Electronic toll collection (ETC) system customer after-sales service order intelligent analysis, dispatching and distribution method

Abstract

The invention relates to the technical field of intelligent traffic systems, and particularly discloses an ETC system customer after-sales service order intelligent analysis, dispatching and distribution method, which comprises the following steps: receiving an after-sales service order submitted by a customer, and collecting fault phenomenon data, a fault position and fault occurrence time; the maintenance emergency degree is analyzed based on the fault occurrence time distribution rule, the influence range and the traffic flow, and the processing priority is evaluated in combination with the time factor and the channel redundancy; identifying a fault type based on the fault phenomenon, and determining service resources required by maintenance of the primary processing order according to the fault type; the position, the working state and the held resource condition of a maintenance team are monitored in real time, and an order is automatically sent according to professional matching and a proximity principle. According to the invention, scientific quantification of order priority, accurate identification of fault types and efficient scheduling of maintenance resources are realized, and the response speed, the resource utilization rate and the user satisfaction of the ETC system after-sales service are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation systems technology, and specifically to a method for intelligent analysis and dispatching of after-sales service orders for ETC (Electronic Toll Collection) systems. Background Technology

[0002] With the widespread application of the Electronic Toll Collection (ETC) system on highways, the efficiency and quality of its after-sales service are directly related to the stability of the road network and the user experience. At present, the ETC system often encounters various faults during operation, such as card reading failure, communication interruption, and equipment damage. Users can submit after-sales service orders through various channels. However, the existing order processing methods mostly rely on manual experience to prioritize, identify fault types, and allocate maintenance resources, which has the following shortcomings: (1) Priority judgment is highly subjective: There is a lack of scientific quantitative assessment of the urgency of faults. They are often sorted based only on the time of fault occurrence or simple description, which can easily lead to delays in the response to important faults.

[0003] (2) Low efficiency in fault location and type identification: The determination of fault location and type relies heavily on manual communication and on-site investigation, which is time-consuming and prone to errors.

[0004] (3) Unreasonable resource allocation: The maintenance team dispatch lacks comprehensive consideration of real-time location, working status and resource matching, often resulting in idle resources or repeated dispatch, which affects the overall service efficiency.

[0005] Therefore, there is an urgent need for an ETC system after-sales service order processing method that can intelligently analyze, automatically dispatch, and accurately allocate orders in order to improve service response speed, resource utilization, and user satisfaction. Summary of the Invention

[0006] To address the above problems, this invention proposes an intelligent analysis and dispatch method for after-sales service orders of ETC system customers. The specific technical solution is as follows: An intelligent analysis and dispatch method for after-sales service orders of ETC system customers, comprising the following steps: S1, Service request reception: receiving after-sales service orders submitted by customers and collecting basic information of the orders, including fault phenomenon data, fault location and fault occurrence time.

[0007] S2. Order Priority Analysis: Based on the distribution pattern of fault occurrence time, the scope of fault impact, and traffic flow information at the fault location, analyze the urgency of repair. Combine the fault occurrence time and the number of lanes at the fault location to comprehensively evaluate the processing priority of orders, and determine the first order to be processed based on the evaluation results.

[0008] S3. Service Resource Requirements Analysis: Identify fault types based on fault phenomenon data of primary processing orders, and determine the service resources required for repair based on fault types.

[0009] S4. Repair Work Order Dispatch: Real-time monitoring of the location information, working status and service resources of each repair team. Based on the fault location and service resources required for the primary order, and according to the preset professional matching rules and proximity allocation rules, the system matches the corresponding repair team, generates a repair work order, and sends it to the matched repair team.

[0010] Compared with existing technologies, the intelligent analysis and dispatching method for ETC system customer after-sales service orders described in this invention has the following beneficial effects: 1. This invention calculates the urgency of repairs by integrating multi-dimensional information such as the time distribution pattern of fault occurrence, the scope of impact, and traffic flow, and combines time factors and redundancy factors to generate objective and unified processing priorities, thereby achieving a scientific quantitative assessment of order priorities and avoiding subjective misjudgments.

[0011] 2. This invention uses information such as license plate number and geographical location to intelligently reconstruct the location of the fault, and accurately identifies the fault type by combining text keyword matching and image similarity analysis, thereby improving the accuracy and efficiency of fault location and type identification and reducing manual intervention.

[0012] 3. This invention improves the first dispatch success rate and response speed by real-time monitoring of the location, working status and resource availability of maintenance teams, combined with the fault location and resource requirements in after-sales service orders, and automatically dispatching orders based on professional matching and proximity principles. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0015] Figure 2 This is a flowchart illustrating the time distribution pattern of fault occurrence according to the present invention.

[0016] Figure 3 This is a flowchart of step S4 of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 As shown, the present invention provides an intelligent analysis and dispatch method for after-sales service orders of ETC system customers, which includes the following steps: S1, Service request reception: receiving after-sales service orders submitted by customers and collecting basic information of the orders, including fault phenomenon data, fault location and fault occurrence time.

[0019] As a preferred option, the specific analysis process for collecting basic order information in step S1 is as follows: S11: Summarize the after-sales service orders submitted by customers through various channels, parse the order content, and extract the fault phenomenon data, which includes fault phenomenon text data and fault phenomenon image data.

[0020] S12: Identify whether the order content contains ETC toll station and lane information.

[0021] If the identification is successful, the fault location will be determined based on the ETC toll station and lane number.

[0022] If identification fails, extract the customer information from the order, obtain the license plate number and the geographical location when the order was submitted, match the nearest ETC toll station based on the geographical location, retrieve the license plate number information of vehicles passing through each lane of the ETC toll station, and determine the lane number the customer passed through by combining the customer's license plate number to obtain the fault location.

[0023] S13: Obtain the time point when the customer's vehicle passed through the channel from the back-end system of the channel used by the customer, and determine the time of the fault.

[0024] It should be noted that the customer information in the after-sales service orders involved in this invention is only used for the purpose of fault location, analysis and diagnosis related to the ETC system.

[0025] S2. Order Priority Analysis: Based on the distribution pattern of fault occurrence time, the scope of fault impact, and traffic flow information at the fault location, analyze the urgency of repair. Combine the fault occurrence time and the number of lanes at the fault location to comprehensively evaluate the processing priority of orders, and determine the first order to be processed based on the evaluation results.

[0026] As a preferred embodiment, the specific process of analyzing the urgency of maintenance in step S2 is as follows: obtain the time distribution pattern of fault occurrence in the order, the time distribution pattern including occasional, continuous and discontinuous, and determine the first maintenance urgency factor of the order according to the preset quantitative mapping relationship between the fault occurrence time distribution pattern and the first maintenance urgency factor.

[0027] The fault level is determined based on the fault location. The impact range corresponding to each fault level is then matched with the current fault's impact range. Based on the preset mapping rules between the fault's impact range and the emergency second factor for maintenance, the emergency second factor for maintenance of the order is determined.

[0028] Retrieve historical traffic flow information for the fault location, obtain its historical average traffic flow, and determine the emergency third factor for the order based on the preset correlation function between traffic flow and emergency third factor.

[0029] The repair urgency level of an order is calculated by weighting and fusing the first, second, and third factors of repair urgency.

[0030] It should be noted that the quantitative mapping relationship between the distribution pattern of fault occurrence time and the first emergency factor for maintenance is set based on historical operation and maintenance data and expert experience. In a specific embodiment, intermittent faults are mapped to a lower emergency factor, continuous faults are mapped to a higher emergency factor, and discontinuous faults are mapped to an intermediate value, thereby establishing a qualitative and quantifiable correspondence to provide standardized input for subsequent calculations.

[0031] It should be noted that determining the fault level based on the fault location is based on the strategic importance and topological structure of different facilities within the ETC system, such as provincial border stations, city main stations, and ramp stations. In a specific embodiment, the fault levels are defined as follows: the first level is key main road toll stations, the second level is regional core toll stations, and the third level is ordinary ramp toll stations, etc. By using a preset database of correspondences between toll station names or numbers and levels, the fault level can be automatically matched and determined based on the fault location information.

[0032] It should be noted that the impact range corresponding to each fault level is predefined based on the function and geographical location of its respective level. In a specific embodiment, the impact range of a first-level fault is the entire provincial highway network, the second level is the road network of the city and surrounding areas, and the third level is local road sections around the station, etc. This setting establishes a qualitative mapping from fault level to macroscopic impact range.

[0033] It should be noted that the mapping rule between the scope of the fault's impact and the second emergency factor for maintenance is set based on the severity level of the impact scope. The wider the impact scope, the higher the emergency factor value. In a specific embodiment, the impact of the entire province is mapped to the highest emergency factor, the impact of a region is mapped to the medium emergency factor, and the impact of a local area is mapped to the lower emergency factor, thereby transforming the qualitative impact into a quantitative indicator that can participate in the weighted calculation.

[0034] It should be noted that the correlation function between the traffic flow and the emergency third factor of maintenance is a monotonically increasing linear function, which aims to map the continuous value of historical average traffic flow into an emergency factor. The greater the traffic flow, the more severe the potential congestion caused by the fault, the higher the urgency of maintenance, and the greater the emergency factor.

[0035] It should be noted that the weights of the first, second, and third emergency maintenance factors can be set based on industry experience or obtained through a limited number of experimental data. For example, one can first collect correlation data between each emergency factor and the degree of emergency maintenance in historical orders, then calculate the correlation coefficient between each emergency factor and the degree of emergency maintenance, use regression analysis or principal component analysis to determine the contribution of each emergency factor, and finally, after normalization, convert the contribution into the weights of each emergency factor, with the sum of the weights being 1.

[0036] It should be noted that the time distribution pattern of fault occurrence reflects the persistence and suddenness of the fault, serving as a time-series basis for judging urgency; the scope of the fault's impact directly reflects the degree of its spread and overall hazard level within the road network; and the traffic flow at the fault location objectively reflects the service load at that location and the actual congestion scale that the fault may cause. By transforming multidimensional qualitative and quantitative indicators into unified and quantifiable assessment results of maintenance urgency, core decision support is provided for the scientific, objective, and automated prioritization of orders, thereby improving the efficiency of after-sales service and the rationality of resource allocation.

[0037] In this embodiment, the present invention calculates the urgency of maintenance by integrating multi-dimensional information such as the time distribution pattern of fault occurrence, the scope of impact, and traffic flow, and generates an objective and unified processing priority by combining time factors and redundancy factors, thereby achieving a scientific quantitative assessment of order priority and avoiding subjective misjudgment.

[0038] As a preferred option, see [reference] Figure 2 As shown, the specific analysis process for obtaining the fault occurrence time distribution pattern in step S2 is as follows: based on the fault location in the order, retrieve the license plate number information of the corresponding ETC toll station lane, and generate a sequence of license plate numbers of the passing vehicles according to the order of passage time.

[0039] Based on the customer's license plate number, determine the specific position of the license plate number in the above sequence, and select several vehicles before and after the customer's vehicle according to preset rules, thereby constructing a queue of passing vehicles to be analyzed, including the preceding vehicles, the customer's vehicle, and the following vehicles.

[0040] Based on the ETC system, query whether the user corresponding to each vehicle in the queue of vehicles to be analyzed has submitted an after-sales service order: if only the customer in the queue has submitted an after-sales service order, it is determined to be an intermittent fault.

[0041] If all users corresponding to all vehicles in the queue submit after-sales service orders, it is determined to be a continuous fault.

[0042] If neither of the above two conditions is met, it is determined to be a discontinuous fault.

[0043] As a preferred embodiment, the specific analysis process for evaluating the order processing priority in step S2 is as follows: sort each after-sales service order according to the order in which the fault occurred, and determine the time priority factor of each order according to the preset mapping relationship between the sorting order and the time priority factor.

[0044] Obtain the number of channels at each fault location in each order, and determine the redundancy factor for each order based on the pre-defined correspondence between the channel number range and the redundancy factor.

[0045] The repair urgency, time priority factor, and redundancy factor of each order are input into a preset processing priority evaluation model to calculate the processing priority of each order. The evaluation model is a function model that characterizes the relationship between repair urgency, time priority factor, redundancy factor, and processing priority.

[0046] It should be noted that the mapping relationship between the sorting order and the time priority factor is set based on the principle that the earlier the fault occurs, the higher the processing priority. In a specific embodiment, orders are sorted and numbered according to the time of fault occurrence, and the time priority factor of the earliest order is set to the maximum value. Subsequently, it decreases linearly or in segments in sequence to establish a quantitative, monotonically decreasing mapping relationship, ensuring that the time factor is reasonably reflected in the priority evaluation.

[0047] It should be noted that the correspondence between the channel number intervals and the redundancy factor is set based on the logic that the more channels there are, the higher the system redundancy. In a specific embodiment, the channel number is divided into multiple intervals, and a redundancy factor is assigned to each interval: the interval with the fewest channels corresponds to the smallest redundancy factor, and the interval with the most channels corresponds to a higher redundancy factor, thereby transforming structural redundancy information into quantifiable evaluation parameters.

[0048] It should be noted that the construction of the processing priority evaluation model involves defining a comprehensive function that maps the three input variables—maintenance urgency, time priority factor, and redundancy factor—to a final processing priority value. Its specific form can be expressed as: ,in Indicates processing priority. These represent the urgency of maintenance, time priority factor, and redundancy factor, respectively. These represent the weights of the preset maintenance urgency level, time priority factor, and redundancy factor, respectively. , The weight allocation can be determined based on historical data analysis and expert experience, aiming to balance the impact of the urgency of the fault itself, the order of occurrence, and the redundancy of the location on the overall priority, and finally output a priority score to support the dispatch decision.

[0049] It should be noted that the urgency of repair objectively reflects the urgency of handling the fault itself; the time priority factor follows the first-come, first-served principle, ensuring the fairness and timeliness of order processing; and the redundancy factor considers the system's fault tolerance capabilities due to the physical structure of the fault location, avoiding over-prioritizing faults with high redundancy but limited actual impact. By assessing processing priority based on urgency, time priority, and redundancy factors, and through the comprehensive consideration and weighing of multi-dimensional indicators, subjective experience-based decisions are transformed into objective, quantifiable, and reusable unified evaluation standards. This scientifically generates a processing priority sequence, optimizes the allocation of repair resources, and improves the overall efficiency of after-sales service and customer satisfaction.

[0050] S3. Service Resource Requirements Analysis: Identify fault types based on fault phenomenon data of primary processing orders, and determine the service resources required for repair based on fault types.

[0051] As a preferred embodiment, the specific analysis process for identifying fault types in step S3 is as follows: extract historical fault phenomenon data corresponding to various faults from the historical fault database.

[0052] By using keyword extraction technology, keywords are extracted from the historical fault phenomenon text data of various faults and the fault phenomenon text data in the primary processing order. By comparing the keywords of the two, the number of matching keywords is counted. Based on the preset mapping relationship between the number of matching keywords and the first matching degree of fault phenomenon, the first matching degree of fault phenomenon in the primary processing order and various faults is calculated.

[0053] Image similarity analysis is performed between the fault phenomenon images of the primary order and the historical fault phenomenon images of various types of faults. Based on the preset correspondence between image similarity and the second matching degree of fault phenomenon, the second matching degree of fault phenomenon in the primary order and various types of faults is calculated.

[0054] The first and second matching degrees of the fault phenomena are summed to obtain the matching degree between the faults in the priority processing order and various types of faults, and the fault type with the highest matching degree is identified as the fault type of the priority processing order.

[0055] It should be noted that the mapping relationship between the number of matching keywords and the first degree of match of the fault phenomenon is set based on the confidence principle of text matching. The more matching keywords, the higher the first degree of match. A linear or piecewise function can be used for mapping, thereby transforming the quantitative feature of keywords into a standardized and quantifiable matching degree index, which is used to objectively measure the similarity of faults at the text level.

[0056] It should be noted that the correspondence between the image similarity and the second matching degree of the fault phenomenon is set according to the principle that the more similar the images are, the higher the probability of matching the fault type. In a specific embodiment, when the image similarity is less than 30%, the second matching degree is 0; when the similarity is between 30% and 80%, the matching degree increases linearly from 0 to 0.8; when the similarity is greater than 80%, the matching degree is directly set to 1.0. This mapping converts the image feature similarity into a unified matching degree score through a preset function, thereby supporting the quantitative comparison and fusion decision of fault features at the visual level.

[0057] It should be noted that text data, through keyword extraction and matching, can objectively reflect the semantic features and descriptive patterns of faults, while image data, through visual similarity calculation, can accurately capture intuitive morphological information such as equipment damage and abnormal status. Identifying fault types based on both textual and image data can comprehensively utilize multimodal information from text and images, overcoming the limitations of a single data source, improving the accuracy and robustness of fault type identification, and providing reliable data support and classification basis for subsequent accurate matching of service resources and improved maintenance efficiency.

[0058] In this embodiment, the present invention uses information such as license plate number and geographical location to intelligently reconstruct the location of the fault, and accurately identifies the fault type by combining text keyword matching and image similarity analysis, thereby improving the accuracy and efficiency of fault location and type identification and reducing manual intervention.

[0059] As a preferred embodiment, the specific analysis process for determining the service resources required for maintenance in step S3 is as follows: based on the historical maintenance case library, equipment topology relationships and signal flow logic, obtain the service resources required for maintenance of various types of faults. The service resources include after-sales technical support personnel, maintenance tools and spare parts.

[0060] Match and determine the service resources required for repair based on the fault type of the primary order.

[0061] It should be noted that obtaining the service resources required for repairing various types of faults is primarily based on a comprehensive analysis of resolved cases recorded in the historical repair case database, the topological relationships of the equipment's physical and functional levels, and signal flow logic. By mining the historical case database, the correspondence between different fault types and the actual manpower, tools, and spare parts consumed can be summarized. By analyzing the equipment topology and signal flow, the types of technical support and physical resources required for fault repair can be inferred from the system level. Its purpose is to establish a precise and reliable mapping from fault type to service resources, providing a data-driven scientific basis for subsequent resource scheduling and dispatch decisions, thereby ensuring repair efficiency and optimizing resource utilization.

[0062] S4. Repair Work Order Dispatch: Real-time monitoring of the location information, working status and service resources of each repair team. Based on the fault location and service resources required for the primary order, and according to the preset professional matching rules and proximity allocation rules, the system matches the corresponding repair team, generates a repair work order, and sends it to the matched repair team.

[0063] As a preferred option, see [reference] Figure 3 As shown, the specific analysis process of step S4 is as follows: S41: Monitor the working status of each maintenance team in real time and determine whether it is in an available state. The working status includes idle and non-idle.

[0064] S42: Monitor the service resources currently held by each maintenance team in real time, compare them with the service resources required for the primary order maintenance, and determine whether they meet the maintenance resource requirements.

[0065] S43: Select maintenance teams that are available and meet the maintenance resource requirements, and mark them as candidate maintenance teams.

[0066] S44: Obtain the real-time location of each candidate repair team, calculate the distance between it and the fault location in the primary processing order, and match the candidate repair team with the closest distance as the repair team responsible for the order.

[0067] S45: Generate a repair work order based on the fault location and fault type in the primary processing order, and issue the repair work order to the matching repair team.

[0068] It should be noted that by comprehensively considering the real-time availability, resource adaptability, and geographical proximity of the repair teams, matching the repair teams responsible for the orders ensures that the selected repair teams have the basic conditions for timely response, all the resources needed to complete the task, and can arrive at the site via the shortest path. This achieves efficient and accurate allocation of repair resources, significantly reducing dispatch delays caused by resource mismatch or excessive distance, improving the first dispatch success rate and repair response efficiency, while optimizing the utilization rate of human resources and materials, ultimately ensuring the quality of after-sales service and customer satisfaction.

[0069] In this embodiment, the present invention automatically dispatches orders based on professional matching and proximity principles by real-time monitoring of the location, working status and resource availability of maintenance teams, combined with the fault location and resource requirements in after-sales service orders, thereby improving the first dispatch success rate and response speed.

[0070] As a preferred embodiment, the specific analysis process for determining whether the maintenance team is in an available state in step S14 is as follows: monitor the working status of the maintenance team in real time, and if it is idle, directly determine that the maintenance team is in an available state.

[0071] If the task is not idle, monitor the progress of the current maintenance task of the maintenance team and predict the remaining time required to complete the task.

[0072] If the remaining time is less than or equal to a preset threshold, the maintenance team is determined to be in an available state.

[0073] If the remaining time exceeds a preset threshold, the maintenance team is determined to be unavailable.

[0074] As a preferred embodiment, the specific analysis process for determining whether the maintenance team meets the maintenance resource requirements in step S42 is as follows: real-time monitoring of the service resources currently held by the maintenance team and comparing them one by one with the service resources required for the primary order maintenance.

[0075] If the following conditions are met simultaneously, the maintenance team is deemed to meet the maintenance resource requirements: (1) The after-sales technical support personnel equipped by the maintenance team meet the requirements of the order maintenance.

[0076] (2) The maintenance tools held by the maintenance team meet the requirements of the order maintenance.

[0077] (3) The spare parts currently in stock by the maintenance team meet the needs of order maintenance.

[0078] If any of the above conditions are not met, the maintenance team is deemed not to meet the maintenance resource requirements.

[0079] It should be noted that "the after-sales technical support personnel meet the needs of the order repair" means that the number and professional skills of the after-sales technical support personnel equipped by the repair team cover the needs of the order repair; "the repair tools meet the needs of the order repair" means that the types and specifications of the repair tools held by the repair team meet the needs of the order repair; and "the spare parts meet the needs of the order repair" means that the spare parts currently in stock by the repair team meet the needs of the order repair in terms of model and quantity.

[0080] In this embodiment, the present invention achieves full-process automation from order acceptance, analysis, order dispatch to execution through multi-source data fusion and model-driven decision-making, significantly improving after-sales service efficiency and resource utilization.

[0081] In this embodiment, the present invention effectively reduces fault handling time and improves user experience and overall operational stability of the ETC system through rapid response, precise maintenance and optimized resource allocation.

[0082] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0083] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0084] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0085] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0086] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent analysis and dispatch of after-sales service orders for ETC systems, characterized in that, Includes the following steps: S1. Service Request Reception: Receive after-sales service orders submitted by customers and collect basic information of the orders, including fault phenomenon data, fault location and fault occurrence time; S2. Order Priority Analysis: Based on the distribution pattern of fault occurrence time, the scope of fault impact, and traffic flow information at the fault location, analyze the urgency of repair. Combine the fault occurrence time and the number of lanes at the fault location to comprehensively evaluate the order processing priority and determine the first order to be processed based on the evaluation results. S3. Service Resource Demand Analysis: Identify fault types based on fault phenomenon data of primary processing orders, and determine the service resources required for repair based on fault types; S4. Repair Work Order Dispatch: Real-time monitoring of the location information, working status and service resources of each repair team. Based on the fault location and service resources required for the primary order, and according to the preset professional matching rules and proximity allocation rules, the system matches the corresponding repair team, generates a repair work order, and sends it to the matched repair team.

2. The intelligent analysis and dispatching method for after-sales service orders in an ETC system according to claim 1, characterized in that: The specific analysis process for collecting basic order information in step S1 is as follows: S11: Summarize the after-sales service orders submitted by customers through various channels, parse the order content, and extract the fault phenomenon data, which includes fault phenomenon text data and fault phenomenon image data. S12: Identify whether the order content contains ETC toll station and lane information; If the identification is successful, the fault location will be determined based on the ETC toll station and lane number. If identification fails, extract the customer information from the order, obtain the license plate number and the geographical location when the order was submitted, match the nearest ETC toll station based on the geographical location, retrieve the license plate number information of vehicles passing through each lane of the ETC toll station, combine the customer's license plate number to determine the lane number the customer passed through, and obtain the fault location. S13: Obtain the time point when the customer's vehicle passed through the channel from the back-end system of the channel used by the customer, and determine the time of the fault.

3. The intelligent analysis and dispatching method for after-sales service orders in an ETC system according to claim 1, characterized in that: The specific process for analyzing the urgency of maintenance in step S2 is as follows: The time distribution pattern of fault occurrence in the order is obtained, and the time distribution pattern includes occasional, continuous and discontinuous occurrence. Based on the preset quantitative mapping relationship between the fault occurrence time distribution pattern and the first emergency factor of maintenance, the first emergency factor of maintenance for the order is determined. The fault level is determined based on the fault location. The impact range corresponding to each fault level is combined with the preset impact range to match the impact range of the current fault. Based on the preset mapping rule between the fault impact range and the second emergency factor for maintenance, the second emergency factor for maintenance of the order is determined. Retrieve historical traffic flow information of the fault location, obtain its historical average traffic flow, and determine the third emergency factor for the order based on the preset correlation function between traffic flow and the third emergency factor for maintenance. The repair urgency level of an order is calculated by weighting and fusing the first, second, and third factors of repair urgency.

4. The intelligent analysis and dispatch method for after-sales service orders in an ETC system according to claim 2, characterized in that: The specific analysis process for obtaining the time distribution pattern of fault occurrence in step S2 is as follows: Based on the fault location in the order, retrieve the license plate number information of the corresponding ETC toll station and the corresponding lane, and generate a sequence of license plate numbers of the passing vehicles according to the order of passage time. Based on the customer's license plate number, determine the specific position of the license plate number in the above sequence, and select several vehicles before and after the customer's vehicle according to preset rules, thereby constructing a queue of passing vehicles to be analyzed, including the preceding vehicles, the customer's vehicle, and the following vehicles. Based on the ETC system, query whether the user corresponding to each vehicle in the queue of vehicles to be analyzed has submitted an after-sales service order: If only this customer submits an after-sales service order in the queue, it is determined to be an intermittent failure; If all users corresponding to all vehicles in the queue submit after-sales service orders, it is determined to be a continuous fault. If neither of the above two conditions is met, it is determined to be a discontinuous fault.

5. The intelligent analysis and dispatching method for after-sales service orders in an ETC system according to claim 1, characterized in that: The specific analysis process for evaluating order processing priority in step S2 is as follows: After-sales service orders are sorted according to the order in which the faults occurred, and the time priority factor of each order is determined according to the preset mapping relationship between the sorting order and the time priority factor. Obtain the number of channels at each fault location in each order, and determine the redundancy factor for each order based on the pre-defined correspondence between the channel number range and the redundancy factor. The repair urgency, time priority factor, and redundancy factor of each order are input into a preset processing priority evaluation model to calculate the processing priority of each order. The evaluation model is a function model that characterizes the relationship between repair urgency, time priority factor, redundancy factor, and processing priority.

6. The intelligent analysis and dispatching method for after-sales service orders in an ETC system according to claim 2, characterized in that: The specific analysis process for identifying the fault type in step S3 is as follows: Extract historical fault phenomenon data corresponding to various types of faults from the historical fault database; By using keyword extraction technology, keywords are extracted from the historical fault phenomenon text data of various faults and the fault phenomenon text data in the primary processing order. By comparing the keywords of the two, the number of matching keywords is counted. Based on the preset mapping relationship between the number of matching keywords and the first matching degree of fault phenomenon, the first matching degree of fault phenomenon in the primary processing order and various faults is calculated. Image similarity analysis is performed between the fault phenomenon images of the faults in the primary processing order and the historical fault phenomenon images of various types of faults. Based on the preset correspondence between image similarity and the second matching degree of fault phenomenon, the second matching degree of fault phenomenon in the primary processing order and various types of faults is calculated. The first and second matching degrees of the fault phenomena are summed to obtain the matching degree between the faults in the priority processing order and various types of faults, and the fault type with the highest matching degree is identified as the fault type of the priority processing order.

7. The intelligent analysis and dispatch method for after-sales service orders in an ETC system according to claim 1, characterized in that: The specific analysis process for determining the service resources required for maintenance in step S3 is as follows: Based on the historical maintenance case library, equipment topology and signal flow logic, the service resources required for maintenance of various types of faults are obtained. The service resources include after-sales technical support personnel, maintenance tools and spare parts. Match and determine the service resources required for repair based on the fault type of the primary order.

8. The intelligent analysis and dispatch method for after-sales service orders in an ETC system according to claim 1, characterized in that: The specific analysis process of step S4 is as follows: S41: Monitor the working status of each maintenance team in real time and determine whether it is in an available state. The working status includes idle and non-idle. S42: Monitor the service resources currently held by each maintenance team in real time, compare them with the service resources required for the primary order maintenance, and determine whether they meet the maintenance resource requirements; S43: Select maintenance teams that are available and meet the maintenance resource requirements, and mark them as candidate maintenance teams; S44: Obtain the real-time location of each candidate repair team, calculate the distance between it and the fault location in the primary processing order, and match the candidate repair team with the closest distance as the repair team responsible for the order; S45: Generate a repair work order based on the fault location and fault type in the primary processing order, and issue the repair work order to the matching repair team.

9. The intelligent analysis and dispatch method for after-sales service orders in an ETC system according to claim 8, characterized in that: The specific analysis process for determining whether the maintenance team is in an available state in step S41 is as follows: The system monitors the work status of maintenance teams in real time. If a team is idle, it is determined that the maintenance team is in an available state. If the task is not idle, monitor the progress of the current maintenance task of the maintenance team and predict the remaining time required to complete the task. If the remaining time is less than or equal to a preset threshold, the maintenance team is determined to be in an available state. If the remaining time exceeds a preset threshold, the maintenance team is determined to be unavailable.

10. The intelligent analysis and dispatching method for after-sales service orders in an ETC system according to claim 8, characterized in that: The specific analysis process for determining whether the maintenance team meets the maintenance resource requirements in step S42 is as follows: The service resources currently available to the maintenance team are monitored in real time and compared one by one with the service resources required for the primary repair orders. If the following conditions are met simultaneously, the maintenance team is deemed to meet the maintenance resource requirements: (1) The after-sales technical support personnel in the maintenance team meet the needs of order maintenance; (2) The repair team has enough repair tools to meet the requirements of the order repair; (3) The spare parts currently in stock by the maintenance team meet the needs of the order maintenance; If any of the above conditions are not met, the maintenance team is deemed not to meet the maintenance resource requirements.

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