User experience quality evaluation method, system, equipment and medium

Abstract

The invention provides a user experience quality evaluation method, system and device and a medium. The method comprises the following steps: obtaining index monitoring data of each to-be-evaluated product in unit time; classifying the index monitoring data according to the index types to obtain corresponding classified index monitoring data; wherein each piece of classified index monitoring data comprises a plurality of different types of special dimension index data; obtaining a special dimension score of each piece of special dimension index data through a preset evaluation model; obtaining a product score of each to-be-evaluated product according to the special dimension score of each piece of special dimension index data; and transversely comparing the to-be-evaluated products according to the product scores and displaying the to-be-evaluated products on a quality scoring interface. According to the method, the user experience quality can be comprehensively evaluated, the accuracy and objectivity of product scores are improved, and reliable data support is provided for optimization iteration of products or services.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a method, system, device, and medium for evaluating quality of user experience. Background Art

[0002] Currently, the quality of user experience (QoE) evaluation models mainly include the Kano model, the cost-benefit model, and the behavioral intention model. These models consider multiple dimensions when evaluating user experience, including functionality, emotional response, interaction experience, etc., aiming to comprehensively evaluate the overall satisfaction of users with products or services; Among them, the Kano model classifies functions into must-have, one-dimensional, and exciting types by distinguishing the types of user expectations to evaluate user satisfaction. However, this model needs to collect data through questionnaires, and there are problems of sample selection bias and low user participation; the cost-benefit model evaluates its value by comparing the costs and benefits of user experience improvement. However, this model is easily affected by external environments and market changes, and requires a large amount of time and resources to collect data and conduct analysis; the behavioral intention model evaluates user experience by predicting user behavioral intentions, but the prediction effects of this model vary in different scenarios, and it requires in-depth understanding of the object to be evaluated.

[0003] It can be seen that the existing technologies have the following disadvantages: 1. The Kano model has sample selection bias, which affects the accuracy of research results, and questionnaires may lead to low user participation, affecting data quality; 2. The cost-benefit model has the problems that the analysis results are easily affected by the external environment, lack accuracy, and the data collection and analysis processes are time-consuming and laborious; 3. The prediction effect of the behavioral intention model depends on the understanding of the object to be evaluated, and its universality is limited.

[0004] Therefore, the existing quality of user experience evaluation models are interfered by multiple uncertain factors, resulting in poor stability and objectivity of evaluation results, and it is difficult to comprehensively and accurately present the true state of user experience. At the same time, in the data collection link, traditional methods represented by questionnaires and user interviews have significant limitations. Specifically, due to the influence of users' subjective emotions, expression abilities, and cognitive differences, the feedback information is prone to be one-sided and ambiguous, which in turn leads to the deviation of the quality of user experience evaluation scores from the actual level, reducing the credibility and application value of evaluation results. Summary of the Invention

[0005] Embodiments of the present invention provide a method, device, and equipment for evaluating quality of user experience, aiming to solve the problems of poor stability and objectivity of evaluation results of existing quality of user experience evaluation models.

[0006] In a first aspect, embodiments of the present invention provide a method for evaluating quality of user experience, the method includes: Obtain the index monitoring data of each product to be evaluated per unit time; Classify the index monitoring data according to the index type to obtain the corresponding classified index monitoring data; wherein, each piece of classified index monitoring data includes multiple special dimension index data of different types; Obtain the special dimension score of each piece of special dimension index data through a preset evaluation model; Obtain the product scores of each product to be evaluated according to the special dimension scores of each piece of special dimension index data; Make a horizontal comparison of each product to be evaluated according to the product scores and display them on the quality scoring interface.

[0007] In a second aspect, an embodiment of the present invention further provides a user experience quality evaluation device, and the device includes: A first acquisition unit, configured to obtain the index monitoring data of each product to be evaluated per unit time; A classification unit, configured to classify the index monitoring data according to the index type to obtain the corresponding classified index monitoring data; wherein, each piece of classified index monitoring data includes multiple special dimension index data of different types; A second acquisition unit, configured to obtain the special dimension score of each piece of special dimension index data through a preset evaluation model; A product score acquisition unit, configured to obtain the product scores of each product to be evaluated according to the special dimension scores of each piece of special dimension index data; A horizontal comparison unit, configured to make a horizontal comparison of each product to be evaluated according to the product scores and display them on the quality scoring interface.

[0008] In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory and a processor, and a computer program is stored on the memory, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0009] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, and the storage medium stores a computer program, and the computer program includes program instructions, and when the program instructions are executed by a processor, the method described in the first aspect can be implemented.

[0010] The present invention provides a method, system, device and medium for evaluating the quality of user experience. The method includes: obtaining the index monitoring data of each product to be evaluated per unit time; classifying each of the index monitoring data according to the index type to obtain the corresponding classified index monitoring data; wherein each of the classified index monitoring data includes a plurality of special dimension index data of different types; obtaining the special dimension score of each of the special dimension index data through a preset evaluation model; obtaining the product score of each product to be evaluated according to the special dimension score of each of the special dimension index data; and making a horizontal comparison of each product to be evaluated according to the product score and displaying it on the quality scoring interface. The present invention has the following beneficial effects: 1. By obtaining the index monitoring data, the negative impact of long surveys on user experience is avoided, and the accuracy and objectivity of product scores are improved; 2. By obtaining a plurality of special dimension index data of different types, the quality of user experience is comprehensively evaluated. This comprehensive evaluation method can conduct an all-round and refined evaluation of the quality of user experience, thereby providing reliable data support for the optimization and iteration of products or services; 3. Through horizontal comparison, the differences in user experience performance of each business segment of the company can be intuitively presented, the key short-board indicators of the business with a lower score can be accurately positioned, data support is provided for formulating refined improvement strategies, and it helps the enterprise to achieve efficient resource allocation and comprehensive improvement of business quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1 It is a schematic flowchart of the method for evaluating the quality of user experience provided by the embodiment of the present invention; Figure 2 It is a schematic block diagram of the device for evaluating the quality of user experience provided by the embodiment of the present invention; Figure 3 It is a schematic block diagram of the electronic device provided by the embodiment of the present invention; Figure 4 It is a schematic diagram of the application environment of the method for evaluating the quality of user experience provided by the embodiment of the present invention; Figure 5 It is a schematic diagram of the tree-like hierarchical structure provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0014] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0015] It should also be understood that the terms used in this specification of the present invention are merely for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in this specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0016] It should be further understood that the term " / and" used in this specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. For the user experience quality evaluation method, please refer to Figure 4 , Figure 4 which is a schematic diagram of the application environment of the user experience quality evaluation method provided by the embodiments of the present invention. The user experience quality evaluation method is applied in an application environment such as Figure 4 . In the user layer, the index monitoring data of each product to be evaluated per unit time is obtained; in the big data filtering layer, the invalid data in the index detection data is filtered out; in the monitoring platform, the index monitoring data is classified according to the index type to obtain the corresponding classified index monitoring data; wherein, each classified index monitoring data includes a plurality of special dimension index data of different types; the classified index monitoring data is displayed in the monitoring platform; in the quality management platform, the special dimension score of each special dimension index data is obtained through a preset evaluation model; the product score of each product to be evaluated is obtained according to the special dimension score of each special dimension index data; and the products to be evaluated are horizontally compared according to the product score and displayed on the quality scoring interface. The present invention can comprehensively evaluate the user experience quality, improve the accuracy and objectivity of the product score, and provide reliable data support for the optimization and iteration of products or services. The present invention will be described in detail through specific embodiments below.

[0017] Figure 1 which is a schematic flowchart of the user experience quality evaluation method provided by the embodiments of the present invention. AsFigure 1 As shown, the method includes the following steps S110 - S150.

[0018] S110. Obtain the index monitoring data of each product to be evaluated per unit time.

[0019] In this embodiment, front - end piling is performed at the user layer to obtain the real - time index monitoring data of each product to be evaluated; among them, the products to be evaluated can be, but are not limited to, application programs, web sites / applications, h5 web pages / applications, mini - program applications, html websites, and Android - embedded websites.

[0020] Specifically, front - end piling essentially "buries points" for key events such as page loading, user operations (such as clicks, swipes, inputs), and interface calls. For example, data collection code is automatically inserted before and after a network request is sent to record request parameters, response time, error information, etc., or when a user triggers behaviors such as button clicks and form submissions, the operation details and page status are silently captured. The whole process does not require manual triggering or perception by the user, ensuring the naturalness and integrity of data collection. The present invention uses front - end piling technology to deeply embed the data collection code into the underlying interaction logic, and the data collection, encapsulation, and uploading are all completed silently in the background, without affecting the page rendering speed and interaction fluency, realizing user - unaware data collection and avoiding the negative impact of long - drawn - out survey problems on the user experience.

[0021] The collected real - time index monitoring data will be transmitted to the big data filtering layer for systematic processing; based on a preset rule engine and intelligent algorithms, the big data filtering layer performs multi - dimensional screening on the data: on the one hand, by setting threshold rules, abnormal data is excluded; on the other hand, data cleaning algorithms are used to automatically identify and correct duplicate records and data with format errors, such as merging redundant monitoring records with the same timestamp and correcting data with mismatched field types. At the same time, for invalid data containing sensitive information or interference, the big data filtering layer will perform desensitization processing or directly intercept it according to the data classification strategy, ensuring that the data entering the subsequent analysis link has high availability and accuracy, and providing reliable data support for user experience quality evaluation, system performance optimization, etc.

[0022] Each of the real - time index monitoring data includes multiple real - time special - dimension index data of different types, and each real - time special - dimension index data includes multiple real - time index data; among them, the index types can be, but are not limited to, functional indexes, user behavior indexes, problem quality indexes, performance indexes, and core business indexes; the present invention can comprehensively evaluate the user experience quality based on the real - time index monitoring data. Specifically, obtain the index monitoring data of each product to be evaluated per unit time, and obtain the product scores of each product to be evaluated regularly based on the index monitoring data.

[0023] S120. Classify each of the above-mentioned index monitoring data according to the index type to obtain corresponding classified index monitoring data; wherein, each of the classified index monitoring data includes a plurality of special dimension index data of different types.

[0024] In this embodiment, the index type may include, but is not limited to, functional index, user behavior index, problem quality index, performance index, and core business index; classify each of the index monitoring data according to the index type to obtain corresponding classified index monitoring data; wherein, each of the classified index monitoring data includes a plurality of special dimension index data of different types; when the index type is a functional index, user behavior index, problem quality index, performance index, and core business index, the classified index monitoring data includes functional index data, user behavior index data, problem quality index data, performance index data, and core business index data. Preferably, the present invention can accurately construct a user portrait based on the classified index monitoring data, so as to accurately identify key features such as the user's behavior pattern, function usage preference, and potential needs.

[0025] In one embodiment, after step S120, it further includes: obtaining the target classified index monitoring data of the target product to be evaluated; wherein, the target classified index monitoring data includes a plurality of target special dimension index data of different types, and each of the target special dimension index data includes a plurality of target index data; for each of the target index data, determine whether it meets the preset suspected problem determination condition; mark the target index data that meets the suspected problem determination condition as a suspected problem; assign a unique error identifier to each of the suspected problems and track and manage each of the suspected problems.

[0026] In this embodiment, obtain the target classified index monitoring data of the target product to be evaluated; wherein, the target classified index monitoring data includes functional index data, user behavior index data, problem quality index data, performance index data, and core business index data; the functional index data includes the number of functions, the number of network interfaces, the number of buttons, and the number of pages; the user behavior index data includes target index data such as the number of active users in 7 days, the total number of users, and the application level; the problem quality index data includes target index data such as the number of suspected problems, the number of confirmed problems, the number of crashes, and the number of freezes; the performance index data includes target index data such as application availability, average interface response time, and the number of slow interface requests; the core business index data includes the success rate of key business function 1, the success rate of key business function 2, the success rate of key business function 3, the index response time of key business function 1, and the index response time of key business function 2 (for details, refer to Table 1): Table 1 The classified index monitoring data is displayed in the data display module of the monitoring platform. In the data analysis module of the monitoring platform, for each of the target index data, it is judged whether it meets the preset suspected problem judgment conditions; the target index data that meets the suspected problem judgment conditions is marked as a suspected problem; here, taking "the success rate of key business function 1" as an example, the target suspected problem judgment conditions associated with "the success rate of key business function 1" are obtained, and it is judged whether "the success rate of key business function 1" is within the success rate threshold range in the target suspected problem judgment conditions; if "the success rate of key business function 1" is within the success rate threshold range, it is determined that it does not meet the target suspected problem judgment conditions; if "the success rate of key business function 1" is not within the success rate threshold range, it is determined that it meets the target suspected problem judgment conditions; the target index data that meets the suspected problem judgment conditions is marked as a suspected problem; a unique error identifier is assigned to each of the suspected problems and each of the suspected problems is tracked and managed; specifically, detailed records of the suspected problems are made, including information such as problem description, occurrence time, occurrence location, affiliated team, the person in charge, etc.; for example, team teamA can view all the problems it is responsible for in the problem management interface of the suspected quality management platform, and the developers of team teamA can click to view the detailed information of the suspected problems and maintain them; further, the status of the problems (such as unprocessed, processing, resolved, etc.) is updated in real time according to the problem handling progress. The present invention can track and manage each of the suspected problems, record the whole process from discovery, processing to resolution, so as to ensure that all suspected problems can be properly handled and improve the quality and performance of the product to be evaluated.

[0027] The present invention also relates to front-end application management, which focuses on the overall management of front-end applications, including: 1. Application configuration management: adjusting various configuration parameters of front-end applications according to different business scenarios and requirements; 2. Usage permission management of front-end applications: determining which personnel or roles can access and use specific front-end applications, ensuring the standardization and security of application usage and many other aspects, and ensuring that front-end applications can serve business needs stably and efficiently.

[0028] Preferably, the present invention also relates to problem filtering. Specifically, developers are allowed to set filtering rules according to their own specific needs and business logics; for example, rules can be formulated according to different dimensions such as the severity / priority of problems, affiliated business fields, problem sources, etc. The system will screen and filter a large amount of information (such as problem records, index data, etc.) according to these custom rules, and only present the relevant content that meets the rule requirements, which helps developers quickly focus on key information and improve the efficiency and accuracy of information processing.

[0029] In one embodiment, after assigning a unique error identifier to each of the suspected problems and tracking and managing each of the suspected problems, the method further includes: obtaining, according to the full-process management data of each of the suspected problems, the problem quantity indicators of the target product to be evaluated in different processing states; wherein, the problem quantity indicators include, but are not limited to, the number of problems to be processed, the number of closed-loop problems, the number of newly added problems, and the number of optimized problems; storing the problem quantity indicators of the target product to be evaluated in each unit time to form sequence data of problem quantity indicators with a time dimension; generating a corresponding problem trend chart / table according to the sequence data of problem quantity indicators and performing visual display.

[0030] In this embodiment, according to the full-process management data of each of the suspected problems, the problem quantity indicators of the target product to be evaluated in different processing states are obtained; wherein, the problem quantity indicators include, but are not limited to, the number of problems to be processed, the number of closed-loop problems, the number of newly added problems, and the number of optimized problems; storing the problem quantity indicators of the target product to be evaluated in each unit time to form sequence data of problem quantity indicators with a time dimension; wherein, the unit time can be set customarily; preferably, the unit time is set to be a month or a week; generating a corresponding problem trend chart / table according to the sequence data of problem quantity indicators and displaying it on the team quality score interface to assist the team in evaluating the development of the target product to be evaluated based on the problem trend chart / table; wherein, the problem trend chart / table is used to reflect the dynamic evolution of the team quality score trend.

[0031] Preferably, the total change trend of all products within the target team can also be determined according to the problem quantity indicators of each product to be evaluated in different processing states; specifically, obtaining the associated products responsible by the target team; determining the team problem quantity indicators according to the problem quantity indicators of each associated product in different processing states; wherein, the team problem quantity indicators include, but are not limited to, the number of team problems to be processed, the number of team closed-loop problems, the number of team newly added problems, and the number of team optimized problems; storing the team problem quantity indicators of the target team in each unit time to form sequence data of team problem quantity indicators with a time dimension; generating a corresponding team problem trend chart / table according to the sequence data of team problem quantity indicators and displaying it on the team quality score interface to assist the team in viewing the change trend of all the products they are responsible for within the unit time based on the team problem trend chart / table, so as to determine whether corresponding optimization measures need to be taken or resource allocation needs to be adjusted, etc.

[0032] S130. Obtaining the special dimension score of each special dimension index data through a preset evaluation model.

[0033] In this embodiment, the classified index monitoring data includes functional index data, user behavior index data, problem quality index data, performance index data, and core business index data. The present invention can obtain the special dimension scores of each piece of the special dimension index data through a pre-constructed evaluation model. Specifically, the evaluation model deeply integrates the five major dimension index systems of functionality, user behavior, problem quality, performance, and core business, and through systematic quantitative analysis and multi-dimensional cross-validation, realizes a comprehensive and accurate evaluation of the user experience quality, providing a scientific and reliable decision-making basis for product iteration optimization and service quality improvement.

[0034] In one embodiment, step S130 includes: determining a corresponding target scoring strategy according to the index type of the special dimension index data; through the evaluation model, calculating the corresponding special dimension index data in combination with the target scoring strategy to obtain the corresponding special dimension score.

[0035] In this embodiment, the index types of the special dimension index data may include, but are not limited to, functional indexes, user behavior indexes, problem quality indexes, performance indexes, and core business indexes. Among them, when the index type of the special dimension index data is a functional index, a user behavior index, or a problem quality index, the first scoring strategy is used for calculation; when the index type of the special dimension index data is a performance index or a core business index, the second scoring strategy is used for calculation.

[0036] In one embodiment, the calculating, through the evaluation model, the corresponding special dimension index data in combination with the target scoring strategy to obtain the corresponding special dimension score includes: if the target scoring strategy is the first scoring strategy, obtaining the ideal value and the actual value of each index data in the corresponding special dimension index data; calculating the ideal value and the actual value of each index data according to the scoring algorithm in the first scoring strategy to obtain the corresponding index quantization score; obtaining the weight coefficient of each index data according to the index weight quantization table in the evaluation model; calculating the index quantization scores and their corresponding weight coefficients according to the weighted summation formula in the evaluation model to obtain the special dimension scores of each piece of the special dimension index data.

[0037] In this embodiment, if the target scoring strategy is the first scoring strategy, obtain the ideal value and the actual value of each index data in the corresponding special dimension index data; wherein, the ideal value can be set according to specific business requirements; according to the scoring algorithm Score = min (100, Actual_Value / Ideal_Value × 100) in the first scoring strategy, calculate the ideal value and the actual value of each index data to obtain the corresponding index quantization score; wherein, Score is the index quantization score, Actual_Value is the actual value, and Ideal_Value is the ideal value; obtain the weight coefficient of each index data according to the index weight quantization table (specifically, refer to Table 2) in the evaluation model, wherein, the weight coefficient can be set according to specific business requirements; according to the weighted summation formula Total_Score = Score1×Q1+Score2×Q2+……+ScoreN×QN in the evaluation model, calculate the index quantization scores and their corresponding weight coefficients to obtain the special dimension scores of each special dimension index data; wherein, Total_Score is the special dimension score of the special dimension index data, Score1 to ScoreN respectively represent the index quantization scores of the 1st to the Nth index data, Q1 to QN respectively represent the weight coefficients corresponding to the 1st to the Nth index data, and N is a positive integer greater than or equal to 1. Table 2 The specific calculation steps for the special dimension score of the functional index data are as follows: Total_Score 1=A×W1+B×W2+C×W3+D×W4, where Total_Score1 is the special dimension score of the functional index data; A = min (100, Actual_Value1 / Ideal_Value1×100), A is the index quantization score of the function quantity, Actual_Value1 is the actual value of the function quantity, and Ideal_Value1 is the ideal value of the function quantity; the calculation processes of the index quantization scores of B, C, D, E, F, G, H, I, J, and K all follow the same calculation logic as A. The specific calculation steps for the special dimension score of the user behavior index data are as follows: Total_Score 2=E×W5+F×W6+ G×W7, where Total_Score 2 is the special dimension score of the user behavior index data. The specific calculation steps for the special dimension score of the problem quality index data are as follows: Total_Score 3 = H×W8 + I×W9 + J×W10 + K×W11, where Total_Score 3 is the score of the special dimension of the problem quality index data.

[0038] In one embodiment, calculating the corresponding special dimension score for the special dimension index data through the evaluation model in combination with the target scoring strategy includes: if the target scoring strategy is the second scoring strategy, obtaining the data threshold interval and the actual value of each index data in the corresponding special dimension index data; evaluating the actual value of the corresponding index data according to the data threshold interval to obtain the corresponding index quantization score; obtaining the weight coefficient of each index data according to the index weight quantization table in the evaluation model; calculating the index quantization scores and their corresponding weight coefficients according to the weighted summation formula in the evaluation model to obtain the special dimension scores of each special dimension index data.

[0039] In this embodiment, if the target scoring strategy is the second scoring strategy, obtain the data threshold interval and the actual value of each index data in the corresponding special dimension index data; evaluate the actual value of the corresponding index data according to the data threshold interval to obtain the corresponding index quantization score; for example, divide the data threshold interval of application availability into the first availability threshold interval, the second availability threshold interval, and the third availability threshold interval, where the first availability threshold interval is set to be greater than 99%, the second availability threshold interval is set to (98%, 99%), and the third availability threshold interval is set to be less than 98%. Each availability threshold interval corresponds to a different index quantization score, and the index quantization score corresponding to each availability threshold interval can be set according to actual business requirements; specifically, if the actual value of application availability is greater than 99%, the index quantization score L of application availability is 0. If the actual value of application availability is within (98%, 99%), the index quantization score L of application availability is 10. If the actual value of application availability is less than 98%, the index quantization score L of application availability is 20; for another example, divide the data threshold interval of the average interface response time into the first response time threshold interval, the second response time threshold interval, and the third response time threshold interval, where the first response time threshold interval is set to be less than the ideal response time, the second response time threshold interval is set to (the ideal response time, the first response time threshold), and the third response time threshold interval is set to be greater than the second response time threshold; if the actual value of the average interface response time is less than the ideal response time, the index quantization score M of the average interface response time is 0. If the actual value of the average interface response time is within (the ideal response time, the first response time threshold), the index quantization score M of the average interface response time is 10. If the actual value of the average interface response time is greater than the second response time threshold, the index quantization score M of the average interface response time is 20; the calculation processes of the index quantization scores of N, O, P, Q, R, and S all follow the same calculation logic as L or M.

[0040] Obtain the weight coefficient of each index data according to the index weight quantization table in the evaluation model (specifically, refer to Table 2); calculate the index quantization scores and their corresponding weight coefficients according to the weighted summation formula Total_Score = Score1×Q1 + Score2×Q2 + …… + ScoreN×QN in the evaluation model to obtain the special dimension scores of each special dimension index data; where Total_Score is the special dimension score of the special dimension index data, Score1 to ScoreN respectively represent the index quantization scores of the 1st to the Nth index data, Q1 to QN respectively represent the weight coefficients corresponding to the 1st to the Nth index data, and N is a positive integer greater than or equal to 1.

[0041] The specific calculation steps for the special dimension score of the performance index data are as follows: Total_Score 4 = L×W12 + M×W13 + N×W14, where Total_Score 4 is the special dimension score of the performance index data; The specific calculation steps for the special dimension score of the core business index data are as follows: Total_Score 5 = O×W15 + P×W16 + Q×W17 + R×W18 + S×W19, where Total_Score 5 is the special dimension score of the core business index data.

[0042] S140. Obtain the product scores of each product to be evaluated based on the special dimension scores of each special dimension index data.

[0043] In this embodiment, the product score = Total_Score 1 + Total_Score 2 - Total_Score 3 - Total_Score 4 - Total_Score 5, where Total_Score1 is the special dimension score of the functional index data, Total_Score 2 is the special dimension score of the user behavior index data, Total_Score 3 is the special dimension score of the problem quality index data, Total_Score 4 is the special dimension score of the performance index data, and Total_Score 5 is the special dimension score of the core business index data; further, a product score constraint mechanism is established to limit the product score within the range of 0 to 100 points.

[0044] In the embodiment of the present invention, the product score can be regarded as a quantitative manifestation of the user's overall feeling of the product. Based on each product score, the present invention realizes the accurate quantitative evaluation and objective value judgment of the user experience quality, effectively avoids the interference of subjective factors, and provides scientific and reliable data support for product optimization iteration and user demand insight.

[0045] Specifically, in the quality management platform of the present invention, the product scores of multiple products to be evaluated are comprehensively evaluated through the evaluation model; the evaluation model includes multiple sub-evaluation models, and each sub-evaluation model corresponds to a product to be evaluated one by one. Each sub-evaluation model includes a comprehensive score layer, and the comprehensive score layer is connected to multiple dimension calculation layers (where the multiple dimension calculation layers include functional indicators, user behavior indicators, problem quality indicators, performance indicators, and core business indicators), and each dimension calculation layer is respectively connected to multiple indicator layers to form a tree-like hierarchical structure (specifically, refer to Figure 5); Wherein, when the indicator type of the associated special dimension indicator data is a functional indicator or a user behavior indicator or a problem quality indicator, the indicator layer has a built-in scoring algorithm module, which is configured with a scoring algorithm. The scoring algorithm module can receive and parse the input indicator data, calculate the ideal value and the actual value of the indicator data according to the scoring algorithm, obtain the corresponding indicator quantitative score, and output it to the dimension calculation layer; when the indicator type of the associated special dimension indicator data is a performance indicator or a core business indicator, the indicator layer can receive and parse the input indicator data, extract the actual value and the preset data threshold interval; compare and analyze the actual value with the data threshold interval, and determine the actual value by The value is in the interval position, and a quantitative conversion is performed according to a preset mapping relationship to obtain the corresponding indicator quantitative score, and the score is output to the dimension calculation layer; the dimension calculation layer is connected to each related indicator layer, and the weight coefficient corresponding to each indicator is stored internally; the dimension calculation layer receives the quantitative score of each indicator from the indicator layer, and based on the weight coefficient, uses the weighted summation formula to calculate, and outputs the corresponding special dimension score; the comprehensive score layer is connected to each dimension calculation layer, and receives the special dimension scores output by each dimension calculation layer by summarizing, and according to the preset integration rules, comprehensively processes the special dimension scores, and finally generates a comprehensive score (i.e., product score) that can reflect the overall user experience of the product.

[0046] In one embodiment, after step S140, the method further includes: storing the product scores of the evaluated products in each unit time to form product score sequence data with a time dimension; generating a corresponding quality score trend chart / table based on the product score sequence data and performing a visual display.

[0047] In this embodiment, the product scores of the product to be evaluated DP1 in each unit time are stored to form a product score sequence data S with a time dimension. DP1 ; According to the product score sequence data S DP1 Generate a corresponding quality score trend chart / table and display it visually to assist developers in evaluating the development of the product to be evaluated DP1 based on the quality score trend chart / table. If the quality score trend chart / table is in a downward trend for a long time, developers can view the detailed interface of the latest product score of the product to be evaluated DP1, view the special dimension scores of each special dimension indicator data and / or the indicator quantitative scores of each indicator data, so as to quickly locate the weak indicators, focus resources on optimizing the weak indicators, and improve the quality of the product to be evaluated DP1.

[0048] S150: Perform a horizontal comparison of the products to be evaluated based on the product scores and display them on a quality scoring interface.

[0049] In this embodiment, by comparing the product scores of each product to be evaluated horizontally and displaying them intuitively on the quality scoring interface, developers and decision makers can quickly locate products with lower scores, and accurately identify the shortcomings of each product in terms of functionality, user behavior, problem quality, performance, core business and other dimensions. This visual comparative analysis method can not only clearly present the differences in quality between products, but also provide scientific and intuitive data support for the formulation of subsequent product optimization directions and the rational allocation of resources, helping to achieve comprehensive improvement in product quality and competitiveness.

[0050] Preferably, team teamA can view the changing trend of the average scores of all the products it is responsible for within a unit time in the problem management interface; specifically, obtain the related products that the target team is responsible for; calculate the average score of all the products that the target team is responsible for within the unit time based on the product scores of each related product within the same unit time; at the same time, process the average score data of multiple consecutive unit times in chronological order to generate average score series data; dynamically display the average score series data in the form of visual charts such as line charts and curve charts in the problem management interface; through visual charts, the target team can intuitively and clearly observe the changing trend of the average scores of all the products it is responsible for within a unit time, including information such as the rising and falling trends and fluctuation range of the scores, thereby assisting the target team to judge whether it is necessary to take corresponding optimization measures or adjust resource allocation based on these trends.

[0051] Preferably, the product scores of each related product in the same unit time are displayed on the team quality scoring interface; at the same time, the indicator data of each related product is presented; the target team can know the quality level of each related product in the unit time according to the differences in scores / indicator data between different related products, and then find out the products with excellent performance or those that need improvement, focus on analyzing the reasons, summarizing the experience, and promoting the continuous improvement of product quality.

[0052] In summary, the present invention has the following beneficial effects: 1. By obtaining indicator monitoring data, the negative impact of lengthy survey questions on user experience is avoided, and the accuracy and objectivity of product scores are improved; 2. By obtaining multiple different types of special dimension indicator data, the user experience quality is comprehensively evaluated. This comprehensive evaluation method can conduct an all-round and refined evaluation of the user experience quality, thereby providing reliable data support for the optimization and iteration of products or services; 3. Through horizontal comparison, the differences in user experience performance of each business segment of the company can be intuitively presented, and the key shortcomings of low-scoring businesses can be accurately located, providing data support for the formulation of refined improvement strategies, and helping enterprises to achieve efficient resource allocation and comprehensive improvement of business quality.

[0053] Figure 2Schematic block diagram of the user experience quality evaluation device provided by an embodiment of the present invention. As Figure 2 shown, corresponding to the above user experience quality evaluation method, the present invention also provides a user experience quality evaluation device, which is configured in an application environment such as Figure 4 . In the user layer, index monitoring data of each product to be evaluated per unit time is obtained; in the big data filtering layer, invalid data in the index detection data is filtered out; in the monitoring platform, the index monitoring data is classified according to the index type to obtain corresponding classified index monitoring data; wherein, each classified index monitoring data includes multiple special dimension index data of different types; the classified index monitoring data is displayed in the monitoring platform; in the quality management platform, the special dimension score of each special dimension index data is obtained through a preset evaluation model; the product score of each product to be evaluated is obtained according to the special dimension score of each special dimension index data; horizontal comparison is performed on each product to be evaluated according to the product score and displayed on the quality scoring interface. The present invention can comprehensively evaluate the user experience quality, improve the accuracy and objectivity of the product score, and provide reliable data support for the optimization and iteration of products or services. Specifically, please refer to Figure 2 , the user experience quality evaluation device 700 includes: A first acquisition unit 701, configured to acquire index monitoring data of each product to be evaluated per unit time; A classification unit 702, configured to classify the index monitoring data according to the index type to obtain corresponding classified index monitoring data; wherein, each classified index monitoring data includes multiple special dimension index data of different types; A second acquisition unit 703, configured to obtain the special dimension score of each special dimension index data through a preset evaluation model; A product score acquisition unit 704, configured to obtain the product score of each product to be evaluated according to the special dimension score of each special dimension index data; A horizontal comparison unit 705, configured to perform horizontal comparison on each product to be evaluated according to the product score and display it on the quality scoring interface.

[0054] In some embodiments, when the second acquisition unit 703 executes the step of obtaining the special dimension score of each special dimension index data through a preset evaluation model, it is specifically configured to: Determine a corresponding target scoring strategy according to the index type of the special dimension index data; through the evaluation model, calculate the corresponding special dimension index data in combination with the target scoring strategy to obtain the corresponding special dimension score.

[0055] In some embodiments, when the second obtaining unit 703 executes the step of calculating the corresponding special dimension score by using the evaluation model in combination with the target scoring strategy for the corresponding special dimension index data, it is specifically configured to: If the target scoring strategy is the first scoring strategy, obtain the ideal value and the actual value of each index data in the corresponding special dimension index data; calculate the ideal value and the actual value of each index data according to the scoring algorithm in the first scoring strategy to obtain the corresponding index quantization score; obtain the weight coefficient of each index data according to the index weight quantization table in the evaluation model; calculate the special dimension scores of each special dimension index data according to the weighted summation formula in the evaluation model for each index quantization score and its corresponding weight coefficient.

[0056] In some embodiments, when the second obtaining unit 703 executes the step of calculating the corresponding special dimension score by using the evaluation model in combination with the target scoring strategy for the corresponding special dimension index data, it is specifically configured to: If the target scoring strategy is the second scoring strategy, obtain the data threshold interval and the actual value of each index data in the corresponding special dimension index data; evaluate the actual value of the corresponding index data according to the data threshold interval to obtain the corresponding index quantization score; obtain the weight coefficient of each index data according to the index weight quantization table in the evaluation model; calculate the special dimension scores of each special dimension index data according to the weighted summation formula in the evaluation model for each index quantization score and its corresponding weight coefficient.

[0057] In some embodiments, after the classification unit 702 executes the step of classifying each index monitoring data according to the index type to obtain the corresponding classified index monitoring data, it is further configured to: Obtain the target classified index monitoring data of the target product to be evaluated; wherein, the target classified index monitoring data includes multiple different types of target special dimension index data, and each target special dimension index data includes multiple target index data; for each target index data, determine whether it meets the preset suspected problem determination condition; mark the target index data that meets the suspected problem determination condition as a suspected problem; assign a unique error identifier to each suspected problem and track and manage each suspected problem.

[0058] In some embodiments, after the classification unit 702 executes the step of assigning a unique error identifier to each suspected problem and tracking and managing each suspected problem, it is further configured to: Based on the full-process management data of each suspected problem, obtain the problem quantity indicators of the target product to be evaluated in different processing states; wherein, the problem quantity indicators include, but are not limited to, the number of problems to be processed, the number of closed-loop problems, the number of newly added problems, and the number of optimized problems; store the problem quantity indicators of the target product to be evaluated within each unit time to form problem quantity indicator sequence data with a time dimension; generate a corresponding problem trend chart / table according to the problem quantity indicator sequence data and perform visual display.

[0059] In some embodiments, after the product score acquisition unit 704 executes the step of obtaining the product scores of each product to be evaluated according to the special dimension scores of each special dimension indicator data, it is further configured to: Store the product scores of the product to be evaluated within each unit time to form product score sequence data with a time dimension; generate a corresponding quality score trend chart / table according to the product score sequence data and perform visual display.

[0060] It should be noted that those skilled in the art can clearly understand that the specific implementation processes of the above user experience quality evaluation device and each unit can refer to the corresponding descriptions in the foregoing method embodiments. For the sake of convenience and brevity of description, they will not be elaborated herein.

[0061] The above user experience quality evaluation device can be implemented in the form of a computer program, and this computer program can run on an electronic device as shown in Figure 3 .

[0062] Please refer to Figure 3 , Figure 3 which is a schematic block diagram of an electronic device provided by an embodiment of the present invention. The electronic device 800 can be a terminal or a server. Among them, the terminal can be an electronic device with communication functions. The server can be an independent server or a server cluster composed of multiple servers.

[0063] Refer to Figure 3 , the electronic device 800 includes a processor 802, a memory, and a network interface 805 connected through a system bus 801. Among them, the memory can include a non-volatile storage medium 803 and an internal memory 804.

[0064] The non-volatile storage medium 803 can store an operating system 8031 and a computer program 8032. The computer program 8032 includes program instructions. When the program instructions are executed, the processor 802 can execute a user experience quality evaluation method.

[0065] The processor 802 is used to provide computing and control capabilities to support the operation of the entire electronic device 800.

[0066] The internal memory 804 provides an environment for the operation of the computer program 8032 in the non-volatile storage medium 803. When the computer program 8032 is executed by the processor 802, the processor 802 can be caused to execute a user experience quality evaluation method.

[0067] The network interface 805 is used for network communication with other devices. Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the electronic device 800 to which the solution of the present invention is applied. The specific electronic device 800 may include more or fewer components than those shown in the figure, or combine some components, or have a different component arrangement.

[0068] Among them, the processor 802 is used to run the computer program 8032 stored in the memory to implement the following steps: Obtain the index monitoring data of each product to be evaluated per unit time; classify each of the index monitoring data according to the index type to obtain the corresponding classified index monitoring data; wherein, each of the classified index monitoring data includes a plurality of special dimension index data of different types; obtain the special dimension score of each of the special dimension index data through a preset evaluation model; obtain the product score of each product to be evaluated according to the special dimension score of each of the special dimension index data; perform horizontal comparison on each product to be evaluated according to the product score and display it on the quality scoring interface.

[0069] In some embodiments, when the processor 802 implements the step of obtaining the special dimension score of each of the special dimension index data through a preset evaluation model, the following steps are specifically implemented: Determine the corresponding target scoring strategy according to the index type of the special dimension index data; through the evaluation model, calculate the corresponding special dimension index data in combination with the target scoring strategy to obtain the corresponding special dimension score.

[0070] In some embodiments, when the processor 802 implements the step of calculating the corresponding special dimension score by calculating the corresponding special dimension index data in combination with the target scoring strategy through the evaluation model, the following steps are specifically implemented: If the target scoring strategy is the first scoring strategy, obtain the ideal value and actual value of each index data in the corresponding special dimension index data; calculate the ideal value and actual value of each index data according to the scoring algorithm in the first scoring strategy to obtain the corresponding index quantization score; obtain the weight coefficient of each index data according to the index weight quantization table in the evaluation model; calculate the special dimension scores of each special dimension index data according to the weighted summation formula in the evaluation model.

[0071] In some embodiments, when the processor 802 implements the step of calculating the corresponding special dimension score by combining the target scoring strategy through the evaluation model for the corresponding special dimension index data, the specific implementation steps are as follows: If the target scoring strategy is the second scoring strategy, obtain the data threshold interval and actual value of each index data in the corresponding special dimension index data; evaluate the actual value of the corresponding index data according to the data threshold interval to obtain the corresponding index quantization score; obtain the weight coefficient of each index data according to the index weight quantization table in the evaluation model; calculate the special dimension scores of each special dimension index data according to the weighted summation formula in the evaluation model.

[0072] In some embodiments, after the processor 802 implements the step of classifying each index monitoring data according to the index type to obtain the corresponding classified index monitoring data, the following steps are further implemented: Obtain the target classified index monitoring data of the target product to be evaluated; wherein, the target classified index monitoring data includes multiple different types of target special dimension index data, and each target special dimension index data includes multiple target index data; for each target index data, determine whether it meets the preset suspected problem determination condition; mark the target index data that meets the suspected problem determination condition as a suspected problem; assign a unique error identifier to each suspected problem and track and manage each suspected problem.

[0073] In some embodiments, after the processor 802 implements the step of assigning a unique error identifier to each suspected problem and tracking and managing each suspected problem, the following steps are further implemented: Obtain the problem quantity indicators of the target product to be evaluated in different processing states according to the full-process management data of each suspected problem; wherein, the problem quantity indicators include but are not limited to the number of problems to be processed, the number of closed-loop problems, the number of new problems, and the number of optimized problems; store the problem quantity indicators of the target product to be evaluated within each unit time to form problem quantity indicator sequence data with a time dimension; generate a corresponding problem trend chart / table according to the problem quantity indicator sequence data and perform visual display.

[0074] In some embodiments, after the processor 802 implements the step of obtaining the product scores of each product to be evaluated according to the special dimension scores of each special dimension index data, the following steps are further implemented: Store the product scores of the product to be evaluated within each unit time to form product score sequence data with a time dimension; generate a corresponding quality score trend chart / table according to the product score sequence data and perform visual display.

[0075] It should be understood that in the embodiments of the present invention, the processor 802 may be a central processing unit (CPU), and this processor 802 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0076] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, and this storage medium is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0077] Therefore, the present invention also provides a storage medium. This storage medium may be a computer-readable storage medium. The storage medium stores a computer program, where the computer program includes program instructions. When the program instructions are executed by the processor, the processor is caused to execute the following steps: Obtain the index monitoring data of each product to be evaluated per unit time; classify each of the index monitoring data according to the index type to obtain the corresponding classified index monitoring data; wherein, each of the classified index monitoring data includes a plurality of special dimension index data of different types; obtain the special dimension score of each of the special dimension index data through a preset evaluation model; obtain the product score of each product to be evaluated according to the special dimension score of each of the special dimension index data; perform a horizontal comparison of each product to be evaluated according to the product score and display it on the quality scoring interface.

[0078] In one embodiment, when the processor executes the program instructions to implement the step of obtaining the special dimension score of each of the special dimension index data through a preset evaluation model, the specific implementation is as follows: Determine the corresponding target scoring strategy according to the index type of the special dimension index data; through the evaluation model, calculate the corresponding special dimension index data in combination with the target scoring strategy to obtain the corresponding special dimension score.

[0079] In one embodiment, when the processor executes the program instructions to implement the step of calculating the corresponding special dimension score by calculating the corresponding special dimension index data in combination with the target scoring strategy through the evaluation model, the specific implementation is as follows: If the target scoring strategy is the first scoring strategy, obtain the ideal value and actual value of each index data in the corresponding special dimension index data; calculate the ideal value and actual value of each index data according to the scoring algorithm in the first scoring strategy to obtain the corresponding index quantization score; obtain the weight coefficient of each index data according to the index weight quantization table in the evaluation model; calculate the special dimension scores of each of the special dimension index data according to the weighted summation formula in the evaluation model for each of the index quantization scores and their corresponding weight coefficients.

[0080] In one embodiment, when the processor executes the program instructions to implement the step of calculating the corresponding special dimension score by calculating the corresponding special dimension index data in combination with the target scoring strategy through the evaluation model, the specific implementation is as follows: If the target scoring strategy is the second scoring strategy, obtain the data threshold interval and the actual value of each index data in the corresponding special dimension index data; evaluate the actual value of the corresponding index data according to the data threshold interval to obtain the corresponding index quantization score; obtain the weight coefficient of each index data according to the index weight quantization table in the evaluation model; calculate the index quantization scores and their corresponding weight coefficients according to the weighted summation formula in the evaluation model to obtain the special dimension scores of each special dimension index data.

[0081] In one embodiment, after the processor executes the program instruction to classify each index monitoring data according to the index type to obtain the corresponding classified index monitoring data, the following steps are further implemented: Obtain the target classified index monitoring data of the target product to be evaluated; wherein, the target classified index monitoring data includes multiple target special dimension index data of different types, and each target special dimension index data includes multiple target index data; for each target index data, determine whether it meets the preset suspected problem judgment condition; mark the target index data that meets the suspected problem judgment condition as a suspected problem; assign a unique error identifier to each suspected problem and track and manage each suspected problem.

[0082] In one embodiment, after the processor executes the program instruction to assign a unique error identifier to each suspected problem and track and manage each suspected problem, the following steps are further implemented: Obtain the problem quantity index of the target product to be evaluated in different processing states according to the full-process management data of each suspected problem; wherein, the problem quantity index includes but is not limited to the number of problems to be processed, the number of closed-loop problems, the number of new problems, and the number of optimized problems; store the problem quantity index of the target product to be evaluated in each unit time to form a problem quantity index sequence data with a time dimension; generate a corresponding problem trend chart / table according to the problem quantity index sequence data and perform visual display.

[0083] In one embodiment, after the processor executes the program instruction to obtain the product scores of each product to be evaluated according to the special dimension scores of each special dimension index data, the following steps are further implemented: Store the product scores of the product to be evaluated in each unit time to form a product score sequence data with a time dimension; generate a corresponding quality scoring trend chart / table according to the product score sequence data and perform visual display.

[0084] The storage medium may be a variety of computer-readable storage media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disc, etc., which can store program codes.

[0085] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0086] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0087] The steps in the method embodiments of the present invention can be adjusted, combined, and deleted according to actual needs. The units in the device embodiments of the present invention can be combined, divided, and deleted according to actual needs. In addition, the functional units in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0088] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable an electronic device (which can be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0089] As described above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for evaluating the quality of user experience, characterized in that, The method includes: Obtaining the index monitoring data of each product to be evaluated per unit time; Classifying the index monitoring data according to the index type to obtain the corresponding classified index monitoring data; wherein, each piece of classified index monitoring data includes multiple special dimension index data of different types; Obtaining the special dimension score of each piece of special dimension index data through a preset evaluation model; Obtaining the product scores of each product to be evaluated according to the special dimension scores of each piece of special dimension index data; Making a horizontal comparison of each product to be evaluated according to the product scores and displaying them on the quality scoring interface.

2. The method for evaluating the quality of user experience according to claim 1, characterized in that, The obtaining the special dimension score of each piece of special dimension index data through a preset evaluation model includes: Determining the corresponding target scoring strategy according to the index type of the special dimension index data; Calculating the corresponding special dimension score for the corresponding special dimension index data through the evaluation model in combination with the target scoring strategy.

3. The method for evaluating the quality of user experience according to claim 2, wherein The calculating the corresponding special dimension score for the corresponding special dimension index data through the evaluation model in combination with the target scoring strategy includes: If the target scoring strategy is the first scoring strategy, obtaining the ideal value and the actual value of each index data in the corresponding special dimension index data; Calculating the ideal value and the actual value of each index data according to the scoring algorithm in the first scoring strategy to obtain the corresponding index quantization score; Obtaining the weight coefficient of each index data according to the index weight quantization table in the evaluation model; Calculating the special dimension scores of each piece of special dimension index data according to the weighted summation formula in the evaluation model for each index quantization score and its corresponding weight coefficient.

4. The method for evaluating the quality of user experience according to claim 2, wherein The calculating the corresponding special dimension score for the corresponding special dimension index data through the evaluation model in combination with the target scoring strategy includes: If the target scoring strategy is the second scoring strategy, obtaining the data threshold interval and the actual value of each index data in the corresponding special dimension index data; Evaluating the actual value of the corresponding index data according to the data threshold interval to obtain the corresponding index quantization score; Obtaining the weight coefficient of each index data according to the index weight quantization table in the evaluation model; Calculating the special dimension scores of each piece of special dimension index data according to the weighted summation formula in the evaluation model for each index quantization score and its corresponding weight coefficient.

5. The method for evaluating the quality of user experience according to claim 1, characterized in that, After classifying the index monitoring data according to the index type to obtain the corresponding classified index monitoring data, it further includes: Obtaining the target classified index monitoring data of the target product to be evaluated; wherein, the target classified index monitoring data includes multiple target special dimension index data of different types, and each target special dimension index data includes multiple target index data; For each target index data, determining whether it meets the preset suspected problem determination condition; Marking the target index data that meets the suspected problem determination condition as a suspected problem. Assign a unique error identifier to each of the suspected problems and track and manage each of the suspected problems.

6. The method for evaluating the quality of user experience according to claim 5, wherein After assigning a unique error identifier to each of the suspected problems and tracking and managing each of the suspected problems, it further includes: According to the full-process management data of each of the suspected problems, obtain the problem quantity indicators of the target product to be evaluated in different processing states; wherein, the problem quantity indicators include, but are not limited to, the number of problems to be processed, the number of closed-loop problems, the number of new problems, and the number of optimized problems; Store the problem quantity indicators of the target product to be evaluated within each unit time to form problem quantity indicator sequence data with a time dimension; Generate a corresponding problem trend chart / table based on the problem quantity indicator sequence data and perform visual display.

7. The method for evaluating the quality of user experience according to claim 1, characterized in that After obtaining the product scores of each product to be evaluated according to the special dimension scores of each of the special dimension indicator data, it further includes: Store the product scores of each product to be evaluated within each unit time to form product score sequence data with a time dimension; Generate a corresponding quality score trend chart / table based on the product score sequence data and perform visual display.

8. A user experience quality evaluation device, characterized in that The device includes: A first acquisition unit for acquiring the index monitoring data of each product to be evaluated within a unit time; A classification unit for classifying each of the index monitoring data according to the index type to obtain corresponding classified index monitoring data; wherein, each of the classified index monitoring data includes a plurality of special dimension indicator data of different types; A second acquisition unit for obtaining the special dimension score of each of the special dimension indicator data through a preset evaluation model; A product score acquisition unit for obtaining the product scores of each product to be evaluated according to the special dimension scores of each of the special dimension indicator data; A horizontal comparison unit for horizontally comparing each product to be evaluated according to the product scores and displaying them on the quality scoring interface.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, and a computer program is stored on the memory. When the processor executes the computer program, the method described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the method described in any one of claims 1-7 can be implemented.

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