first statistical result, the preset service hours resources, the preset service ratio, and the employee participation rate. The resources obtained from the repair include the product of the first statistical result, the preset service man-hour resources, and the employee participation rate. The resources obtained from spare parts sales include the product of the second statistical result and the employee participation rate. The second statistical result is obtained by calculating the third product of the number of service station business services, the repair ratio, the average repair man-hours, and the spare parts service resources corresponding to the fault type for each fault type. The service subsidy resources for outings include the product of the number of services provided by the service station, the average mileage traveled, the mileage subsidy resources, and the employee participation rate.

5. The method according to claim 3, characterized in that, The business requirement data includes the required repair duration, and the method further includes: Based on the historical retention of the target battery and the future sales volume of the target battery in the target area, predict the total stock of the target battery in the target area; Based on the total number of vehicles and the first statistical result, the required maintenance time corresponding to the target area is predicted.

6. The method according to claim 1, characterized in that, The employee service data includes employee participation rate, and the business demand data includes demand repair time. The first constraint includes: the employee participation rate is less than or equal to the participation rate threshold; The second constraint includes: the resource consumption return cycle is within a set cycle range; The third constraint includes: the product of the number of service station employees, the preset number of working days, the maximum working hours of employees, the number of service station layouts, and the employee participation rate corresponding to the target area is greater than or equal to the first target value and less than or equal to the second target value; the first target value and the second target value are determined according to the required repair time.

7. The method according to any one of claims 1 to 6, characterized in that, Based on the number of service stations that match the target area and the locations of the existing service stations in the target area, determine the locations of service stations in the target area that match the number of service stations.

8. The method according to claim 7, characterized in that, The step of determining the location of service stations in the target area that matches the number of service station layouts based on the number of service station layouts matching the target area and based on the locations of already deployed service stations in the target area includes: Determine a service station layout scheme, wherein the service station layout scheme includes candidate layout locations consistent with the number of service stations; Based on the number of service station layout schemes and the locations of existing service stations within the target area, determine the locations of service stations within the target area that match the number of service station layouts.

9. The method according to claim 8, characterized in that, The number of service station layout schemes is one; determining the service station layout locations in the target area that match the number of service station layouts based on the number of service station layout schemes and the locations of already laid-out service stations in the target area includes: For each candidate layout location in the service station layout scheme, taking the candidate layout location as the center, and provided that the distance between any two locations is within a preset range, the candidate layout location is updated based on the clustering results of the service object locations in the target area to obtain the first target layout location; wherein, the distance between any two locations includes the distance between the candidate layout location and the already laid-out service station location, as well as the distance between different candidate layout locations; The first target layout position corresponding to each candidate layout position is determined as the service station layout position in the target area that is consistent with the number of service station layouts.

10. The method according to claim 8, characterized in that, The number of service station layout schemes is multiple; determining the service station layout locations in the target area that match the number of service station layout schemes based on the number of service station layout schemes and the locations of already laid-out service stations in the target area includes: For each candidate layout location in each service station layout scheme, taking the candidate layout location as the center, and provided that the distance between any two locations is within a preset range, the candidate layout location is updated based on the clustering results of the service object locations in the target area to obtain a second target layout location; wherein, the distance between any two locations includes the distance between the candidate layout location and the already laid-out service station location, as well as the distance between different candidate layout locations; For each second target layout location, with the second target layout location as the center, the weight of the service object within the service radius corresponding to the second target layout location is calculated, and the target statistical results between the service object weights corresponding to each candidate layout location in each service station layout scheme are obtained. Among the target statistical results corresponding to the multiple service station layout schemes, the candidate layout position in the service station layout scheme corresponding to the largest target statistical result is determined as the service station layout position in the target area that is consistent with the number of service station layouts.

11. A service station planning and layout device, characterized in that, The device includes: The acquisition module is used to acquire environmental resource data and historical service information that match the target area; The first prediction module is used to analyze the historical service information and predict employee service data and business demand data that match the target area, as well as resource consumption data and service obtained resource data corresponding to the employee service data. The second prediction module, based on the environmental resource data, the resource consumption data, and the resource acquisition data, predicts the resource consumption return cycle that matches the resource consumption data. The determination module is used to determine the number of service stations to be deployed in the target area under the condition of the shortest resource consumption return cycle, based on a first constraint, a second constraint, and a third constraint; wherein, the first constraint represents the constraint corresponding to the employee service data, the second constraint represents the constraint corresponding to the resource consumption return cycle, and the third constraint represents the constraint between the business demand data, the employee service data, and the number of service stations to be deployed.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 10.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 10.

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