Vehicle service management platform

Abstract

The invention belongs to the field of vehicle service, relates to a data analysis technology, is used for solving the problems that matched service recommendation cannot be provided for a user and service type optimization cannot be carried out according to the service condition of the user in the prior art, and particularly relates to a vehicle service management platform which comprises a server, the server is in communication connection with a correlation analysis module, a service management module, a store recommendation module, a store management module and a database; the association analysis module is used for performing association analysis on service types of the vehicle service management platform: generating an analysis period, obtaining all service data of the vehicle service management platform in the analysis period, and screening associated objects through the service data; according to the invention, association analysis can be carried out on the service type of the vehicle service management platform, the service data in the analysis period is analyzed to obtain the association data set of the vehicle interval, the corresponding service combination package can be generated for the vehicle interval according to the association data set, and the user experience is improved.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle services, relates to data analysis technology, and specifically is a vehicle service management platform. Background Art

[0002] Vehicle service refers to the industry that provides a full range of services for automobiles, including car shopping guides, insurance, decoration, and maintenance. It involves various car-related matters during the entire use process of the car, from purchasing to replacing the car. The purpose of automobile service is to ensure the safety, reliability and comfort of the car, extend the service life of the car, and improve the performance and appearance of the car.

[0003] The vehicle service management platform in the prior art is unable to perform correlation analysis between platform service items and user experience, and is thus unable to provide users with supporting service recommendations and optimize service types based on user service conditions, resulting in the inability to guarantee user experience.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a vehicle service management platform to solve the problem that the prior art cannot provide users with matching service recommendations and optimize service types according to user service conditions;

[0006] The technical problem to be solved by the present invention is: how to provide a vehicle service management platform that can provide users with supporting service recommendations and optimize service types according to user service conditions.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A vehicle service management platform includes a server, wherein the server is communicatively connected to a correlation analysis module, a service management module, a store recommendation module, a store management module, and a database;

[0009] The association analysis module is used to perform association analysis on the service types of the vehicle service management platform: generate an analysis cycle, obtain all service data of the vehicle service management platform within the analysis cycle, screen the associated objects through the service data, obtain the brand, model and new car price of the service vehicle in the user data of the associated objects, form a vehicle range by the highest and lowest values ​​of the new car prices of the service vehicles of all associated objects, divide the vehicle range into several vehicle intervals, obtain the associated data set of the vehicle intervals, and send the associated data sets of all vehicle intervals to the mobile phone terminal of the manager through the server;

[0010] The service management module is used to manage and analyze the service quality of the vehicle service management platform: mark the user whose service type number is one in all user data of the user as the analysis object, mark the service type in the user data of the analysis object as an independent type, mark the number of services corresponding to the analysis object in the analysis period as the service value of the analysis object, and mark the analysis object as a lost object or a maintenance object through the service value; mark the ratio of the service number of the maintenance object corresponding to the independent type in the analysis period to the service number of the lost object as the maintenance coefficient of the independent type, and judge whether the service quality of the independent type in the analysis period meets the requirements through the maintenance coefficient;

[0011] The store recommendation module is used to perform service store recommendation analysis on users;

[0012] The store management module is used to supervise, manage and analyze the service quality of vehicle service stores.

[0013] Furthermore, the process of acquiring associated objects includes: service data includes user information, service type, service time, service vehicle and service personnel; all service data corresponding to the same user information are marked as user data of the user, and users whose service types in all user data are not less than L1 are marked as associated objects.

[0014] Furthermore, the process of acquiring the associated data set of the vehicle interval includes: marking the associated objects whose new car prices of the service vehicles are within the vehicle interval as matching objects of the vehicle interval; forming a type set of the vehicle interval from the service types of all matching objects within the vehicle interval, marking the number of elements of the service type in the type set as the associated value of the service type, marking the service type with the largest associated value as the associated type, marking the user data containing the associated type in the matching object as associated data, and forming the associated data set of the vehicle interval from all service types corresponding to the associated data of all matching objects.

[0015] Furthermore, the specific process of marking the analysis object as a lost object or a maintenance object includes: retrieving the service threshold of an independent type through the database, and comparing the service value with the service threshold: if the service value is less than the service threshold, the corresponding analysis object is marked as a lost object; if the service value is greater than or equal to the service threshold, the corresponding analysis object is marked as a maintenance object.

[0016] Furthermore, the specific process of determining whether the service quality of the independent type during the analysis period meets the requirements includes: comparing the maintenance coefficient with the preset maintenance threshold: if the maintenance coefficient is less than the maintenance threshold, it is determined that the service quality of the independent type during the analysis period does not meet the requirements, the independent type is marked as a training type, a training optimization signal is generated, and the training optimization signal and the training type are sent to the administrator's mobile terminal through the server; if the maintenance coefficient is greater than or equal to the maintenance threshold, it is determined that the service quality of the independent type during the analysis period meets the requirements.

[0017] Furthermore, the specific process of the store recommendation module for analyzing service store recommendations for users includes: when a user needs vehicle service, the user uploads demand information on the user side, and the demand information includes the user's location information, demand type and service vehicle; draws a circle with the user's location as the center and R1 as the radius, and marks the obtained circular area as the initial screening area, and marks the vehicle service stores in the initial screening area as initial screening objects, obtains the direct distance data ZJ, idle data KX and training data PX of the initial screening objects, and performs numerical calculations to obtain the recommendation coefficient TJ, and performs numerical calculations to obtain the recommendation coefficient TJ of the initial screening objects; marks the initial screening objects as elimination objects or recommendation objects through the recommendation coefficient TJ; arranges all recommended objects in descending order according to the value of the recommendation coefficient TJ to obtain a recommendation sequence, and sends the recommendation sequence to the user's mobile terminal through the server.

[0018] Furthermore, the specific process of marking the initial screening object as an elimination object or a recommended object includes: obtaining the recommended threshold value TJmin through the database, and comparing the recommended coefficient TJ with the recommended threshold value TJmin: if the recommended coefficient TJ is less than the recommended threshold value TJmin, it is determined that the initial screening object does not meet the recommended requirements, and the corresponding initial screening object is marked as an elimination object; if the recommended coefficient TJ is greater than or equal to the recommended threshold value TJmin, it is determined that the initial screening object meets the recommended requirements, and the corresponding initial screening object is marked as a recommended object.

[0019] Furthermore, the specific process of the store management module supervising, managing and analyzing the service quality of the vehicle service store includes: retrieving the serial number of the recommended object finally selected by the user in the recommendation sequence and marking it as a supervision value, and no processing is performed for the time being when the supervision value is one; when the supervision value is not one, marking the vehicle service store with a serial number less than the supervision value as an autonomous elimination object; marking the number of times the vehicle service store is marked as an autonomous elimination object during the analysis period as the autonomous elimination value of the vehicle service store, and comparing the autonomous elimination value with a preset autonomous elimination threshold: if the autonomous elimination value is less than the autonomous elimination threshold, it is determined that the service quality of the vehicle service store meets the requirements; if the autonomous elimination value is greater than or equal to the autonomous elimination threshold, it is determined that the service quality of the vehicle service store does not meet the requirements, and the corresponding vehicle service store is marked as a supervised store, and all supervised stores are sent to the mobile phone terminal of the manager through the server.

[0020] The present invention has the following beneficial effects:

[0021] 1. The association analysis module can be used to conduct association analysis on the service types of the vehicle service management platform, and analyze the service data within the analysis period to obtain the association data set of the vehicle interval. Then, the corresponding service package can be generated for the vehicle interval based on the association data set, providing users with a full range of automobile services and improving user experience;

[0022] 2. The service management module can be used to manage and analyze the service quality of the vehicle service management platform, obtain the maintenance coefficient of the independent type from the perspective of service type evaluation, and provide feedback on the user satisfaction when the independent type is implemented based on the maintenance coefficient, so as to screen out unqualified training types and conduct targeted training for the training types, thereby improving user experience and reasonably allocating training resources;

[0023] 3. The store recommendation module can be used to analyze service stores for users. The recommendation coefficient is obtained through comprehensive analysis and calculation based on multiple objective visual data of vehicle service stores. The matching degree between vehicle service stores and users is fed back through the recommendation coefficient. The recommended objects that have been screened are prioritized and a recommendation sequence is generated, which improves service convenience while retaining the user's right to choose.

[0024] 4. The store management module can be used to supervise, manage and analyze the service quality of vehicle service stores. The autonomous elimination value of the vehicle service store can be generated according to the user's autonomous selection. The user's subjective satisfaction with the vehicle service store can be fed back based on the autonomous elimination value. The supervised stores can then be screened based on the feedback results, the service supervision of the supervised stores can be strengthened, and the overall service quality of the platform can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0027] Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Embodiment 1: Figure 1 As shown, a vehicle service management platform includes a server, and the server is communicatively connected with a correlation analysis module, a service management module, a store recommendation module, a store management module and a database.

[0030] The association analysis module is used to perform association analysis on the service types of the vehicle service management platform: generate an analysis cycle, obtain all service data of the vehicle service management platform within the analysis cycle, the service data includes user information, service type, service time, service vehicle and service personnel; mark all service data corresponding to the same user information as the user data of the user, mark users whose service types are not less than L1 in all user data of the user as associated objects, obtain the brand, model and new car price of the service vehicle in the user data of the associated object, and form a vehicle range with the highest and lowest values ​​of the new car prices of all service vehicles of the associated objects, divide the vehicle range into several vehicle intervals, and mark the associated objects whose new car prices of the service vehicles are within the vehicle interval as Matching objects of vehicle intervals; the service types of all matching objects within the vehicle interval constitute a type set of the vehicle interval, the number of elements of the service type in the type set is marked as the associated value of the service type, the service type with the largest associated value is marked as the associated type, the user data of the matching object containing the associated type is marked as associated data, all service types corresponding to the associated data of all matching objects constitute an associated data set of the vehicle interval, and the associated data sets of all vehicle intervals are sent to the mobile phone terminal of the manager through the server; the service data within the analysis period is analyzed to obtain the associated data set of the vehicle interval, and then the corresponding service combination package can be generated for the vehicle interval according to the associated data set, so as to provide users with comprehensive automobile services and improve user experience.

[0031] The service management module is used to manage and analyze the service quality of the vehicle service management platform: mark the users whose service type number is one in all the user data of the users as analysis objects, mark the service type in the user data of the analysis object as an independent type, mark the number of services corresponding to the analysis object within the analysis period as the service value of the analysis object, retrieve the service threshold of the independent type through the database, and compare the service value with the service threshold: if the service value is less than the service threshold, mark the corresponding analysis object as a lost object; if the service value is greater than or equal to the service threshold, mark the corresponding analysis object as a maintenance object; mark the ratio of the service number of the maintenance object corresponding to the independent type within the analysis period to the service number of the lost object as the independent type. Maintenance coefficient, compare the maintenance coefficient with the preset maintenance threshold: if the maintenance coefficient is less than the maintenance threshold, it is determined that the service quality of the independent type during the analysis period does not meet the requirements, the independent type is marked as a training type, a training optimization signal is generated, and the training optimization signal and the training type are sent to the administrator's mobile terminal through the server; if the maintenance coefficient is greater than or equal to the maintenance threshold, it is determined that the service quality of the independent type during the analysis period meets the requirements; the maintenance coefficient of the independent type is obtained from the perspective of service type evaluation, and user satisfaction during the implementation of the independent type is fed back according to the maintenance coefficient, so as to screen out unqualified training types, conduct targeted training for the training types, improve user experience and reasonably allocate training resources.

[0032] The store recommendation module is used to perform service store recommendation analysis for users: when users need vehicle services, they upload demand information on the user side. The demand information includes the user's location information, demand type, and service vehicle; draw a circle with the user's location as the center and R1 as the radius, mark the obtained circular area as the initial screening area, mark the vehicle service stores in the initial screening area as initial screening objects, and obtain the direct distance data ZJ, idle data KX, and training data PX of the initial screening objects; the direct distance data ZJ is the straight-line distance value between the location of the initial screening object and the user's location, the idle data KX is the number of idle personnel of the service demand type in the initial screening object, and the training data PX is the number of times the demand type in the initial screening object has been marked as a training type in the most recent L2 analysis cycles; through the formula The recommendation coefficient TJ of the initial screening object is obtained, where k1, k2 and k3 are all proportional coefficients, and k1>k2>k3>1; the recommendation threshold TJmin is obtained through the database, and the recommendation coefficient TJ is compared with the recommendation threshold TJmin: if the recommendation coefficient TJ is less than the recommendation threshold TJmin, it is determined that the initial screening object does not meet the recommendation requirements, and the corresponding initial screening object is marked as a rejection object; if the recommendation coefficient TJ is greater than or equal to the recommendation threshold TJmin, it is determined that the initial screening object meets the recommendation requirements, and the corresponding initial screening object is marked as a recommended object; all recommended objects are arranged in descending order according to the value of the recommendation coefficient TJ to obtain a recommendation sequence, and the recommendation sequence is sent to the user's mobile terminal through the server; the recommendation coefficient is obtained by comprehensive analysis and calculation based on multiple objective visualization data of the vehicle service store, and the matching degree between the vehicle service store and the user is fed back through the recommendation coefficient, and the recommended objects that have completed the screening are prioritized and a recommendation sequence is generated to improve the service convenience while retaining the user's right to choose.

[0033] The store management module is used to supervise, manage and analyze the service quality of vehicle service stores: retrieve the serial number of the recommended object finally selected by the user in the recommendation sequence and mark it as the supervision value. When the supervision value is one, no processing is done; when the supervision value is not one, the vehicle service store with a serial number less than the supervision value is marked as an autonomous elimination object; the number of times the vehicle service store is marked as an autonomous elimination object during the analysis period is marked as the autonomous elimination value of the vehicle service store, and the autonomous elimination value is compared with the preset autonomous elimination threshold: if the autonomous elimination value is less than the autonomous elimination threshold, it is determined that the service quality of the vehicle service store meets the requirements; if the autonomous elimination value is greater than or equal to the autonomous elimination threshold, it is determined that the service quality of the vehicle service store does not meet the requirements, and the corresponding vehicle service store is marked as a supervision store, and all supervision stores are sent to the mobile phone terminal of the manager through the server; the autonomous elimination value of the vehicle service store is generated according to the user's autonomous selection, and the user's subjective satisfaction with the vehicle service store is fed back according to the autonomous elimination value, and then the supervision stores are screened according to the feedback results, the service supervision of the supervision stores is strengthened, and the overall service quality of the platform is improved.

[0034] Embodiment 2: Figure 2 As shown, a vehicle service management method comprises the following steps:

[0035] Step 1: Perform association analysis on the service types of the vehicle service management platform: Generate an analysis cycle, the vehicle range is composed of the highest and lowest values ​​of the new car prices of the service vehicles of all associated objects, the vehicle range is divided into several vehicle intervals, and all service types corresponding to the associated data of all matching objects constitute the associated data set of the vehicle interval;

[0036] Step 2: Perform management analysis on the service quality of the vehicle service management platform: mark the user with one service type in all the user data as the analysis object, mark the number of services corresponding to the analysis object in the analysis period as the service value of the analysis object, obtain the maintenance coefficient of the independent type through the service value, and judge whether the service quality of the independent type in the analysis period meets the requirements through the maintenance coefficient;

[0037] Step 3: Analyze the service store recommendations for users: When users need vehicle services, they upload their demand information on the user side. According to the demand information, the initial screening objects are marked as excluded objects or recommended objects. All recommended objects are arranged in descending order according to the value of the recommendation coefficient TJ to obtain a recommendation sequence.

[0038] Step 4: Conduct supervision and management analysis on the service quality of vehicle service stores and screen the supervision stores among the vehicle service stores.

[0039] A vehicle service management platform generates an analysis cycle when working, wherein the vehicle range is formed by the highest and lowest values ​​of the new car prices of the service vehicles of all associated objects, the vehicle range is divided into a number of vehicle intervals, and the associated data set of the vehicle interval is formed by all service types corresponding to the associated data of all matching objects; a user with one service type in all user data of the user is marked as an analysis object, the number of services corresponding to the analysis object in the analysis cycle is marked as the service value of the analysis object, the maintenance coefficient of the independent type is obtained through the service value, and whether the service quality of the independent type in the analysis cycle meets the requirements is judged through the maintenance coefficient; when a user needs vehicle service, the user uploads the demand information on the user side, marks the pre-screened object as a rejection object or a recommendation object according to the demand information, and arranges all the recommended objects in descending order according to the value of the recommendation coefficient TJ to obtain a recommendation sequence; the service quality of vehicle service stores is supervised, managed and analyzed, and the supervision stores among the vehicle service stores are screened.

[0040] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0041] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula A technician in this field collects multiple groups of sample data and sets corresponding recommended coefficients for each group of sample data; substitutes the set recommended coefficients and the collected sample data into the formula, and any three formulas constitute a three-variable linear equation system. The calculated coefficients are screened and averaged, and the values ​​of k1, k2, and k3 are obtained to be 4.25, 2.81, and 2.06, respectively;

[0042] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the technical personnel in this field preliminarily set the corresponding recommended coefficient for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the recommended coefficient is proportional to the value of the idle data.

[0043] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0044] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A vehicle service management platform, characterized in that: It includes a server, wherein the server is communicatively connected with a correlation analysis module, a service management module, a store recommendation module, a store management module and a database; The association analysis module is used to perform association analysis on the service types of the vehicle service management platform: generate an analysis cycle, obtain all service data of the vehicle service management platform within the analysis cycle, screen the associated objects through the service data, obtain the brand, model and new car price of the service vehicle in the user data of the associated objects, form a vehicle range by the highest and lowest values ​​of the new car prices of the service vehicles of all associated objects, divide the vehicle range into several vehicle intervals, obtain the associated data set of the vehicle intervals, and send the associated data sets of all vehicle intervals to the mobile phone terminal of the manager through the server; The service management module is used to manage and analyze the service quality of the vehicle service management platform: mark the user whose service type number is one in all user data of the user as the analysis object, mark the service type in the user data of the analysis object as an independent type, mark the number of services corresponding to the analysis object in the analysis period as the service value of the analysis object, and mark the analysis object as a lost object or a maintenance object through the service value; mark the ratio of the service number of the maintenance object corresponding to the independent type in the analysis period to the service number of the lost object as the maintenance coefficient of the independent type, and judge whether the service quality of the independent type in the analysis period meets the requirements through the maintenance coefficient; The store recommendation module is used to perform service store recommendation analysis on users; The store management module is used to supervise, manage and analyze the service quality of vehicle service stores.

2. A vehicle service management platform according to claim 1, characterized in that: The process of obtaining associated objects includes: service data includes user information, service type, service time, service vehicle and service personnel; all service data corresponding to the same user information are marked as user data of the user, and users whose service types are not less than L1 in all user data of the user are marked as associated objects.

3. A vehicle service management platform according to claim 2, characterized in that: The process of obtaining the associated data set of the vehicle interval includes: marking the associated objects whose new car prices of the service vehicles are within the vehicle interval as matching objects of the vehicle interval; forming a type set of the vehicle interval by the service types of all matching objects within the vehicle interval, marking the number of elements of the service type in the type set as the associated value of the service type, marking the service type with the largest associated value as the associated type, marking the user data containing the associated type in the matching object as associated data, and forming the associated data set of the vehicle interval by all service types corresponding to the associated data of all matching objects.

4. A vehicle service management platform according to claim 3, characterized in that: The specific process of marking an analysis object as a lost object or a maintenance object includes: retrieving the service threshold of an independent type through the database, and comparing the service value with the service threshold: if the service value is less than the service threshold, the corresponding analysis object is marked as a lost object; if the service value is greater than or equal to the service threshold, the corresponding analysis object is marked as a maintenance object.

5. A vehicle service management platform according to claim 4, characterized in that: The specific process of determining whether the service quality of the independent type during the analysis period meets the requirements includes: comparing the maintenance coefficient with the preset maintenance threshold: if the maintenance coefficient is less than the maintenance threshold, it is determined that the service quality of the independent type during the analysis period does not meet the requirements, the independent type is marked as a training type, a training optimization signal is generated, and the training optimization signal and the training type are sent to the mobile terminal of the administrator through the server; if the maintenance coefficient is greater than or equal to the maintenance threshold, it is determined that the service quality of the independent type during the analysis period meets the requirements.

6. A vehicle service management platform according to claim 5, characterized in that: The specific process of the store recommendation module for analyzing service store recommendations for users includes: when a user needs vehicle service, the user uploads demand information on the user side, and the demand information includes the user's location information, demand type and service vehicle; draws a circle with the user's location as the center and R1 as the radius, and marks the obtained circular area as the initial screening area, and marks the vehicle service stores in the initial screening area as initial screening objects, obtains the direct distance data ZJ, idle data KX and training data PX of the initial screening objects, and performs numerical calculations to obtain the recommendation coefficient TJ; the initial screening objects are marked as elimination objects or recommendation objects through the recommendation coefficient TJ; all recommended objects are arranged in descending order according to the value of the recommendation coefficient TJ to obtain a recommendation sequence, and the recommendation sequence is sent to the user's mobile terminal through the server.

7. A vehicle service management platform according to claim 6, characterized in that: The specific process of marking the initial screening object as an elimination object or a recommended object includes: obtaining the recommended threshold value TJmin through the database, and comparing the recommended coefficient TJ with the recommended threshold value TJmin: if the recommended coefficient TJ is less than the recommended threshold value TJmin, it is determined that the initial screening object does not meet the recommended requirements, and the corresponding initial screening object is marked as an elimination object; if the recommended coefficient TJ is greater than or equal to the recommended threshold value TJmin, it is determined that the initial screening object meets the recommended requirements, and the corresponding initial screening object is marked as a recommended object.

8. A vehicle service management platform according to claim 7, characterized in that: The specific process of the store management module supervising, managing and analyzing the service quality of vehicle service stores includes: retrieving the serial number of the recommended object finally selected by the user in the recommendation sequence and marking it as the supervision value, and no processing is performed for the time being when the supervision value is one; when the supervision value is not one, the vehicle service store with a serial number less than the supervision value is marked as an autonomous elimination object; the number of times the vehicle service store is marked as an autonomous elimination object during the analysis period is marked as the autonomous elimination value of the vehicle service store, and the autonomous elimination value is compared with the preset autonomous elimination threshold: if the autonomous elimination value is less than the autonomous elimination threshold, it is determined that the service quality of the vehicle service store meets the requirements; if the autonomous elimination value is greater than or equal to the autonomous elimination threshold, it is determined that the service quality of the vehicle service store does not meet the requirements, and the corresponding vehicle service store is marked as a supervision store, and all supervision stores are sent to the mobile phone terminal of the manager through the server.

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