A method for intelligent matching of tire businesses based on geographic location

Abstract

The present application belongs to the technical field of intelligent information recommendation, and discloses a kind of tire merchant intelligent matching method based on geographic location, comprising: obtaining user current position coordinate and tire fault description, obtain emergency response area set in combination with real-time traffic condition;Emergency response area set is filtered and availability is checked to position, and preliminary repair alternative group is obtained;Based on preliminary repair alternative group, repair-recovery pairing chain is constructed, and service chain pairing set is obtained;User preference weight adjustment and history feedback integration are executed to service chain pairing set, and chain whole matching score is calculated, and preferred service chain list is obtained;Based on preferred service chain list, interactive path planning is pushed to user, and user confirmed service chain execution plan is obtained;The implementation process of service chain execution plan is monitored, and merchant association database is updated in real time to continuously provide optimal pairing merchant;Significantly improve service response efficiency and user experience coherence under emergency scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent information recommendation, more particularly, to a tire business intelligent matching method based on geographic location. BACKGROUND

[0002] With the continuous growth of the number of cars and the deep popularization of mobile internet technology, local life service platforms based on geographic location have become an important bridge connecting users and offline businesses. In the field of automobile after-sales service, tire repair and replacement is a high-frequency and essential emergency service scenario, and the convenience and reliability of service acquisition directly affect the safety and service experience of car owners. Especially during the process of driving on urban roads and highways, sudden tire damage needs to be quickly located to nearby reliable service businesses, and after completing the repair and replacement, the standardized recycling of waste tires has become an environmental responsibility and actual demand that users are increasingly concerned about.

[0003] However, the existing tire service acquisition method has significant defects in service process continuity, which is particularly prominent in emergency scenarios. Specifically, when a car owner encounters tire damage during driving, he or she usually needs to first search for nearby repair businesses through a search engine or a map application. In this stage, the car owner needs to browse business information one by one, compare location distances, and view scattered user reviews. After completing the selection, the car owner goes to receive tire repair or replacement services. After the service is completed, if the car owner needs to dispose of the waste tire, he or she must restart the search process and again filter and decide in the recycling business database. The artificial fragmentation of this service chain causes the user to experience multiple independent information retrieval and business evaluation processes in a short period of time, and each decision is accompanied by repeated judgments on the credibility of the business. More importantly, there is a lack of information association between repair businesses and recycling businesses, and users cannot predict whether there is a matching recycling service near the selected repair point, nor can they know the overall service quality evaluation of different business combinations. This information island phenomenon forces users to invest additional time and cognitive costs in multi-stage service planning under the pressure of vehicle breakdown. Although the current mainstream local service platform provides business location display and basic scoring functions, these functions are designed only for single service transactions and cannot integrate the complete service chain from fault emergency to subsequent disposal. Users can only rely on manual memory and secondary operations to link the previous and subsequent stages, forcing the service that could have been completed continuously to be divided into multiple isolated events, ultimately reducing the service response efficiency in emergency scenarios and weakening the user's sense of control and satisfaction over the overall service process.

[0004] In view of this, the present application proposes a tire business intelligent matching method based on geographic location to solve the above problems. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purposes, the present application provides the following technical scheme: a tire merchant intelligent matching method based on geographic position, comprising:

[0006] Step S1: obtaining the current position coordinates and tire failure description input by the user, generating a failure surrounding area range in combination with real-time traffic conditions, and obtaining an emergency response zoning set;

[0007] Step S2: performing position filtering and availability checking on the repair service providers in the emergency response zoning set, eliminating service providers exceeding the response time limit, and obtaining a preliminary repair service provider set;

[0008] Step S3: for each service provider in the preliminary repair service provider set, querying the geographic proximity and cooperation record of the associated recycling service provider, constructing a repair-recycling pairing chain, and obtaining a service chain pairing set;

[0009] Step S4: performing user preference weight adjustment and historical feedback integration on each pairing chain in the service chain pairing set and calculating the overall matching score of the chain, and obtaining an optimal service chain list;

[0010] Step S5: based on the optimal service chain list, pushing an interactive path planning to the user, including continuous navigation guidance from the repair point to the recycling point, and obtaining a user-confirmed service chain execution plan;

[0011] Step S6: monitoring the implementation process of the service chain execution plan, collecting real-time service feedback and updating the associated service provider database in real time to continuously provide the optimal paired service providers.

[0012] Further, step S1 comprises:

[0013] Step S11: collecting the current position coordinates and tire failure description from the user device, verifying the coordinate accuracy and supplementing the failure type details, and obtaining a failure positioning record;

[0014] Step S12: querying real-time traffic conditions in combination with the failure positioning record, estimating the radiation radius of the failure surrounding area, dynamically adjusting the radiation radius to avoid congested road sections, and obtaining an expanded response boundary;

[0015] Step S13: partitioning the land blocks within the expanded response boundary, preferentially marking the highway or urban trunk road area, and obtaining a partition priority sequence;

[0016] Step S14: integrating the failure positioning record based on the partition priority sequence to form an emergency response zoning set.

[0017] Further, step S12 comprises:

[0018] Step S121: Extract the current location coordinates from the fault location record, access the external traffic data source to obtain the surrounding road network congestion index, and obtain the road network congestion mapping;

[0019] Step S122: Calculate the initial radiation radius based on the road network congestion mapping, introduce fault type details as a correction factor, extend the radius to cover alternative paths, and obtain the preliminary radiation range;

[0020] Step S123: Perform a congestion avoidance scan on the initial radiation range, and shift the boundary line segment by segment to bypass the highly congested area to obtain the optimized radiation radius;

[0021] Step S124: Bind the optimized radiation radius to the current location coordinates to construct an extended response boundary that includes obstacle avoidance path hints.

[0022] Further, step S2 includes:

[0023] Step S21: Set location filtering thresholds and availability check criteria based on the emergency response zoning set. The availability check criteria include merchant business hours and inventory matching requirements.

[0024] Step S22: Calculate the straight-line distance and estimated arrival time of each repair shop within the emergency response zone set from its current location coordinates, and remove shops whose distance exceeds the location filtering threshold or whose arrival time exceeds the response time limit to obtain the distance qualified group;

[0025] Step S23: Verify the current availability of each repair shop in the qualified availability group, check whether the inventory matches the tire fault description, remove shops with insufficient inventory or not in operation, and obtain the qualified availability group.

[0026] Step S24: Perform cross-comparison of multiple vendors for the availability qualified group, and give priority to vendors with backup vehicle support to obtain the initial repair alternative group.

[0027] Further, step S3 includes:

[0028] Step S31: Select each repair merchant from the preliminary repair candidate group, query the location coordinates and cooperation frequency of its historical cooperative recycling merchants, and obtain the recycling associated candidate set;

[0029] Step S32: Calculate the geographical proximity of each recycling merchant in the candidate set of recycling associations to the corresponding repair merchant, introduce cooperation records as affinity weighting, and obtain the proximity affinity score;

[0030] Step S33: Screen recycling merchants based on proximity affinity score, construct exclusive recycling pairings for each repair merchant, and remove pairings with affinity scores below the affinity threshold to obtain a preliminary chain draft;

[0031] Step S34: Perform environmental compliance verification on the preliminary chain draft, confirm the processing qualifications of the recycling merchants and integrate them into the matching, forming a service chain matching set.

[0032] Further, step S32 includes:

[0033] Step S321: Extract the location coordinates of each recycling merchant from the recycling association candidate set, perform path simulation with the location coordinates of the corresponding repair merchants, calculate the actual travel distance, and obtain the distance reference value;

[0034] Step S322: Query the cooperation record based on the distance reference value, count the historical cooperation frequency and convert it into affinity weight, dynamically amplify the weight of low-distance matching, and obtain the weighted proximity score;

[0035] Step S323: Perform multi-dimensional adjustment on the weighted proximity score, integrate the processing capacity of the recycling merchant as an additional factor, and obtain the comprehensive proximity affinity score;

[0036] Step S324: Bind the comprehensive proximity affinity score with the recycling association candidate set, record the potential service continuity prompt of the matching, and form a proximity affinity score set.

[0037] Further, step S4 includes:

[0038] Step S41: Select each matching chain from the service chain matching set, extract the basic attributes of the repair and recycling merchants, and obtain the chain attribute summary;

[0039] Step S42: Apply weight adjustment to the chain attribute summary based on the user input preference settings, and obtain the preference weighted summary;

[0040] Step S43: Query the historical feedback database, integrate user evaluations related to the chain attribute summary, and accumulate positive feedback and deduct negative records item by item, to obtain feedback points;

[0041] Step S44: Fuse the preference weighted summary and the feedback points, calculate the overall matching score, and form a preferred service chain list.

[0042] Further, step S5 includes:

[0043] Step S51: Sort and extract the front matching chain from the preferred service chain list, show the user an interactive map view of the repair point and the recycling point, and obtain a visual chain preview;

[0044] Step S52: Based on the visual chain preview, generate a continuous path plan from the current location coordinates to the repair point and then to the recycling point, and obtain a path guide draft;

[0045] Step S53: User interaction confirmation is performed on the path guide draft, allowing manual adjustment of the recycling order or addition of a backup path, and a confirmed adjustment set is obtained;

[0046] Step S54: The confirmed adjustment set is converted into an executable format, including appointment reminders and emergency contact channels, to form a user-confirmed service chain execution plan.

[0047] Further, step S52 includes:

[0048] Step S521: Lock the coordinate sequence of the repair point and the recycling point from the visual chain preview, and concatenate the current position coordinates to form a path chain to obtain an initial path sequence;

[0049] Step S522: Access real-time traffic updates, evaluate congestion at each turning point of the initial path sequence, and automatically insert detour sub-paths to minimize delay to obtain an optimized path chain;

[0050] Step S523: Inject service timing prompts into the optimized path chain to obtain a timing-enhanced chain;

[0051] Step S524: Synchronize the timing-enhanced chain with the visual chain preview to build a path guide draft containing voice navigation support.

[0052] Further, step S6 includes:

[0053] Start location tracking based on the user-confirmed service chain execution plan, record the actual completion time points of the repair and recycling stages, and obtain an execution progress log;

[0054] Embed real-time feedback collection points in the execution progress log to guide the user to submit service quality evaluations and problem descriptions to obtain an instant feedback set;

[0055] Classify and summarize the instant feedback set, extract positive elements to strengthen business association, and deduct negative elements to adjust cooperation affinity to obtain a feedback optimization summary;

[0056] Inject the feedback optimization summary into the business association database to update the data of the service chain pairing set, ensuring dynamic adaptability of subsequent matching to continuously provide the best matched business.

[0057] The technical effects and advantages of the tire business intelligent matching method based on geographic location are:

[0058] The application realizes rapid service range definition in a fault scene by receiving current position coordinates of a user and a tire fault description, and generating an emergency response zoning set in combination with real-time traffic conditions; the timeliness and accessibility of emergency services are ensured by eliminating merchants exceeding the response time limit through position filtering and availability checking of repair merchants to obtain a preliminary repair option set; the originally fragmented repair service and recycling service are integrated into a continuous service chain pairing set by querying the geographic proximity and cooperation records of repair merchants to recycling merchants, solving the problem of multiple independent searches and decisions required by the user; the overall quality of the service chain is comprehensively evaluated instead of only focusing on a single service node by adjusting the user preference weight and integrating historical feedback to calculate the overall matching score of the chain; the multi-stage service is seamlessly connected by pushing an interactive path planning including continuous navigation guidance from the repair point to the recycling point to the user, eliminating the cognitive burden and operation complexity of the user in an emergency state; a dynamic feedback mechanism of service quality is formed by monitoring the service chain execution process and updating the merchant association database in real time, ensuring continuous optimization of subsequent matching; the application integrates scattered service nodes into a complete service chain, realizes one-stop service planning from fault emergency to waste disposal, and significantly improves the service response efficiency and user experience continuity in an emergency scenario. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 FIG. 1 is a schematic diagram of a tire merchant intelligent matching method based on geographic position according to the application;

[0060] Figure 2 FIG. 2 is a schematic diagram of a tire merchant intelligent matching system based on geographic position according to the application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0062] Embodiment 1

[0063] Please refer to Figure 1 The tire merchant intelligent matching method based on geographic position according to the embodiment includes the following steps:

[0064] Step S1: Obtain the current position coordinates and tire fault description input by the user, generate a fault surrounding area range in combination with real-time traffic conditions, and obtain an emergency response zoning set.

[0065] In the case of tire breakdown, users often need to quickly obtain repair and recycling services to reduce roadside stay time and environmental impact. This method first collects key input information from user devices such as smartphones or car navigation systems to ensure real-time and targeted matching. The current location coordinates are used to locate the breakdown point, and the tire breakdown description (such as "flat tire" or "severe wear") is used to filter the matching business service type. By integrating real-time traffic conditions, the response area is set to avoid congested areas, thereby improving the efficiency of emergency response.

[0066] Specifically, this embodiment collects current location coordinates and tire breakdown description from user devices, verifies coordinate accuracy and supplements breakdown type details to obtain breakdown location records. Coordinate accuracy verification is performed by checking GPS signal strength. If it is lower than the preset threshold (which is set to 10 meters of accuracy based on the average GPS error in urban environments to ensure that the location deviation does not exceed 5% of the safe response distance), the phone base station positioning is supplemented as an auxiliary. Breakdown type detail supplement is based on keyword matching of user description, for example, after entering "flat tire", the system automatically associates "immediate replacement" and "old tire recycling" labels to form a complete breakdown location record. For example, assuming that the user is located at a certain road segment and the breakdown description is "flat tire on the left front wheel", the breakdown location record includes coordinates, timestamp, and label set {flat tire, immediate repair, recycling demand}.

[0067] Query real-time traffic conditions based on breakdown location records, estimate the radiation radius of the breakdown surrounding area, and dynamically adjust the radiation radius to avoid congested road segments to obtain an expanded response boundary. Real-time traffic conditions are obtained by accessing Gaode Map API to obtain surrounding road network data, including congestion index (0 to 10 levels, 0 for smooth, 10 for severe congestion). The initial radiation radius is set to 5 kilometers (based on the average urban speed of 40 kilometers per hour and the emergency response time limit of 15 minutes: distance = speed x time, but considering the backup path, the experience value is adjusted to 5 kilometers), then dynamically adjusted according to the congestion index: if the congestion index of a road segment exceeds 7, it is shifted 0.5 to 1 kilometers in the direction adjacent to the low congestion. For example, at the user's location, the query shows that the east main road has a congestion index of 8 and the west side has a congestion index of 3, so the radiation radius is expanded to 6 kilometers to the west and compressed to 4 kilometers to the east, forming an expanded response boundary.

[0068] Divide the land within the expanded response boundary into zones, and mark the highway or urban main road area first to obtain a zone priority sequence. The zoning division uses a grid method to evenly divide the area within the boundary into 1 kilometer x 1 kilometer square grids, and then sorts the road types according to priority: the weight of the highway is 3 (because of its strong connectivity, it is convenient for quick response), the weight of the urban main road is 2, and the weight of the branch road is 1. The zone priority sequence is arranged in descending order of weight, for example, the sequence is [highway grid A (weight 3), main road grid B (weight 2)...].

[0069] Integrate the fault location records based on the priority sequence of the zoning set, and form an emergency response zoning set containing multiple buffer zones. The multiple buffer zones include an inner layer (0-2 km, highest priority, for immediate response) and an outer layer (2-5 km, backup layer), and the final emergency response zoning set contains multiple priority-ordered area units, ensuring coverage of more than 95% (coverage target based on historical fault data statistics).

[0070] Step S2: Perform location filtering and availability check on the repair service providers in the emergency response zoning set, remove service providers that exceed the response time limit, and obtain a preliminary repair candidate group.

[0071] To ensure timely response of service providers, this step focuses on dual filtering of geography and operational availability, removes unsuitable options, and narrows down the candidate range. The location filtering threshold is set to a predicted arrival time of no more than 15 minutes (based on the experience value of user tolerance time in emergency scenarios, and reference to the response time limit of insurance industry standards), and the availability check criteria include service provider business hours (current time within business hours) and inventory matching requirements (at least 1 tire of matching fault type in inventory).

[0072] Based on the emergency response zoning set, set the location filtering threshold and availability check criteria, calculate the straight-line distance and predicted arrival time of each repair service provider in the emergency response zoning set to the current location coordinates, remove service providers with a distance exceeding the location filtering threshold or a time exceeding the response time limit, and obtain a distance-qualified group. The straight-line distance is calculated using the Haversine formula. The predicted arrival time is the straight-line distance divided by the average vehicle speed (initial speed 40 km / h, adjusted to real-time traffic speed).

[0073] Verify the current availability of each repair service provider in the distance-qualified group, check if the inventory matches the tire fault description, remove service providers with insufficient inventory or non-operating status, and obtain an availability-qualified group. Availability verification is performed by querying the service provider database: business hours check if the current time is within 9:00-21:00, and inventory matching uses fault label comparison, such as "flat tire" requires at least 1 summer tire of 205 / 55R16 size in inventory. Assuming that in the distance-qualified group, service provider A has inventory matching (2 tires of 205 / 55R16 size), and service provider C is not in business, it is removed.

[0074] Perform multi-service provider cross-comparison on the availability-qualified group, preferentially retain service providers with backup vehicle support, and obtain a preliminary repair candidate group. Cross-comparison calculates the backup vehicle index of each service provider (number of vehicles / daily order volume, index threshold > 0.5), and preferentially retains service providers with high index. For example, in the qualified group, service provider A has an index of 0.8 and service provider D has an index of 0.3, then the former is retained.

[0075] Step S3: For each merchant in the preliminary repair alternative set, query its geographical proximity and cooperation records with associated recyclers, build repair-recycling pairing chains, and obtain a service chain pairing set.

[0076] After tire repair, old tires often need to be recycled to meet environmental protection requirements. This step builds a closed-loop chain of repair and recycling to improve service continuity through geographical proximity and historical cooperation. The geographical proximity threshold is set to 5 kilometers (based on the experience value of recycling logistics efficiency, and referring to the transfer distance standard of the environmental protection department), and the affinity threshold is set to 0.6 (based on the statistical threshold of historical cooperation success rate).

[0077] Select each repair merchant from the preliminary repair alternative set, query the location coordinates and cooperation frequency of its historical cooperation recyclers, and obtain a recycling association candidate set. The query is based on the merchant association database, and the cooperation frequency refers to the number of cooperation times in the past 6 months.

[0078] Calculate the geographical proximity of each recycler in the recycling association candidate set to the corresponding repair merchant, introduce cooperation records as affinity weighting, and obtain a proximity affinity score. Extract the location coordinates of each recycler in the recycling association candidate set, and perform path simulation with the location coordinates of the corresponding repair merchant to calculate the actual travel distance, obtaining a distance benchmark. The path simulation uses the A algorithm, considering road network weights, and the distance benchmark is the total length of the simulated path.

[0079] Based on the distance benchmark, query the cooperation records, count the historical cooperation frequency and convert it to affinity weight, dynamically amplify the weight of low-distance pairing, and obtain a weighted proximity score. The affinity weight is the ratio of cooperation frequency to maximum frequency, and then the weighted proximity score ; wherein, represents the distance benchmark, represents the affinity weight, represents the amplification factor (based on distance, for example, if the distance < 3 kilometers, the amplification factor is 1.2).

[0080] Perform multi-dimensional adjustment on the weighted proximity score, incorporate the processing capacity of recyclers as an additional factor to ensure that high-capacity merchants are prioritized, and obtain a comprehensive proximity affinity score. The processing capacity factor is the ratio of current capacity to maximum capacity, and the comprehensive proximity affinity score = weighted proximity score x (1 + processing capacity factor x 0.3).

[0081] Bind the comprehensive proximity affinity score with the recycling association candidate set, record the potential service continuity hints of the pairing, and form a proximity affinity score set.

[0082] Based on the proximity affinity score, the recycling merchants are screened, and the exclusive recycling pairing of each repair merchant is constructed. The pairing with an affinity lower than the affinity threshold (set to 0.6 in this embodiment) is excluded, and a preliminary chain draft is obtained. For example, merchant A scores 0.330 with recycling merchant X, which is lower than 0.6 and is excluded; and scores 0.150 with recycling merchant Y, which is lower than 0.6 and is excluded.

[0083] The preliminary chain draft is subjected to environmental compliance verification, the processing qualification of the recycling merchant is confirmed and integrated into the pairing, and a service chain pairing set containing continuous service guarantee is formed. The compliance verification checks the validity period of the qualification certificate (validity period > 6 months), for example, recycling merchant X is qualified, and the chain {A repair-X recycling, continuity prompt: transfer within 5 minutes after repair} is formed.

[0084] Step S4: For each pairing chain in the service chain pairing set, user preference weight adjustment and historical feedback integration are performed, and the overall matching score of the chain is calculated to obtain a preferred service chain list.

[0085] For personalized matching, this step integrates user preferences (such as environmental priority, cost sensitivity, etc.) and historical feedback to ensure that the score reflects the actual value. The sum of the preference weights is 1, the environmental priority weight is adjusted based on the proportion of environmental concerns in the user survey, and the cost sensitivity weight is adjusted based on the user's choice of price in the survey.

[0086] From the service chain pairing set, the basic attributes of the repair and recycling merchants of each pairing chain are extracted to obtain a chain attribute summary. The attributes include price (repair fee and recycling subsidy), environmental level (1-5 levels), and time length. For example, chain 1: price 500 yuan, environmental level 4, time length 20 minutes.

[0087] Based on the user's input preference settings, the chain attribute summary is applied with weight adjustment, emphasizing environmental recycling priority or cost sensitivity factors, to obtain a preference weighted summary. Normalize the values in the chain attribute summary, use the maximum and minimum normalization method to obtain the normalized value of each attribute, calculate the preference weighted summary based on the normalized value, and the calculation method is: ; Wherein, represents the preference weighted summary, represents the price normalized value, represents the cost weight, represents the environmental level normalized value, represents the environmental weight, represents the time length normalized value, represents the time length weight, which is adjusted according to the user's settings.

[0088] Query the historical feedback database, integrate user evaluations related to the chain attribute summary, accumulate positive feedback item by item and deduct negative records to obtain feedback points. The accumulation basis of positive feedback is the ratio of positive evaluation number to total evaluation number, and the accumulation basis of negative feedback is the negative number of the ratio of negative evaluation number to total evaluation number.

[0089] Fuse the preference weighted summary and the feedback points to calculate the overall matching score, ensure that the score reflects the continuity and reliability of the service chain, and form a preferred service chain list.

[0090] Step S5: Based on the preferred service chain list, push the interactive path planning to the user, including continuous navigation guidance from the repair point to the recycling point, and obtain the user confirmed service chain execution plan.

[0091] This step provides visualization and interaction to improve user experience and ensure that the plan is executable. The interactive map uses Baidu Map SDK and supports zooming and path editing.

[0092] From the preferred service chain list, extract the front pairing chain in order, show the user an interactive map view of the repair point and recycling point, and obtain a visual chain preview. For example, push Chain 1 map, mark user point A, repair B, and recycling C.

[0093] Based on the visual chain preview, generate a continuous path planning from the current location coordinates to the repair point and then to the recycling point, embed real-time traffic updates to optimize the turning points, and obtain a path guide draft. From the visual chain preview, lock the coordinate sequence of the repair point and the recycling point, and concatenate them into a path chain with the current location coordinates to obtain an initial path sequence.

[0094] Access real-time traffic updates, evaluate congestion at each turning point of the initial path sequence, automatically insert detour sub-paths to minimize delay, and obtain an optimized path chain. If the congestion evaluation is >5, insert a sub-path, and the total delay reduction is calculated as the original delay minus the sub-path saving.

[0095] Inject service timing prompts to the optimized path chain to ensure a seamless transition from repair to recycling after completion, and obtain a timing enhanced chain. For example, prompt "arrive at repair at 14:45, complete recycling at 15:00".

[0096] Synchronize the timing enhanced chain with the visual chain preview to build a path guide draft that includes voice navigation support.

[0097] Perform user interactive confirmation on the path guide draft, allowing manual adjustment of recycling order or addition of backup paths, and obtain a confirmed adjustment set; convert the confirmed adjustment set into an executable format, including appointment reminders and emergency contact channels, and form a user confirmed service chain execution plan. For example, after user confirmation, send a plan SMS appointment.

[0098] Step S6: Monitor the implementation process of the service chain execution plan, collect real-time service feedback and update the merchant association database in real time to continuously provide the optimal matched merchant.

[0099] For closed-loop optimization, this step tracks and feeds back iterations in real time. Position tracking uses device GPS, with a 5-minute reporting interval (based on experience interval balancing battery consumption). Position tracking is initiated based on user confirmation of the service chain execution plan, records the actual completion time points of the maintenance and recycling stages, and obtains an execution progress log. For example, the log records "14:50 maintenance completed, 15:05 recycling started". Embedding real-time feedback collection points in the execution progress log guides the user to submit service quality evaluations and problem descriptions, obtaining an instant feedback set. For example, a questionnaire is pushed after maintenance, collecting "service satisfaction 5 points, problem: none". The instant feedback set is classified and summarized, with positive elements extracted to strengthen merchant association and negative elements deducted to adjust cooperation affinity, obtaining a feedback optimization summary. Positive reinforcement: affinity +0.1; negative deduction: -0.2. The feedback optimization summary is injected into the merchant association database, updating the data of the service chain matching set to ensure dynamic adaptability of subsequent matching, continuously providing the optimal matched merchant. For example, after updating, the affinity of merchant A is increased to 0.7, increasing the priority next time.

[0100] This embodiment realizes rapid service range definition in the fault scenario by receiving the current position coordinates of the user and the tire fault description, and combining real-time traffic conditions; ensures the timeliness and accessibility of emergency services by excluding merchants that exceed the response time limit through location filtering and availability checks of maintenance merchants, obtaining a preliminary maintenance candidate group; solves the problem of multiple independent searches and decisions required by the user by integrating the originally fragmented maintenance service and recycling service into a continuous service chain matching set through querying the geographic proximity and cooperation records of the maintenance merchant and the recycling merchant; realizes comprehensive evaluation of the overall quality of the service chain rather than focusing on a single service node by adjusting the user preference weight and integrating historical feedback to calculate the overall matching score of the chain; seamlessly connects multiple-stage services by pushing interactive path planning to the user, including continuous navigation guidance from the maintenance point to the recycling point, eliminating the cognitive burden and operational complexity of the user in the emergency state; forms a dynamic feedback mechanism for service quality by monitoring the service chain execution process and updating the merchant association database in real time, ensuring continuous optimization of subsequent matching; the invention integrates dispersed service nodes into a complete service chain, realizing one-stop service planning from fault emergency to waste disposal, significantly improving the service response efficiency and user experience continuity in the emergency scenario.

[0101] Embodiment 2

[0102] Please refer to Figure 2As shown, the embodiments not described in detail see example 1 described in the content, provide a kind of based on geographical position's tire merchant intelligent matching system, comprising:

[0103] Data acquisition module: obtain the current position coordinates and tire failure description input by user, generate the range of surrounding area of failure in combination with real-time traffic conditions, obtain emergency response zoning set;

[0104] Screening module: the repair business in emergency response zoning set is filtered by position and availability check, eliminate the business exceeding response time limit, obtain preliminary repair candidate group;

[0105] Matching module: for each business in preliminary repair candidate group, query the geographical proximity and cooperation record of its associated recycling business, build repair-recycling matching chain, obtain service chain matching set;

[0106] Preferred module: for each matching chain in service chain matching set, execute user preference weight adjustment and historical feedback integration and calculate chain overall matching score, obtain preferred service chain list;

[0107] Confirmation module: based on preferred service chain list, push interactive path planning to user, including continuous navigation instruction from repair point to recycling point, obtain user confirmed service chain execution plan;

[0108] Feedback module: monitor the implementation process of service chain execution plan, collect real-time service feedback and update business association database in real time to continuously provide optimal matching business.

[0109] The above only for the preferred embodiment of the present application, and not for limiting the present application, although the present application is described in detail with reference to the foregoing embodiments, for the person skilled in the art, still can be modified to the technical scheme recorded in the foregoing each embodiment, or equivalent replacement to part of technical features. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included in the protection scope of the present application.

Claims

1. A location-based intelligent matching method for tire vendors, characterized in that, include: Step S1: Obtain the current location coordinates and tire fault description input by the user, and generate the surrounding area of ​​the fault by combining it with real-time traffic conditions to obtain the emergency response zoning set; Step S2: Filter the repair vendors within the emergency response zone set by location and check their availability, remove vendors that have exceeded the response time limit, and obtain a preliminary repair candidate group; Step S3: For each merchant in the initial repair candidate group, query the geographical proximity and cooperation records of its associated recycling merchants, construct a repair-recycling pairing chain, and obtain a service chain pairing set; Step S4: Perform user preference weight adjustment and historical feedback integration on each pairing chain in the service chain pairing set, and calculate the overall matching score of the chain to obtain the preferred service chain list; Step S5: Based on the preferred service chain list, push interactive route planning to the user, including continuous navigation guidance from the repair point to the recycling point, and obtain the service chain execution plan confirmed by the user; Step S6: Monitor the implementation process of the service chain execution plan, collect real-time service feedback, and update the merchant association database in real time to continuously provide the best matched merchants.

2. The location-based intelligent matching method for tire vendors according to claim 1, characterized in that, Step S1 includes: Step S11: Collect the current location coordinates and tire fault description from the user equipment, verify the coordinate accuracy and supplement the fault type details to obtain the fault location record; Step S12: Combine fault location records to query real-time traffic conditions, estimate the radiation radius of the area surrounding the fault, dynamically adjust the radiation radius to avoid congested road sections, and obtain the extended response boundary; Step S13: Divide the land parcels within the extended response boundary into zones, prioritizing the marking of highway or urban arterial road areas to obtain a zone priority sequence; Step S14: Integrate fault location records based on partition priority sequence to form an emergency response zoning set.

3. The location-based intelligent matching method for tire vendors according to claim 2, characterized in that, Step S12 includes: Step S121: Extract the current location coordinates from the fault location record, access the external traffic data source to obtain the surrounding road network congestion index, and obtain the road network congestion mapping; Step S122: Calculate the initial radiation radius based on the road network congestion mapping, introduce fault type details as a correction factor, extend the radius to cover alternative paths, and obtain the preliminary radiation range; Step S123: Perform a congestion avoidance scan on the initial radiation range, and shift the boundary line segment by segment to bypass the highly congested area to obtain the optimized radiation radius; Step S124: Bind the optimized radiation radius to the current location coordinates to construct an extended response boundary that includes obstacle avoidance path hints.

4. The location-based intelligent matching method for tire vendors according to claim 1, characterized in that, Step S2 includes: Step S21: Set location filtering thresholds and availability check criteria based on the emergency response zoning set. The availability check criteria include merchant business hours and inventory matching requirements. Step S22: Calculate the straight-line distance and estimated arrival time of each repair shop within the emergency response zone set from its current location coordinates, and remove shops whose distance exceeds the location filtering threshold or whose arrival time exceeds the response time limit to obtain the distance qualified group; Step S23: Verify the current availability of each repair shop in the qualified availability group, check whether the inventory matches the tire fault description, remove shops with insufficient inventory or not in operation, and obtain the qualified availability group. Step S24: Perform cross-comparison of multiple vendors for the availability qualified group, and give priority to vendors with backup vehicle support to obtain the initial repair alternative group.

5. The location-based intelligent matching method for tire vendors according to claim 1, characterized in that, Step S3 includes: Step S31: Select each repair merchant from the preliminary repair candidate group, query the location coordinates and cooperation frequency of its historical cooperative recycling merchants, and obtain the recycling associated candidate set; Step S32: Calculate the geographical proximity of each recycling merchant in the candidate set of recycling associations to the corresponding repair merchant, introduce cooperation records as affinity weighting, and obtain the proximity affinity score; Step S33: Screen recycling merchants based on proximity affinity score, construct exclusive recycling pairings for each repair merchant, and remove pairings with affinity scores below the affinity threshold to obtain a preliminary chain draft; Step S34: Perform environmental compliance verification on the initial draft chain, confirm the processing qualifications of recycling merchants and integrate them into the pairing to form a service chain pairing set.

6. The location-based intelligent matching method for tire vendors according to claim 5, characterized in that, Step S32 includes: Step S321: Extract the location coordinates of each recycling merchant from the recycling association candidate set, perform path simulation with the location coordinates of the corresponding repair merchant, calculate the actual travel distance, and obtain the distance benchmark value; Step S322: Query cooperation records based on distance benchmark values, count historical cooperation frequency and convert it into affinity weight, dynamically amplify the weight of low-distance pairings, and obtain weighted proximity score; Step S323: Perform multi-dimensional adjustments on the weighted proximity score, incorporating the processing capacity of the recycling vendor as an additional factor to obtain a comprehensive proximity affinity score; Step S324: Bind the comprehensive proximity affinity score to the recycling association candidate set, record the potential service continuity hints of the pairing, and form a proximity affinity score set.

7. The location-based intelligent matching method for tire vendors according to claim 1, characterized in that, Step S4 includes: Step S41: Select each pairing chain from the service chain pairing set, extract the basic attributes of its repair and recycling merchants, and obtain the chain attribute summary; Step S42: Apply weight adjustments to the chain attribute summary based on user-inputted preference settings to obtain a preference-weighted summary; Step S43: Query the historical feedback database, integrate user reviews related to the chain attribute summary, add positive feedback item by item and deduct negative records to obtain feedback points; Step S44: Combine the preference-weighted summary with the feedback integral to calculate the overall matching score and form a list of preferred service chains.

8. The location-based intelligent matching method for tire vendors according to claim 1, characterized in that, Step S5 includes: Step S51: Sort and extract the preceding pairing chains from the preferred service chain list, display an interactive map view of repair points and recycling points to the user, and obtain a visual chain preview; Step S52: Generate a continuous path plan from the current location coordinates to the maintenance point and then to the recycling point based on the visual chain preview, and obtain a path guidance draft; Step S53: Perform user interaction confirmation on the draft path guidance, allowing manual adjustment of the recycling order or addition of alternative paths, to obtain a confirmed adjustment set; Step S54: Convert the confirmation adjustment set into an executable format, including appointment reminders and emergency contact channels, to form a service chain execution plan for user confirmation.

9. The location-based intelligent matching method for tire vendors according to claim 8, characterized in that, Step S52 includes: Step S521: Locate the coordinate sequence of the maintenance point and recycling point from the visual chain preview, and connect it with the current location coordinates to form a path chain to obtain the initial path sequence; Step S522: Access real-time traffic updates, perform congestion assessment at each turning point of the initial path sequence, automatically insert detour sub-paths to minimize delay, and obtain an optimized path chain; Step S523: Inject service timing hints into the optimized path chain to obtain the timing enhancement chain; Step S524: Synchronize the temporal enhancement chain with the visual chain preview to build a draft path guidance that includes voice navigation support.

10. The location-based intelligent matching method for tire vendors according to claim 1, characterized in that, Step S6 includes: Based on user-confirmed service chain execution plan start location tracking, the actual completion time of the maintenance and recycling phases is recorded to obtain the execution progress log; Embed real-time feedback collection points in the execution progress log to guide users to submit service quality evaluations and problem descriptions, and obtain an instant feedback set. The real-time feedback sets are categorized and summarized. Positive elements are extracted to strengthen merchant relationships, and negative elements are removed to adjust the degree of cooperation affinity, resulting in a feedback optimization summary. The feedback optimization summary is injected into the merchant association database to update the data of the service chain matching set, ensuring the dynamic adaptability of subsequent matching to continuously provide the best matched merchants.

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