Dynamic matching and optimizing method for automobile aftermarket service resources

Abstract

The invention relates to the technical field of automobile aftermarket service resource optimization, in particular to an automobile aftermarket service resource dynamic matching and optimizing method, which comprises the following steps: acquiring service demand information and historical cases of a target area to generate a feature data set, and generating a matching service resource set through weight setting and feature comparison; and an optimization mark set is generated by combining the resource position and the real-time capability information, and dynamic optimization processing is realized based on comparison of a standard and current resource configuration data. According to the invention, dynamic demands can be accurately identified, resources can be flexibly matched, the service efficiency and customer satisfaction can be significantly improved, and automobile aftermarket service upgrading can be promoted.

Description

Technical Field

[0001] This invention belongs to the field of automotive aftermarket service technology, specifically a method for dynamic matching and optimization of automotive aftermarket service resources. Background Technology

[0002] Dynamic matching and optimization of automotive aftermarket service resources are crucial for improving service efficiency and customer satisfaction. Traditional models often rely on manual experience or simple rules for resource allocation, such as assigning demands to nearby or relevant service providers based on static information like service location and business scope. However, this approach lacks precise understanding of demand characteristics, struggles to adapt to dynamically changing needs, and is prone to resource mismatch. For example, service providers with insufficient repair capabilities may receive complex fault requests, leading to service delays and decreased customer satisfaction. Furthermore, regarding the reuse of historical cases, current technologies merely store historical service cases without in-depth feature analysis, making it difficult to extract effective experience from massive amounts of cases to guide resource allocation. When faced with new demands, the inability to quickly match suitable resources results in delayed service responses, increased duplication of work, and a significant reduction in service efficiency.

[0003] In terms of resource assessment and optimization, existing technologies have significant shortcomings. The assessment of service resources often remains at a superficial level, focusing on static indicators such as service point location and basic business scope, while neglecting the real-time status and dynamic adaptability of resources. For example, it fails to consider the real-time on-duty status of maintenance personnel, the immediate availability of equipment, and the flexible adjustment capability of service resources under different demand cycles. Furthermore, after discovering discrepancies between resources and demand, there is a lack of effective optimization and adjustment methods, failing to promptly identify and correct unreasonable service resource allocation caused by factors such as new vehicle iterations and policy changes. For instance, the shortage of new energy vehicle repair resources in specific areas, without timely replenishment, results in a long-term mismatch between service resources and actual demand, leading to low overall utilization efficiency and hindering the high-quality development of the automotive aftermarket service industry.

[0004] Existing technologies have significant shortcomings in areas such as demand capture, reuse of historical cases, and resource assessment and optimization. There is an urgent need for a method that can accurately and dynamically match service resources with demand to solve problems such as resource mismatch and delayed service response, thereby improving service efficiency and customer satisfaction, and promoting the refined operation and high-quality development of the automotive aftermarket service industry. Based on this background, this invention proposes an innovative method for dynamic matching and optimization of automotive aftermarket service resources. It aims to achieve efficient matching and proactive optimization of service resources through multi-dimensional data collection, in-depth feature mining, dynamic comparative analysis, and a closed-loop optimization mechanism to meet the dynamic changes in market demand. Summary of the Invention

[0005] This invention addresses the problems of uneven allocation and low matching degree of automotive aftermarket service resources in existing technologies by providing a method for dynamic matching and optimization of automotive aftermarket service resources. The method achieves precise matching of service resources and demand through multi-dimensional data collection, in-depth feature mining, and dynamic optimization processing, thereby improving service efficiency and customer satisfaction.

[0006] This invention provides a method for dynamic matching and optimization of automotive aftermarket service resources, comprising the following steps:

[0007] S1, collect automotive service demand information and historical service cases in the target area, and extract service demand feature dataset and historical service case feature dataset;

[0008] S2, based on the service demand feature dataset and the historical service case feature dataset, performs feature matching analysis to generate a matching service resource set;

[0009] S3: Collect service resource location information and real-time service capability information of the target area, process and analyze it with the matching service resource set, and generate a service resource optimization tag set;

[0010] S4. Based on the service resource optimization tag set, extract the standard service resource configuration dataset and the current service resource configuration dataset, compare them to generate the first information set and the second information set, and perform dynamic matching optimization processing to obtain the matching result.

[0011] Furthermore, in step S1, collecting vehicle service demand information and historical service cases in the target area specifically includes the following operations:

[0012] The system acquires service demand information from the target area, including but not limited to multi-dimensional data such as service type, urgency, vehicle type, and timeliness requirements, to form an original service demand dataset. It also acquires historical service cases from the target area, including but not limited to multi-dimensional data such as service resource type, service time, and service effect, to form an original historical case dataset. The system performs feature weighting on the original service demand dataset and the original historical case dataset to generate a service demand feature dataset and a historical service case feature dataset.

[0013] The feature weighting process includes weighting each dimension based on pre-established demand feature weights and case feature weights, highlighting high-weight features, and using normalization to adjust the total weights to 1 to prevent weight bias from causing matching distortion.

[0014] Furthermore, in step S2, feature matching analysis is performed based on the service demand feature dataset and the historical service case feature dataset to generate a matching service resource set, specifically including the following steps:

[0015] Weights are set for service demand feature dimensions and case feature dimensions, generating demand feature weights and case feature weights respectively. The service demand feature dataset is then weighted using a normalized weight set to generate an adjusted service demand feature dataset. This adjusted dataset is compared with historical service case feature datasets to generate an initial matching service resource set. The highest-scoring service cases from the initial matching service resource set are selected to form the first matching result. The next highest-scoring service cases are selected to form the second matching result. The matching service resource set is directly determined based on the first matching result. For the second matching result, the first and second matching feature data are output, and the weights are adjusted using the demand feature weights, case feature weights, and historical service case datasets to generate the matching service resource set.

[0016] The feature comparison uses a cosine similarity algorithm to calculate the similarity between feature vectors, so as to quantify the similarity between the service demand feature dataset and the historical service case feature dataset in dimensions such as service type, urgency, and resource type; the screening process is based on a preset similarity threshold to ensure the relevance and reliability of the matching results.

[0017] Specifically, in step S3, the location information and real-time service capability information of the target area are collected, and processed and analyzed with the matching service resource set to generate a service resource optimization tag set. This specifically includes the following operations:

[0018] The system collects location information of service resources, including the location and coverage of service outlets; it also collects real-time capability information of service resources, including the current on-duty status of service personnel and the availability of equipment; it evaluates service resources based on their location and real-time capability information, generating a matching service resource evaluation set; it compares the matching service resource set with the matching service resource evaluation set to generate a resource matching comparison result; and it extracts a set of matching difference segments from the resource matching comparison result that are greater than or equal to a preset resource matching difference threshold, marks these segments for service resource optimization, and generates a service resource optimization marker set.

[0019] The evaluation process comprehensively considers the multi-dimensional features of location information and real-time capability information to ensure the comprehensiveness and accuracy of the evaluation results; the comparison process quickly locates configuration deviations by scanning the marked segments in the comparison results item by item, thereby improving the targeting of subsequent optimizations.

[0020] Further, in step S4, the standard service resource configuration dataset and the current service resource configuration dataset are extracted based on the service resource optimization tag set, compared to generate a first information set and a second information set, and dynamic matching optimization processing is performed to obtain the matching result. Specifically, this includes the following steps:

[0021] The process involves: collecting the current service resource configuration dataset for the corresponding segment of the service resource optimization marker set; comparing the standard service resource configuration dataset with the current service resource configuration dataset in the corresponding segment to generate configuration data comparison results; extracting service resource optimization marker segments that are the same as those in the standard and current service resource configuration datasets based on the configuration data comparison results to generate the first information set; extracting service resource optimization marker segments that are different from those in the standard and current service resource configuration datasets based on the configuration data comparison results to generate configuration data difference segment results; collecting service resource configuration data again for the configuration data difference segment results to generate a difference service resource configuration dataset; comparing the difference service resource configuration dataset with the standard service resource configuration dataset item by item to generate a comparison difference result set; and extracting inconsistent service resource optimization marker segments from the comparison difference result set to generate the final optimized matching result.

[0022] The comparison process is completed by matching the position and value of the corresponding segments, revealing specific inconsistencies and supporting refined resource optimization decisions. The results of the difference segments are used to adjust resource allocation, avoid waste, and improve system stability and resource utilization efficiency.

[0023] The beneficial effects of this invention are as follows:

[0024] By collecting data from multiple dimensions and deeply mining features, we accurately identify scattered and dynamically changing needs, making service resources more aligned with actual scenarios and reducing idleness and waste. Based on feature correlation analysis, we quickly extract experience from massive historical cases to guide resource allocation and shorten service response time. We build a complete data and process system, from discovering differences to optimizing the closed loop, adjusting resource configuration in real time, and continuously improving service efficiency. Through precise and dynamic service resource adaptation, we significantly improve service response efficiency and customer satisfaction.

[0025] In summary, this invention, through innovative design across the entire process, solves the problems of uneven distribution and low matching degree of service resources in existing technologies, and promotes the upgrading and development of automotive aftermarket services. Attached Figure Description

[0026] Figure 1 This is a flowchart of a method for dynamic matching and optimization of automotive aftermarket service resources according to the present invention;

[0027] Figure 2 This is a schematic diagram illustrating the steps of a historical service case dataset for a dynamic matching and optimization method for automotive aftermarket service resources according to the present invention. Detailed Implementation

[0028] This invention provides a method for dynamic matching and optimization of automotive aftermarket service resources. Through multi-dimensional data collection, in-depth feature mining, and dynamic optimization processing, it achieves precise matching between service resources and demand. The following is in conjunction with the appendix... Figure 1 and attached Figure 2 The specific embodiments of the present invention will be described in detail below.

[0029] like Figure 1 As shown, the method of this invention includes multiple steps, forming a closed-loop management from data collection to final optimization processing. First, S1 is executed, which involves collecting automotive service demand information and historical service cases from the target area, extracting a service demand feature dataset and a historical service case feature dataset. In practical applications, service demand information includes, but is not limited to, multi-dimensional data such as service type, urgency level, vehicle model, and timeliness requirements. For example, within a target area of ​​a city, a user may request professional repair services for a new energy vehicle battery system, requiring immediate response. This demand information is collected through online service platforms or offline service outlets, forming the original service demand dataset. Simultaneously, historical service cases include multi-dimensional data such as service resource type, service time, and service effectiveness. For example, a fixed store may have successfully completed a new energy vehicle battery system repair task, taking two hours, with positive user feedback. This historical case data is also compiled into the original historical case dataset. Subsequently, feature weighting is applied to the original service demand dataset and the original historical case dataset to generate the service demand feature dataset and the historical service case feature dataset. This process weights each dimension based on pre-established demand feature weights and case feature weights, highlighting high-weight features. For example, setting a higher weight for urgency ensures that requests for rapid response are prioritized. Furthermore, normalization is used to adjust the sum of the weights to 1, preventing weight bias from distorting the match. Normalization is the process of adjusting the original weights proportionally; for example, if the original weights are 0.4 and 0.6, normalization maintains the proportions but the sum is 1, thus ensuring the stability and reliability of the matching results.

[0030] Next, step S2 is executed, performing feature matching analysis based on the service demand feature dataset and the historical service case feature dataset to generate a matching service resource set. Weights are assigned to the service demand feature dimension and the case feature dimension, generating demand feature weights and case feature weights respectively. The service demand feature dataset is then weighted using a normalized weight set to generate a service demand feature adjustment dataset. Subsequently, the service demand feature adjustment dataset is compared with the historical service case feature dataset to generate an initial matching service resource set. The feature comparison uses a cosine similarity algorithm to calculate the similarity between feature vectors, quantifying the similarity between the service demand feature dataset and the historical service case feature dataset in dimensions such as service type, urgency, and resource type. For example, if the service demand is for the repair of a new energy vehicle battery system, and a certain fixed store has repeatedly completed similar tasks in historical cases, then the similarity score between the two in the service type and service resource type dimensions is high. Next, the service cases with the highest matching degree are selected from the initial matching service resource set to form the first matching result; the service cases with the second highest scores are selected to form the second matching result. The system directly determines the matching service resource set based on the first matching result. For the second matching result, it outputs both first and second matching feature data. These are then combined with demand feature weights, case feature weights, and historical service case datasets to adjust the weights and generate the matching service resource set. The filtering process is based on a preset similarity threshold; for example, service cases with a similarity higher than 0.8 are included in the first matching result, while those with a similarity between 0.6 and 0.8 are included in the second matching result. This tiered filtering mechanism ensures the relevance and reliability of the matching results while providing alternative solutions to address resource shortages.

[0031] After completing the feature matching analysis, step S3 is executed to collect service resource location information and real-time service capability information for the target area. This information is then processed and analyzed against the matched service resource set to generate a service resource optimization marker set. Service resource location information includes the location and coverage area of ​​service outlets, while real-time service capability information includes the current on-duty status of service personnel and the availability of equipment. For example, a fixed store located in the city center covers a radius of 5 kilometers, currently has two technicians on duty, and the equipment is available. This information is collected in real-time via IoT devices or manual input to ensure data freshness and accuracy. Subsequently, service resources are evaluated based on the service resource location information and real-time service capability information to generate a matched service resource evaluation set. The evaluation process comprehensively considers the multi-dimensional features of location information and real-time capability information to ensure the comprehensiveness and accuracy of the evaluation results. The matched service resource set is compared with the matched service resource evaluation set to generate resource matching comparison results. The comparison process quickly locates configuration deviations by scanning the marked segments in the comparison results item by item, improving the targeting of subsequent optimizations. For example, if the service capacity of a fixed store is insufficient to meet current demand, this segment is identified as a discrepancy, thus forming a set of matching discrepancy segments. From the resource matching comparison results, a set of matching discrepancy segments that are greater than or equal to a preset resource matching discrepancy threshold is extracted and used for service resource optimization early warning marking, generating a service resource optimization marker set. For example, if the matching discrepancy of a segment exceeds 20%, an optimization early warning mark is issued for that segment, indicating that further adjustments to resource allocation are needed.

[0032] Finally, S4 is executed. Based on the service resource optimization marker set, the standard service resource configuration dataset and the current service resource configuration dataset are extracted and compared to generate a first information set and a second information set. Dynamic matching optimization processing is then performed to obtain the matching result. The current service resource configuration dataset for the corresponding segment of the service resource optimization marker set is collected. The standard service resource configuration dataset and the current service resource configuration dataset are compared in the corresponding segment to generate configuration data comparison results. For example, if a segment in the standard service resource configuration dataset has a CPU allocation of 4 cores, while the corresponding segment in the current service resource configuration dataset has 3 cores, then this segment is identified as a difference. Based on the configuration data comparison results, service resource optimization marker segments that are the same as those in the standard service resource configuration dataset and the current service resource configuration dataset are extracted to generate the first information set. Based on the configuration data comparison results, service resource optimization marker segments that are different from those in the standard service resource configuration dataset and the current service resource configuration dataset are extracted to generate configuration data difference segment results. Service resource configuration data is collected again for the configuration data difference segment results to generate a difference service resource configuration dataset. The difference service resource configuration dataset is compared item by item with the standard service resource configuration dataset to generate a comparison difference result set 16. For example, network bandwidth markers in the dataset are compared one by one. If the standard is 100Mbps and the difference dataset is 80Mbps, it is recorded as a difference. Inconsistent service resource optimization marker segments are extracted from the comparison result set to generate the final optimized matching result. This process is completed by matching the position and value of the corresponding segments, revealing specific inconsistencies and supporting refined resource optimization decisions. For example, if the storage capacity of a certain segment is inconsistent, the segment is extracted and summarized into an information set for targeted configuration correction, improving the efficiency and stability of overall service resources.

[0033] In practical applications, the method of this invention can be widely applied in the automotive aftermarket service sector. For example, in the repair service of new energy vehicles in a certain city, users submit battery system fault repair requests through an online platform, requiring immediate response. The system generates a matching service resource set based on information such as the service type, vehicle model, and timeliness requirements input by the user, combined with historical service case data. Subsequently, the system collects the location information and real-time service capability information of the service resources, assesses the adaptability of each service outlet, and generates a service resource optimization mark set. If a fixed store is found to have insufficient service capability, an optimization warning mark is issued for that section, and resource allocation is adjusted through dynamic matching optimization. Finally, the system generates optimized matching results and assigns tasks to mobile service teams with the corresponding technical capabilities and located nearby, ensuring timely service delivery to meet user needs. This entire process, through innovative design, solves the problems of uneven service resource allocation and low matching degree in existing technologies, significantly improving service response efficiency and customer satisfaction.

[0034] In summary, this invention, through multi-dimensional data collection and in-depth feature mining, accurately identifies dispersed and dynamically changing needs, enabling service resource matching to better align with actual scenarios and reducing idleness and waste. Based on feature correlation analysis, it quickly extracts experience from massive historical cases to guide resource allocation, shortening service response time. A complete data and process system is constructed, from identifying discrepancies to optimizing the closed loop, adjusting resource allocation in real time to continuously improve service efficiency. Through precise and dynamic service resource adaptation, it promotes the upgrading and development of automotive aftermarket services.

Claims

1. A method for dynamic matching and optimization of automotive aftermarket service resources, characterized in that... Includes the following steps: Collect automotive service demand information and historical service cases in the target area, and extract service demand feature datasets and historical service case feature datasets; Feature matching analysis is performed based on service demand feature dataset and historical service case feature dataset to generate a matching service resource set; service resource location information and real-time service capability information of target area are collected and processed with the matching service resource set to generate a service resource optimization tag set; Based on the service resource optimization tag set, the standard service resource configuration dataset and the current service resource configuration dataset are extracted, compared to generate the first information set and the second information set, and dynamic matching optimization processing is performed to obtain the final optimized matching result.

2. The method as described in claim 1, characterized in that... When collecting automotive service demand information and historical service cases in the target area, service demand information is obtained from the target area, including multi-dimensional data such as service type, urgency, vehicle type, and timeliness requirements, forming the original service demand dataset; historical service cases are obtained from the target area, including multi-dimensional data such as service resource type, service time, and service effect, forming the original historical case dataset; feature weighting is performed on the original service demand dataset and the original historical case dataset to generate the service demand feature dataset and the historical service case feature dataset.

3. The method as described in claim 2, characterized by the following features The weighting process includes weighting each dimension based on pre-established demand feature weights and case feature weights, highlighting high-weight features, and using normalization to adjust the total weight to 1.

4. The method as described in claim 1, characterized in that... When performing feature matching analysis based on service demand feature dataset and historical service case feature dataset, weights are set for the service demand feature dimension and the case feature dimension, and demand feature weights and case feature weights are generated respectively; the service demand feature dataset is then adjusted using a normalized weight set to generate an adjusted service demand feature dataset. The service demand feature adjustment dataset is compared with the historical service case feature dataset to generate an initial matching service resource set.

5. The method as described in claim 4, characterized in that... The comparison uses the cosine similarity algorithm to calculate the similarity between feature vectors, quantifying the similarity between the service demand feature dataset and the historical service case feature dataset in terms of service type, urgency, and resource type. The screening process is based on a preset similarity threshold to ensure the relevance and reliability of the matching results.

6. The method as described in claim 1, characterized in that... When collecting service resource location information and real-time service capability information in the target area, the location information of the service resources is collected, including the location and coverage of the service outlets; the real-time capability information of the service resources is collected, including the current on-duty status of service personnel and the availability status of equipment. Service resources are evaluated based on their location information and real-time service capability information, and a matching service resource evaluation set is generated.

7. The method as described in claim 6, characterized in that... The matching service resource set is compared with the matching service resource evaluation set to generate a resource matching comparison result; from the resource matching comparison result, a set of matching difference segments that are greater than or equal to the preset resource matching difference magnitude threshold are extracted, and service resource optimization early warning marks are generated to generate a service resource optimization mark set.

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