Car washing service management system based on car washing information platform

Abstract

The invention relates to the technical field of service management, and discloses a car washing service management system based on a car washing information platform. Comprising a merchant portrait combination module for combining distribution data and preferential data of available merchants into merchant portraits, a portrait library conversion module for constructing a portrait library, a user request construction module for constructing user requests with position features, a target merchant screening module for screening target merchants from candidate merchants, and a target merchant recommendation module. Recommending the target merchants to the user in sequence; according to the method, association identification can be carried out on different dealers with relatively high similar overlap ratio in the portrait library, and a dual independent screening query effect of a user on a target merchant on a car washing information platform can be realized by combining primary screening of position features and associated channels and a secondary screening mode of user requests and candidate merchants, so that the user experience is improved. It is ensured that the screened target merchant can meet the actual demand of the user, and the effect of car washing service management is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of service management. More specifically, the present invention relates to a car wash service management system based on a car wash information platform. Background Art

[0002] With the continuous growth of the car ownership, the traditional offline reservation car wash and phone query car wash service management mode can no longer meet the increasingly intense car wash service demand. With the continuous development of the Internet, a car wash service management system that integrates multi-dimensional data such as merchant information, user evaluations, and geographical location data has initially achieved the effects of online display, query reservation, and service management of car wash services, and has greatly promoted the intelligent, networked, and healthy development of car wash service management.

[0003] The patent application with the publication number CN118396723A discloses a method, system, device, and storage medium for pushing car wash service information of users, including extracting the historical car wash data and corresponding historical condition data of the user's vehicle, and performing standardized processing, conducting a correlation analysis on the historical car wash data and historical condition data to obtain the user's habitual set, constructing a user portrait, predicting the user's car wash time based on a preset ARIMA prediction model and historical car wash data, generating an optimal push time and personalized recommendation strategy, and pushing car wash service information; through in-depth analysis of the user's historical car wash data and conditional factors, as well as the construction of a personalized label set and user portrait, it can provide car wash service recommendations that are extremely close to the user's personal habits and preferences, increasing the attractiveness of the service and user satisfaction; The existing car wash service management system collects the user's historical car wash data and current car wash demand information to comprehensively describe the user's car wash service characteristics, and then screens out merchants that match the car wash service characteristics at one time. For example, in the above patent application, through the correlation analysis of historical car wash data and historical condition data, a user portrait is constructed to provide the car wash service recommendations required by the user. However, the method of screening by portrait matching at one time will result in uneven degrees of correlation and matching between the multiple merchants screened out and the user's service needs, and it is impossible to ensure that all the multiple merchants screened are highly correlated and similar to the user's service needs, thus reducing the accuracy of recommending merchants to users.

[0004] In view of this, the present invention proposes a car wash service management system based on a car wash information platform to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solution: A car wash service management system based on a car wash information platform, which is applied to a car wash information platform and includes: The merchant profile combination module collects the distribution data and preferential data of registered merchants, screens out available merchants from the registered merchants based on the service management criteria, and combines the distribution data and preferential data of the available merchants into a merchant profile. The service management criteria are as follows: Exclude the registered merchants with the first abnormal data or the second abnormal data; The profile library conversion module constructs an information library with portrait units distributed in a ring, imports the merchant profile into the portrait units, and establishes an association channel between the contour points of the portrait units with associated features, prompting the information library to be converted into a profile library; The user request construction module collects the real-time location and historical user tags of the user, fuses the real-time location and historical user tags, and constructs a user request with location features; The target merchant screening module combines the location features with the association channel, screens out candidate merchants from the available merchants, and performs profile matching between the user request and the candidate merchants to screen out the target merchants; The target merchant recommendation module formulates a recommendation priority based on the car wash recommendation parameters of the target merchants and recommends the target merchants to the user in order.

[0006] Furthermore, the distribution data includes the store address, business hours, word-of-mouth level, service type, and type price; the preferential data includes the coupon type and the number of coupons; The screening method for available merchants is as follows: Count the quantity of the distribution data and the quantity of the preferential data among the registered merchants one by one, and record them as the first quantity value and the second quantity value respectively; When the first quantity value is less than 5, record the distribution data as the first abnormal data; When the second quantity value is less than 2, record the preferential data as the second abnormal data; Exclude the registered merchants with the first abnormal data or the second abnormal data, and record the remaining registered merchants as available merchants, obtaining A available merchants.

[0007] Furthermore, the combination method of the merchant profile is as follows: Establish A portrait contours with three vertically parallel distributed tag layers respectively, and record the three tag layers as the basic layer, the behavior layer, and the value layer in the order from bottom to top; Set S tag positions in the basic layer, the behavior layer, and the value layer respectively, and draw tag dividing lines between adjacent tag positions; Import the store addresses, business hours, and word-of-mouth levels of the A available merchants into the tag positions of the A basic layers one by one, import the service types and type prices of the A available merchants into the tag positions of the A behavior layers one by one, and import the coupon types and the number of coupons of the A available merchants into the tag positions of the A value layers one by one; Remove the redundant tag bits in A basic layers, A behavior layers, and A value layers respectively, so as to generate A merchant portraits from A portrait outlines.

[0008] Furthermore, the method for constructing the information library is as follows: Establish a database with A blank data bits arranged in a ring, and draw a circular bit outline line on the outside of each of the A blank data bits one by one to generate A portrait units; Mark a point on the bit outline line near the previous bit outline line and near the next bit outline line respectively, denoted as the first outline point and the second outline point; Measure the distance from the first outline point to the previous bit outline line and the distance from the second outline point to the next bit outline line one by one, and denote them as the first distance value and the second distance value respectively; Continuously adjust the positions of the first outline point and the second outline point until both the first distance value and the second distance value reach the minimum value, and then stop the adjustment to construct the information library.

[0009] Furthermore, when identifying associated features, split the same type of distribution data or preferential data within the tag bits of two portrait units into two element sets through natural language processing technology; When the number of elements with the same meaning in the two element sets is greater than or equal to one-half of the total number of elements in any one of the element sets, mark the elements with the same meaning within the tag bits of the two portrait units as associated features.

[0010] Furthermore, the method for converting the portrait library is as follows: Import A merchant portraits into the A portrait units of the information library one by one, number the A portrait units in ascending order according to the import sequence, and summarize the two portrait units with associated features into a unit group to obtain C unit groups; In the C unit groups, mark the second outline point of the portrait unit with a smaller number as the channel start point, mark the first outline point of the portrait unit with a larger number as the channel end point, and establish a two-way conduction channel between the channel start point and the channel end point to obtain C associated channels; Remark the associated features of the C unit groups on the corresponding associated channels respectively, so as to convert the information library into a portrait library.

[0011] Furthermore, the historical user tags include service unit price range, service demand type, and service reputation lower limit; The method for constructing the user request is as follows: Establish a basic request with three blank request bits, and denote the three blank request bits as the first request bit, the second request bit, and the third request bit respectively; Import the user's service unit price range, service demand type, and service reputation lower limit into the first request position, the second request position, and the third request position respectively to generate a unit price request position, a type request position, and a reputation request position; Assign the unit price request position, the type request position, and the reputation request position the ranking symbols DJW, LXW, and KBW respectively to convert the basic request into a user request; Set a feature box on the user request and import the user's real-time position into the feature box to generate a user request with location features.

[0012] Furthermore, the screening method for candidate merchants is as follows: Import the location features of the user request and the store addresses of A available merchants in the portrait library onto the same electronic map to obtain a request point and A store points; Measure the request distance values from the request point to the A store points one by one, and record the store points with request distance values less than the distance upper limit value as candidate points to obtain D candidate points; Mark the portrait units corresponding to the D candidate points one by one in the information library, and count the number of associated features on the associated channels connected to the portrait units; Eliminate the portrait units at both ends of the associated channels with the number of associated features being 1, and record the available merchants corresponding to the remaining portrait units as candidate merchants to obtain E candidate merchants.

[0013] Furthermore, the screening method for target merchants is as follows: Use natural language processing technology to identify the text parts and numerical parts of the service unit price range, service type, service reputation lower limit, reputation level, service type, and type price respectively, and extract the numerical values in the numerical parts; When the numerical value of the service unit price range coincides with the numerical value of the type price, record the type price as the matching data; When the numerical value of the service demand type coincides with the numerical value of the service type, record the service type as the matching data; When the numerical value of the service reputation lower limit is less than or equal to the numerical value of the reputation level, record the reputation level as the matching data; Count the number of matching data among the E candidate merchants one by one, and record the candidate merchants with the number of matching data being 3 as target merchants to obtain F target merchants.

[0014] Furthermore, the car wash recommendation parameters include the driving distance value, the driving duration value, and the remaining work station value; The recommendation priority is: the priority of the driving duration value is higher than the priority of the driving distance value, and the priority of the driving distance value is higher than the priority of the remaining work station value; The method of recommending target merchants to users in order is as follows: Recommend the F target merchants to the user in ascending order of driving duration values; When there are target merchants with the same driving duration value, recommend the target merchants to the user in ascending order of driving distance values; When there are target merchants with the same driving duration value and driving distance value, recommend the target merchants to the user in descending order of remaining workstations; When there are target merchants with the same driving distance value, driving duration value, and remaining workstation value, randomly recommend the target merchants to the user.

[0015] The technical effects and advantages of a car wash service management system based on a car wash information platform according to the present invention: (1): By combining distribution data and preferential data into a merchant portrait and fusing the merchant portrait with portrait units, a portrait library with an association channel is constructed, so as to collect, fuse, and summarize and display the car wash service information of registered and qualified merchants in the car wash information platform in multiple dimensions and accurately. And by performing an association conduction operation on portrait units with associated features through the association channel, different dealers with a high similarity overlap in the portrait library can be associated and identified, avoiding the negative interference brought by different dealers with a low similarity overlap in the subsequent screening and identification process, and facilitating the accurate query operation of associated dealers in the future.

[0016] (2): By fusing the real-time position with historical user tags into a user request with position characteristics, the real-time nature and characteristics of the user request can be more prominent, improving the matching efficiency and accuracy when the user request queries portrait units in the portrait library later. And by combining the first-level screening of position characteristics and the association channel, and the second-level screening method of user requests and candidate merchants, a double independent screening and query effect of target merchants by the user in the car wash information platform can be achieved, avoiding the problem of inaccurate screening results existing in a single screening method, and effectively combining the factors of the matching and association similarity degree between different target merchants and user requests, ensuring that the selected target merchants can meet the actual needs of users, and effectively improving the effect of car wash service management.

[0017] (3): By formulating a recommendation priority to recommend target merchants in an orderly manner, the orderly, accurate, and reasonable recommendation operation of target merchants can be realized according to the high and low degree of association similarity between target merchants and user requests, ensuring that the car wash service management system can quickly and orderly recommend relevant car wash information that meets the needs to the user in the first time, and further improving the effect of car wash service management. Brief Description of the Drawings

[0018] Figure 1Schematic diagram of the architecture of a car wash service management system based on a car wash information platform provided in Embodiment 1 of the present invention; Figure 2 Schematic flowchart of a car wash service management method based on a car wash information platform provided in Embodiment 2 of the present invention. Detailed implementation manners

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Embodiment 1: Please refer to Figure 1 As shown, a car wash service management system based on a car wash information platform described in this embodiment is applied to a car wash information platform and includes: A merchant portrait combination module, which collects the distribution data and preferential data of registered merchants, screens out available merchants from the registered merchants based on service management criteria, and combines the distribution data and preferential data of the available merchants into a merchant portrait; A registered merchant refers to a car wash distribution terminal that has completed the information registration of car wash service management in the car wash information platform as of the current moment and has obtained the right to perform normal car wash information management in the car wash information platform. In this embodiment, the car wash distribution terminal is a car wash store.

[0021] Distribution data refers to the inherent store registration information of a registered merchant when registering in the car wash information platform, so that the distribution data can represent the inherent information of the registered merchant; The distribution data includes store address, business hours, word-of-mouth level, service type, and type price; Specifically, the store address refers to the location of the car wash store of the registered merchant in the car wash information platform, which is used to accurately represent the car wash address of the registered merchant. The business hours refer to the normal business hours of the store of the registered merchant in the car wash information platform. The word-of-mouth level refers to the service word-of-mouth evaluation level of the registered merchant in the car wash information platform. The service type refers to the car wash types available for selection by the registered merchant in the car wash information platform. The type price refers to the single charge price of each car wash type of the registered merchant in the car wash information platform. The store address, business hours, word-of-mouth level, service type, and type price are obtained by querying the merchant database in the registration database in the car wash information platform.

[0022] The database in the registered database car wash information platform used to record and save various data. The registered database includes a merchant database and a user database. The merchant database is used to record and save the data of dealers, and the user database is used to record and save the data of users.

[0023] It should be noted that through the accurate collection of store address, business hours, word-of-mouth level, service type, and type price, multi-dimensional acquisition of the inherent registration information of registered merchants can be carried out. At the same time, when changes and updates occur in the store address, business hours, word-of-mouth level, service type, and type price of registered merchants, the changed and updated store address, business hours, word-of-mouth level, service type, and type price also need to be updated and replaced in real time.

[0024] The preferential data refers to the car wash service preferential information of registered merchants at the current moment in the car wash information platform, which is used to accurately represent the current car wash preferential situation of registered merchants; The preferential data includes coupon type and coupon quantity; Specifically, the coupon type refers to the type of car wash preferential service vouchers issued by registered merchants for car wash services at the current moment, which can represent the specific car wash preferential type, and the coupon quantity refers to the number of coupon types issued by registered merchants at the current moment; the coupon type and coupon quantity are obtained by querying the merchant database of the car wash information platform.

[0025] After obtaining the distribution data and preferential data of registered merchants, it is necessary to identify the obtained data, and under the restriction of service management guidelines, identify and screen whether the registered merchants meet the conditions for subsequent car wash service management, and record the registered merchants that meet the conditions for subsequent car wash service management as available merchants.

[0026] The service management guidelines are: excluding registered merchants with first abnormal data or second abnormal data; this can ensure the rationality of the quantity of distribution data and preferential data of the available merchants left after screening, avoid registered merchants lacking distribution data and preferential data from participating in subsequent car wash service management operations, and thus improve the screening threshold of available merchants.

[0027] The screening method for available merchants is: Count the quantity of distribution data and the quantity of preferential data among registered merchants one by one, and record them as the first quantity value and the second quantity value respectively; When the first quantity value is less than 5, it indicates that there is a phenomenon of quantity shortage in the distribution data, and the distribution data is recorded as the first abnormal data; When the second quantity value is less than 2, it indicates that there is a phenomenon of quantity shortage in the preferential data, and the preferential data is recorded as the second abnormal data; Exclude the registered merchants with the first abnormal data or the second abnormal data, and record the remaining registered merchants as available merchants, obtaining A available merchants.

[0028] After screening out the available merchants, the distribution data and preferential data of the available merchants can be combined and summarized, and the combined and summarized data is constructed into a merchant portrait, so that the merchant portrait can comprehensively represent the car washing service situation shown by the available merchants in the car washing information platform and serve as the judgment basis for subsequent users to select available merchants as car washing objects; The combination method of the merchant portrait is as follows: Establish A portrait outlines with three tag layers distributed parallel up and down respectively, and in the order from bottom to top, the three tag layers are respectively recorded as the basic layer, the behavior layer and the value layer; the portrait outline refers to a merchant portrait without any substantial content, and the tag layer is the smallest unit that composes the portrait outline and provides import position limits for different types of data in the subsequent distribution data and preferential data; Set S tag positions in the basic layer, the behavior layer and the value layer respectively, and draw tag dividing lines between adjacent two tag positions; the tag position is used to position a specific data in the distribution data and preferential data to ensure the independence of each data, and the tag dividing line is used to separate the data in adjacent two tag positions to avoid the data in the tag positions at adjacent positions from intersecting and arranging disorderly; Import the store addresses, business hours and word-of-mouth levels of A available merchants into the tag positions of A basic layers one by one, import the service types and type prices of A available merchants into the tag positions of A behavior layers one by one, and import the coupon types and coupon quantities of A available merchants into the tag positions of A value layers one by one; Exclude the redundant tag positions in A basic layers, A behavior layers and A value layers respectively, so that A portrait outlines generate A merchant portraits.

[0029] It should be noted that each available merchant has and only has one corresponding merchant portrait, ensuring that the relevant information of each available merchant in the car washing information platform can be completely and comprehensively integrated and summarized together, thereby facilitating subsequent identification and matching operations.

[0030] The portrait library conversion module constructs an information library with portrait units distributed in a ring, imports the merchant portraits into the portrait units, and establishes an association channel between the portrait units with associated features, so that the information library is converted into a portrait library; An information database refers to a database into which no substantial merchant portraits have been imported, and enables the information database to serve as the basis for constructing subsequent portrait databases. A portrait unit is the smallest constituent unit of the information database and provides an accurate position limit for the import of merchant portraits, enabling a merchant portrait to correspond to a portrait unit; The method for constructing the information database is as follows: Establish a database with A blank data bits arranged in a ring, and draw a circular bit contour line on the outside of each of the A blank data bits one by one to generate A portrait units; a blank data bit refers to a data bit without any substantial content. By drawing the bit contour line, each blank data bit can play a role in closed protection, ensuring that the data in each data bit will not leak, and improving data security; Mark a point on the bit contour line near the previous bit contour line and near the next bit contour line respectively, denoted as the first contour point and the second contour point; the first contour point and the second contour point are the two endpoints used to construct the association channel and provide an accurate position limit for the establishment of the association channel; Measure the distance from the first contour point to the previous bit contour line and the distance from the second contour point to the next bit contour line one by one, and denote them as the first distance value and the second distance value respectively; Continuously adjust the positions of the first contour point and the second contour point until the first distance value and the second distance value both reach the minimum value, and then stop the adjustment to construct the information database.

[0031] After constructing the information database, the merchant portraits can be imported into each portrait unit of the information database one by one, so that each portrait unit contains data with practical significance. After importing the merchant portraits into the portrait units, there may be similar or identical distribution data or preferential data between different merchant portraits, so there will be an association between different portrait units. Therefore, the corresponding data of the similar or identical distribution data or preferential data is recorded as an association feature; Specifically, when judging whether there is an association feature between any two portrait units, it is necessary to compare the similarity overlap degree of the distribution data or preferential data in the label bits of any two portrait units, and determine that there is an association feature between the two portrait units with a similarity overlap degree greater than the calibrated overlap degree.

[0032] The similarity overlap degree is a numerical representation of the similarity degree of the distribution data or preferential data with the same meaning in the label bits of two portrait units, and can be specifically judged by comparing the number of similar elements between the distribution data or preferential data with the same meaning; Exemplarily, through natural language processing technology, the distribution data or preferential data of the same type within the tag bits of two portrait units are elementarily split to form two element sets. When the number of elements with the same meaning in the two element sets is greater than or equal to one-half of the total number of elements in any one of the element sets, it is determined that there is an association feature between the two portrait units, and the elements with the same meaning in the tag bits of the two portrait units are recorded as the association feature.

[0033] The association channel is a data channel used for two-way transmission of distribution data or verification data between two portrait units with an association feature, and realizes the association query effect of car wash service management between the portrait units with an association feature.

[0034] The portrait library refers to an information library that can contain the complete information of portrait units and association channels, enabling the portrait library to comprehensively integrate the car wash information of all available merchants and laying a foundation for subsequent user query management of car wash information; The conversion method of the portrait library is as follows: Import A merchant portraits into the A portrait units of the information library one by one. According to the order of import, sequentially number the A portrait units in ascending order, and summarize the two portrait units with an association feature into a unit group to obtain C unit groups; In the C unit groups, record the second contour point of the portrait unit with a smaller number as the channel starting point, record the first contour point of the portrait unit with a larger number as the channel end point, and establish a two-way conduction channel between the channel starting point and the channel end point to obtain C association channels; Remark the association features of the C unit groups on the corresponding association channels respectively, prompting the information library to be converted into a portrait library.

[0035] It should be noted that the method of remarking the association feature on the association channel can clearly and explicitly identify the type and attributes of each association channel, and is convenient for subsequent precise query and service management of the car wash information in the portrait library.

[0036] The user request construction module collects the real-time location and historical user tags of the user, fuses the real-time location and historical user tags, and constructs a user request with location features; The real-time location refers to the current geographical location of the user who needs to query and manage car wash information, which can provide a comparison of the location distance for the subsequent screening and query of available merchants; the real-time location is obtained through real-time positioning query on the electronic map.

[0037] The historical user tag refers to the specific information generated when the user performs operations such as car wash services within the past time period on the car wash information platform, which can comprehensively display the user's specific car wash service consumption situation in the past; Historical user tags include service unit price range, service demand type, and lower limit of service reputation; Specifically, the service unit price range refers to the minimum to maximum values of the car wash unit price for each car wash by the user during the past period within the car wash information platform. The service demand type refers to the types of all car washes by the user during the past period within the car wash information platform. The lower limit of service reputation refers to the lowest value of the reputation level of the corresponding car wash store by the user during the past period within the car wash information platform. The service unit price range, service demand type, and lower limit of service reputation are obtained by querying the user database of the car wash information platform.

[0038] After obtaining the user's real-time location and historical user tags, at this time, the real-time location and historical user tags can be subjected to information fusion between different information, so as to effectively and accurately fuse different types of information together, and finally generate a user request that conforms to the user's behavior during the past period on the car wash information platform; After constructing the user request, the user request at this time can only be used to represent the user's historical information as a whole. In order to highlight the real-time nature of the user request and improve the query and matching efficiency of the user request with the portrait library in the follow-up, it is necessary to mark the location feature on the user request, so that the location feature can accurately represent the specific location of the specific car wash service management of the user request, and is the key point for subsequent matching and screening with the merchant portrait; The construction method of the user request is as follows: Establish a basic request with three blank request positions, and record the three blank request positions as the first request position, the second request position, and the third request position respectively; Import the user's service unit price range, service demand type, and lower limit of service reputation into the first request position, the second request position, and the third request position respectively to generate a unit price request position, a type request position, and a reputation request position; Assign the rank symbols DJW, LXW, and KBW to the unit price request position, the type request position, and the reputation request position respectively, so that the basic request is converted into a user request; the rank symbol is the basis for distinguishing and identifying the unit price request position, the type request position, and the reputation request position, and maintains the uniqueness of each request position; Set a feature box on the user request, and import the user's real-time location into the feature box to generate a user request with a location feature. The feature box is a blank data group used to provide a note for the location feature of the user request to ensure that the real-time location can be associated with the user request and noted.

[0039] It should be noted that the constructed user request with a location feature serves as the query basis for subsequent car wash service management in the portrait library, and provides a screening basis with double screening conditions for the subsequent query and screening of the required merchants.

[0040] The target merchant screening module combines location features with associated channels to screen candidate merchants from available merchants, and performs portrait matching between the user request and the candidate merchants to screen out target merchants from the candidate merchants; Candidate merchants refer to the available merchants corresponding to the merchant portraits initially screened from the portrait library based on the location features of the user request and in combination with the associated channels. At this time, the candidate merchants are only the results of preliminary screening, resulting in a relatively large number of candidate merchants, and all candidate merchants can maintain a basic association with the user request; When screening candidate merchants, it is necessary to perform sequential screening in the manner of location feature - associated channel - candidate merchant; Specifically, first, import the location features of the user request and the store addresses of A available merchants in the portrait library into the same electronic map to obtain a request point and A store points; Then, measure the request distance values from the request point to the A store points one by one, and record the store points with request distance values less than the distance upper limit value as candidate points to obtain D candidate points; the distance upper limit value refers to the maximum value of the distance length between the user's real-time location and the store address of the candidate merchant on the electronic map, so as to provide a numerical limit on the distance and location of the candidate merchants that meet the subsequent user service requirements; Next, mark the portrait units corresponding to the D candidate points in the information library one by one, and count the number of associated features on the associated channels connected to the portrait units; Finally, eliminate the portrait units at both ends of the associated channels with the number of associated features being 1, and record the available merchants corresponding to the remaining portrait units as candidate merchants to obtain E candidate merchants.

[0041] After screening out the candidate merchants, at this time, the candidate merchants can maintain a basic association with the user request in terms of location distance, but the candidate merchants may not necessarily meet the requirements of other dimensional data in the user request, and it is still necessary to perform a second screening process on the candidate merchants to obtain target merchants, so that the target merchants can be used as the final distributors associated and matched with the user request; When performing the second screening of the target merchants, it is necessary to perform portrait matching on the specific data between the user request and the candidate merchants, so as to represent the degree of matching compliance between the user request and the candidate merchants, and improve the screening accuracy of the target merchants; The screening method for target merchants is as follows: Perform portrait matching on the service unit price range, service demand type, and service reputation lower limit of the user request with the reputation level, service type, and type price of the E candidate merchants respectively; Using natural language processing technology, respectively identify the text part and the numerical part of the service unit price range, service type, lower limit of service reputation, reputation level, service type, and type price, and extract the numerical value in the numerical part; the text part and the numerical part are respectively used to concisely represent the content of the text and the content of the number for each piece of data, and the numerical value is used to directly represent the numerical size of the numerical part; When the numerical value of the service unit price range coincides with the numerical value of the type price, it indicates that there is a matching phenomenon in the single - time price of car washing between the user request and the candidate merchant, and then the type price is recorded as matching data; When the numerical value of the service demand type coincides with the numerical value of the service type, it indicates that there is a matching phenomenon in the type of car washing between the user request and the candidate merchant, and then the service type is recorded as matching data; When the numerical value of the lower limit of service reputation is less than or equal to the numerical value of the reputation level, it indicates that there is a matching phenomenon in the reputation evaluation level of the car - washing store between the user request and the candidate merchant, and then the reputation level is recorded as matching data; Count the number of matching data among E candidate merchants one by one, and record the candidate merchant with the number of matching data being 3 as the target merchant, obtaining F target merchants.

[0042] It should be noted that the selected target merchants can be directly recommended to the user as car - washing service dealers, enabling the user to arbitrarily select one of the target merchants as the car - washing object. In actual situations, the number of target merchants is usually not less than 1 to ensure that multiple different car - washing dealers can be provided for the user to choose from.

[0043] The target merchant recommendation module obtains the car - washing recommendation parameters of the target merchants, formulates the recommendation priority, and recommends the target merchants to the user in order according to the recommendation priority; The car - washing recommendation parameters refer to multi - dimensional data that affect the order of the user's choice of the target merchant as the best car - washing dealer, and serve as the basis for formulating the recommendation priority for the order of recommending the target merchants to the user; The car - washing recommendation parameters include the driving distance value, driving duration value, and remaining work - station value; Specifically, the driving distance value refers to the length of the driving distance on the electronic map from the user's real - time location to the store address of the target merchant. When the driving distance value is larger, the probability of the user choosing the target merchant is smaller, and the recommendation priority of the target merchant is lower. The driving duration value refers to the length of the driving time on the electronic map from the user's real - time location to the store address of the target merchant. When the driving duration value is larger, the probability of the user choosing the target merchant is smaller, and the recommendation priority of the target merchant is lower; both the driving distance value and the driving duration value are obtained by querying the electronic map.

[0044] The remaining station value refers to the number of idle car wash stations that the target merchant can provide externally at the current moment. The greater the remaining station value, the greater the probability that the user will choose the target merchant, and the higher the recommendation priority of the target merchant. The remaining station value is obtained by querying the merchant database in the car wash information platform.

[0045] When formulating the recommendation priority, it is necessary to consider from three dimensions: the driving distance value, the driving duration value, and the remaining station value of the target merchant. Specifically, since the driving duration value directly affects the length of time for the user to reach the target merchant and has the greatest impact on the user's experience of car wash recommendations, the priority of the driving duration value is the highest. The remaining station value does not directly affect the length of time for the user to reach the target merchant and has the least impact on the user's experience of car wash recommendations, so the priority of the remaining station value is the lowest. To sum up, the recommendation priority is: the priority of the driving duration value is higher than that of the driving distance value, and the priority of the driving distance value is higher than that of the remaining station value.

[0046] After formulating the recommendation priority, at this time, it is necessary to recommend the F target merchants to the user in order according to the recommendation priority, so as to achieve an orderly, accurate and reasonable recommendation effect for the F target merchants, and meet the user's management needs for car wash services as much as possible. Specifically, the method of recommending target merchants to the user in order is as follows: Compare the driving duration values of the F target merchants one by one, and recommend the F target merchants to the user in turn in the order of increasing driving duration value. When there are target merchants with the same driving duration value, recommend the target merchants to the user in turn in the order of increasing driving distance value. When there are target merchants with the same driving duration value and driving distance value, recommend the target merchants to the user in turn in the order of decreasing remaining station value. When there are target merchants with the same driving distance value, driving duration value and remaining station value, randomly recommend the target merchants to the user.

[0047] It should be noted that usually, due to the influence of external traffic environment factors and random in-store factors of car wash stores, there is almost no situation where the driving distance value, driving duration value and remaining station value of target merchants are all the same at the same time. By analyzing the situation where the driving distance value, driving duration value and remaining station value are all the same under ideal conditions in this embodiment, the rigor and comprehensiveness of the recommendation of target merchants to the user can be ensured.

[0048] In this embodiment, by combining distribution data and preferential data into a merchant portrait, and fusing the merchant portrait with portrait units, a portrait library with associated channels is constructed, so as to collect, fuse and summarize the car wash service information of qualified merchants registered in the car wash information platform in a multi-dimensional and accurate manner, and perform an associated conduction operation on portrait units with associated features through the associated channels, so as to perform associated recognition on different distributors with a high similarity overlap in the portrait library, avoiding the negative interference brought by different distributors with a low similarity overlap in the subsequent screening and recognition process, and facilitating the accurate query operation on associated distributors in the future.

[0049] By fusing the real-time location with historical user tags into a user request with location features, the real-time nature and characteristics of the user request can be more prominent, improving the matching efficiency and accuracy when the user request is queried with portrait units in the portrait library in the future. Combining the first-level screening of location features and associated channels, and the second-level screening method of user requests and candidate merchants, a dual independent screening and query effect of the target merchant by the user on the car wash information platform can be achieved, avoiding the problem of inaccurate screening results existing in the single screening method, and effectively combining the factors of the matching and association similarity degree between different target merchants and user requests, ensuring that the selected target merchant can meet the actual needs of the user, and effectively improving the effect of car wash service management.

[0050] By formulating a recommendation priority to recommend target merchants in an orderly manner, the orderly, accurate and reasonable recommendation operation of target merchants can be realized according to the high and low degree of the association similarity between the target merchants and the user requests, ensuring that the car wash service management system can quickly and orderly recommend relevant car wash information that meets the needs to the user in the first time, and further improving the effect of car wash service management.

[0051] Embodiment 2: Please refer to Figure 2 As shown in the figure, for the parts not described in detail in this embodiment, please refer to the description content of Embodiment 1. A car wash service management method based on a car wash information platform is provided, which is applied to the car wash information platform and implemented based on a car wash service management system based on a car wash information platform, including: S1: Collect the distribution data and preferential data of registered merchants, screen out available merchants from the registered merchants based on service management criteria, and combine the distribution data and preferential data of the available merchants into a merchant portrait; S2: Construct an information library with portrait units distributed in a ring shape, import the merchant portrait into the portrait units, and establish an associated channel between the contour points of the portrait units with associated features, so that the information library is converted into a portrait library; S3: Collect the real-time location of the user and historical user tags, fuse the real-time location and historical user tags, and construct a user request with location features; S4: Combine the location features with the associated channels, screen out candidate merchants from the available merchants, and perform portrait matching between the user request and the candidate merchants to screen out the target merchants from the candidate merchants; S5: Determine the recommendation priority according to the car wash recommendation parameters of the target merchants and recommend the target merchants to the user in sequence.

[0052] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered within the protection scope of the present invention.

Claims

1. A car wash service management system based on a car wash information platform, which is applied to the car wash information platform, and is characterized in that Including: A merchant portrait combination module, which collects the distribution data and preferential data of registered merchants, screens out available merchants from the registered merchants based on service management criteria, and combines the distribution data and preferential data of the available merchants into merchant portraits. The service management criteria are: excluding the registered merchants with the first abnormal data or the second abnormal data; A portrait library conversion module, which constructs an information library with portrait units distributed in a ring shape, imports the merchant portraits into the portrait units, and establishes association channels between the contour points of the portrait units with associated features, so that the information library is converted into a portrait library; A user request construction module, which collects the real-time location and historical user tags of the user, fuses the real-time location and historical user tags, and constructs a user request with location features; A target merchant screening module, which combines the location features with the association channels, screens out candidate merchants from the available merchants, and performs portrait matching between the user request and the candidate merchants, and screens out target merchants from the candidate merchants; A target merchant recommendation module, which formulates a recommendation priority according to the car wash recommendation parameters of the target merchants, and recommends the target merchants to the user in order; 2. The car wash service management system based on a car wash information platform according to claim 1, characterized in that, The distribution data includes store address, business hours, word-of-mouth level, service type and type price; the preferential data includes coupon type and coupon quantity; The screening method for available merchants is: Count the quantity of distribution data and the quantity of preferential data in the registered merchants one by one, and record them as the first quantity value and the second quantity value respectively; When the first quantity value is less than 5, record the distribution data as the first abnormal data; When the second quantity value is less than 2, record the preferential data as the second abnormal data; Exclude the registered merchants with the first abnormal data or the second abnormal data, and record the remaining registered merchants as available merchants, obtaining A available merchants; 3. A car wash service management system based on a car wash information platform according to claim 2, characterized in that, The combination method of merchant portraits is: Establish A portrait contours with three label layers distributed parallel up and down respectively, and in the order from bottom to top, record the three label layers as the basic layer, the behavior layer and the value layer respectively; Set S label positions in the basic layer, the behavior layer and the value layer respectively, and draw label dividing lines between adjacent two label positions; Import the store addresses, business hours and word-of-mouth levels of A available merchants into the label positions of A basic layers one by one, import the service types and type prices of A available merchants into the label positions of A behavior layers one by one, and import the coupon types and coupon quantities of A available merchants into the label positions of A value layers one by one; Exclude the redundant label positions in A basic layers, A behavior layers and A value layers respectively, so that A portrait contours generate A merchant portraits; 4. The car wash service management system based on a car wash information platform according to claim 3, wherein, The construction method of the information library is: Establish a database with A blank data positions arranged in a ring shape, and draw circular bit contour lines on the outside of A blank data positions one by one to generate A portrait units; Mark a point position on the bit contour line and close to the previous bit contour line and close to the next bit contour line respectively, and record them as the first contour point and the second contour point; Measure the distances from the first contour point to the previous contour line and from the second contour point to the next contour line one by one, and record them as the first distance value and the second distance value respectively; Continuously adjust the positions of the first contour point and the second contour point until the adjustment stops when both the first distance value and the second distance value reach the minimum, and construct an information database.

5. A car wash service management system based on a car wash information platform according to claim 4, characterized in that, When identifying associated features, use natural language processing technology to split the distribution data or preferential data of the same type within the label bits of the two portrait units into elements, forming two element sets; When the number of elements with the same meaning in the two element sets is greater than or equal to one-half of the total number of elements in any one of the element sets, record the elements with the same meaning within the label bits of the two portrait units as associated features.

6. The car wash service management system based on a car wash information platform according to claim 5, wherein, The conversion method of the portrait database is as follows: Import A merchant portraits into the A portrait units of the information database one by one, sequentially number the A portrait units in ascending order according to the import sequence, and summarize the two portrait units with associated features into a unit group to obtain C unit groups; In the C unit groups, record the second contour point of the portrait unit with a smaller number as the channel start point, record the first contour point of the portrait unit with a larger number as the channel end point, and establish a two-way conductive channel between the channel start point and the channel end point to obtain C associated channels; Mark the associated features of the C unit groups on the corresponding associated channels respectively, so that the information database is converted into a portrait database.

7. The car wash service management system based on a car wash information platform according to claim 6, characterized in that, Historical user tags include service unit price range, service demand type, and service reputation lower limit; The construction method of the user request is as follows: Establish a basic request with three blank request bits, and record the three blank request bits as the first request bit, the second request bit, and the third request bit respectively; Import the service unit price range, service demand type, and service reputation lower limit of the user into the first request bit, the second request bit, and the third request bit respectively to generate a unit price request bit, a type request bit, and a reputation request bit; Assign the unit price request bit, the type request bit, and the reputation request bit the rank symbols DJW, LXW, and KBW respectively, so that the basic request is converted into a user request; Set a feature box on the user request, and import the user's real-time position into the feature box to generate a user request with position features.

8. The car wash service management system based on a car wash information platform according to claim 7, characterized in that, The screening method for candidate merchants is as follows: Import the position feature of the user request and the store addresses of A available merchants in the portrait database onto the same electronic map to obtain a request point and A store points; Measure the request distance values from the request point to the A store points one by one, and record the store points with request distance values less than the distance upper limit as candidate points to obtain D candidate points; Mark the portrait units corresponding to the D candidate points in the information database one by one, and count the number of associated features on the associated channels connected to the portrait units; Eliminate the portrait units at both ends of the associated channels with the number of associated features being 1, and record the available merchants corresponding to the remaining portrait units as candidate merchants to obtain E candidate merchants.

9. The car wash service management system based on a car wash information platform according to claim 8, characterized in that, The screening method for target merchants is as follows: Identify the text parts and numerical parts of the service unit price range, service type, lower limit of service reputation, reputation level, service type, and type price respectively through natural language processing technology, and extract the numerical values in the numerical parts; When there is an overlap between the numerical value of the service unit price range and the numerical value of the type price, record the type price as the matching data; When there is an overlap between the numerical value of the service demand type and the numerical value of the service type, record the service type as the matching data; When the numerical value of the lower limit of service reputation is less than or equal to the numerical value of the reputation level, record the reputation level as the matching data; Count the number of matching data among E candidate merchants one by one, and record the candidate merchants with the number of matching data being 3 as the target merchants, obtaining F target merchants.

10. A car wash service management system based on a car wash information platform according to claim 9, characterized in that, The car wash recommendation parameters include the driving distance value, driving duration value, and remaining workbench value; The recommendation priority is: the priority of the driving duration value is higher than that of the driving distance value, and the priority of the driving distance value is higher than that of the remaining workbench value; The method of recommending target merchants to users in order is: Recommend the F target merchants to the user in turn in the order of increasing driving duration value; When the driving duration values of the target merchants are the same, recommend the target merchants to the user in turn in the order of increasing driving distance value; When the driving duration values and driving distance values of the target merchants are both the same, recommend the target merchants to the user in turn in the order of decreasing remaining workbench value; When the driving distance values, driving duration values, and remaining workbench values of the target merchants are all the same, randomly recommend the target merchants to the user.

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