A comprehensive recommendation system and method for emergency tire repair at night

Abstract

The present invention discloses a comprehensive recommendation system and method for emergency tire repairs at night, which specifically relates to the technical field of automobile repair service recommendations. First, a comprehensive tire repair recommendation database is constructed to obtain user tire repair request information. Through a nighttime tire repair intelligent comprehensive recommendation module and a nighttime tire repair active search recommendation module, intelligent comprehensive recommendations and active search recommendations are performed based on the tire repair request information. Then, a first nighttime tire repair recommendation list and a second nighttime tire repair recommendation list are generated respectively. Finally, a comprehensive recommendation evaluation is performed on nighttime tire repairs, and human-computer interaction is performed based on the abnormal evaluation results. The present invention combines the user's historical repair records, automatic recommendations and active search recommendations, and provides recommendation accuracy, ensuring that users can obtain effective repair services in a timely manner when emergency tire failures occur at night.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile repair service recommendation, and in particular to a comprehensive recommendation system and method for emergency tire repair at night. Background Art

[0002] When driving at night, sudden tire failures (such as blowouts, air leaks, etc.) are a common and difficult problem. Due to factors such as poor visibility at night, uneven distribution of maintenance resources, and information asymmetry, users often find it difficult to quickly find suitable maintenance services.

[0003] With existing repair recommendation methods, users can often only find nearby repair shops through traditional search engines or map software. In terms of matching mechanisms, traditional models lack automatic and reasonable order allocation. After users post repair requests, they may be closed and unable to provide timely assistance, or remain unanswered for extended periods. Traditional methods have a fixed and limited search range, typically only providing recommendations for repairs within a short distance. For repair shops that have accepted orders but remain unresponsive for extended periods, there's a lack of effective oversight and handling mechanisms, leaving users passively waiting and unable to adjust their repair service choices.

[0004] At present, although there are some automobile repair service platforms on the market, most of them lack special optimization for nighttime emergency tire repair scenarios. For example, these platforms usually only make simple merchant recommendations based on the user's geographic location, and do not fully consider the particularities of nighttime repair services, such as merchant business status, repair timeliness, and search range flexibility. As a result, users find it difficult to obtain a satisfactory repair service experience in actual use, and may even fall into difficulties due to the inability to repair tires in time, such as being unable to continue their journey or being forced to stay. Therefore, the development of a comprehensive recommendation system and method that can effectively solve the problem of emergency tire repair at night is of great practical significance. Summary of the Invention

[0005] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a comprehensive recommendation system and method for emergency tire repair at night, so as to solve the problems of the existing repair recommendation method lacking accuracy and flexibility as mentioned in the above-mentioned background art.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a comprehensive recommendation system for emergency tire repairs at night, comprising:

[0007] Tire Repair Recommendation Comprehensive Management Center: This center uses database technology to store and manage various data generated during system operation, including user information database, merchant information database, order information database, and the existing tire failure standard text database, to build a comprehensive tire repair recommendation database.

[0008] Nighttime Tire Repair Request Initiation Module: When a tire fails at night, the user is prompted to enter personal verification information through the mobile device interface. After verification, a tire repair request is initiated and the obtained tire repair request information is transmitted to the Nighttime Tire Repair Intelligent Comprehensive Recommendation Module and the Nighttime Tire Repair Active Search and Recommendation Module;

[0009] Nighttime tire repair intelligent comprehensive recommendation module: used to retrieve merchant information from the merchant information database based on tire repair request information, obtain the first nighttime tire repair recommendation list, and push it to the user;

[0010] Nighttime tire repair active search and recommendation module: Based on tire repair request information, it provides users with an active search function, retrieves merchant information from the merchant information database based on the user's active search text, generates a second nighttime tire repair recommendation list, and pushes it to the user;

[0011] Nighttime tire repair comprehensive recommendation and evaluation module: Through big data analysis technology, the parameters generated in the tire repair recommendation process are evaluated, and human-computer interaction is carried out based on abnormal evaluation results.

[0012] Preferably, a comprehensive recommendation method for emergency tire repair at night includes:

[0013] S1: Using database technology, we store and manage various data generated during system operation, including user information database, merchant information database, order information database, and existing tire failure standard text database, to build a comprehensive database for tire repair recommendations;

[0014] S2: When a tire fails at night, the user is prompted to enter personal verification information through the mobile device interface. After passing the verification, a tire repair request is initiated and tire repair request information is obtained;

[0015] S3: retrieves merchant information from a merchant information database based on the tire repair request information obtained in S2, obtains a first nighttime tire repair recommendation list, and pushes it to the user;

[0016] S4: Based on the tire repair request information obtained in S2, an active search function is provided for the user. Merchant information is retrieved from the merchant information database according to the user's active search text, and a second night tire repair recommendation list is generated and pushed to the user;

[0017] S5: Use big data analysis technology to evaluate the parameters generated during the tire repair recommendation process, and conduct human-computer interaction based on abnormal evaluation results.

[0018] Technical effects and advantages of the present invention:

[0019] 1. The present invention uses a nighttime tire repair request initiation module to verify the vehicle location information uploaded by the user, ensuring the accuracy of the vehicle location information and enabling repair vendors to accurately locate the user. This allows the user to quickly and conveniently obtain repair services, resolving the user's urgent need for nighttime tire repair services.

[0020] 2. Targeting the unique situation of tire failures at night, this invention proposes a flexible search distance threshold range recommendation mechanism, effectively resolving the difficulty in obtaining repair services caused by the fixed search range of traditional recommendation systems. By automatically expanding the search range, the likelihood of users finding repair services is greatly increased, ensuring that users can receive timely assistance in emergency situations.

[0021] 3. The present invention adopts a multi-dimensional recommendation mechanism, combines the user's historical maintenance records, and combines automatic recommendations with active search recommendations. It fully considers the user's personalized needs and preferences, provides accurate recommendations, and ensures that users can obtain effective repair services in a timely manner when an emergency tire failure occurs at night, thereby improving the safety and convenience of users' nighttime driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0023] Figure 2 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0025] The embodiments described are clearly and completely. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] Example 1:

[0027] See also Figure 1 As shown, the present invention provides a comprehensive recommendation system for emergency tire repairs at night, including a comprehensive management center for recommending tire repairs at night, a module for initiating a request for tire repairs at night, an intelligent comprehensive recommendation module for recommending tire repairs at night, an active search and recommendation module for recommending tire repairs at night, and a comprehensive recommendation evaluation module for recommending tire repairs at night.

[0028] The nighttime tire maintenance recommendation comprehensive management center is connected to the remaining modules, the nighttime tire maintenance request initiation module is connected to the nighttime tire maintenance intelligent comprehensive recommendation module and the nighttime tire maintenance active search recommendation module, and the nighttime tire maintenance comprehensive recommendation evaluation module is connected to the nighttime tire maintenance request initiation module, the nighttime tire maintenance intelligent comprehensive recommendation module and the nighttime tire maintenance active search recommendation module respectively.

[0029] Tire Repair Recommendation Comprehensive Management Center: This center uses database technology to store and manage various data generated during system operation, including user information database, merchant information database, order information database, and the existing tire failure standard text database, to build a comprehensive tire repair recommendation database.

[0030] What needs to be specifically explained in this embodiment is that user information includes user information including but not limited to user basic information, historical maintenance records, preference settings, etc.; merchant information includes merchant name, address, latitude and longitude coordinates, business hours, service items including tire repair, price range, user reviews, contact information, etc.; at the same time, the database also updates the status information of each repair shop in real time to ensure that the server can obtain the most accurate data and provide users with accurate repair service recommendations; order information includes but not limited to order details, status change records, etc.; tire fault standard text database includes but not limited to abnormal tire pressure, tire blowout, tire leakage, etc.; through database technology, the tire repair recommendation comprehensive database has been encrypted to ensure data security, integrity and efficient retrieval.

[0031] Nighttime Tire Repair Request Initiation Module: When a tire fails at night, the user is prompted to enter personal verification information through the mobile device interface. After verification, a tire repair request is initiated and the obtained tire repair request information is transmitted to the Nighttime Tire Repair Intelligent Comprehensive Recommendation Module and the Nighttime Tire Repair Active Search and Recommendation Module;

[0032] Nighttime tire repair intelligent comprehensive recommendation module: used to retrieve merchant information from the merchant information database based on tire repair request information, obtain the first nighttime tire repair recommendation list, and push it to the user;

[0033] Nighttime tire repair active search and recommendation module: Based on tire repair request information, it provides users with an active search function, retrieves merchant information from the merchant information database based on the user's active search text, generates a second nighttime tire repair recommendation list, and pushes it to the user;

[0034] Nighttime tire repair comprehensive recommendation and evaluation module: Through big data analysis technology, the parameters generated in the tire repair recommendation process are evaluated to obtain evaluation results, and human-computer interaction is performed based on abnormal evaluation results.

[0035] In this embodiment, the nighttime tire maintenance request initiating module obtains tire maintenance request information, including the following steps:

[0036] A1: When a tire fails at night, the user is prompted to enter personal verification information on a mobile device (e.g., a mobile phone, iPad, etc.). After passing the verification, a tire repair request is initiated, including the owner's contact information, vehicle location (obtained via GPS), tire fault type (obtained via the system's standard tire fault text database), and tire repair notes.

[0037] What needs to be specifically explained in this embodiment is that when a user uses the user terminal application for the first time, he needs to register, fill in personal information and set a login password. After successful registration, the user can use the registered account and password to log in to the application, which is personal verification information; the tire failure repair note information function is used to describe the tire failure in more detail or actively add the merchant name. It is an optional field, such as a left front tire blowout, a tear on the tire surface, and the owner's contact information, vehicle location information and tire failure type are required fields.

[0038] A2: Verify the vehicle location information uploaded by the user. If the user is using this system for the first time, combine the GPS positioning function and the merchant information database to identify the user's location as the origin. Whether there is a service provider within the maximum allowed radius (for example, 20 kilometers, for the purpose of emergency repair or fast repair) is there? If not, a prompt will be given. If not, continue to determine whether it is in a non-road area (such as a lake, inside a building), if it triggers a secondary positioning confirmation pop-up window ("It is detected that your current location may be abnormal, do you need to reposition?"); If the user is not using this system for the first time, obtain the distance Δd between the current location and historical commonly used parking spots (such as home, company, etc.) and the time interval Δt from the last searched parking spot. The time interval Δt can be in minutes, hours, etc., and obtain the vehicle location abnormality index A bs , A bs =e -λΔt ×I(Δd>d th ), I() is the indicator function, with values of 1 and 0. If Δd>d th , I() returns a value of 1, otherwise it returns 0, d th is the distance threshold, λ is the time decay coefficient, which reflects the “timeliness” of abnormal events. Recent events have higher weights (for example, anomalies from 1 hour ago are more worthy of attention than anomalies from 1 day ago). bs If the value is greater than the corresponding threshold, an alarm is immediately triggered and the tire repair recommendation integrated management center's review mechanism is triggered, such as contacting the user by phone for verification. Finally, the verified tire repair request information is obtained, including the owner's contact information, vehicle location information, tire repair request initiation time, expected repair arrival time, tire fault type, and tire fault repair notes.

[0039] In this embodiment, the nighttime tire maintenance intelligent comprehensive recommendation module obtains a first nighttime tire maintenance recommendation list, including the following steps:

[0040] B1: First, using big data technology, retrieve a list of businesses offering tire repair services, BL1, from the user's historical repair records. This list is then combined with a list of businesses in the business information database, BL2, to obtain a list of businesses offering tire repair services, BL3. Second, using a map function, the distance between each business address in BL3 and the user's vehicle location is traversed to obtain a distance dataset, Dd(BL3), from BL3.

[0041] B2: Combine the tire repair request time t and the business hours t(bh) with a spatial index query algorithm (such as the R-tree algorithm) f(s i ) filter out the first distance threshold range Dd1 th Merchants in business status, traverse each merchant f(s i ) value, statistics f(s i ) whose value is 1 constitutes the final merchant list BL4. , Dd(BL3) i Indicates the distance to the i-th merchant, t(bh) i represents the business hours of the i-th merchant, f(s i )=1 means that the merchant belongs to the merchant list BL4, f(s i )=0 means that the merchant does not belong to the merchant list BL4;

[0042] In this embodiment, it should be specifically explained that the nighttime tire maintenance intelligent comprehensive recommendation module has a dynamic geo-fencing function, which can dynamically expand the search to include the first distance threshold range Dd1 th , for example 3-5 kilometers, the second distance threshold range Dd2 th , for example 5-10 kilometers, the third distance threshold range Dd3 th , for example, 10-20 kilometers; R-tree (Region Tree) is an efficient tree structure designed specifically for spatial data indexing. It is represented by two-dimensional geographic coordinates (latitude and longitude) or three-dimensional spatial objects (such as buildings) and supports range queries (such as "query for repair shops within a 5-kilometer radius") and nearest neighbor queries (such as "find the three nearest repair shops").

[0043] B3: Then, through big data analysis technology, the number of merchants in the merchant list BL4 |BL4| is counted. If |BL4|≥k, k represents the minimum number of merchants to be screened, for example, 3, then B4 is entered. If |BL4|<k, the first dynamic expansion search mechanism is triggered, and step B2 is repeated to convert the spatial index query algorithm f(s i ) in the first distance threshold range Dd1 th Updated to the second distance threshold Dd2 th , get the first updated merchant list BL4 first ; BL4 first |Continue to compare with threshold k, if |BL4 first |≥k, then enter B4, if |BL4 first |<k, the second dynamic expansion search mechanism is triggered, and step B2 is repeated to convert the spatial index query algorithm f(s i ) in the second distance threshold Dd2 th Updated to the third distance threshold Dd3 th , get the second updated merchant list BL4 sec ; BL4 sec |Continue to compare with threshold k, if |BL4 sec |≥k, then enter B4, if |BL4 sec |<k, then the mechanism of suggesting calling roadside assistance is triggered;

[0044] Specifically, considering the urgency of nighttime tire failures and the limited availability of repair resources, the intelligent recommendation module not only searches for businesses within the user-defined range of three to five kilometers, but also, if no suitable businesses are found within that range, automatically expands the search to ten, twenty, or even further kilometers until a suitable business is found. This significantly increases the user's likelihood of finding repair services and avoids the dilemma of being unable to obtain timely assistance due to distance constraints.

[0045] B4: Using big data analysis technology, the most recently updated merchant list obtained in B3 is sorted from high to low according to the results returned by the priority scoring model, resulting in merchant list BL5, which is the target first-time nighttime tire repair recommendation list, including merchant name, address, business hours, service items, price range, service rating, contact information, distance, and estimated time of arrival. The priority scoring model is: ,Score(s i ) represents the priority score of the i-th merchant, sr i represents the service rating of the i-th merchant, max(sr i ) represents the maximum value of the corresponding service score, t irepresents the estimated arrival time of the i-th merchant, which can be in minutes, hours, etc. γ represents the adjustment factor of the estimated arrival time, which is obtained based on historical data. a1, a2, and a3 represent the corresponding weights, for example, a1=0.4, a2=0.3, and a3=0.3;

[0046] In this embodiment, the nighttime tire maintenance active search and recommendation module obtains the second nighttime tire maintenance recommendation list, including the following steps:

[0047] C1: First, the user selects a search text according to the active search function prompt information. The search function prompt information includes the merchant name, service rating, distance, estimated repair price, and estimated arrival time. If the natural language processing technology detects that the user input is a merchant name, the merchant information database is traversed to retrieve the merchant name. If the merchant name is retrieved and the merchant is open for business, a second nighttime tire repair recommendation list is generated for the user. If no search results are found or the merchant is not open for business, a prompt message is sent to the user. If a merchant name is not detected, the merchants are sorted according to the optimal principle, including sorting from high to low service rating, selecting a preset number n0 (for example, 5) of merchants to form a merchant list BL5, sorting from near to far distance, selecting the same preset number of merchants to form a merchant list BL6, sorting from fast to slow estimated arrival time, selecting the same preset number of merchants to form a merchant list BL7, and sorting from low to high estimated repair price, selecting the same preset number of merchants to form a merchant list BL8, wherein the preset number n0 of merchants are open for business.

[0048] C2: Using big data analysis technology, based on service ratings, distance, estimated repair price, and estimated arrival time, a comprehensive analysis is performed on the matching merchant lists BL5, BL6, BL7, and BL8 to obtain the recommended optimal index (RMOI) for each merchant. i , the formula is:

[0049] ,

[0050] sr j represents the service rating of the jth merchant, j∈n0, DI j Indicates the distance to the j-th merchant, td j represents the estimated arrival time of the j-th merchant, pr j represents the estimated repair price of the jth merchant, b1, b2, b3 and b4 represent the corresponding weights, for example, b1=0.2, b2=0.3, b3=0.3 and b4=0.2; finally, according to the recommended optimal index RMOI iSort the n0 merchants from high to low to obtain a merchant list BL8, where |BL8| ≥ threshold k. This list is used as the target second nighttime tire repair recommendation list and pushed to the user.

[0051] What needs to be specifically explained in this embodiment is that the present invention is a comprehensive recommendation system for emergency tire repairs, and the default repair item is tire repair.

[0052] The comprehensive recommendation and evaluation module for nighttime tire maintenance in this embodiment uses big data analysis technology to evaluate the parameters generated during the recommendation process to obtain an evaluation result, including the following steps:

[0053] D1: First, use timestamp technology to obtain the time t when the system starts processing the user's maintenance request start , tire repair request initiation time t and set response time threshold t th , get the user request response efficiency coefficient REC, REC=t th / (t start -t); then record the actual time t from when the user starts searching to when they confirm the target merchant sea and set time t base , we can get the recommended efficiency coefficient REA for tire maintenance, REA=1-(t sea -t base ) / t base Finally, the comprehensive recommended efficiency index of tire repair is obtained through big data analysis technology, ZREC, which is equal to REC + REA.

[0054] D2: Using big data analysis technology, the tire repair comprehensive recommendation efficiency index ZREC is compared with the threshold ZREC0. If ZREC is greater than or equal to ZREC0, it indicates that the tire repair comprehensive recommendation efficiency is good. Otherwise, it indicates an abnormality. The abnormal evaluation result is transmitted to the system administrator terminal for human-computer interaction.

[0055] What needs to be specifically explained in this implementation is that the evaluation abnormality results are transmitted to the system administrator terminal for human-computer interaction, so that the system administrator can take corresponding measures in a timely manner, such as optimizing the tire repair recommendation system database and storing merchant information by regional partitions; for example, in the process of generating the recommendation list, key information is highlighted and key services such as "night business" and "free towing" are marked with icons in the list items.

[0056] Example 2:

[0057] See also Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description of Example 1. A comprehensive recommendation method for emergency tire repair at night is provided, which includes the following steps:

[0058] S1: Using database technology, we store and manage various data generated during system operation, including user information database, merchant information database, order information database, and existing tire failure standard text database, to build a comprehensive database for tire repair recommendations;

[0059] S2: When a tire fails at night, the user is prompted to enter personal verification information through the mobile device interface. After passing the verification, a tire repair request is initiated and tire repair request information is obtained;

[0060] S3: retrieves merchant information from a merchant information database based on the tire repair request information obtained in S2, obtains a first nighttime tire repair recommendation list, and pushes it to the user;

[0061] S4: Based on the tire repair request information obtained in S2, an active search function is provided for the user. Merchant information is retrieved from the merchant information database according to the user's active search text, and a second night tire repair recommendation list is generated and pushed to the user;

[0062] S5: Use big data analysis technology to evaluate the parameters generated during the tire repair recommendation process, and conduct human-computer interaction based on abnormal evaluation results.

[0063] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict.

[0064] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A comprehensive recommendation system for emergency tire repair at night, characterized by: include: Tire Repair Recommendation Comprehensive Management Center: This center uses database technology to store and manage various data generated during system operation, including user information database, merchant information database, order information database, and the existing tire failure standard text database, to build a comprehensive tire repair recommendation database. Nighttime Tire Repair Request Initiation Module: When a tire fails at night, the user is prompted to enter personal verification information through the mobile device interface. After verification, a tire repair request is initiated and the obtained tire repair request information is transmitted to the Nighttime Tire Repair Intelligent Comprehensive Recommendation Module and the Nighttime Tire Repair Active Search and Recommendation Module; The tire repair request information in the nighttime tire repair request initiation module includes: verifying the vehicle location information uploaded by the user; if the user is using the system for the first time, combining the GPS positioning function and the merchant information database to identify the user location as the origin, whether there is a service provider within the maximum allowed radius; if not, prompting; if it is, continuing to determine whether it is in a non-road area, if it is, triggering a secondary positioning confirmation pop-up window; if the user is not using the system for the first time, obtaining the distance Δd between the current location and the historical commonly used parking points and the time interval Δt from the most recently searched parking point, and obtaining the vehicle location abnormality index A. bs , A bs =e -λΔt ×I(Δd>d th ), I() is the indicator function, with values of 1 and 0. If Δd>d th , I() returns a value of 1, otherwise it returns 0, d th is the distance threshold, λ is the time attenuation coefficient, A bs If the value is greater than the corresponding threshold, an alarm is immediately triggered and the review mechanism of the tire repair recommendation integrated management center is triggered. Finally, the verified tire repair request information is obtained, including the owner's contact information, vehicle location information, tire repair request initiation time, expected repair arrival time, tire fault type, and tire fault repair notes. Nighttime tire repair intelligent comprehensive recommendation module: used to retrieve merchant information from the merchant information database based on tire repair request information, obtain the first nighttime tire repair recommendation list, and push it to the user; The nighttime tire maintenance intelligent comprehensive recommendation module obtains a first nighttime tire maintenance recommendation list, including: B1: First, using big data technology, retrieve a list of businesses offering tire repair services, BL1, from the user's historical repair records. This list is then combined with a list of businesses in the business information database, BL2, to obtain a list of businesses offering tire repair services, BL3. Second, using a map function, the distance between each business address in BL3 and the user's vehicle location is traversed to obtain a distance dataset, Dd(BL3), from BL3. B2: Combine the tire repair request time t and the business hours t(bh) through the spatial index query algorithm f(s i ) filter out the first distance threshold range Dd1 th Merchants in business status, traverse each merchant f(s i ) value, statistics f(s i ) whose value is 1 constitutes the final merchant list BL4. , Dd(BL3) i Indicates the distance to the i-th merchant, t(bh) i represents the business hours of the i-th merchant, f(s i )=1 means that the merchant belongs to the merchant list BL4, f(s i )=0 means that the merchant does not belong to the merchant list BL4; B3: Then, through big data analysis technology, count the number of merchants in the merchant list BL4 |BL4|. If |BL4|≥k, where k represents the minimum number of merchants to be screened, then proceed to B4. If |BL4|<k, then trigger the first dynamic expansion search mechanism and repeat step B2. The spatial index query algorithm f(s i ) in the first distance threshold range Dd1 th Updated to the second distance threshold Dd2 th , get the first updated merchant list BL4 first ; BL4 first |Continue to compare with threshold k, if |BL4 first |≥k, then enter B4, if |BL4 first |<k, the second dynamic expansion search mechanism is triggered, and step B2 is repeated to convert the spatial index query algorithm f(s i ) in the second distance threshold Dd2 th Updated to the third distance threshold Dd3 th , get the second updated merchant list BL4 sec ; BL4 sec |Continue to compare with threshold k, if |BL4 sec |≥k, then enter B4, if |BL4 sec |<k, then the mechanism of suggesting calling roadside assistance is triggered; B4: Using big data analysis technology, the most recently updated merchant list obtained in B3 is sorted from high to low according to the results returned by the priority scoring model, resulting in merchant list BL5, which is the target first-time nighttime tire repair recommendation list, including merchant name, address, business hours, service items, price range, service rating, contact information, distance, and estimated time of arrival. The priority scoring model is: ,Score(s i ) represents the priority score of the i-th merchant, sr i represents the service rating of the i-th merchant, max(sr i ) represents the maximum value of the corresponding service score, t i represents the estimated arrival time of the i-th merchant, γ represents the adjustment factor of the estimated arrival time, and a1, a2 and a3 represent the corresponding weights respectively; Nighttime tire repair active search and recommendation module: Based on tire repair request information, it provides users with an active search function, retrieves merchant information from the merchant information database based on the user's active search text, generates a second nighttime tire repair recommendation list, and pushes it to the user; Nighttime tire repair comprehensive recommendation and evaluation module: This module uses big data analysis technology to evaluate the parameters generated during the tire repair recommendation process, obtains evaluation results, and conducts human-computer interaction based on abnormal evaluation results; Obtaining the evaluation result in the nighttime tire maintenance comprehensive recommendation evaluation module includes the following steps: D1: First, use timestamp technology to obtain the time t when the system starts processing the user's maintenance request start , tire repair request initiation time t and set response time threshold t th , get the user request response efficiency coefficient REC, REC=t th / (t start -t); then record the actual time t from when the user starts searching to when they confirm the target merchant sea and set time t base , we can get the recommended efficiency coefficient REA for tire maintenance, REA=1-(t sea -t base ) / t base Finally, the comprehensive recommended efficiency index of tire repair is obtained through big data analysis technology, ZREC, which is equal to REC + REA. D2: Using big data analysis technology, the tire repair comprehensive recommendation efficiency index ZREC is compared with the threshold ZREC0. If ZREC is greater than or equal to ZREC0, it indicates that the tire repair comprehensive recommendation efficiency is good. Otherwise, it indicates an abnormality. The abnormal evaluation result is transmitted to the system administrator terminal for human-computer interaction.

2. The comprehensive recommendation system for emergency tire repair at night according to claim 1, characterized in that: The tire repair request information in the nighttime tire repair request initiation module includes: when a tire fails at night, the user is prompted to enter personal verification information through the mobile device interface, and after the verification is passed, a tire repair request is initiated, uploading the owner's contact information, vehicle location information, tire failure type, and tire failure repair notes.

3. The comprehensive recommendation system for emergency tire repair at night according to claim 1, characterized in that: Obtaining a second nighttime tire maintenance recommendation list in the nighttime tire maintenance active search and recommendation module includes the following steps: C1: First, the user selects a search text according to the active search function prompt information. The search function prompt information includes the merchant name, service rating, distance, estimated repair price, and estimated arrival time. If the natural language processing technology detects that the user input is a merchant name, the merchant information database is traversed to retrieve the merchant name. If the merchant name is retrieved and the merchant is open for business, a second nighttime tire repair recommendation list is generated for the user. If no search results are found or the merchant is not open for business, a prompt message is sent to the user. If a merchant name is not detected, the merchants are sorted according to the optimal principle, including sorting from high to low service rating, selecting a preset number n0 of merchants to form a merchant list BL5, sorting from near to far distance, selecting the same preset number of merchants to form a merchant list BL6, sorting from fast to slow estimated arrival time, selecting the same preset number of merchants to form a merchant list BL7, and sorting from low to high estimated repair price, selecting the same preset number of merchants to form a merchant list BL8. The preset number n0 of merchants are open for business.

4. The comprehensive recommendation system for emergency tire repair at night according to claim 1, characterized in that: The second nighttime tire maintenance recommendation list is obtained by the nighttime tire maintenance active search and recommendation module, further comprising the following steps: C2: Using big data analysis technology, based on service ratings, distance, estimated repair price, and estimated arrival time, a comprehensive analysis is performed on the matching merchant lists BL5, BL6, BL7, and BL8 to obtain the recommended optimal index (RMOI) for each merchant. i , the formula is: ， sr j represents the service rating of the jth merchant, j∈n0, DI j Indicates the distance to the jth merchant, td j represents the estimated arrival time of the j-th merchant, pr j represents the estimated repair price of the jth merchant, b1, b2, b3 and b4 represent the corresponding weights respectively; finally, according to the recommended optimal index RMOI i Sort the n0 merchants from high to low to obtain a merchant list BL8, where |BL8| ≥ threshold k. This list is used as the target second nighttime tire repair recommendation list and pushed to the user.

5. A comprehensive recommendation method for emergency tire repair at night, used for using the comprehensive recommendation system for emergency tire repair at night according to any one of claims 1 to 4, characterized in that: include: S1: Using database technology, we store and manage various data generated during system operation, including user information database, merchant information database, order information database, and existing tire failure standard text database, to build a comprehensive database for tire repair recommendations; S2: When a tire fails at night, the user is prompted to enter personal verification information through the mobile device interface. After passing the verification, a tire repair request is initiated and tire repair request information is obtained; S3: retrieves merchant information from a merchant information database based on the tire repair request information obtained in S2, obtains a first nighttime tire repair recommendation list, and pushes it to the user; S4: Based on the tire repair request information obtained in S2, an active search function is provided for the user. Merchant information is retrieved from the merchant information database according to the user's active search text, and a second night tire repair recommendation list is generated and pushed to the user; S5: Use big data analysis technology to evaluate the parameters generated during the tire repair recommendation process, obtain evaluation results, and conduct human-computer interaction based on abnormal evaluation results.

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