Charging service evaluation quality optimization method, system and device and medium

Abstract

The invention provides a charging service evaluation quality optimization method, system and device and a medium, and relates to the technical field of charging services, and the method comprises the steps: obtaining user feedback suggestions, carrying out the semantic combination of the user feedback suggestions based on a preset combination rule, and obtaining a standardized combination topic; based on a semantic matching technology, automatically mapping the standardized combined topic to a specific service index in a charging service quality index system considering objective operation data, and establishing a mapping association relationship between user feedback opinions and the specific service index; on the basis of the mapping association relationship, specific service indexes with high association frequency are counted and identified as key problems; and generating a service optimization scheme based on the key problem. According to the method, conversion from fuzzy user feedback to accurate operation decision is realized through the technical process of semantic combination, association mapping, statistical recognition and scheme generation, so that accurate positioning, quantitative analysis and decision support are carried out on user experience problems, and the satisfaction degree of users is improved.

Description

Technical Field

[0001] This invention relates to the field of charging service technology, and specifically to a method, system, device, and medium for optimizing the quality of charging service evaluation. Background Technology

[0002] In the current intelligent connected vehicle ecosystem, charging services, being high-frequency and high-demand, have seen their service quality and user experience become key determinants of a platform's core competitiveness. However, traditional service evaluation systems, such as the NPS (Net Promoter Score) model, exhibit significant limitations when directly applied to these new charging services. The fundamental reason lies in the highly heterogeneous nature of charging services across different scenarios, the high frequency of interactions, and the complexity of the service chain. Existing charging service quality evaluation models lack timeliness when processing complex data and lack a unified and scientific evaluation "yardstick." Current national and industry standards mostly focus on technical specifications such as the physical safety and electrical performance of charging piles and interfaces. These standards define the lower limit of "usability" but fail to quantify the "satisfactory" experience. This directly results in the non-uniformity of evaluation standards and an attribution gap between user subjective perception and objective operational indicators. Without an effective correlation model, we cannot accurately map user evaluations to specific objective operational parameters, leading to a lack of data support for operational decisions.

[0003] Therefore, in the intelligent vehicle network ecosystem, the traditional charging service quality evaluation system suffers from a serious disconnect between subjective feedback and objective operational data. This makes it difficult for operators to pinpoint the real reasons for user dissatisfaction, resulting in a lack of data support for service optimization decisions and ultimately failing to improve user satisfaction. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, the present invention provides a method for optimizing the quality of charging service evaluation, comprising: Obtain user feedback and perform semantic merging of user feedback based on preset merging rules to obtain standardized merged topics; Based on semantic matching technology, standardized merged topics are automatically mapped to specific service indicators in the charging service quality indicator system that takes into account objective operational data, and a mapping relationship between user feedback and specific service indicators is established. Based on the mapping relationship, specific service indicators with high association frequency are statistically identified as key issues; Based on the key issues, service optimization solutions are generated.

[0005] Preferably, the construction of the charging service quality indicator system includes: We analyze the technical clauses related to charging service quality from multiple source standard documents and conduct a structured analysis of the technical clauses from multiple dimensions. The technical terms, which are analyzed in a structured manner, are transformed into multiple service indicators, and the mapping attributes and NPS importance level of each service indicator are marked. Based on mapping attributes and NPS importance levels, multiple service indicators are categorized, merged, and deduplicated to construct a hierarchical charging service quality indicator system.

[0006] Preferably, the technical terms are analyzed in a structured manner from multiple dimensions, including: The technical terms are assessed in a structured manner from multiple dimensions, including user perceptibility, quantifiable metrics, operational interveneability, comparable results, and correlation with user satisfaction. If the assessment results of user perceptibility, quantifiability of indicators, and operational intervention all meet the preset conditions, then the technical terms will be included in the indicator candidate pool. Based on the results of the comparison of results and the correlation with user satisfaction, the priority order of the candidate indicators transformed from the technical terms in the indicator candidate pool is determined.

[0007] Preferably, the service indicators include at least category service indicators, sub-category service indicators, and specific service indicators; wherein, the category service indicators include at least one of stability and reliability indicators, experience indicators, facility availability indicators, economic indicators, and billing and trust indicators; Stability and reliability indicators include at least one of availability indicators, communication stability indicators, and maintenance timeliness indicators; among them, availability indicators include at least one of equipment availability rate, charging pile abnormal stop rate, and charging pile availability rate; communication stability indicators include at least charging gun stability; and maintenance timeliness indicators include at least fault repair time. The experience indicators should include at least the following: convenience indicators, waiting time indicators, comfort indicators, speed indicators, and transparency indicators. Among them, convenience indicators include at least one of charging smoothness, startup success rate, and navigation accuracy; waiting time indicators include at least the queue length; comfort indicators include at least the environmental comfort; speed indicators include at least the charging speed satisfaction; and transparency indicators include at least one of the following: completeness of guidance and location accuracy. Facilities availability indicators should include at least coverage indicators and community availability indicators; coverage indicators should include at least highway / urban fast charging coverage rate; community availability indicators should include at least the effectiveness of releasing occupied resources. Economic indicators should include at least one of the following: perceived cost indicators; perceived cost indicators include at least one of the following: price satisfaction, parking fee satisfaction, and service fee satisfaction. Billing and trust metrics should include at least billing accuracy metrics; billing accuracy metrics should include at least the billing error rate. The mapping attributes of service metrics include at least direct correlation, indirect correlation, and weak correlation; The importance of NPS is categorized as high, medium, and low.

[0008] Preferably, user feedback is semantically merged based on preset merging rules to obtain standardized merged topics, including: Based on a pre-defined bidirectional topic mapping rule base, user feedback is mapped to corresponding standard merged topics. The bidirectional topic mapping rule base defines the forward mapping relationship from multiple original expressions to merged topics, as well as the reverse index from original expressions to merged topics. The rules of the bidirectional topic mapping rule base can be dynamically adjusted through configuration files.

[0009] Preferably, based on semantic matching technology, standardized merged topics are automatically mapped to specific service indicators in a charging service quality indicator system that considers objective operational data, establishing a mapping relationship between user feedback and specific service indicators, including: Analyze the charging service quality indicator system to obtain the hierarchical structure and hierarchical indicator description text; Based on hierarchical structure and hierarchical indicator description text, an indicator feature library for multi-level semantic matching is constructed. Based on the semantic understanding model and the matching logic guided by the hierarchical structure, the standardized merged topics are matched and calculated with the indicator feature library to determine the corresponding specific service indicators and generate a preliminary mapping relationship. Based on preset verification rules, the initial mapping relationship is verified to obtain the final mapping association relationship.

[0010] Preferably, based on preset verification rules, the initial mapping relationship is verified to obtain the final mapping association relationship, including: Calculate the keyword overlap rate between the hierarchical indicator description texts corresponding to the standardized merged topics and the initial mapping relationship; Using a pre-built concept mapping dictionary, semantic standardization transformation of synonyms or near-synonyms is performed on the words in the standardized merged topic to obtain the semantic standardization transformation results; Based on keyword overlap rate and semantic standardization conversion results, the preliminary mapping relationship is confirmed or re-matched to obtain the final mapping association relationship.

[0011] Preferably, based on key issues, service optimization solutions are generated, including: The specific service indicators corresponding to the key issues are sorted according to their correlation frequency, and the top-ranked indicators are selected as key improvement targets. For key improvement objectives, generate structural optimization suggestions that include corresponding indicator names, descriptions of associated high-frequency topics, and specific improvement measures, as a service optimization plan.

[0012] Based on the same inventive concept, the present invention also provides an optimization system for evaluating the quality of charging services, the system comprising: The semantic merging module is used to obtain user feedback and perform semantic merging on the user feedback based on preset merging rules to obtain standardized merged topics. The association mapping module is used to automatically map standardized merged topics to specific service indicators of the charging service quality indicator system that takes into account objective operational data, based on semantic matching technology, and to establish a mapping relationship between user feedback and specific service indicators. The statistical identification module is used to statistically identify specific service indicators with high association frequency as key issues based on mapping relationships; The solution generation module is used to generate service optimization solutions based on key issues.

[0013] Preferably, the system also includes a module for constructing a charging service quality indicator system, used for: We analyze the technical clauses related to charging service quality from multiple source standard documents and conduct a structured analysis of the technical clauses from multiple dimensions. The technical terms, which are analyzed in a structured manner, are transformed into multiple service indicators, and the mapping attributes and NPS importance level of each service indicator are marked. Based on mapping attributes and NPS importance levels, multiple service indicators are categorized, merged, and deduplicated to construct a hierarchical charging service quality indicator system.

[0014] Preferably, the module for constructing the charging service quality indicator system is specifically used for: The technical terms are assessed in a structured manner from multiple dimensions, including user perceptibility, quantifiable metrics, operational interveneability, comparable results, and correlation with user satisfaction. If the assessment results of user perceptibility, quantifiability of indicators, and operational intervention all meet the preset conditions, then the technical terms will be included in the indicator candidate pool. Based on the results of the comparison of results and the correlation with user satisfaction, the priority order of the candidate indicators transformed from the technical terms in the indicator candidate pool is determined.

[0015] Preferably, the service indicators include at least category service indicators, sub-category service indicators, and specific service indicators; wherein, the category service indicators include at least one of stability and reliability indicators, experience indicators, facility availability indicators, economic indicators, and billing and trust indicators; Stability and reliability indicators include at least one of availability indicators, communication stability indicators, and maintenance timeliness indicators; among them, availability indicators include at least one of equipment availability rate, charging pile abnormal stop rate, and charging pile availability rate; communication stability indicators include at least charging gun stability; and maintenance timeliness indicators include at least fault repair time. The experience indicators should include at least the following: convenience indicators, waiting time indicators, comfort indicators, speed indicators, and transparency indicators. Among them, convenience indicators include at least one of charging smoothness, startup success rate, and navigation accuracy; waiting time indicators include at least the queue length; comfort indicators include at least the environmental comfort; speed indicators include at least the charging speed satisfaction; and transparency indicators include at least one of the following: completeness of guidance and location accuracy. Facilities availability indicators should include at least coverage indicators and community availability indicators; coverage indicators should include at least highway / urban fast charging coverage rate; community availability indicators should include at least the effectiveness of releasing occupied resources. Economic indicators should include at least one of the following: perceived cost indicators; perceived cost indicators include at least one of the following: price satisfaction, parking fee satisfaction, and service fee satisfaction. Billing and trust metrics should include at least billing accuracy metrics; billing accuracy metrics should include at least the billing error rate. The mapping attributes of service metrics include at least direct correlation, indirect correlation, and weak correlation; The importance of NPS is categorized as high, medium, and low.

[0016] Preferably, the semantic merging module is specifically used for: Based on a pre-defined bidirectional topic mapping rule base, user feedback is mapped to corresponding standard merged topics. The bidirectional topic mapping rule base defines the forward mapping relationship from multiple original expressions to merged topics, as well as the reverse index from original expressions to merged topics. The rules of the bidirectional topic mapping rule base can be dynamically adjusted through configuration files.

[0017] Preferably, the association mapping module is specifically used for: Analyze the charging service quality indicator system to obtain the hierarchical structure and hierarchical indicator description text; Based on hierarchical structure and hierarchical indicator description text, an indicator feature library for multi-level semantic matching is constructed. Based on the semantic understanding model and the matching logic guided by the hierarchical structure, the standardized merged topics are matched and calculated with the indicator feature library to determine the corresponding specific service indicators and generate a preliminary mapping relationship. Based on preset verification rules, the initial mapping relationship is verified to obtain the final mapping association relationship.

[0018] Preferably, the association mapping module is specifically used for: Calculate the keyword overlap rate between the hierarchical indicator description texts corresponding to the standardized merged topics and the initial mapping relationship; Using a pre-built concept mapping dictionary, semantic standardization transformation of synonyms or near-synonyms is performed on the words in the standardized merged topic to obtain the semantic standardization transformation results; Based on keyword overlap rate and semantic standardization conversion results, the preliminary mapping relationship is confirmed or re-matched to obtain the final mapping association relationship.

[0019] Preferably, the solution generation module specifically includes: The specific service indicators corresponding to the key issues are sorted according to their correlation frequency, and the top-ranked indicators are selected as key improvement targets. For key improvement objectives, generate structural optimization suggestions that include corresponding indicator names, descriptions of associated high-frequency topics, and specific improvement measures, as a service optimization plan.

[0020] Based on the same inventive concept, the present invention also provides an electronic device, comprising: at least one processor and a memory; wherein the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an optimization method for evaluating the quality of charging services as described above is implemented.

[0021] Based on the same inventive concept, the present invention also provides a readable storage medium having an executable program stored thereon, which, when executed, implements the aforementioned method for optimizing the quality of charging service evaluation.

[0022] Compared with the closest existing technology, the present invention has the following beneficial effects: This invention provides a method for optimizing the quality of charging service evaluation, comprising: obtaining user feedback; semantically merging the user feedback based on preset merging rules to obtain standardized merged topics; automatically mapping the standardized merged topics to specific service indicators in a charging service quality indicator system that considers objective operational data based on semantic matching technology, establishing a mapping relationship between user feedback and specific service indicators; statistically identifying specific service indicators with high correlation frequency as key issues based on the mapping relationship; and generating service optimization solutions based on the key issues. This invention, through the technical process of semantic merging, correlation mapping, statistical identification, and solution generation, realizes the transformation from vague user feedback to precise operational decisions, thereby accurately locating, quantifying, and supporting user experience issues, and improving user satisfaction. Attached Figure Description

[0023] Figure 1A flowchart illustrating the optimization method for evaluating the quality of charging services provided by this invention; Figure 2 A detailed flowchart of the method for optimizing the evaluation quality of charging services provided by this invention. Figure 3 A structural diagram of the optimization system for evaluating the quality of charging services provided by this invention; Figure 4 A schematic diagram of the electronic device provided by the present invention. Detailed Implementation

[0024] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0025] Example 1: This invention provides a method for optimizing the evaluation quality of charging services, specifically, Figure 1 A flowchart illustrating the method for optimizing the quality of charging service evaluation provided in this embodiment of the invention is shown in the figure, including the following steps: S101: Obtain user feedback, perform semantic merging of user feedback based on preset merging rules, and obtain standardized merged topics; S102: Based on semantic matching technology, standardized merged topics are automatically mapped to specific service indicators in the charging service quality indicator system that takes into account objective operational data, and a mapping relationship between user feedback and specific service indicators is established. S103: Based on the mapping relationship, statistically identify specific service indicators with high association frequency as key issues; S104: Generate service optimization solutions based on key issues.

[0026] This invention achieves the transformation from vague user feedback to precise operational decisions through a technical process of semantic merging, association mapping, statistical identification, and scheme generation. This enables precise positioning, quantitative analysis, and decision support for user experience issues, thereby improving user satisfaction.

[0027] To address the core pain points in current charging service NPS applications, such as scenario fragmentation and macro-level attribution, this invention inputs user feedback into a pre-built charging service quality indicator system, thereby identifying high-frequency key issues and ultimately generating service optimization solutions.

[0028] In some optional implementations, this invention constructs a charging service quality indicator system. The specific construction process includes: parsing technical clauses related to charging service quality from multi-source standard documents, and performing a structured analysis of these technical clauses from multiple dimensions; transforming the structured technical clauses into multiple service indicators, and labeling the mapping attributes and NPS importance level of each service indicator; based on the mapping attributes and NPS importance level, classifying, merging, and deduplicating the multiple service indicators to construct a hierarchical charging service quality indicator system.

[0029] The technical terms are assessed in a structured manner from multiple dimensions, including: user perceptibility, quantifiability of indicators, operational interveneability, comparability of results, and correlation with user satisfaction. If the assessment results for user perceptibility, quantifiability of indicators, and operational interveneability all meet the preset conditions, the technical terms are included in the indicator candidate pool. Based on the assessment results for comparability of results and correlation with user satisfaction, the priority order of the candidate indicators transformed from the technical terms in the indicator candidate pool is determined.

[0030] Service indicators include at least category service indicators, sub-category service indicators, and specific service indicators; among them, category service indicators include at least one of the following: stability and reliability indicators, experience indicators, facility availability indicators, economic indicators, and billing and trust indicators. Stability and reliability indicators include at least one of availability indicators, communication stability indicators, and maintenance timeliness indicators; among them, availability indicators include at least one of equipment availability rate, charging pile abnormal stop rate, and charging pile availability rate; communication stability indicators include at least charging gun stability; and maintenance timeliness indicators include at least fault repair time. The experience indicators should include at least the following: convenience indicators, waiting time indicators, comfort indicators, speed indicators, and transparency indicators. Among them, convenience indicators include at least one of charging smoothness, startup success rate, and navigation accuracy; waiting time indicators include at least the queue length; comfort indicators include at least the environmental comfort; speed indicators include at least the charging speed satisfaction; and transparency indicators include at least one of the following: completeness of guidance and location accuracy. Facilities availability indicators should include at least coverage indicators and community availability indicators; coverage indicators should include at least highway / urban fast charging coverage rate; community availability indicators should include at least the effectiveness of releasing occupied resources. Economic indicators should include at least one of the following: perceived cost indicators; perceived cost indicators include at least one of the following: price satisfaction, parking fee satisfaction, and service fee satisfaction. Billing and trust metrics should include at least billing accuracy metrics; billing accuracy metrics should include at least the billing error rate. The mapping attributes of service metrics include at least direct correlation, indirect correlation, and weak correlation; The importance of NPS is categorized as high, medium, and low.

[0031] The following section provides a detailed explanation of the construction process of the charging service quality indicator system, using a specific implementation example.

[0032] Initially, the core dimensions of the evaluation were established through research and analysis, and the entire service chain was thoroughly analyzed to identify key service touchpoints. Then, this invention will establish a high-quality charging service evaluation index system. This process adopts a multi-stage technical approach, systematically extracting service quality-related technical clauses from multiple industry standard documents to form a raw evidence base; through a specific mapping mechanism, the engineered technical clauses are transformed into user-perceptible service indicators, and their importance is marked; to ensure the integrity of the system, all indicators will be merged and deduplicated to construct a unified, hierarchical indicator architecture; thus completing this quantifiable, monitorable, and optimizable evaluation index system.

[0033] In the specific implementation process, regarding the applicability of NPS in charging services: Firstly, through systematic literature review and in-depth benchmarking against industry leaders, the core dimensions and key indicators that NPS evaluation needs to focus on were gradually clarified, laying a theoretical and practical foundation for subsequent methodological design. Subsequently, the user journey mapping methodology was introduced to break down the entire user process from initial contact, consultation and communication, order placement and payment, service fulfillment, to after-sales follow-up. In this process, key touchpoints and pain points affecting the experience were meticulously identified. Based on these touchpoints, the reflectivity and guiding value of NPS results for actual service quality were improved.

[0034] 2. Regarding the establishment of evaluation indicators for high-quality charging services on the platform: This system aims to address the issue of "macro-level attribution." It is divided into three steps.

[0035] (1) Knowledge Extraction: The construction of the indicator system begins with in-depth analysis and structured extraction of industry knowledge. First, a series of key standards and specifications, such as the "Operation and Management Service Specifications for Electric Vehicle Charging and Swapping Facilities in Urban Public Facilities" and the "Guiding Opinions of the General Office of the State Council on Further Building a High-Quality Charging Infrastructure System", were systematically reviewed. With the help of intelligent analysis methods, all technical clauses related to service quality were accurately identified. On this basis, each clause was further structured and judged from five evaluation dimensions through multiple rounds of semantic understanding and rule matching. If the judgments of questions ① to ③ are all affirmative, the indicator is included in the candidate pool; questions ④ and ⑤ are used to help determine its priority order in the system: ① Perceptibility: Can users directly perceive the changes in service quality reflected by the indicator? (If yes, it belongs to the direct perception category; if it is an indirect impact, it is classified as the indirect category) ② Quantifiability: Can the indicator be numerically measured or measured by quantiles (such as P50, P95) based on a unified standard? ③ Intervention: Can the operation or product side effectively improve the performance of the indicator through strategy optimization or function iteration? ④ Comparability: Does this indicator support horizontal or vertical comparisons across different stations, time periods, or regions? ⑤ Correlation with user satisfaction: Does this indicator clearly correlate with core elements of user experience, such as charging speed, reliability, and price fairness—dimensions that typically have strong theoretical correlations? The construction of the indicator system begins with knowledge extraction.

[0036] (2) Indicator Transformation and Labeling: In the indicator transformation and labeling stage, a dual labeling system was constructed to map the engineering technical terms into service evaluation dimensions that users can perceive. Specifically, by introducing a rule mapping engine, each indicator is assigned two key labels: one is the "mapping strength" of its relationship with the original technical content, which is divided into three categories: direct, indirect, and weak; the other is the "NPS importance" level (high, medium, and low) based on service science and user experience theory. For example, "energy efficiency" itself does not directly affect user perception, but by deconstructing it into user-side key dimensions such as "charging speed," "stability," and "cost rationality," it can be effectively associated with the NPS impact path. We classify the indicators a priori based on service design theory: high importance indicators cover user-sensitive dimensions such as speed, convenience, reliability, and billing transparency; medium importance includes secondary sensitive elements such as power fluctuation, reservation experience, and information release; while policy-oriented content (such as V2G pilot projects) is classified as medium to low importance because it has a weaker connection with daily experience. This mechanism not only achieves semantic conversion from technical language to user language, but also lays a logically clear and highly interpretable analytical foundation for subsequent indicator optimization and experience management.

[0037] (3) System construction: To ensure the uniqueness and integrity of the system, all indicators extracted from cross-files will be merged and deduplicated, and a unified, hierarchical L1-L2-L3 indicator architecture will be constructed.

[0038] Table 1 shows a schematic diagram of the L1-L2-L3 indicator architecture of the charging service quality indicator system.

[0039]

[0040] Table 1 In this invention, user feedback is semantically merged based on preset merging rules to obtain standardized merged topics. This includes mapping user feedback to corresponding standard merged topics according to a preset bidirectional topic mapping rule library. The bidirectional topic mapping rule library defines a forward mapping relationship from multiple original expressions to merged topics, as well as a reverse index from original expressions to merged topics. The rules of the bidirectional topic mapping rule library can be dynamically adjusted through configuration files.

[0041] In one specific implementation, this invention performs topic standardization processing on user feedback data with scores not equal to 100 (not perfect). It achieves the merging and structuring of sub-topics through preset mapping rules, solving the problem of "heterogeneous similarities" in user feedback (such as the normalization of expressions like "slow charging" and "poor charging speed"), and providing standardized input for subsequent indicator system association. Technically, this invention establishes a bidirectional mapping rule library of "merged topics – sub-topics." On one hand, it defines high-frequency merged topics and their covered sub-topic sets (forward mapping); on the other hand, it automatically generates an index table of "sub-topics → merged topics" (reverse mapping), supporting the rapid location of the standardized merged topic to which any user opinion belongs when processing it. This rule library supports dynamic addition, deletion, and modification based on configuration files, and can be flexibly adjusted as business expands, adapting to different business scenarios and domain corpora. It is an evolvable rule-driven standardization mechanism. Furthermore, it performs statistical analysis on the merged datasets, automatically calculating the frequency of occurrence and the number of sub-topics included in each merged topic, and outputting structured results in descending order of frequency, achieving visualization and quantitative analysis from "original opinions" to "standardized topic distribution." Furthermore, by traversing the directory structure, the system automatically filters Excel files containing user feedback by file extension and outputs a list of file paths, providing a unified data entry point for subsequent batch reading and processing.

[0042] In this way, on the one hand, the normalization problem of "different expressions of the same topic but different features" (such as "slow charging" and "poor charging speed") in user feedback is solved through a two-way mapping rule base and dynamic configuration mechanism, providing standardized input for subsequent automatic association with the indicator system; on the other hand, through the structured visualization of topic merging results and the fully automated file screening process, an automatic pipeline from multi-source user feedback to standardized topic statistics is realized, which significantly improves the scalability and engineering level of user opinion mining.

[0043] In some optional implementations, based on semantic matching technology, standardized merged topics are automatically mapped to specific service indicators in a charging service quality indicator system that considers objective operational data, establishing a mapping relationship between user feedback and specific service indicators. This includes: parsing the charging service quality indicator system to obtain the hierarchical structure and hierarchical indicator description text; constructing an indicator feature library for multi-level semantic matching based on the hierarchical structure and hierarchical indicator description text; matching and calculating the standardized merged topics with the indicator feature library based on a semantic understanding model and the matching logic guided by the hierarchical structure to determine the corresponding specific service indicators and generate a preliminary mapping relationship; and verifying the preliminary mapping relationship based on preset verification rules to obtain the final mapping relationship.

[0044] The process involves verifying the initial mapping relationship based on preset verification rules to obtain the final mapping relationship. This includes: calculating the keyword overlap rate between the standardized merged topic and the hierarchical indicator description text corresponding to the initial mapping relationship; performing semantic standardization transformation on the words in the standardized merged topic using a preset concept mapping dictionary to obtain the semantic standardization transformation result; and confirming or re-matching the initial mapping relationship based on the keyword overlap rate and the semantic standardization transformation result to obtain the final mapping relationship.

[0045] In one specific implementation, this invention automatically maps standardized user feedback topics, such as merged topics and sub-topics, to L1 major categories, L2 subcategories, and L3 specific indicators, allowing the indicator system to dynamically expand and adjust based on user feedback. Technically, the system reads existing indicator files through an indicator system architecture parsing module, extracts the L1 / L2 / L3 hierarchical relationships, constructs hierarchical mappings and an L3 indicator feature library, providing structured benchmark data for semantic matching. Subsequently, a multi-level classification prompt word generation module generates L1 / L2 / L3 classification prompt words for different levels. These prompt words include a list of existing indicators at the current level, the text of the topic to be classified, and classification rules. "Semantic priority weights" are embedded in the prompt words (e.g., if the keyword overlap between a sub-topic and an indicator is ≥60%, it is matched first), improving classification accuracy. Furthermore, the prompts are sent to the Semantic Understanding API (Application Programming Interface) to obtain the hierarchical classification results corresponding to the topics. Then, a dual matching mechanism of "keyword overlap rate + concept mapping dictionary" is used for result verification and correction. This considers both explicit keywords and uses concept mappings such as "slow → speed / duration" to address biases caused by synonyms and near-synonyms. Finally, a three-level classification is performed on a large batch of user feedback, and the mapping relationships are cached to avoid duplicate calculations. A list of newly added indicators, a report of unmatched topics, and a mapping cache table are output, thereby achieving dynamic updates and traceable evolution of the indicator system. Furthermore, semantic priority weights can be embedded in the prompts to improve classification accuracy.

[0046] In some optional implementations, a service optimization plan is generated based on the key issues, including: sorting the specific service indicators corresponding to the key issues according to their association frequency, selecting the top-ranked indicators as key improvement targets; and generating structural optimization suggestions for the key improvement targets, including the corresponding indicator names, descriptions of associated high-frequency topics, and specific improvement measures, as a service optimization plan.

[0047] In one specific implementation, this invention batch processes user feedback data from multiple source Excel files. Through high-frequency topic identification and indicator mapping, it generates targeted service improvement suggestions, solving the problems of low efficiency and weak targeting in traditional manual analysis, and realizing a closed loop of indicator optimization based on user feedback. Specifically, it first automatically retrieves all Excel files containing user feedback and intelligently identifies the "rating column" and "feedback content column" using header keywords, retaining only valid question data with a rating ≠ 100, automatically completing data cleaning and filtering for subsequent analysis. Based on this, this invention uses a high-frequency topic-indicator mapping module to perform frequency statistics on sub-topics, filtering out high-frequency questions, and associating L3 indicators through two mapping strategies: on the one hand, it directly reuses the rule-based standardized topic rule mapping results to ensure efficient matching of standardized topics; on the other hand, it calls the semantic matching interface of the semantic understanding-based user feedback and indicator system fusion system to dynamically semantically associate unstandardized topics. Finally, the improvement suggestion generation module, based on the high-frequency topic-metric mapping results, sorts the metrics by correlation strength and extracts the improvement directions corresponding to the top 3 core metrics (e.g., "L3 metric 'charging efficiency' has the highest correlation with topics, so it is recommended to optimize the charging algorithm"). It then generates a structured report (including metric name, number of related topics, and specific suggestions), outputting directly applicable optimization solutions that directly support business decisions.

[0048] In summary, this invention establishes a hierarchical (L1-L3) indicator relationship and optimizes its weight allocation to identify key factors affecting NPS scores. Beyond superficial data correlation, it delves deeper and automatically establishes the correlation between subjective user experiences (e.g., low NPS scores) and objective technical indicators of specific service processes (e.g., API timeouts). In this way, this invention aims to achieve precise attribution from macro-level NPS scores to technical indicators, enabling managers to accurately focus on the service processes most in need of improvement. Through a refined understanding of the "entire service chain," it provides comprehensive and accurate micro-level insights. Its purpose is to overcome the limitations of existing technologies that focus on "macro-level correlation" rather than "micro-level attribution," enabling more accurate and faster discovery and resolution of individual user service issues, thereby achieving true intelligent decision support. Specifically, through a complete technology chain of "data standardization → semantic fusion → optimized output," it combines rule engines with semantic understanding to achieve automated and dynamic integration of user feedback and the indicator system, improving the adaptability of the indicator system to actual needs and the accuracy of improvement suggestions.

[0049] This invention achieves (1) on the basis of systematically sorting out multiple relevant standard documents, completing clause analysis, extracting and structuring L3 level service evaluation indicators, constructing an L1-L2-L3 graded indicator system covering L1 dimensions such as "experience, billing and trust, stability and reliability, facility availability", and clarifying the data source, calculation scope, mapping attributes and NPS importance level for each indicator, forming a machine-readable and clearly structured indicator dictionary. (2) Constructing a multi-file data automated processing pipeline to realize the automatic reading, cleaning and storage of multi-source table data and user feedback; extracting and splitting "small topics" from user opinions, realizing the extraction of key field scores and topics in user feedback, filtering of non-full score data, splitting of small topics and word frequency statistics, and by merging similar characteristic topics such as "long waiting time / need to queue", the relationship between user feedback and standardized indicators is initially established, realizing the integration of user feedback and indicator system, improving the adaptability of indicator system to actual needs and the accuracy of improvement suggestions.

[0050] like Figure 2 The diagram shows a flowchart of the method for optimizing the quality of charging service evaluation provided by the present invention. It includes three modules: (1) a rule-based user feedback topic standardization processing tool, including mapping rule construction for merging topic processing; merging topic details visualization for displaying standardized merged topics; and target file filtering for filtering out files containing feedback data; (2) a user feedback and indicator system fusion system based on semantic understanding, including an indicator system structure parsing module for constructing hierarchical mapping relationships; a semantic matching and API module for performing matching verification; and a result fusion and output module for completing the classification cache mapping relationship and outputting the fusion result; (3) an indicator optimization suggestion generation system based on multi-source feedback data, including multi-source data batch processing for filtering out effective data; high-frequency topic identification for associating the merged mapping result with specific service indicators through rule mapping and semantic mapping; and an improvement suggestion generation module for generating improvement directions or feasible suggestions to obtain service optimization solutions.

[0051] The problem with existing technologies is that users' "subjective evaluations" cannot be effectively linked to the "objective facts" in the background. When a user gives a low NPS score at a charging station due to payment failure and expresses dissatisfaction, existing technologies cannot automatically associate this "low score" with objective technical problems. Currently, a new technological solution is needed that can break down the barriers between subjective and objective data, constructing an evaluation and attribution model that integrates NPS scores with objective operational indicators, thereby achieving precise localization, quantitative analysis, and decision support for user experience issues. Currently, some companies have introduced NPS evaluation systems, integrating and analyzing survey data, behavioral data, network and operational data, and customer service tickets, embedding them into user-oriented 4G service management strategies. NPS scores reflect users' level of acceptance and recommendation of 4G services, thus guiding service management direction. However, existing technologies only start with survey data, resulting in limited user coverage, high costs, and an inability to pinpoint deeper business problems. Therefore, this invention aims to construct an "attribution model" to solve the problem of its inability to accurately locate specific service links.

[0052] Existing technologies suffer from severe deficiencies in the integration and governance of subjective evaluation data and objective operational data, creating data silos between "macro-statistics" and "micro-facts," hindering accurate individual attribution analysis. Due to a lack of scenario-based domain knowledge constraints for complex charging services (such as high-speed fast charging and destination slow charging), existing technologies struggle to deeply understand the changes in user experience weight across different scenarios. Insufficient understanding of the charging industry's unique "full-service chain" (from search to payment) and multi-dimensional service indicators keeps analysis results at a macro level, detached from the realities of specific service stages, and unable to accurately identify micro-level problems. The insufficient mapping between subjective feedback and objective indicators—based on "correlation" rather than "causation"—further exacerbates the difficulty in deep data integration. These factors combined prevent existing technologies from comprehensively and deeply understanding the complex mapping relationship between user feedback and specific service indicators, hindering a comprehensive grasp of service quality issues at both macro and micro levels, thus severely impacting the accuracy of problem diagnosis and solution development. Ultimately, this lag and macro-level understanding of complex relationships severely restricts the monitoring and decision support for user experience and platform service quality, resulting in the inability to provide comprehensive and accurate micro-level insights, and leaving service optimization and resource allocation without precise basis.

[0053] The purpose of this invention is to provide a novel intelligent analysis system and method, aiming to fundamentally solve the challenge of deep integration and governance of subjective evaluation data and objective operational data in intelligent vehicle-to-everything (V2X) charging services. This invention overcomes the core technical shortcomings of existing technologies in processing subjective NPS and objective operational data, namely, the "macro-level analysis" and the "non-scenario-based model." It breaks down the data silos between "macro-statistics" and "micro-facts," linking isolated user subjective feedback with objective industry indicators to achieve accurate individual attribution analysis. This invention constructs a completely new evaluation and optimization system capable of integrating multi-dimensional data, scientifically quantifying the true weight of indicators such as charging speed, equipment availability, and price, accurately identifying and prioritizing the resolution of issues with the greatest impact on user satisfaction, and ensuring that operational resources are allocated to the most critical improvement points.

[0054] This invention solves the problem of inconsistent evaluation standards. It establishes a unified and scientific evaluation "yardstick," whose advantage lies in its ability to transform obscure technical terms such as national and industry standards into service indicators (such as device availability and payment convenience) that are understandable and implementable by the operations team and perceptible to users. By scientifically quantifying the true weight of various indicators (such as charging speed and price), it can accurately identify and prioritize the issues that have the greatest impact on user satisfaction. This allows managers to stop guessing and ensure that limited operational resources are precisely allocated to the most critical improvement points, achieving the highest service improvement efficiency.

[0055] Example 2: Based on the same inventive concept, this invention also provides an optimization system for evaluating the quality of charging services, the system structure of which is as follows: Figure 3 As shown, the system includes: The semantic merging module 301 is used to obtain user feedback and perform semantic merging on the user feedback based on preset merging rules to obtain standardized merged topics. The association mapping module 302 is used to automatically map standardized merged topics to specific service indicators of the charging service quality indicator system that takes into account objective operational data based on semantic matching technology, and to establish a mapping relationship between user feedback and specific service indicators. The statistical identification module 303 is used to statistically identify specific service indicators with high association frequency as key issues based on the mapping relationship; Solution generation module 304 is used to generate service optimization solutions based on key issues.

[0056] Preferably, the system also includes a module for constructing a charging service quality indicator system, used for: We analyze the technical clauses related to charging service quality from multiple source standard documents and conduct a structured analysis of the technical clauses from multiple dimensions. The technical terms, which are analyzed in a structured manner, are transformed into multiple service indicators, and the mapping attributes and NPS importance level of each service indicator are marked. Based on mapping attributes and NPS importance levels, multiple service indicators are categorized, merged, and deduplicated to construct a hierarchical charging service quality indicator system.

[0057] Preferably, the module for constructing the charging service quality indicator system is specifically used for: The technical terms are assessed in a structured manner from multiple dimensions, including user perceptibility, quantifiable metrics, operational interveneability, comparable results, and correlation with user satisfaction. If the assessment results of user perceptibility, quantifiability of indicators, and operational intervention all meet the preset conditions, then the technical terms will be included in the indicator candidate pool. Based on the results of the comparison of results and the correlation with user satisfaction, the priority order of the candidate indicators transformed from the technical terms in the indicator candidate pool is determined.

[0058] Preferably, the service indicators include at least category service indicators, sub-category service indicators, and specific service indicators; wherein, the category service indicators include at least one of stability and reliability indicators, experience indicators, facility availability indicators, economic indicators, and billing and trust indicators; Stability and reliability indicators include at least one of availability indicators, communication stability indicators, and maintenance timeliness indicators; among them, availability indicators include at least one of equipment availability rate, charging pile abnormal stop rate, and charging pile availability rate; communication stability indicators include at least charging gun stability; and maintenance timeliness indicators include at least fault repair time. The experience indicators should include at least the following: convenience indicators, waiting time indicators, comfort indicators, speed indicators, and transparency indicators. Among them, convenience indicators include at least one of charging smoothness, startup success rate, and navigation accuracy; waiting time indicators include at least the queue length; comfort indicators include at least the environmental comfort; speed indicators include at least the charging speed satisfaction; and transparency indicators include at least one of the following: completeness of guidance and location accuracy. Facilities availability indicators should include at least coverage indicators and community availability indicators; coverage indicators should include at least highway / urban fast charging coverage rate; community availability indicators should include at least the effectiveness of releasing occupied resources. Economic indicators should include at least one of the following: perceived cost indicators; perceived cost indicators include at least one of the following: price satisfaction, parking fee satisfaction, and service fee satisfaction. Billing and trust metrics should include at least billing accuracy metrics; billing accuracy metrics should include at least the billing error rate. The mapping attributes of service metrics include at least direct correlation, indirect correlation, and weak correlation; The importance of NPS is categorized as high, medium, and low.

[0059] Preferably, the semantic merging module is specifically used for: Based on a pre-defined bidirectional topic mapping rule base, user feedback is mapped to corresponding standard merged topics. The bidirectional topic mapping rule base defines the forward mapping relationship from multiple original expressions to merged topics, as well as the reverse index from original expressions to merged topics. The rules of the bidirectional topic mapping rule base can be dynamically adjusted through configuration files.

[0060] Preferably, the association mapping module is specifically used for: Analyze the charging service quality indicator system to obtain the hierarchical structure and hierarchical indicator description text; Based on hierarchical structure and hierarchical indicator description text, an indicator feature library for multi-level semantic matching is constructed. Based on the semantic understanding model and the matching logic guided by the hierarchical structure, the standardized merged topics are matched and calculated with the indicator feature library to determine the corresponding specific service indicators and generate a preliminary mapping relationship. Based on preset verification rules, the initial mapping relationship is verified to obtain the final mapping association relationship.

[0061] Preferably, the association mapping module is specifically used for: Calculate the keyword overlap rate between the hierarchical indicator description texts corresponding to the standardized merged topics and the initial mapping relationship; Using a pre-built concept mapping dictionary, semantic standardization transformation of synonyms or near-synonyms is performed on the words in the standardized merged topic to obtain the semantic standardization transformation results; Based on keyword overlap rate and semantic standardization conversion results, the preliminary mapping relationship is confirmed or re-matched to obtain the final mapping association relationship.

[0062] Preferably, the solution generation module specifically includes: The specific service indicators corresponding to the key issues are sorted according to their correlation frequency, and the top-ranked indicators are selected as key improvement targets. For key improvement objectives, generate structural optimization suggestions that include corresponding indicator names, descriptions of associated high-frequency topics, and specific improvement measures, as a service optimization plan.

[0063] Example 3: Based on the same inventive concept, such as Figure 4As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0064] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in a readable storage medium to implement the corresponding method flow or corresponding function, so as to realize the steps of the charging service evaluation quality optimization method in the above embodiments.

[0065] Example 4: Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the readable storage medium here can include both the built-in storage medium of the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the storage medium to implement the steps of the charging service evaluation quality optimization method in the above embodiments.

[0066] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0068] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0070] 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 its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A method for optimizing the evaluation quality of charging services, characterized in that, include: Obtain user feedback, and semantically merge the user feedback based on preset merging rules to obtain standardized merged topics; Based on semantic matching technology, the standardized merged topics are automatically mapped to specific service indicators in the charging service quality indicator system that takes into account objective operational data, thereby establishing a mapping relationship between the user feedback and the specific service indicators. Based on the mapping relationship, specific service indicators with high association frequency are statistically identified as key issues. Based on the aforementioned key issues, a service optimization plan is generated.

2. The method according to claim 1, characterized in that, The construction of the charging service quality indicator system includes: The technical clauses related to charging service quality are analyzed from multiple source standard documents, and the technical clauses are analyzed in a structured manner from multiple dimensions. The technical terms, which are analyzed in a structured manner, are transformed into multiple service indicators, and the mapping attributes and NPS importance level of each service indicator are marked. Based on the mapping attributes and NPS importance levels, the multiple service indicators are categorized, merged, and deduplicated to construct a hierarchical charging service quality indicator system.

3. The method according to claim 2, characterized in that, The structured analysis of the technical terms from multiple dimensions includes: The technical terms are assessed in a structured manner from multiple dimensions, including user perceptibility, quantifiability of indicators, operational interventionability, comparability of results, and correlation with user satisfaction. If the assessment results of user perceptibility, quantifiability of indicators, and operational intervention all meet the preset conditions, then the aforementioned technical terms will be included in the indicator candidate pool. Based on the results of the comparison and the correlation with user satisfaction, the priority order of the candidate indicators transformed by the technical terms in the indicator candidate pool is determined.

4. The method according to claim 1, characterized in that, The service indicators include at least category service indicators, sub-category service indicators, and specific service indicators; wherein, the category service indicators include at least one of stability and reliability indicators, experience indicators, facility availability indicators, economic indicators, and billing and trust indicators. The stability and reliability indicators include at least one of availability indicators, communication stability indicators, and maintenance timeliness indicators; wherein, the availability indicators include at least one of equipment availability rate, charging pile abnormal stop rate, and charging pile availability rate; the communication stability indicators include at least the stability of the charging gun; and the maintenance timeliness indicators include at least the fault repair time. The experience indicators include at least convenience indicators, waiting indicators, comfort indicators, speed indicators, and transparency indicators; wherein, the convenience indicators include at least one of charging smoothness, startup success rate, and navigation accuracy; the waiting indicators include at least queuing time; the comfort indicators include at least environmental comfort; the speed indicators include at least charging speed satisfaction; and the transparency indicators include at least one of guidance completeness and location accuracy. The facility availability indicators include at least coverage indicators and community availability indicators; the coverage indicators include at least highway / urban fast charging coverage rate; the community availability indicators include at least the effectiveness of releasing occupied resources. The economic indicators include at least one cost perception indicator; the cost perception indicator includes at least one of price satisfaction, parking fee satisfaction, and service fee satisfaction. The billing and trust metrics include at least a billing accuracy metric; the billing accuracy metric includes at least a billing error rate. The mapping attributes of the service metrics include at least direct correlation, indirect correlation, and weak correlation; The NPS importance is categorized as high, medium, and low.

5. The method according to claim 1, characterized in that, The process of semantically merging user feedback based on preset merging rules to obtain standardized merged topics includes: According to a preset bidirectional topic mapping rule library, the user feedback opinions are mapped to corresponding standard merged topics; wherein, the bidirectional topic mapping rule library defines a forward mapping relationship from multiple original expressions to merged topics, and a reverse index from original expressions to merged topics; the rules of the bidirectional topic mapping rule library can be dynamically adjusted through configuration files.

6. The method according to claim 1, characterized in that, The method based on semantic matching technology automatically maps the standardized merged topics to specific service indicators in the charging service quality indicator system that considers objective operational data, establishing a mapping relationship between user feedback and the specific service indicators, including: The charging service quality index system is analyzed to obtain the hierarchical structure and hierarchical index description text; Based on the hierarchical structure and hierarchical indicator description text, an indicator feature library for multi-level semantic matching is constructed. Based on the semantic understanding model, and according to the matching logic guided by the hierarchical structure, the standardized merged topics are matched and calculated with the indicator feature library to determine the corresponding specific service indicators and generate a preliminary mapping relationship. Based on preset verification rules, the preliminary mapping relationship is verified to obtain the final mapping association relationship.

7. The method according to claim 6, characterized in that, The step of verifying the preliminary mapping relationship based on preset verification rules to obtain the final mapping association relationship includes: Calculate the keyword overlap rate between the standardized merged topics and the hierarchical indicator description texts corresponding to the preliminary mapping relationship; Using a pre-built concept mapping dictionary, the words in the standardized merged topic are semantically standardized by converting them into synonyms or near-synonyms, and the semantic standardization conversion results are obtained. Based on the keyword overlap rate and semantic standardization conversion results, the preliminary mapping relationship is confirmed or re-matched to obtain the final mapping association relationship.

8. The method according to claim 1, characterized in that, The process of generating a service optimization solution based on the aforementioned key issues includes: The specific service indicators corresponding to the key issues are sorted according to their correlation frequency, and the top-ranked indicators are selected as key improvement targets. For the aforementioned key improvement objectives, structural optimization suggestions are generated, including corresponding indicator names, descriptions of associated high-frequency topics, and specific improvement measures, as a service optimization plan.

9. An optimization system for evaluating the quality of charging services, characterized in that, include: The semantic merging module is used to obtain user feedback and perform semantic merging on the user feedback based on preset merging rules to obtain standardized merged topics. The association mapping module is used to automatically map the standardized merged topics to specific service indicators of the charging service quality indicator system that takes into account objective operational data based on semantic matching technology, and to establish a mapping relationship between the user feedback and the specific service indicators. The statistical identification module is used to statistically identify specific service indicators with high association frequency as key issues based on the mapping relationship; The solution generation module is used to generate service optimization solutions based on the aforementioned key issues.

10. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method for optimizing the quality of charging service evaluation as described in any one of claims 1 to 9 is implemented.

11. A readable storage medium, characterized in that, It contains an execution program, which, when executed, implements the method for optimizing the quality of charging service evaluation as described in any one of claims 1 to 9.

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