Road rescue order distribution method and system for electric bicycles

Abstract

The invention belongs to the technical field of computer data models, and particularly relates to a road rescue order distribution method and system for electric bicycles, and the method comprises the steps: receiving a road rescue order request of a user through a user terminal, analyzing the order request, and selecting information according to a rescue mode of the user, the method comprises the following steps: determining that a door-to-door rescue mode or a shop rescue mode is combined with a big data model to match rescue resources meeting conditions, distributing an order to the matched rescue resources, the rescue resources comprising a maintenance worker and a maintenance shop, the maintenance worker receiving the order through a mobile terminal, and the maintenance shop sending the order to the mobile terminal. The maintenance store receives an order through a store management terminal, after the rescue resource receives the order, service confirmation is carried out on the order, and the service confirmation comprises order receiving confirmation of a maintenance worker or the maintenance store; and after the user confirms the maintenance service, the user pays related fees to complete the order service, and the method has the effects of improving the allocation efficiency of the rescue order and optimizing resource allocation.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data models, and in particular to a method and system for distributing electric bicycle road rescue orders. Background Art

[0002] As an environmentally friendly and convenient means of transportation, electric bicycles are becoming increasingly popular for daily urban commuting. However, this has led to a growing demand for roadside assistance, particularly for new modes of transportation like electric bicycles, where demand for repair and rescue services is rapidly increasing. Traditional rescue operations, which typically rely on manual phone calls to answer calls and assign tasks, are inefficient and difficult to respond to. This not only impacts the timeliness and effectiveness of rescue services, but also affects user experience and satisfaction.

[0003] To address these issues, several roadside assistance solutions based on mobile internet technology have emerged on the market, such as those that enable the submission and initial allocation of rescue orders through smartphone applications. These applications can more effectively collect rescue information and improve the efficiency of information transmission. However, these systems still have limitations. For example, there is a lack of comprehensive methods for optimizing order scheduling algorithms. This can lead to scheduling imbalances and uneven task distribution when faced with a large number of simultaneous requests, thus affecting rescue efficiency and service quality.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides the following technical solutions:

[0006] A method for distributing electric bicycle roadside assistance orders, comprising:

[0007] receiving a roadside assistance order request from a user through a user terminal, the request including the user's geographic location information, vehicle fault description information, and assistance mode selection information;

[0008] Parsing the order request to extract the user's geographic location information, vehicle fault description information, and rescue mode selection information;

[0009] Based on the user's rescue mode selection information, determine whether to use the door-to-door rescue mode or the in-store rescue mode. Based on the user's geographic location information and vehicle fault description information, combined with the big data model, match qualified rescue resources, such as repair technicians and repair shops;

[0010] The order is distributed to the matched rescue resources, which include repair technicians and repair shops. The repair technicians receive the order through mobile terminals, and the repair shops receive the order through shop management terminals. After the rescue resources receive the order, they provide service confirmation for the order, which includes order confirmation from the repair technician or repair shop.

[0011] After the user confirms the maintenance service, he / she pays the relevant fees and completes the order service.

[0012] Furthermore, the rescue resource matching step includes screening out maintenance shops and maintenance technicians within a certain range of the user's location based on the user's geographic location information, further screening out qualified rescue resources based on the service scope of the maintenance shops and the service area of the maintenance technicians, screening out maintenance technicians or maintenance shops with corresponding maintenance skills based on the vehicle fault description information in combination with the big data model, and optimizing the matching results based on the historical service quality, response speed and user evaluation of the maintenance technicians or maintenance shops.

[0013] Furthermore, the construction of the big data model includes predicting the user's rescue needs based on the user's order history, rescue mode preference and geographic location preference, evaluating the matching priority of the repair master or repair store based on the historical order volume, service quality, response speed and service capabilities, dynamically adjusting the matching results based on the real-time location, busy and idle status and service resources of the repair master or repair store, and dynamically optimizing the rescue resource matching strategy based on the real-time order volume, rescue resource distribution and changes in user demand.

[0014] Furthermore, the order distribution step includes calculating the optimal dispatching plan based on the rescue resource matching results and combining it with a big data model, distributing the order to the most qualified rescue resource, notifying the repair technician and repair shop to grab the order within a specified time, and the rescue resource that successfully grabs the order accepting the order and providing service. Based on the rescue resource's geographical location, service capabilities, historical performance, and user preferences, multi-dimensional priority sorting rules are set to optimize order distribution efficiency.

[0015] The priority of rescue resources is calculated based on their geographical location, service capabilities, historical performance, and user preferences. The formula is as follows:

[0016] P i =w1·S geo,i +w2·S cap,i +w3·S his,i +w4·S pref,i ,

[0017] Among them, P i The priority score of rescue resource i, S geo,i is the geographical location score of rescue resource i, S cap,iis the service capability score of rescue resource i, S his,i is the historical performance score of rescue resource i, S pref,i is the user preference score of rescue resource i, w1, w2, w3 and w4 are the weights of each proportion respectively;

[0018] Based on the priority of rescue resources and order distribution rules, the optimal dispatching plan is calculated using the following formula:

[0019]

[0020] Among them, O opt is the optimal dispatching plan, n is the number of rescue resources, C i The order-taking capability of rescue resource i;

[0021] Based on the real-time location of rescue resources and the user's geographic location, the rescue path is optimized. The calculation is as follows:

[0022]

[0023] Among them, d k,k+1 is the distance from node k to node k+1, and m is the total number of nodes;

[0024] By combining big data models and mathematical calculation formulas, accurate matching of rescue resources and optimal distribution of orders can be achieved.

[0025] Furthermore, the order distribution step also includes, during the order execution process, if rescue resources are unable to accept orders or services are interrupted, the platform will re-match rescue resources and re-distribute orders; users will file complaints about services through user terminals, and the platform will process complaint records, including investigation and verification, coordination and resolution, and feedback results; the platform will collect and analyze order data, rescue resource data, and service evaluation data, and optimize rescue resource matching algorithms and service processes;

[0026] Conduct internal platform reviews of order completion status based on user feedback, including the repair technician's order acceptance time, completion time, and completion results, and conduct internal audits based on user feedback. If the repair technician is dissatisfied with the operation, the platform will implement rewards and penalties, reassign orders, and compensate for user satisfaction.

[0027] Comprehensive order acceptance time, completion time and completion result scores are scored by building an internal audit calculation model. The formula is as follows:

[0028] S0=w a ×S1+w b ×S2+w c ×S3,

[0029] Among them, S0 is the comprehensive score, between 0-100, S1 is the order score, S2 is the completion score, S3 is the result score, w a 、w b With w c are the corresponding weight proportions respectively;

[0030] When user satisfaction falls below a certain threshold, the platform provides compensation, calculated as follows:

[0031]

[0032] Among them, S A is the user satisfaction score, F is the total maintenance cost, and k is the replenishment multiple.

[0033] Furthermore, the order distribution includes when a repairman’s qualification to accept orders is suspended due to service quality issues or orders need to be redistributed, the platform will reallocate orders based on the comprehensive scores and availability of other repairmen, prioritize them based on the comprehensive scores, order-taking capabilities and geographical locations of the repairmen, and dynamically adjust order distribution based on the real-time order volume and the busy and idle status of the repairmen. For users who are dissatisfied with the repair service, the platform will calculate the compensation amount based on the user satisfaction score and repair costs, and pay the compensation in a timely manner.

[0034] Furthermore, the order distribution includes users paying the repair fee through the user terminal. The fee includes the on-site fee, advance payment, and repair fee. Users evaluate the repair service, and the evaluation includes service quality, response speed, and the professionalism of the repair technician / repair shop. The platform archives the order information, including order details, service records, and user reviews. The user reviews and service records are fed back into the big data model to optimize subsequent order distribution and rescue resource matching.

[0035] The platform monitors the order execution process in real time, including the real-time location of rescue resources, service progress, and service quality. During the order execution process, if an emergency occurs, the platform will activate the emergency response mechanism, coordinate rescue resources to quickly handle the problem, analyze the utilization efficiency and service quality of rescue resources based on big data models, dynamically adjust the distribution and service scope of rescue resources, and improve overall service capabilities.

[0036] According to one aspect of the present invention, a system for distributing roadside assistance orders for electric bicycles is provided, comprising:

[0037] An order receiving module is used to receive a user's roadside assistance request, the request including the user's location information, fault information, and rescue mode selection;

[0038] The rescue resource matching module is used to match qualified repair technicians or repair shops based on the user's location information, fault information, and rescue mode selection;

[0039] Path optimization module, which is used to optimize rescue routes and calculate estimated arrival times based on real-time traffic data and the real-time location of rescue resources;

[0040] The internal review module is used to review the repair technician's order acceptance time, completion time, and completion results based on user feedback, and to reward or punish or redistribute orders based on user satisfaction;

[0041] The order redistribution module is used to reallocate orders to other qualified rescue resources when the repair technician is unable to meet the order requirements;

[0042] The user satisfaction compensation module is used to calculate and distribute compensation amounts based on user satisfaction scores to improve user satisfaction.

[0043] According to one aspect of the present invention, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program and the processor implements the above steps when executing the computer program.

[0044] According to one aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps described above are implemented.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention discloses a method and system for distributing roadside assistance orders for electric bicycles. The method and system receive a roadside assistance order request from a user through a user terminal, wherein the request includes the user's geographic location information, vehicle fault description information, and rescue mode selection information; the order request is parsed to extract the user's geographic location information, vehicle fault description information, and rescue mode selection information; based on the user's rescue mode selection information, a door-to-door assistance mode or an in-store assistance mode is determined; based on the user's geographic location information and vehicle fault description information, a big data model is combined to match qualified rescue resources, wherein the rescue resources include repair masters and repair stores; the order is distributed to the matched rescue resources, and the rescue resources are matched. It includes repair masters and repair shops. The repair masters receive orders through mobile terminals, and the repair shops receive orders through store management terminals. After the rescue resources receive the order, they confirm the service of the order. The service confirmation includes the order confirmation of the repair master or the repair shop. After the user confirms the repair service, he pays the relevant fees and completes the order service. It has the ability to improve the allocation efficiency of rescue orders and optimize resource allocation through innovative technical solutions, so as to provide electric bicycle users with more efficient, fast and satisfactory road rescue services. Through improved scheduling algorithms and real-time traffic data analysis capabilities, the system will achieve more accurate rescue task allocation, thereby further improving rescue timeliness and service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0048] Figure 1 This is an overall schematic diagram of a method for distributing roadside assistance orders for electric bicycles according to the present invention;

[0049] Figure 2 This is a schematic diagram of a framework for a roadside assistance order distribution system for electric bicycles according to the present invention;

[0050] Figure 3 The present invention is a schematic diagram of a computer structure used in an electric bicycle road rescue order distribution system. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0052] like Figure 1-Figure 3As shown, the present application provides a method for distributing roadside assistance orders for electric bicycles, comprising:

[0053] S1: Receive a roadside assistance order request from a user through a user terminal, the request including the user's geographic location information, vehicle fault description information, and rescue mode selection information;

[0054] S2: Parse the order request to extract the user's geographic location information, vehicle fault description information, and rescue mode selection information;

[0055] S3: Determine whether to use a door-to-door rescue mode or a store-based rescue mode based on the user's rescue mode selection information, and match eligible rescue resources, including repair technicians and repair stores, based on the user's geographic location information and vehicle fault description information in combination with a big data model;

[0056] S4: Distribute the order to the matched rescue resources, which include repair technicians and repair shops. The repair technicians receive the order via a mobile terminal, and the repair shops receive the order via a shop management terminal. After the rescue resources receive the order, they perform service confirmation on the order, which includes order acceptance confirmation from the repair technician or the repair shop.

[0057] S5: After the user confirms the repair service, he / she pays the relevant fees and completes the order service.

[0058] Furthermore, the rescue resource matching step includes screening out maintenance shops and maintenance technicians within a certain range of the user's location based on the user's geographic location information, further screening out qualified rescue resources based on the service scope of the maintenance shops and the service area of the maintenance technicians, screening out maintenance technicians or maintenance shops with corresponding maintenance skills based on the vehicle fault description information in combination with the big data model, and optimizing the matching results based on the historical service quality, response speed and user evaluation of the maintenance technicians or maintenance shops.

[0059] Furthermore, the construction of the big data model includes predicting the user's rescue needs based on the user's order history, rescue mode preference and geographic location preference, evaluating the matching priority of the repair master or repair store based on the historical order volume, service quality, response speed and service capabilities, dynamically adjusting the matching results based on the real-time location, busy and idle status and service resources of the repair master or repair store, and dynamically optimizing the rescue resource matching strategy based on the real-time order volume, rescue resource distribution and changes in user demand.

[0060] In one embodiment, the system collects information such as the user's order history, rescue mode preferences, and geographic location preferences. It records information such as the repair technician's and repair shop's historical order volume, service quality, response speed, service capabilities, real-time location, busy / idle status, and service resources. It also organizes the user's vehicle fault description and builds a fault type database. It also obtains real-time dynamic information such as order volume, rescue resource distribution, and traffic conditions.

[0061] Data cleaning and preprocessing involves removing invalid or erroneous data, such as invalid location information and duplicate order records. Data from different sources and formats is uniformly converted to ensure consistency and comparability. Useful features are extracted from the raw data, such as user location preferences and repair technician skill tags.

[0062] Based on a user's order history, rescue mode preferences, and geographic location, machine learning algorithms (such as time series prediction, random forests, or neural networks) are used to predict a user's rescue needs. A scoring system or ranking mechanism is established to assess the matching priority of repairmen and stores based on their historical order volume, service quality, response speed, and service capabilities. Real-time data analysis and optimization algorithms (such as dynamic programming or genetic algorithms) are used to dynamically adjust matching results, taking into account the real-time location, busyness, and service resources of repairmen and stores.

[0063] The system is divided into an order receiving module, a rescue resource matching module, a route optimization module, an internal review module, an order redistribution module, and a user satisfaction compensation module, ensuring the independence and maintainability of each module. A real-time data processing engine (such as stream processing technology) is integrated to ensure the system can quickly respond to and handle real-time changes. Through A / B testing, performance analysis, and user feedback, the system's algorithms and matching strategies are continuously optimized to improve system accuracy and efficiency.

[0064] Perform functional testing on each module of the system to ensure that it can operate normally and achieve its intended functions. Evaluate the system's performance under high concurrency and large data volumes to ensure that the system can still operate stably under extreme conditions. Invite real users to participate in testing, collect user feedback, and further optimize system design and user experience. Deploy the system to the actual operating environment to ensure system stability and reliability. Establish a system monitoring mechanism to monitor the system's operating status and performance indicators in real time to promptly identify and resolve potential problems. Based on system operating data and user feedback, continuously improve system functions and performance to maintain the system's competitiveness and adaptability.

[0065] Furthermore, the order distribution step includes calculating the optimal dispatching plan based on the rescue resource matching results and combining it with a big data model, distributing the order to the most qualified rescue resource, notifying the repair technician and repair shop to grab the order within a specified time, and the rescue resource that successfully grabs the order accepting the order and providing service. Based on the rescue resource's geographical location, service capabilities, historical performance, and user preferences, multi-dimensional priority sorting rules are set to optimize order distribution efficiency.

[0066] The priority of rescue resources is calculated based on their geographical location, service capabilities, historical performance, and user preferences. The formula is as follows:

[0067] P i =w1·S geo,i +w2·S cap,i +w3·S his,i +w4·S pref,i ,

[0068] Among them, P i The priority score of rescue resource i, S geo,i is the geographical location score of rescue resource i, S cap,i is the service capability score of rescue resource i, S his,i is the historical performance score of rescue resource i, S pref,i is the user preference score of rescue resource i, w1, w2, w3 and w4 are the weights of each proportion respectively;

[0069] Based on the priority of rescue resources and order distribution rules, the optimal dispatching plan is calculated using the following formula:

[0070]

[0071] Among them, O opt is the optimal dispatching plan, n is the number of rescue resources, C i The order-taking capability of rescue resource i;

[0072] Based on the real-time location of rescue resources and the user's geographic location, the rescue path is optimized. The calculation is as follows:

[0073]

[0074] Among them, d k,k+1 is the distance from node k to node k+1, and m is the total number of nodes;

[0075] By combining big data models and mathematical calculation formulas, accurate matching of rescue resources and optimal distribution of orders can be achieved.

[0076] In one embodiment, the user's rescue needs are predicted based on their historical behavior, geographic location, and rescue mode preferences. The user's historical order data (time, location, fault type, and rescue mode selection) is used. The user's geographic location information (real-time location, typical location) and user preferences (rescue mode preference, repair shop preference) are used. The user's real-time rescue needs and their rescue mode preference (on-site rescue or in-store rescue) are also used.

[0077] Match the most qualified rescue resources based on their geographic location, service capabilities, and service quality. The geographic location of the repairman / repair shop; the service capabilities of the repairman / repair shop (skill level, tool equipment); the historical service quality of the repairman / repair shop (user reviews, response speed); real-time order volume and the busy / idle status of the rescue resource. Optimize rescue routes based on real-time traffic data and the real-time location of the rescue resource. Calculate the priority of the rescue resource based on its geographic location, service capabilities, historical performance, and user preferences. Calculate the optimal dispatch plan based on the priority of the rescue resource and order distribution rules.

[0078] Optimize the rescue path based on the real-time location of the rescue resources and the user's geographic location. Calculate the estimated arrival time of the rescue resources based on real-time traffic data and the driving speed of the rescue resources. Path optimization is a key link in order distribution. The following are the specific steps and mathematical formulas for path optimization. Use distance formulas (such as Euclidean distance or Manhattan distance) to calculate the distance from the rescue resources to the user. Use the shortest path algorithm (such as Dijkstra algorithm or A* algorithm) to optimize the rescue path. Calculate the estimated arrival time of the rescue resources based on the path length and real-time traffic data. By combining big data models and mathematical calculation formulas, accurate matching of rescue resources and optimal distribution of orders can be achieved. Through these methods, the response speed and user satisfaction of road rescue services can be significantly improved, while optimizing the utilization efficiency of rescue resources.

[0079] Furthermore, the order distribution step also includes, during the order execution process, if rescue resources are unable to accept orders or services are interrupted, the platform will re-match rescue resources and re-distribute orders; users will file complaints about services through user terminals, and the platform will process complaint records, including investigation and verification, coordination and resolution, and feedback results; the platform will collect and analyze order data, rescue resource data, and service evaluation data, and optimize rescue resource matching algorithms and service processes;

[0080] Conduct internal platform reviews of order completion status based on user feedback, including the repair technician's order acceptance time, completion time, and completion results, and conduct internal audits based on user feedback. If the repair technician is dissatisfied with the operation, the platform will implement rewards and penalties, reassign orders, and compensate for user satisfaction.

[0081] Comprehensive order acceptance time, completion time and completion result scores are scored by building an internal audit calculation model. The formula is as follows:

[0082] S0=w a ×S1+w b ×S2+w c ×S3,

[0083] Among them, S0 is the comprehensive score, between 0-100, S1 is the order score, S2 is the completion score, S3 is the result score, w a 、w b With w c are the corresponding weight proportions respectively;

[0084] When user satisfaction falls below a certain threshold, the platform provides compensation, calculated as follows:

[0085]

[0086] Among them, S A is the user satisfaction score, F is the total maintenance cost, and k is the replenishment multiple.

[0087] In one embodiment, data collection includes repair technician data: order acceptance time, completion time, repair results, etc.; user feedback: satisfaction ratings, review content, etc.; order information: order amount, service type, etc.; order acceptance timeliness rating: evaluates the repair technician's speed in accepting orders; completion timeliness rating: evaluates the repair technician's efficiency in completing tasks; and completion result rating: evaluates the quality of repair tasks, combining user satisfaction and platform inspection results.

[0088] A weighted average of the order acceptance time, completion time, and completion result scores is used to determine the overall repair technician score. Based on this overall score, repair technicians are rewarded or penalized. Repair technicians with low scores may have their order acceptance qualifications suspended or their priority reduced. Orders that require reallocation are reassigned based on the overall scores and availability of other repair technicians. For users dissatisfied with the service, compensation is calculated and distributed based on their satisfaction score and order amount.

[0089] The order acceptance timeliness score measures how quickly a repair technician accepts orders and how efficiently they complete tasks. This score is based on user satisfaction and platform quality inspection results. It combines order acceptance timeliness, completion timeliness, and completion results.

[0090] When user satisfaction falls below a certain threshold, the platform provides compensation. Ratings above 90: Bonuses or honorary titles are awarded. Consecutive high ratings: Priority increases for repair technicians, increasing their chances of receiving orders. Ratings below 70: Bonuses partially deducted or order eligibility suspended. Repeated low ratings: Priority reduction for repair technicians, reducing their chances of receiving orders.

[0091] When a technician's eligibility to accept orders is suspended due to service quality issues or orders need to be reassigned, the platform will reallocate orders based on the comprehensive ratings and availability of other technicians. Reassignment rules may include prioritizing technicians based on their comprehensive ratings, order-taking capabilities, and geographic location. Order allocation is dynamically adjusted based on real-time order volume and the technician's availability.

[0092] For users dissatisfied with repair services, the platform calculates compensation based on the user satisfaction score and repair costs, and promptly disburses compensation. Compensation is typically provided in the form of vouchers, coupons, or cash. After verifying user feedback, the platform promptly disburses compensation, enhancing user trust and satisfaction.

[0093] By establishing an internal review mechanism based on user feedback, combined with mathematical models and reward and punishment measures, the platform can effectively improve the quality of repair services and user satisfaction. Furthermore, a rationally designed order redistribution and user satisfaction compensation mechanism ensures efficient platform operation and a positive user experience. This approach not only helps enhance the platform's competitiveness but also strengthens user trust and loyalty.

[0094] Furthermore, the order distribution includes when a repairman’s qualification to accept orders is suspended due to service quality issues or orders need to be redistributed, the platform will reallocate orders based on the comprehensive scores and availability of other repairmen, prioritize them based on the comprehensive scores, order-taking capabilities and geographical locations of the repairmen, and dynamically adjust order distribution based on the real-time order volume and the busy and idle status of the repairmen. For users who are dissatisfied with the repair service, the platform will calculate the compensation amount based on the user satisfaction score and repair costs, and pay the compensation in a timely manner.

[0095] Furthermore, the order distribution includes users paying the repair fee through the user terminal. The fee includes the on-site fee, advance payment, and repair fee. Users evaluate the repair service, and the evaluation includes service quality, response speed, and the professionalism of the repair technician / repair shop. The platform archives the order information, including order details, service records, and user reviews. The user reviews and service records are fed back into the big data model to optimize subsequent order distribution and rescue resource matching.

[0096] The platform monitors the order execution process in real time, including the real-time location of rescue resources, service progress, and service quality. During the order execution process, if an emergency occurs, the platform will activate the emergency response mechanism, coordinate rescue resources to quickly handle the problem, analyze the utilization efficiency and service quality of rescue resources based on big data models, dynamically adjust the distribution and service scope of rescue resources, and improve overall service capabilities.

[0097] According to one aspect of the present invention, a system for distributing roadside assistance orders for electric bicycles is provided, comprising:

[0098] An order receiving module is used to receive a user's roadside assistance request, the request including the user's location information, fault information, and rescue mode selection;

[0099] The rescue resource matching module is used to match qualified repair technicians or repair shops based on the user's location information, fault information, and rescue mode selection;

[0100] Path optimization module, which is used to optimize rescue routes and calculate estimated arrival times based on real-time traffic data and the real-time location of rescue resources;

[0101] The internal review module is used to review the repair technician's order acceptance time, completion time, and completion results based on user feedback, and to reward or punish or redistribute orders based on user satisfaction;

[0102] The order redistribution module is used to reallocate orders to other qualified rescue resources when the repair technician is unable to meet the order requirements;

[0103] The user satisfaction compensation module is used to calculate and distribute compensation amounts based on user satisfaction scores to improve user satisfaction.

[0104] In one embodiment, order reception involves a user initiating a rescue request through a mobile app or web interface, providing location information, a description of the fault, and a rescue mode selection (e.g., quick response, professional repair, etc.). Upon receiving the request, the order reception module records the user's request information and passes it to the rescue resource matching module.

[0105] Geographic location screening uses the user's location information to filter out repair technicians and repair shops within a certain range of the user. Fault type matching combines the fault description provided by the user and uses big data models to match repair technicians or repair shops with corresponding repair skills.

[0106] Service quality assessment adjusts matching priorities based on the repairman's historical service quality (such as user reviews and response speed). Dynamic optimization dynamically adjusts matching results based on the repairman's real-time location, busyness, and service resources.

[0107] Real-time traffic data acquisition: Integrates with a real-time traffic information API to obtain traffic conditions between the user's location and the rescue resource's location. Path calculation: Uses path optimization algorithms (such as Dijkstra or A*) to calculate the optimal rescue route. Estimated Time of Arrival (ETA) calculation: Calculates the estimated arrival time of rescue resources based on the optimized route and real-time traffic data.

[0108] Order acceptance timeliness assessment calculates the difference between the repairman's order acceptance time and the expected time, and determines the order acceptance timeliness score. Completion timeliness assessment calculates the difference between the repairman's task completion time and the expected time, and determines the completion timeliness score. Service quality assessment combines user satisfaction scores and platform quality inspection results to assess the repairman's completion score. Comprehensive score calculation takes a weighted average of the order acceptance timeliness, completion timeliness, and completion score to determine the repairman's overall score. Reward and punishment enforcement: Based on the comprehensive score, repairmen are rewarded or punished, such as receiving bonuses or suspending their order acceptance qualifications.

[0109] Redistribution conditions are determined. When a repair technician fails to meet order requirements due to service quality issues or other reasons, order redistribution is triggered. Re-matching of rescue resources is performed. Based on the comprehensive scores and availability of remaining rescue resources, a qualified repair technician or repair shop is matched. Order allocation is executed, and the order is redistributed to the new rescue resource, and the user is notified of the new rescue arrangements.

[0110] Satisfaction score collection: collect user satisfaction scores through user feedback channels.

[0111] Compensation calculation: Calculate the compensation amount based on the user's satisfaction score and order amount. Compensation distribution: Disburse the calculated compensation amount to the user in the form of vouchers, coupons, or cash to improve user satisfaction.

[0112] The electric bicycle road rescue order distribution system of the present invention has the following beneficial effects:

[0113] Accurate matching: By comprehensively considering geographical location, fault type, rescue mode and service quality, accurate rescue resource matching is achieved to improve rescue efficiency.

[0114] Efficient distribution, based on real-time traffic data and dynamic adjustment mechanisms, optimizes rescue routes, shortens rescue time, and improves user experience.

[0115] Service quality assurance, through internal audits and reward and punishment mechanisms, encourages maintenance technicians to improve service quality and ensures that users receive high-quality rescue services.

[0116] User satisfaction is improved. Through the user satisfaction compensation mechanism, compensation is provided for unsatisfactory services, enhancing users' trust and loyalty to the platform.

[0117] The system is flexible and supports dynamic adjustment and expansion, which can adapt to changes in different scenarios and needs, and improve the adaptability and maintainability of the system.

[0118] This invention addresses the challenges of traditional rescue services, such as inaccurate resource matching, slow response times, inconsistent service quality, and low user satisfaction, by building an intelligent e-bike roadside assistance order distribution system. By integrating user needs, rescue resources, and real-time data, it achieves precise matching and efficient dispatch, significantly improving rescue efficiency and user experience. This system has broad application prospects and can provide more reliable and efficient rescue services for e-bike users.

[0119] In one embodiment, the system architecture design includes a module division order receiving module, which serves as the entrance to the system, receives the user's rescue request, and passes the request information to the rescue resource matching module. The rescue resource matching module screens out qualified repairmen or repair shops based on the user's location, fault type and rescue mode. The path optimization module calculates the optimal rescue path and estimates the arrival time based on real-time traffic data. The internal review module evaluates the repairman's order acceptance time, completion time and service quality to determine whether the order needs to be redistributed. The order redistribution module rematches and distributes orders when the repairman cannot meet the order requirements.

[0120] The user satisfaction compensation module calculates and distributes compensation amounts based on user satisfaction scores to improve user satisfaction.

[0121] The user initiates a rescue request. The user submits the rescue request through a mobile application or web interface, providing location information, fault description, and rescue mode selection. The order receiving module processes the request, receives the user's request information, and passes it to the rescue resource matching module. The rescue resource matching module screens resources and selects qualified repairmen or repair shops based on the user's location, fault type, and rescue mode. The path optimization module calculates the optimal path and, based on real-time traffic data, calculates the rescue path and estimates the arrival time. The internal review module evaluates the service quality, evaluates the repairman's order acceptance time, completion time, and service quality, and determines whether the order needs to be redistributed. The order redistribution module redistributes orders and, when necessary, reallocates orders to other qualified rescue resources. The user satisfaction compensation module provides compensation and calculates and distributes the compensation amount based on the user's satisfaction score to improve the user experience.

[0122] The system interface design includes a user interface, including a mobile app and a web interface, for users to submit rescue requests and check order status. A repairman interface allows repairmen to receive orders, view task details, and provide feedback on task completion. A repair shop interface allows repair shops to receive orders, schedule repair tasks, and provide feedback on task completion. An external data interface integrates a real-time traffic data API to obtain real-time traffic conditions.

[0123] The database design includes a user information database, which stores users' personal information, order history, rescue mode preferences, and location preferences. A rescue resource database records detailed information about repair technicians and repair shops, including their skill level, tool availability, historical service quality, response speed, and real-time location. A vehicle fault database organizes user descriptions of vehicle faults and builds a fault type database. An order database stores detailed information about all orders, including order status, rescue route, estimated arrival time, and user satisfaction rating.

[0124] Technology selection includes front-end technology, using React Native or Flutter to develop mobile applications, and using React or Vue.js to develop web interfaces. Back-end technology, using Node.js or Python (Django / Flask) to develop back-end services to handle logic such as order reception, rescue resource matching, and route optimization. Database, using MySQL or MongoDB to store user information, rescue resource information, order information, and vehicle fault information. Real-time traffic data API, integrating Google Maps API or Amap API to obtain real-time traffic conditions. Message queue, using RabbitMQ or Kafka to handle order distribution and notifications under high concurrency. Containerization and orchestration, using Docker containerized applications and Kubernetes for container orchestration to improve the scalability and maintainability of the system.

[0125] System deployment involves selecting a cloud service provider, such as AWS, Alibaba Cloud, or Tencent Cloud, to deploy the system's backend services and database. Load balancing involves using the cloud service provider's load balancer to distribute user requests to different servers, improving the system's processing power. For autoscaling, configure an autoscaling policy to dynamically adjust the number of servers based on system load to ensure stable operation. Monitoring and logging involve using Prometheus and Grafana for system monitoring, and ELK (Elasticsearch, Logstash, Kibana) for log management to promptly identify and resolve system issues.

[0126] Security measures include data encryption, using SSL / TLS protocols to encrypt user data transmission and ensure data security during transmission. Access control uses OAuth2.0 or JWT for identity authentication and access control, ensuring that only authorized users can access system resources. Data backup and recovery: Regular database backups and configured data recovery strategies prevent data loss and system failures. Security audits: Regular security audits are conducted to identify system security vulnerabilities and potential risks, and timely repairs and improvements are implemented.

[0127] Functional testing includes order reception testing, which tests whether the system can correctly receive the user's rescue request and pass it to the rescue resource matching module. Rescue resource matching testing, which tests whether the system can accurately match qualified repairmen or repair shops based on the user's location, fault type and rescue mode. Path optimization testing, which tests whether the system can calculate the optimal rescue route based on real-time traffic data and accurately estimate the arrival time. Internal audit testing, which tests whether the system can evaluate the service quality of repairmen and decide whether orders need to be redistributed. Order redistribution testing, which tests whether the system can rematch and reassign orders to other qualified rescue resources when necessary. User satisfaction compensation testing, which tests whether the system can calculate and distribute compensation amounts based on user satisfaction scores to improve user experience.

[0128] Performance testing includes high-concurrency testing, which simulates high-concurrency scenarios and tests the system's processing capabilities to ensure stable operation under high load. Response time testing tests the system's response time under varying loads to ensure a smooth and timely user experience. System scalability testing tests the system's scalability to verify whether the system's processing capacity can be linearly increased after adding servers.

[0129] Security testing includes penetration testing, which simulates hacker attacks to test system security and identify potential vulnerabilities and risks. Data protection testing tests the system's data encryption and access control mechanisms to ensure the security and privacy of user data. Fault recovery testing tests the system's fault recovery capabilities to verify whether it can quickly recover and resume service after a system failure.

[0130] User acceptance testing includes user experience testing, which involves real users participating in the testing to collect feedback and optimize system design and user experience. Functional integrity testing ensures that all functional modules of the system function properly and meet user needs and expectations. System stability testing tests the stability of the system over long periods of time to ensure that it does not crash or experience performance degradation.

[0131] System monitoring and maintenance includes real-time monitoring, using monitoring tools to monitor the system's operating status and performance indicators in real time, promptly identifying and resolving potential issues. Log management involves regularly analyzing system logs to identify abnormal behavior and potential risks, and implementing preventive measures and improvements. System updates involve regularly updating system software and hardware, fixing known vulnerabilities, and improving system security and performance. User feedback collection involves gathering user feedback and suggestions through user surveys, feedback channels, and data analysis to understand their needs and expectations. Functional optimization involves optimizing system functional modules based on user feedback and needs, improving user experience and service quality. Performance optimization involves analyzing system performance data to optimize system algorithms and architecture, enhancing system processing power and response speed.

[0132] Security optimization includes vulnerability repair and regular security audits to identify and repair system security vulnerabilities and improve system security. Security policy updates are based on the latest security threats and attack methods to update system security policies and protection measures to ensure system security. User education is carried out through user education and publicity to enhance user network security awareness and prevent security risks caused by improper user operation.

[0133] Through the detailed design and implementation described above, this invention provides an efficient, intelligent, and flexible e-bike roadside assistance order distribution system. This system significantly improves rescue efficiency and user experience through precise resource matching, optimized rescue routes, rigorous service quality audits, and a user satisfaction compensation mechanism. The system's modular design, high scalability, and security enable it to adapt to diverse application scenarios and changing needs, promising broad application prospects and market competitiveness.

[0134] The present invention discloses a method and system for distributing roadside assistance orders for electric bicycles. The method and system receive a roadside assistance order request from a user through a user terminal, wherein the request includes the user's geographic location information, vehicle fault description information, and rescue mode selection information; the order request is parsed to extract the user's geographic location information, vehicle fault description information, and rescue mode selection information; based on the user's rescue mode selection information, a door-to-door assistance mode or an in-store assistance mode is determined; based on the user's geographic location information and vehicle fault description information, a big data model is combined to match qualified rescue resources, wherein the rescue resources include repair masters and repair stores; the order is distributed to the matched rescue resources, and the rescue resources are matched. It includes repair masters and repair shops. The repair masters receive orders through mobile terminals, and the repair shops receive orders through store management terminals. After the rescue resources receive the order, they confirm the service of the order. The service confirmation includes the order confirmation of the repair master or the repair shop. After the user confirms the repair service, he pays the relevant fees and completes the order service. It has the ability to improve the allocation efficiency of rescue orders and optimize resource allocation through innovative technical solutions, so as to provide electric bicycle users with more efficient, fast and satisfactory road rescue services. Through improved scheduling algorithms and real-time traffic data analysis capabilities, the system will achieve more accurate rescue task allocation, thereby further improving rescue timeliness and service quality.

[0135] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the above-mentioned method for distributing electric bicycle road rescue orders.

[0136] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for distributing road rescue orders for electric bicycles.

[0137] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0138] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0139] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for distributing electric bicycle roadside assistance orders, characterized in that: include: receiving a roadside assistance order request from a user through a user terminal, the request including the user's geographic location information, vehicle fault description information, and assistance mode selection information; Parsing the order request to extract the user's geographic location information, vehicle fault description information, and rescue mode selection information; Based on the user's rescue mode selection information, determine whether to use the door-to-door rescue mode or the in-store rescue mode. Based on the user's geographic location information and vehicle fault description information, combined with the big data model, match qualified rescue resources, such as repair technicians and repair shops; The order is distributed to the matched rescue resources, which include repair technicians and repair shops. The repair technicians receive the order through mobile terminals, and the repair shops receive the order through shop management terminals. After the rescue resources receive the order, they provide service confirmation for the order, which includes order confirmation from the repair technician or repair shop. After the user confirms the maintenance service, he / she pays the relevant fees and completes the order service.

2. A method for distributing electric bicycle roadside assistance orders according to claim 1, characterized in that: The rescue resource matching step includes screening out repair shops and repair masters within a certain range of the user's location based on the user's geographic location information, further screening out qualified rescue resources based on the service scope of the repair shop and the service area of the repair master, screening out repair masters or repair shops with corresponding repair skills based on the vehicle fault description information combined with the big data model, and optimizing the matching results based on the historical service quality, response speed and user evaluation of the repair master or repair shop.

3. The method for distributing electric bicycle roadside assistance orders according to claim 1, characterized in that: The construction of the big data model includes predicting the user's rescue needs based on the user's order history, rescue mode preference and geographic location preference, evaluating the matching priority of the repairman or repair store based on its historical order volume, service quality, response speed and service capability, dynamically adjusting the matching results based on the real-time location, busy and idle status and service resources of the repairman or repair store, and dynamically optimizing the rescue resource matching strategy based on the real-time order volume, rescue resource distribution and changes in user demand.

4. The method for distributing electric bicycle roadside assistance orders according to claim 1, characterized in that: The order distribution step includes calculating the optimal dispatching plan based on the rescue resource matching results and combining it with a big data model, distributing the order to the most qualified rescue resource, notifying the repair technician and repair shop to grab the order within a specified time, and the rescue resource that successfully grabs the order accepting the order and providing the service. Based on the rescue resource's geographical location, service capabilities, historical performance, and user preferences, multi-dimensional priority sorting rules are set to optimize order distribution efficiency; The priority of rescue resources is calculated based on their geographical location, service capabilities, historical performance, and user preferences. The formula is as follows: P i =w1·S geo,i +w2·S cap,i +w3·S his,i +w4·S pref,i , Among them, P i The priority score of rescue resource i, S geo,i is the geographical location score of rescue resource i, S cap,i is the service capability score of rescue resource i, S his,i is the historical performance score of rescue resource i, S pref,i is the user preference score of rescue resource i, w1, w2, w3 and w4 are the weights of each proportion respectively; Based on the priority of rescue resources and order distribution rules, the optimal dispatching plan is calculated using the following formula: Among them, O opt is the optimal dispatching plan, n is the number of rescue resources, C i The order-taking capability of rescue resource i; Based on the real-time location of rescue resources and the user's geographic location, the rescue path is optimized. The calculation is as follows: Among them, d k,k+1 is the distance from node k to node k+1, and m is the total number of nodes; By combining big data models and mathematical calculation formulas, accurate matching of rescue resources and optimal distribution of orders can be achieved.

5. The method for distributing electric bicycle roadside assistance orders according to claim 1, characterized in that: The order distribution step also includes, during the order execution process, if rescue resources are unable to accept orders or services are interrupted, the platform will re-match rescue resources and re-distribute orders; users will file complaints about services through user terminals, and the platform will process complaint records, including investigation and verification, coordination and resolution, and feedback results; the platform will collect and analyze order data, rescue resource data, and service evaluation data, and optimize rescue resource matching algorithms and service processes; Conduct internal platform reviews of order completion status based on user feedback, including the repair technician's order acceptance time, completion time, and completion results, and conduct internal audits based on user feedback. If the repair technician is dissatisfied with the operation, the platform will implement rewards and penalties, reassign orders, and compensate for user satisfaction. Comprehensive order acceptance time, completion time and completion result scores are scored by building an internal audit calculation model. The formula is as follows: S0=w a ×S1+w b ×S2+w c ×S3, Among them, S0 is the comprehensive score, between 0-100, S1 is the order score, S2 is the completion score, S3 is the result score, w a 、w b With w c are the corresponding weight proportions respectively; When user satisfaction falls below a certain threshold, the platform provides compensation, calculated as follows: Among them, S A is the user satisfaction score, F is the total maintenance cost, and k is the replenishment multiple.

6. The method for distributing electric bicycle roadside assistance orders according to claim 5, characterized in that: The order distribution includes when a repairman's qualification to accept orders is suspended due to service quality issues or orders need to be redistributed, the platform will reallocate orders based on the comprehensive scores and availability of other repairmen, prioritize them based on the comprehensive scores, order-taking capabilities and geographical locations, and dynamically adjust order distribution based on real-time order volume and the busy and idle status of the repairmen. For users who are dissatisfied with the repair service, the platform will calculate the compensation amount based on the user satisfaction score and repair costs, and pay the compensation in a timely manner.

7. The method for distributing roadside assistance orders for electric bicycles according to claim 5, characterized in that: The order distribution includes users paying the repair fee through the user terminal. The fee includes the on-site fee, prepayment and repair fee. Users evaluate the repair service, and the evaluation includes service quality, response speed and the professionalism of the repair technician / repair shop. The platform archives the order information, including order details, service records and user reviews, and feeds user reviews and service records into the big data model to optimize subsequent order distribution and rescue resource matching. The platform monitors the order execution process in real time, including the real-time location of rescue resources, service progress, and service quality. During the order execution process, if an emergency occurs, the platform will activate the emergency response mechanism, coordinate rescue resources to quickly handle the problem, analyze the utilization efficiency and service quality of rescue resources based on big data models, dynamically adjust the distribution and service scope of rescue resources, and improve overall service capabilities.

8. A system for distributing roadside assistance orders for electric bicycles, characterized in that: include: An order receiving module is used to receive a user's roadside assistance request, the request including the user's location information, fault information, and rescue mode selection; The rescue resource matching module is used to match qualified repair technicians or repair shops based on the user's location information, fault information, and rescue mode selection; Path optimization module, which is used to optimize rescue routes and calculate estimated arrival times based on real-time traffic data and the real-time location of rescue resources; The internal review module is used to review the repair technician's order acceptance time, completion time, and completion results based on user feedback, and to reward or punish or redistribute orders based on user satisfaction; The order redistribution module is used to reallocate orders to other qualified rescue resources when the repair technician is unable to meet the order requirements; The user satisfaction compensation module is used to calculate and distribute compensation amounts based on user satisfaction scores to improve user satisfaction.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for distributing electric bicycle road rescue orders according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for distributing electric bicycle road rescue orders according to any one of claims 1 to 6 are implemented.

Images