After-sales service management method based on intelligent scheduling and automatic order sending

Abstract

The invention relates to the technical field of intelligent management, and discloses an after-sales service management method based on intelligent scheduling and automatic order dispatching, and the method comprises the steps: obtaining the maintenance and after-sales work order of a customer, and extracting the work order information; classifying the work order information according to a preset classification rule, generating different types of work orders, and distributing the work orders to a to-be-processed queue; matching a plurality of engineers for the work order to be processed by adopting an intelligent matching algorithm, and calculating an engineer score based on factors such as skill matching, load, response time efficiency and historical evaluation; assigning engineer processing work orders according to the scores, marking the work orders as to-be-processed work orders if the work orders cannot be matched, and transferring to manual auditing; if the distribution is successful, tracking the position of the engineer and the service progress in real time, and pushing information to the client; after the work order is completed, client evaluation is collected, and engineer scores are updated in real time. According to the method, the problem that a multi-dimensional scoring judgment, real-time position information tracking and service process visualization feedback mechanism is lacked can be solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology, and more specifically, to an after-sales service management method based on intelligent scheduling and automated order dispatch. Background Technology

[0002] As enterprises continuously improve their digital service capabilities, after-sales service management is gradually shifting from manual dispatching to intelligent and automated processing. In traditional after-sales service processes, the classification of work orders, assignment of engineers, and customer communication often rely on manual judgment and experience, resulting in problems such as low dispatch efficiency, long response times, insufficient engineer matching accuracy, and ineffective utilization of customer feedback. This makes it difficult to meet the needs of large-scale, multi-regional, and high-response after-sales service.

[0003] While some service management systems with dispatch algorithms have emerged in the existing technology, most rely solely on simple matching based on a single parameter (such as geographical location or skill tag), lacking multi-dimensional scoring, real-time location tracking, and service process visualization feedback mechanisms. Furthermore, customer evaluation results are often treated as statistical data for centralized analysis, failing to be deeply integrated with the engineer's dispatch logic, resulting in a lack of adaptive optimization capabilities in dynamic dispatch strategies.

[0004] Therefore, there is an urgent need for an after-sales service management method that can achieve intelligent work order classification, multi-dimensional engineer rating matching, dynamic tracking of the service process, and closed-loop updates of evaluation, in order to improve the accuracy of work order dispatch, the utilization rate of engineer resources, and customer satisfaction. Summary of the Invention

[0005] In view of this, the present invention proposes an after-sales service management method based on intelligent scheduling and automated order dispatch to solve the problems of lacking multi-dimensional scoring judgment, real-time location information tracking and service process visualization feedback mechanism.

[0006] The after-sales service management method based on intelligent scheduling and automated order dispatch proposed in this invention includes:

[0007] Obtain customer maintenance and after-sales work orders, extract information from customer maintenance and after-sales work orders, and obtain work order information;

[0008] The work order information is classified and analyzed according to the preset classification rules to obtain different types of work order information, and then the different types of work order information are assigned to the corresponding waiting queues.

[0009] The work order information in the queue to be processed is matched with multiple engineers using an intelligent matching algorithm, and scores are calculated for each engineer to obtain an engineer rating.

[0010] Work order information is assigned to corresponding engineers based on engineer ratings for work order processing. If a work order cannot be matched with a corresponding engineer, it is marked as pending and transferred to manual processing. If a work order is matched with a corresponding engineer, the engineer's location and service progress are tracked in real time, and the information is sent to the customer.

[0011] Once a work order is completed, customer feedback on the engineer is collected and recorded in the feedback system to update the engineer's rating in real time.

[0012] Furthermore, the work order information includes service type, location information, and fault type;

[0013] The preset classification rules include service classification rules, fault classification rules, and urgency classification rules.

[0014] Furthermore, the specific content of obtaining different types of work order information is as follows: the service type within the work order information is divided into repair, installation, consultation and upgrade categories according to the service classification rules within the preset classification rules;

[0015] The fault types in the work order information are classified into hardware faults, software faults, operational errors, and other categories according to the preset classification rules.

[0016] The urgency classification rule within the preset classification rules divides work order information into high priority, medium priority, and low priority according to the fault classification rules.

[0017] Work order information that simultaneously meets the same service type, same fault type, same urgency level, and same location information will be grouped into the same category, thus obtaining different types of work order information.

[0018] Furthermore, the intelligent matching algorithm used to match multiple engineers with the work order information in the queue to be processed specifically involves: extracting the classification results of the work order information, including service type, location information, fault type and urgency, and constructing a multi-dimensional feature vector of the target work order information;

[0019] Retrieve a list of engineers currently online from the engineer database and obtain the professional skill tags for each engineer;

[0020] The similarity is calculated based on the professional skill tags and the service type and fault type in the work order information to obtain a similarity value. When the similarity value is greater than or equal to 90%, multiple engineers are matched to obtain a set of candidate engineers.

[0021] Furthermore, the specific content of calculating scores for multiple engineers is as follows: further obtain the current workload, historical evaluations, and distance from the customer for multiple engineers in the candidate engineer set; obtain a skill matching score based on professional skill tags; obtain a load balancing score based on the current workload; obtain an evaluation score based on historical evaluations; obtain a response timeliness score based on distance from the customer; and obtain the engineer score by weighting the skill matching score, load balancing score, evaluation score, and response timeliness score.

[0022] Furthermore, the skill matching score is based on the similarity between the engineer's professional skill tags and the current work order information service type and fault type, and the similarity is directly proportional to the skill matching score.

[0023] The load balancing score is calculated based on the engineer’s current workload and average processing capacity per unit time. The load coefficient and the load balancing score are inversely proportional.

[0024] The evaluation score is determined based on the average evaluation value of customers' service attitude, processing efficiency and fault resolution effect in the engineer's historical evaluations. The average evaluation value and the evaluation score are directly proportional.

[0025] The response time score is calculated based on the estimated response time between the engineer and the customer's location. The estimated response time is inversely proportional to the response time score. The estimated response time is calculated based on real-time traffic data, commuting route length, and historical average movement speed.

[0026] Furthermore, the process of assigning corresponding engineers to work order information based on engineer ratings for work order processing is as follows: If a work order cannot be matched with a corresponding engineer, it is marked as pending and transferred to manual processing. Specifically, the matching relationship is judged based on the similarity value. When either the similarity value is less than 90% or the similarity value is more than 90% but the corresponding engineer's rating is less than 80, it is determined that the current work order cannot be matched with a qualified engineer. The work order is marked as pending and transferred to the manual review queue. At the same time, a manual dispatch reminder message is generated and pushed to the backend dispatcher interface, along with the work order information and the reason for the inability to match.

[0027] Furthermore, if the work order information matches the corresponding engineer, the engineer's location and service progress are tracked in real time, and the information is sent to the customer. Specifically, after the engineer receives and confirms the work order information, the engineer's location information is obtained in real time. The real-time distance and estimated arrival time are calculated by combining the location information in the work order information. At the same time, the engineer's service progress is monitored. The customer receives the basic information, location information and estimated arrival time of the dispatched engineer via SMS.

[0028] Furthermore, the specific content of obtaining the engineer's location information in real time is as follows: the service status tracking system is automatically started to periodically locate and collect the engineer's current location to obtain location information, which is obtained through the GPS module of the engineer's mobile terminal;

[0029] The service progress includes six stages: order accepted, en route, arrived, in service, pending confirmation, and completed.

[0030] Furthermore, the process of collecting customer feedback on engineers and recording it in the feedback system to update engineer ratings in real time after the work order is completed is as follows: When the service progress is in the completed stage, the customer will receive a push notification for evaluation. The customer will access the evaluation interface through an SMS link to provide a comprehensive evaluation of the engineer's service. The comprehensive evaluation serves as the input parameter for updating the engineer's rating, and is weighted and averaged with historical evaluations to update the engineer's rating.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] This invention provides an after-sales service management method based on intelligent scheduling and automated dispatch. By introducing a multi-dimensional classification mechanism based on service type, location information, fault type, and urgency, it achieves refined management and efficient categorization of work orders, improving pre-dispatch processing efficiency and accuracy. The invention employs a comprehensive scoring mechanism combining skill matching, load balancing, customer evaluation, and response timeliness to evaluate engineers based on multiple factors, significantly improving the rationality of work order and engineer matching and service success rate. Furthermore, the invention constructs a dynamic intelligent scheduling mechanism that automatically marks work orders as pending and transfers them to manual review when a suitable engineer cannot be matched, ensuring service continuity and scheduling security. Finally, the invention directly feeds customer evaluation results into the engineer scoring system, achieving real-time score updates through weighted fusion with historical evaluation data, constructing a service quality self-learning mechanism, and enhancing the system's intelligent scheduling evolution capabilities. Attached Figure Description

[0033] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0034] Figure 1 This is a flowchart of an after-sales service management method based on intelligent scheduling and automated order dispatch, according to an embodiment of the present invention. Detailed Implementation

[0035] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0036] See Figure 1 As shown, this embodiment of the invention provides an after-sales service management method based on intelligent scheduling and automated order dispatch, including:

[0037] S1: Obtain customer maintenance and after-sales work orders, extract information from customer maintenance and after-sales work orders, and obtain work order information;

[0038] S2: Classify and analyze the work order information according to the preset classification rules to obtain different types of work order information, and allocate the different types of work order information to the corresponding waiting queues.

[0039] S3: Use an intelligent matching algorithm to match multiple engineers with the work order information in the queue to be processed, and calculate the scores of the multiple engineers to obtain the engineer rating;

[0040] S4: Assign the work order information to the corresponding engineer according to the engineer rating to process the work order. If the work order information cannot be matched with the corresponding engineer, it is marked as pending and transferred to manual processing. If the work order information is matched with the corresponding engineer, the engineer's location and service progress are tracked in real time and the information is sent to the customer.

[0041] S5: After a work order is completed, collect customer feedback on the engineer and record it in the feedback system to update the engineer's rating in real time.

[0042] In some embodiments of this application, the work order information includes service type, location information, and fault type;

[0043] The preset classification rules include service classification rules, fault classification rules, and urgency classification rules.

[0044] In some embodiments of this application, the specific content of different types of work order information is as follows: the service type in the work order information is divided into repair, installation, consultation and upgrade categories according to the service classification rules within the preset classification rules;

[0045] The fault types in the work order information are classified into hardware faults, software faults, operational errors, and other categories according to the preset classification rules.

[0046] The urgency classification rule within the preset classification rules divides work order information into high priority, medium priority, and low priority based on the fault classification rules.

[0047] Work order information that simultaneously meets the same service type, same fault type, same urgency level, and same location information will be grouped into the same category, thus obtaining different types of work order information.

[0048] Specifically, the service type field in the work order information is categorized into repair (e.g., component replacement, fault repair), installation (e.g., equipment deployment, line laying), consultation (e.g., remote guidance, troubleshooting), and upgrade (e.g., system version update, equipment function expansion) based on preset fault classification rules and the customer's fault description and equipment fault code, using keyword matching and problem symptom analysis methods. The fault type field is categorized into hardware faults (e.g., component damage, wire breakage) and software faults (e.g., system crash, program error) based on preset fault classification rules and the customer's fault description and equipment fault code. The emergency classification rules categorize work order information into three types: high priority (requiring immediate response, such as production interruption), medium priority (standard response, such as functional limitations), and low priority (which can be delayed, such as advisory requests), based on the importance of the fault type, the Service Level Agreement (SLA), and the customer's level. Work orders that simultaneously possess the same service type, fault type, emergency level, and location information are grouped into the same category, forming a structured work order information grouping. This provides a basis for batch processing and resource allocation in subsequent scheduling, resulting in a structured set of different types of work order information.

[0049] It should be noted that the above classification method enables standardized processing and rapid categorization of a large amount of heterogeneous work order information, effectively improving the visibility and controllability of work order management. The combination of multi-dimensional classification tags not only supports accurate work order dispatch but also facilitates unified scheduling and priority control of batch work orders. It further improves the efficiency of targeted allocation of engineer resources, reduces redundant scheduling and inefficient responses, provides accurate prerequisites for subsequent scoring and matching and path optimization, and enhances the overall intelligence level and service response quality of the system.

[0050] In some embodiments of this application, the intelligent matching algorithm is used to match multiple engineers with work order information in the queue to be processed. Specifically, the classification results of the work order information are extracted, including service type, location information, fault type and urgency, and a multi-dimensional feature vector of the target work order information is constructed.

[0051] Retrieve a list of engineers currently online from the engineer database and obtain the professional skill tags for each engineer;

[0052] The similarity is calculated based on the professional skill tags and the service type and fault type in the work order information to obtain a similarity value. When the similarity value is greater than or equal to 90%, multiple engineers are matched to obtain a set of candidate engineers.

[0053] Specifically, the classification result fields of the work order information in the queue to be processed are extracted, including service type (e.g., repair, installation), location information (geographic coordinates or area code), fault type (e.g., hardware failure, software anomaly), and urgency level (high, medium, low). The above information is encoded into structured input features to construct a multi-dimensional feature vector for the target work order. The data of engineers currently online are retrieved from the engineer information database, including engineer professional skill tags (e.g., proficient equipment models, operating system types, fault handling experience), service coverage area, current task status, and available time period. A matching algorithm based on tag vector similarity (e.g., cosine similarity, Jaccard similarity coefficient, etc.) is used to perform a bidirectional association comparison between the engineer skill tags and the service type and fault type of the work order information, and the similarity value between each engineer and the target work order is calculated. After the similarity calculation is completed, all engineers with similarity values ​​greater than or equal to a preset threshold (e.g., 90%) are selected to generate a preliminary candidate engineer set.

[0054] It should be noted that by converting work order information into multi-dimensional feature vectors and introducing an engineer skill tag comparison mechanism, accurate matching between work orders and engineers is achieved. By setting a similarity threshold, it is ensured that only engineers who meet the high matching requirements are included in the candidate set, avoiding the problem of mis-assignment caused by low suitability. This significantly improves the intelligence of work order assignment and the efficiency of engineer resource matching, which not only shortens the scheduling decision time but also improves the service success rate. It provides a high-quality alternative resource foundation for subsequent scoring, ranking, and scheduling allocation, and enhances the overall system response capability and service quality control capability.

[0055] In some embodiments of this application, a score is calculated for multiple engineers to obtain the engineer score. Specifically, the current workload, historical evaluations, and distance from the customer are obtained for multiple engineers in the candidate engineer set. A skill matching score is obtained based on professional skill tags, a load balancing score is obtained based on the current workload, an evaluation score is obtained based on historical evaluations, and a response timeliness score is obtained based on the distance from the customer. The skill matching score, load balancing score, evaluation score, and response timeliness score are weighted and scored to obtain the engineer score.

[0056] Specifically, after obtaining the candidate engineer set, the current workload information of each engineer is extracted sequentially, including the number of work orders received, average task duration, and available time period; historical evaluation data is retrieved to extract indicators such as average rating, negative review rate, and service feedback completeness rate in past services; combining the location information of the work order information with the engineer's current location, the actual service path length and estimated arrival time between the two are calculated to generate response distance and timeliness parameters; the skill fit is evaluated based on the similarity value between the professional skill tags and the service type and fault type of the target work order information, and a skill matching score is generated on a percentage basis; a load balancing score is generated based on the ratio between workload and average time processing capacity; historical evaluation results are converted into standard scoring indicators, and recent services are given higher weight based on a time decay function to generate a comprehensive evaluation score; finally, the skill matching score, load balancing score, historical evaluation score, and response timeliness score are multiplied by preset weighting coefficients, and a weighted scoring model (such as linear weighting, hierarchical weighting, etc.) is used to calculate the engineer score for each engineer, which will serve as the core basis for order dispatch ranking.

[0057] It should be noted that by constructing a multi-dimensional scoring index system, not only can the degree of matching of engineers with the current work order be comprehensively measured, but their work status and service quality can also be dynamically reflected. This effectively avoids scheduling bias that may occur under a single scoring standard, and improves the fairness and scientific nature of the matching. The weighted scoring mechanism supports flexible adjustment of strategy weights, and can dynamically optimize scheduling strategies according to different business scenarios (such as peak periods and emergency failures). Ultimately, it can realize the intelligent order dispatch logic of "capability priority, timely response, customer satisfaction, and balanced resources", thereby improving overall scheduling efficiency and customer service experience.

[0058] In some embodiments of this application, the skill matching score is based on the similarity between the engineer's professional skill tags and the current work order information service type and fault type, and the similarity is directly proportional to the skill matching score;

[0059] The load balancing score is calculated based on the engineer’s current workload and average processing capacity per unit time. The load factor and the load balancing score are inversely proportional.

[0060] The evaluation score is determined based on the average customer ratings for service attitude, processing efficiency, and troubleshooting effectiveness in the engineer's historical evaluations. The average rating score is directly proportional to the evaluation score.

[0061] The response time score is calculated based on the estimated response time between the engineer and the customer's location. The estimated response time is inversely proportional to the response time score. The estimated response time is calculated based on real-time traffic data, commuting route length, and historical average movement speed.

[0062] It should be noted that the engineer scoring system constructs a multi-dimensional engineer capability evaluation model reflecting "skill matching, resource availability, service reputation, and response timeliness" through the collaborative calculation of four indicators. Each score has quantifiable input parameters and clearly defined output scoring logic, ensuring the transparency and interpretability of system scheduling decisions. Skill matching strengthens the principle of prioritizing technical adaptation and avoids misassignment. Load balancing score improves the overall utilization of scheduling resources and prevents over-concentration. Evaluation score introduces a service quality feedback mechanism to enhance service experience. Response timeliness score ensures efficient arrival and improves response efficiency. The overall scoring system provides structured support for the service scheduling process and dynamic optimization of engineer resources, serving as the core foundation for accurate order dispatch and intelligent scheduling.

[0063] In some embodiments of this application, work order information is assigned to corresponding engineers based on engineer ratings for work order processing. If a work order cannot be matched with a corresponding engineer, it is marked as pending and transferred to manual processing. Specifically, the matching relationship is judged based on the similarity value. When either the similarity value is less than 90% or the similarity value is greater than 90% but the corresponding engineer's rating is less than 80, it is determined that the current work order information cannot be matched with a qualified engineer. The work order information is marked as pending and transferred to the manual review queue. At the same time, a manual scheduling reminder message is generated and pushed to the background scheduling personnel interface, along with the work order information and the reason for the inability to match (such as insufficient similarity, low rating, etc.).

[0064] It should be noted that by setting a "dual judgment threshold" (skill similarity and scoring threshold), the system effectively avoids assigning orders to low-quality or unsuitable engineers, ensuring service quality. Orders that cannot be assigned through intelligent matching are uniformly transferred to a manual review queue with detailed reasons for failure, improving the efficiency of dispatchers' intervention and decision-making basis. Layered collaboration between automation and manual dispatching is achieved, ensuring intelligent order assignment while building a fault tolerance and manual supplementation mechanism to enhance system stability and service continuity.

[0065] In some embodiments of this application, if the work order information matches the corresponding engineer, the engineer's location and service progress are tracked in real time, and the information is sent to the customer. Specifically, after the engineer receives and confirms the work order information, the engineer's location information is obtained in real time, and the real-time distance and estimated arrival time are calculated by combining the location information in the work order information. At the same time, the engineer's service progress is monitored, and the customer receives the basic information, location information and estimated arrival time of the dispatched engineer via SMS.

[0066] It should be noted that by automatically acquiring and dynamically pushing engineer locations and service status, the visibility of the service process and the customer's perceived experience have been significantly improved; the estimated arrival time calculation, combined with real-time traffic information and historical commuting efficiency, makes customer expectations more accurate and reduces complaints and misunderstandings caused by waiting; the transparent display of engineer information enhances customer trust, and the phased feedback on service status can effectively reduce customer doubts caused by information asymmetry.

[0067] In some embodiments of this application, the real-time acquisition of engineer location information specifically involves: automatically starting a service status tracking system to periodically locate and collect the engineer's current location to obtain location information, which is obtained through the GPS module of the engineer's mobile terminal;

[0068] The service progress includes six stages: order accepted, en route, arrived, in service, pending confirmation, and completed.

[0069] In some embodiments of this application, after the work order is processed, customer evaluations of the engineer are collected and recorded in the feedback system to update the engineer's rating in real time. Specifically, when the service progress is in the completed stage, the customer will receive a push evaluation request. The customer enters the evaluation interface through the SMS link to conduct a comprehensive evaluation of the engineer's service. The comprehensive evaluation serves as the input parameter for updating the engineer's evaluation rating, and is weighted and averaged with historical evaluations to update the engineer's evaluation rating.

[0070] Specifically, when the service progress status corresponding to a work order is updated to the completed stage, an evaluation request is immediately pushed to the customer. The push method includes SMS link, App notification, or web pop-up. The customer enters the standardized evaluation interface by clicking the link. The evaluation interface has multiple rating dimensions, including service attitude, fault handling efficiency, on-time arrival, communication satisfaction, and overall experience. The customer can rate each item based on the actual service experience. Each rating dimension is scored on a 5-point or 10-point scale. The system calculates the comprehensive evaluation score corresponding to the work order based on the rating data submitted by the customer. In addition, customers can fill in free text evaluations. The text content can be used for subsequent manual review and service quality improvement. The comprehensive evaluation score is used as the quality indicator of the current service behavior and input into the feedback system. Combined with the engineer's historical evaluation records, a time decay weighted average model is used for updating to ensure that recent services have higher weight and long-term trends are smoother.

[0071] It should be noted that the customer-initiated service evaluation mechanism enables the quantitative collection and continuous monitoring of service quality; the detailed evaluation dimensions help to accurately identify service shortcomings, and text feedback enhances qualitative analysis capabilities; the evaluation results are fed back to the engineer scoring system in real time and dynamically weighted and updated, constructing a closed-loop self-adjusting mechanism, so that the scores not only reflect historical performance but also closely reflect the current state; the scientific nature and adaptability of scheduling decisions are improved, prompting engineers to maintain service enthusiasm and quality awareness, thereby improving the overall operational efficiency of after-sales service and user satisfaction.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An after-sales service management method based on intelligent scheduling and automated order dispatch, characterized in that: include: Obtain customer maintenance and after-sales work orders, extract information from customer maintenance and after-sales work orders, and obtain work order information; The work order information is classified and analyzed according to the preset classification rules to obtain different types of work order information, and then the different types of work order information are assigned to the corresponding waiting queues. The work order information in the queue to be processed is matched with multiple engineers using an intelligent matching algorithm, and scores are calculated for each engineer to obtain an engineer rating. Work order information is assigned to corresponding engineers based on engineer ratings for work order processing. If a work order cannot be matched with a corresponding engineer, it is marked as pending and transferred to manual processing. If a work order is matched with a corresponding engineer, the engineer's location and service progress are tracked in real time, and the information is sent to the customer. Once a work order is completed, customer feedback on the engineer is collected and recorded in the feedback system to update the engineer's rating in real time.

2. The after-sales service management method based on intelligent scheduling and automated order dispatch according to claim 1, characterized in that, The work order information includes service type, location information, and fault type; The preset classification rules include service classification rules, fault classification rules, and urgency classification rules.

3. The after-sales service management method based on intelligent scheduling and automated order dispatch according to claim 2, characterized in that, The specific content of the different types of work order information obtained is as follows: the service type in the work order information is divided into repair, installation, consultation and upgrade categories according to the service classification rules within the preset classification rules; The fault types in the work order information are classified into hardware faults, software faults, operational errors, and other categories according to the preset classification rules. The urgency classification rule within the preset classification rules divides work order information into high priority, medium priority, and low priority according to the fault classification rules. Work order information that simultaneously meets the same service type, same fault type, same urgency level, and same location information will be grouped into the same category, thus obtaining different types of work order information.

4. The after-sales service management method based on intelligent scheduling and automated order dispatch according to claim 3, characterized in that, The work order information in the queue to be processed is matched with multiple engineers using an intelligent matching algorithm. Specifically, the classification results of the work order information are extracted, including service type, location information, fault type and urgency, and a multi-dimensional feature vector of the target work order information is constructed. Retrieve a list of engineers currently online from the engineer database and obtain the professional skill tags for each engineer; The similarity is calculated based on the professional skill tags and the service type and fault type in the work order information to obtain a similarity value. When the similarity value is greater than or equal to 90%, multiple engineers are matched to obtain a set of candidate engineers.

5. The after-sales service management method based on intelligent scheduling and automated order dispatch according to claim 4, characterized in that, The specific content of calculating scores for multiple engineers is as follows: further obtain the current workload, historical evaluations, and distance from the customer for multiple engineers in the candidate engineer set; obtain a skill matching score based on professional skill tags; obtain a load balancing score based on the current workload; obtain an evaluation score based on historical evaluations; obtain a response timeliness score based on distance from the customer; and obtain the engineer score by weighting the skill matching score, load balancing score, evaluation score, and response timeliness score.

6. The after-sales service management method based on intelligent scheduling and automated order dispatch according to claim 5, characterized in that, The skill matching score is based on the similarity between the engineer's professional skill tags and the current work order information service type and fault type. The similarity score is directly proportional to the skill matching score. The load balancing score is calculated based on the engineer’s current workload and average processing capacity per unit time. The load coefficient and the load balancing score are inversely proportional. The evaluation score is determined based on the average evaluation value of customers' service attitude, processing efficiency and fault resolution effect in the engineer's historical evaluations. The average evaluation value and the evaluation score are directly proportional. The response time score is calculated based on the estimated response time between the engineer and the customer's location. The estimated response time is inversely proportional to the response time score. The estimated response time is calculated based on real-time traffic data, commuting route length, and historical average movement speed.

7. The after-sales service management method based on intelligent scheduling and automated order dispatch according to claim 6, characterized in that, The process involves assigning work order information to corresponding engineers based on their ratings. If a work order cannot be matched with a suitable engineer, it is marked as pending and transferred to manual processing. Specifically, the matching relationship is judged based on the similarity value. If either the similarity value is less than 90% or the similarity value is greater than 90% but the corresponding engineer's rating is less than 80, the work order is determined to be unable to match a suitable engineer. The work order is then marked as pending and transferred to the manual review queue. Simultaneously, a manual dispatch reminder is generated and pushed to the backend dispatcher's interface, along with the work order information and the reason for the mismatch.

8. The after-sales service management method based on intelligent scheduling and automated order dispatch according to claim 7, characterized in that, If the work order information matches the corresponding engineer, the engineer's location and service progress will be tracked in real time, and the information will be sent to the customer. Specifically, after the engineer receives and confirms the work order information, the engineer's location information will be obtained in real time. The real-time distance and estimated arrival time will be calculated by combining the location information in the work order information. At the same time, the engineer's service progress will be monitored. The customer will receive the basic information, location information and estimated arrival time of the dispatched engineer via SMS.

9. The after-sales service management method based on intelligent scheduling and automated order dispatch according to claim 8, characterized in that, The specific content of obtaining the engineer's location information in real time is as follows: the service status tracking system is automatically started to periodically locate and collect the engineer's current location to obtain location information, which is obtained through the GPS module of the engineer's mobile terminal; The service progress includes six stages: order accepted, en route, arrived, in service, pending confirmation, and completed.

10. The after-sales service management method based on intelligent scheduling and automated order dispatch according to claim 9, characterized in that, Once the work order is completed, the customer's evaluation of the engineer is collected and recorded in the feedback system to update the engineer's rating in real time. Specifically, when the service progress is in the completed stage, the customer will receive a push notification for evaluation. The customer enters the evaluation interface through an SMS link to give a comprehensive evaluation of the engineer's service. The comprehensive evaluation serves as the input parameter for updating the engineer's evaluation rating, and is weighted and averaged with historical evaluations to update the engineer's evaluation rating.

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