A display method for evaluating information, related devices, equipment, and storage media

Abstract

This application discloses a method, related apparatus, device, and storage medium for displaying evaluation information. The method includes: acquiring N sets of parameters for a target service provider during a data collection period, provided that evaluation triggering conditions are met; determining N evaluation scores for each parameter set in the N sets; determining the target service provider's comprehensive service score for the data collection period based on the N evaluation scores; and displaying the target service provider's comprehensive evaluation information in response to an information query operation for the target service provider. The comprehensive evaluation information includes the comprehensive service score and evaluation information for N dimensions, with each dimension including the corresponding dimension's evaluation score and a threshold value. This application enables ride-hailing service providers to clearly understand their service level and competitiveness on ride-hailing aggregation platforms, and helps them quickly identify problems.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, related apparatus, device, and storage medium for displaying evaluation information. Background Technology

[0002] In today's booming mobile travel industry, ride-hailing aggregation platforms have become key players in the transportation sector. Essentially, these platforms act as online service hubs, allowing users to access information from multiple ride-hailing service providers within a single interface, enabling convenient one-stop booking and greatly enriching user choices.

[0003] In related technologies, ride-hailing service providers assign a service score to drivers based on the orders they fulfill, taking into account factors such as user reviews and complaints, responsible cancellations, peak-hour order acceptance, cumulative online time, compliance score, and safety score. Ride-hailing service providers then use this service score as the basis for order dispatch decisions.

[0004] The inventors have discovered at least the following problems with current solutions: ride-hailing service providers lack information about their own service levels and competitiveness on ride-hailing aggregation platforms. This results in a lack of information for their operational decisions, hindering the improvement of service quality. Currently, no effective solution has been proposed to address these issues. Summary of the Invention

[0005] This application provides a method, related apparatus, device, and storage medium for displaying evaluation information. This enables ride-hailing service providers to clearly understand their service level and competitiveness on ride-hailing aggregation platforms. Simultaneously, it helps ride-hailing service providers quickly identify problems, thereby contributing to improving service quality.

[0006] In view of this, this application provides a method for displaying evaluation information, including:

[0007] Under the condition of meeting the evaluation triggering conditions, obtain the N-type parameter set of the target service provider for the data collection period. The N-type parameter set includes at least one of the service parameter set, security parameter set, customer service parameter set and event parameter set, and N is an integer greater than or equal to 1.

[0008] Based on each of the N parameter sets, determine N evaluation scores, where each evaluation score is calculated based on one set of parameters.

[0009] Based on N evaluation scores, determine the target service provider's overall service score for the data collection period;

[0010] In response to an information query operation targeting a service provider, the system displays the service provider's comprehensive evaluation information. This comprehensive evaluation information includes a comprehensive service score and evaluation information for N dimensions. The evaluation information for each dimension includes the corresponding evaluation score and the evaluation score threshold.

[0011] Another aspect of this application provides an evaluation information display device, comprising:

[0012] The acquisition module is used to acquire the set of N types of parameters of the target service provider for the data collection period when the evaluation trigger conditions are met. The set of N types of parameters includes at least one of the service parameter set, security parameter set, customer service parameter set and event parameter set, and N is an integer greater than or equal to 1.

[0013] The determination module is used to determine N evaluation scores based on each of the N parameter sets, where each evaluation score is calculated based on one set of parameters.

[0014] The determination module is also used to determine the comprehensive service score of the target service provider during the data collection period based on N evaluation scores;

[0015] The display module is used to respond to information query operations targeting a service provider and display the comprehensive evaluation information of the service provider. The comprehensive evaluation information includes a comprehensive service score and evaluation information in N dimensions. The evaluation information in each dimension includes the evaluation score and the evaluation score threshold for the corresponding dimension.

[0016] In one possible design, in another implementation of another aspect of the embodiments of this application, the conditions for satisfying the evaluation triggering condition include at least one of the following:

[0017] The data collection period has ended;

[0018] or,

[0019] Obtain evaluation statistics requests for the target service provider.

[0020] In one possible design, in another implementation of another aspect of the embodiments of this application, the service parameter set includes at least one of non-passenger cancellation rate, average pick-up distance per ride, price overestimation rate, customer complaint rate, and negative review rate;

[0021] The determination module is specifically used to standardize the average pick-up distance when the service parameter set includes the average pick-up distance per unit, so as to obtain the standard rate of pick-up distance.

[0022] The service evaluation score is obtained by weighting and summing at least one of the following: non-passenger cancellation rate, pick-up distance standard rate, price overestimation rate, customer complaint rate, and negative review rate, among N evaluation scores.

[0023] In one possible design, in another implementation of another aspect of the embodiments of this application, the set of security parameters includes at least one of order compliance rate, security-related customer complaint rate, resale order rate, and business license upload rate;

[0024] The determination module is specifically used to perform a weighted summation of at least one of the following: order compliance rate, security-related customer complaint rate, resale order rate, and business license upload rate, to obtain a security evaluation score from N evaluation scores.

[0025] In one possible design, in another implementation of another aspect of the embodiments of this application, the customer service parameter set includes at least one of the case closure rate of a first time range, the case closure rate of a second time range, and the case closure rate of a third time range, wherein the first time range is smaller than the second time range, and the second time range is smaller than the third time range.

[0026] The determination module is specifically used to perform a weighted summation of at least one of the case closure rates for the first time period, the second time period, and the third time period to obtain the customer service evaluation score from N evaluation scores.

[0027] In one possible design, in another implementation of another aspect of the embodiments of this application, the event class parameter set includes at least one of the occurrence count of first-level events, the occurrence count of second-level events, and the occurrence count of third-level events, wherein the importance of first-level events is higher than the importance of second-level events, and the importance of second-level events is higher than the importance of third-level events.

[0028] The determination module is specifically used to determine the event evaluation score among N evaluation scores based on at least one of the occurrence counts of Level 1 events, Level 2 events, and Level 3 events.

[0029] In one possible design, in another implementation of another aspect of the embodiments of this application,

[0030] The determination module is specifically used to determine the initial service score based on the service score, security score, customer service score, and incident score when there are N evaluation scores, including service evaluation score, security score, customer service score, and incident evaluation score.

[0031] The event evaluation score is deducted from the initial service score to obtain the target service provider's comprehensive service score during the data collection period.

[0032] In one possible design, in another implementation of another aspect of the embodiments of this application,

[0033] The determination module is also used to determine the quality level of the target service provider during the data collection period based on N evaluation scores;

[0034] The display module is also used to display the quality level of the target service provider during the data collection period after responding to an information query operation targeting the target service provider.

[0035] In one possible design, in another implementation of another aspect of the embodiments of this application,

[0036] The determination module is specifically used to traverse the evaluation scores among N evaluation scores starting from the root node of the decision tree, wherein the decision tree includes a root node and at least two leaf nodes;

[0037] When traversing to the leaf nodes of the decision tree, the quality level indicated by the leaf node is taken as the quality level of the target service provider during the data collection period.

[0038] In one possible design, in another implementation of another aspect of the embodiments of this application, the N-type parameter set includes a service parameter set, a security parameter set, a customer service parameter set, and an event parameter set;

[0039] The acquisition module is also used to acquire the grading intervals corresponding to various service indicators, various security indicators, various customer service indicators, and various event indicators.

[0040] The display module is also used to highlight the service index corresponding to the abnormal parameter if there is an abnormal parameter in the service parameter set according to the hierarchical range corresponding to each service index.

[0041] The display module is also used to highlight the safety index corresponding to the abnormal parameter if there is an abnormal parameter in the set of safety parameters according to the classification intervals corresponding to each safety index.

[0042] The display module is also used to highlight the customer service indicators corresponding to the abnormal parameters if there are abnormal parameters in the customer service parameter set according to the grade intervals corresponding to each customer service indicator.

[0043] The display module is also used to highlight the event-type indicators corresponding to the abnormal parameters if there are abnormal parameters in the event-type parameter set according to the hierarchical intervals corresponding to each event-type indicator.

[0044] In one possible design, in another implementation of another aspect of the embodiments of this application,

[0045] The display module is also used to respond to a region query operation for a target service provider and display various parameters of the target service provider for at least one region, wherein each parameter is derived from at least one of service parameters, security parameters, customer service parameters, and event parameters.

[0046] In one possible design, in another implementation of another aspect of the embodiments of this application, the evaluation information display device further includes a generation module and a push module;

[0047] The generation module is used to generate the target service provider's pending approval information if the target service provider meets the conditions for handling violations.

[0048] The push module is used to send penalty notifications to the target service provider in response to the approval of pending information.

[0049] In one possible design, in another implementation of another aspect of the embodiments of this application,

[0050] The display module is also used to respond to graphical viewing operations for the target service provider and display at least one of the following: service score trend chart, order completion rate chart, user feedback chart, problem analysis chart, and customer service analysis chart corresponding to the target service provider;

[0051] The service score trend chart is used to reflect the changes in the overall service score of the target service provider within a preset time period.

[0052] The order completion rate chart is used to reflect the order completion status of the target service provider within a preset time period;

[0053] User feedback charts are used to reflect users' evaluation of the target service provider;

[0054] Problem analysis diagrams are used to reflect the target service provider's response to comprehensive problems;

[0055] Customer service analysis charts are used to reflect the target service provider's response to customer service-related questions.

[0056] In one possible design, in another implementation of another aspect of the embodiments of this application,

[0057] The acquisition module is also used to acquire T sets of historical parameters of the target service provider, wherein each set of historical parameters includes at least one of service parameters, security parameters, customer service parameters and event parameters for a historical time period, and T is an integer greater than or equal to 1;

[0058] The generation module is also used to construct a linear regression model based on T sets of historical parameters;

[0059] The acquisition module is also used to acquire the current parameter set of the target service provider;

[0060] The acquisition module is also used to obtain the predicted service score of the target service provider based on the current parameter set through a linear regression model.

[0061] In one possible design, in another implementation of another aspect of the embodiments of this application,

[0062] The acquisition module is also used to acquire T sets of historical parameters of the target service provider, wherein each set of historical parameters includes at least one of service parameters, security parameters, customer service parameters and event parameters for a historical time period, and T is an integer greater than or equal to 1;

[0063] The generation module is also used to generate T feature vectors based on T sets of historical parameters;

[0064] The determination module is also used to select K feature vectors from T feature vectors as K cluster centers, where K is an integer greater than 1 and less than T;

[0065] The determination module is also used to calculate the distance between each of the T feature vectors and the K cluster centers.

[0066] The generation module is also used to assign each of the T feature vectors to the cluster corresponding to the shortest cluster center until the convergence condition is met, and obtain K clustering results.

[0067] In one possible design, in another implementation of another aspect of the embodiments of this application,

[0068] The acquisition module is also used to acquire the target historical parameter set of the target service provider, wherein the target historical parameter set includes at least one of service parameters, security parameters, customer service parameters and event parameters for the target historical time period;

[0069] The generation module is also used to construct the first feature vector based on the target's historical parameter set;

[0070] The acquisition module is also used to obtain a predicted evaluation score for at least one dimension based on the first feature vector through a time series model.

[0071] In one possible design, in another implementation of another aspect of the embodiments of this application,

[0072] The generation module is also used to construct the second feature vector corresponding to the target service provider based on the set of N types of parameters;

[0073] The acquisition module is also used to obtain a predicted probability distribution based on the second feature vector through an anomaly detection model, wherein each element in the predicted probability distribution corresponds to an anomaly category;

[0074] The determination module is also used to determine the anomaly detection results of the target service provider based on the predicted probability distribution.

[0075] In another aspect, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the methods described above.

[0076] Another aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the methods described above.

[0077] Another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the methods described above.

[0078] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0079] This application provides a method for displaying evaluation information. First, under the condition that the evaluation triggering condition is met, N sets of parameters for the target service provider during the data collection period are obtained. Then, the corresponding evaluation score is obtained according to each set of parameters. Therefore, based on the N evaluation scores, the comprehensive service score of the target service provider during the data collection period is determined. When responding to an information query operation targeting the target service provider, the comprehensive evaluation information of the target service provider is displayed. This comprehensive evaluation information includes the comprehensive service score and evaluation information from N dimensions. It is evident that ride-hailing service providers can clearly understand their own service level and competitiveness on ride-hailing aggregation platforms through comprehensive evaluation information. Simultaneously, based on the evaluation scores from different dimensions, specific problems can be quickly identified, thus providing a strong basis for the operational decisions of ride-hailing service providers and helping to improve the service quality of ride-hailing service providers. Attached Figure Description

[0080] Figure 1 This is a schematic diagram illustrating the application of this application's embodiments in a collaborative adjustment scenario;

[0081] Figure 2 This is a schematic diagram illustrating an application of this application in a user service optimization scenario;

[0082] Figure 3 This is a schematic diagram illustrating the application of this embodiment in a marketing scenario;

[0083] Figure 4This is a schematic diagram of an implementation environment for the evaluation information display method in this application.

[0084] Figure 5 This is a schematic diagram of a system architecture for the evaluation information display method in the embodiments of this application;

[0085] Figure 6 This is a flowchart illustrating the evaluation information display method in an embodiment of this application;

[0086] Figure 7 This is a schematic diagram illustrating the various indicators of the target service provider in terms of comprehensive dimensions in the embodiments of this application;

[0087] Figure 8 This is a schematic diagram illustrating the acquisition of quality levels based on decision trees in an embodiment of this application;

[0088] Figure 9 This is a schematic diagram illustrating various indicators of the target service provider at the regional level in the embodiments of this application;

[0089] Figure 10 This is a schematic diagram of the service provider penalty process in the embodiments of this application;

[0090] Figure 11 This is a schematic diagram of a service segment trend chart in an embodiment of this application;

[0091] Figure 12 This is a schematic diagram of an order completion rate graph in an embodiment of this application;

[0092] Figure 13 This is a schematic diagram of a user feedback graph in an embodiment of this application;

[0093] Figure 14 This is a schematic diagram of a problem analysis diagram in an embodiment of this application;

[0094] Figure 15 This is a schematic diagram of a customer service analysis diagram in an embodiment of this application;

[0095] Figure 16 This is a schematic diagram of an evaluation information display device in an embodiment of this application;

[0096] Figure 17 This is a schematic diagram of the structure of a terminal in an embodiment of this application. Detailed Implementation

[0097] This application provides a method, related apparatus, device, and storage medium for displaying evaluation information. This enables ride-hailing service providers to clearly understand their service level and competitiveness on ride-hailing aggregation platforms. Simultaneously, it helps ride-hailing service providers quickly identify problems, thereby contributing to improving service quality.

[0098] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0099] Ride-hailing aggregation platforms are service platforms that aggregate multiple ride-hailing service providers, offering users travel solutions. These platforms can gather vehicle resources, driver information, and pricing strategies from various partners in real time. Passengers can simply place an order with one click on the platform, and the system will use algorithms to select the best-matching service from multiple ride-hailing service providers, achieving efficient order dispatch. This not only provides passengers with more travel options but also improves travel efficiency and optimizes the overall travel experience. The service score system in the ride-hailing industry, based on the ride-hailing service providers, evaluates the service quality of their drivers, facilitating driver management.

[0100] Currently, ride-hailing service providers need to manually inquire about their customer feedback. Ride-hailing aggregation platforms primarily rely on passenger ratings and comments after their rides. However, these platforms need clear rules and quality assessment systems to guide service providers in managing their drivers and ensuring a positive user experience. Due to varying levels of regulation across regions and differences in coverage and service quality among service providers, providers often lack awareness of their own service quality and, in a competitive environment, cannot determine whether they meet platform requirements or remain competitive.

[0101] Based on this, this application provides a method for generating service scores for ride-hailing service providers. On one hand, by quantifying service standards and management strategies, communication efficiency with service providers is improved. This allows for rapid identification of the match between market demand and the service capacity of different service providers, facilitating the development of differentiated traction strategies for different cities and different transport capacities. On the other hand, service providers can log into the Software as a Service (SaaS) platform to view relevant evaluation data, clearly understand the competitiveness of different service dimensions within the platform's ecosystem, and quickly pinpoint specific problems through early warning values, thereby facilitating the estimation of investment costs, improvement difficulty, and improvement cycle.

[0102] Before introducing the specific methods of this application, the application scenarios of this application will be illustrated by example. It should be understood that the following application scenarios are merely illustrative and are not limited to these examples.

[0103] Application Scenario 1: Adjustment of Cooperation;

[0104] After generating comprehensive evaluation information for each service provider, the ride-hailing aggregation platform incentivizes or adjusts cooperation strategies based on the comprehensive evaluation information to improve overall service quality and operational efficiency.

[0105] For example, please refer to Figure 1 , Figure 1 This is a schematic diagram illustrating the application of this application's embodiment in a cooperative adjustment scenario. As shown in the figure, the ride-hailing aggregation platform's management interface displays comprehensive evaluation information for different service providers. Service providers with higher comprehensive service scores can receive more preferential treatment, such as being offered popular routes and priority order allocation. After clicking the "Preferential Treatment" button, the ride-hailing aggregation platform's operators can further select the service type. For service providers with lower comprehensive service scores, the number of orders dispatched can be reduced, and they can be required to rectify issues within a specified time. After clicking the "Require Rectification" button, the ride-hailing aggregation platform's operators can automatically send rectification messages to the corresponding service providers. If a service provider repeatedly fails to meet the evaluation standards, the cooperation can be terminated.

[0106] Application Scenario 2: User Service Optimization;

[0107] After generating comprehensive evaluation information for each service provider, the ride-hailing aggregation platform uses this information to select higher-quality service providers and prioritizes recommending them to users to improve their travel experience.

[0108] For example, please refer to Figure 2 , Figure 2 This is a schematic diagram illustrating an application of this invention in a user service optimization scenario. As shown in the figure, the ride-hailing application interface displays the identifiers of high-quality service providers to facilitate user selection. Furthermore, suitable service providers can be intelligently recommended to users based on their historical usage preferences and the comprehensive service score of the service providers.

[0109] Application Scenario 3: Marketing Promotion;

[0110] After generating comprehensive evaluation information for each service provider, the online car aggregation platform ranks and publishes the rankings based on the comprehensive service scores within the evaluation information. Top-ranked service providers can then engage in marketing to enhance their market competitiveness.

[0111] For example, please refer to Figure 3 , Figure 3 This is a schematic diagram illustrating an application of this application in a marketing scenario. As shown, the ride-hailing aggregation platform can regularly hold selection events for outstanding service providers and display the top three service providers by comprehensive service score on the platform's official website. Furthermore, the platform can promote these high-quality service providers through its social media accounts, official website, and other channels, thereby enhancing their brand awareness and reputation.

[0112] It should be noted that the above application scenarios are merely examples, and the evaluation information display method provided in this embodiment can also be applied to other scenarios, which are not limited here.

[0113] The method provided in this application can be applied to... Figure 4 The illustrated implementation environment includes a terminal 401 and a server 402, and the terminal 401 and server 402 can communicate via a network 403. The network 403 uses standard communication technologies and / or protocols, typically the Internet, but can also be any network, including but not limited to Bluetooth, a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), mobile, private networks, or any combination of virtual private networks. In some embodiments, customized or dedicated data communication technologies may be used to replace or supplement the aforementioned data communication technologies.

[0114] The terminal 401 involved in this application includes, but is not limited to, mobile phones, tablets, laptops, desktop computers, intelligent voice interaction devices, virtual reality devices, smart home appliances, vehicle terminals, and aircraft. The client is deployed on the terminal 401 and can run on the terminal 401 via a browser, a standalone application (APP), or a mini-program.

[0115] The server 402 involved in this application can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence (AI) platforms.

[0116] In the above implementation environment, in step A1, under the condition that the evaluation triggering condition is met, server 402 obtains the N sets of parameters from the target service provider for the data collection period. In step A2, server 402 calculates the evaluation score corresponding to each set of parameters. In step A3, server 402 determines the comprehensive service score of the target service provider for the data collection period based on the N evaluation scores. In step A4, the N evaluation scores and the comprehensive service score are used as the comprehensive evaluation information of the target service provider. In step A5, the user triggers an information query operation for the target service provider through terminal 401, thereby generating a corresponding information query request. In step A6, terminal 401 sends the information query request to server 402 through network 403. In step A7, server 402 feeds back the comprehensive evaluation information of the target service provider to terminal 401 through network 403. In step A8, terminal 401 displays the comprehensive evaluation information.

[0117] This application proposes a layered backend architecture based on a big data technology framework and intelligent algorithm model, aiming to provide a highly available, scalable, secure, reliable, and low-latency solution. This architecture, through its layered design, effectively improves the system's stability, maintainability, and performance. Please refer to [link / reference]. Figure 5 , Figure 5 This is a schematic diagram of a system architecture for the evaluation information display method in this application embodiment. As shown in the figure, the system architecture includes a presentation layer, a business layer, and a data layer. Specifically:

[0118] I. The presentation layer includes the user terminal, development platform, and operation platform (i.e., SaaS platform);

[0119] (1) The user terminal serves as the business C-end entry point, providing functions such as user ride-hailing ordering, driver and passenger display, driver and passenger contact, payment and feedback. It also has the ability to collect behavior tracking data, providing data support for the business layer and data layer.

[0120] (2) The open platform provides business capability output function and supports data transmission between external and internal systems.

[0121] (3) The operation platform displays data analysis results through visualization tools and supports various data display formats (e.g., reports, charts, etc.).

[0122] II. The business layer includes the access gateway, service platform, and external systems;

[0123] (1) The access gateway is responsible for traffic identification, control and scheduling. Through the plug-in orchestration design, it can flexibly and scalably handle access traffic from different scenarios and channels. The service itself is stateless and has good horizontal scalability to ensure system reliability.

[0124] (2) The service platform adopts a microservice architecture to decompose domain services into modules, abstracting independent domain services such as orders, reviews, accounts, and instant messaging (IM). These domain services are highly cohesive, loosely coupled, easily reusable, and have good scalability to support rapid orchestration and access by the business front end. Data transmission between services is supported through an event bus, and the bus data can be directly connected to the data layer.

[0125] (3) External systems provide external data interaction, collaboration and specific business support.

[0126] III. The data layer includes a data acquisition module, a data processing module, an intelligent algorithm module, a quality assessment module, and a visualization analysis module;

[0127] (1) The data acquisition module is responsible for obtaining dynamic demand data and real-time service data from ride-hailing aggregation platforms. This data is then uniformly converged into an open-source stream processing platform (Kafka) via asynchronous messages generated by the service middleware and heterogeneous binary logs (binlog) from the database for use by the data layer. Specific data includes, but is not limited to: order data (e.g., price information, order quantity, order completion time, order cancellation rate, etc.), user feedback data (e.g., user ratings, user reviews, user complaints, etc.), vehicle data (e.g., vehicle location, driver-passenger route, pick-up distance, vehicle status, etc.), driver data (e.g., driver order acceptance status, driver rating, etc.), transportation provider data (e.g., trip pricing information, license information, etc.), customer service data (e.g., customer service efficiency, service rating, etc.), and event information.

[0128] (2) The data processing module preprocesses the collected data, including data cleaning, data filtering, and data normalization. The preprocessed data will be used as input to the intelligent algorithm module.

[0129] (3) The intelligent algorithm module uses machine learning and data mining techniques to analyze and model the preprocessed data.

[0130] (4) The service quality assessment module calculates the service score for each service provider based on the analysis results of the intelligent algorithm module.

[0131] (5) The visualization analysis module displays the service quality assessment results in the form of charts and dashboards, which makes it easier for service providers to identify problems and make targeted improvements.

[0132] Based on the above introduction, the method for displaying evaluation information in this application will be described below. Please refer to [link / reference]. Figure 6In this application embodiment, the method for displaying evaluation information can be completed independently by the server, independently by the terminal, or jointly by the terminal and the server. The method provided in this application includes:

[0133] S601. Under the condition of meeting the evaluation triggering conditions, obtain the N-type parameter set of the target service provider for the data collection period, wherein the N-type parameter set includes at least one of the service parameter set, security parameter set, customer service parameter set and event parameter set, and N is an integer greater than or equal to 1.

[0134] In one or more embodiments, when the evaluation triggering conditions are met, the aggregation platform obtains a set of N types of parameters from the target service provider during the data collection period. The data collection period can be one month or one week, etc., and is not limited here. The set of N types of parameters includes at least one of a service parameter set, a security parameter set, a customer service parameter set, and an event parameter set.

[0135] S602. Based on each of the N parameter sets, determine N evaluation scores, where each evaluation score is calculated based on one set of parameters.

[0136] In one or more embodiments, each set of parameters is used to calculate an evaluation score for one dimension. For example, the service parameter set is used to calculate a service evaluation score; a higher service evaluation score indicates better service quality from the target service provider. For example, the security parameter set is used to calculate a security evaluation score; a higher security evaluation score indicates better security from the target service provider. For example, the customer service parameter set is used to calculate a customer service evaluation score; a higher customer service evaluation score indicates better customer service quality from the target service provider. For example, the event parameter set is used to calculate an event evaluation score; a higher event evaluation score indicates a higher event risk from the target service provider.

[0137] S603. Based on N evaluation scores, determine the target service provider's comprehensive service score during the data collection period;

[0138] In one or more embodiments, obtaining N evaluation scores is equivalent to obtaining ratings from different dimensions. Based on this, the evaluation scores belonging to positive evaluations can be weighted and summed, and the evaluation scores of negative evaluations can be deducted, thereby obtaining the comprehensive service score of the target service provider during the data collection period.

[0139] S604. In response to an information query operation targeting a service provider, display the comprehensive evaluation information of the service provider. The comprehensive evaluation information includes a comprehensive service score and evaluation information for N dimensions. The evaluation information for each dimension includes the evaluation score for the corresponding dimension and the evaluation score threshold.

[0140] In one or more embodiments, the terminal initiates an information query operation targeting a specific service provider, thereby generating a corresponding information query request. This request carries the identifier of the target service provider, enabling the aggregation platform to extract comprehensive evaluation information of the target service provider based on this identifier and push this comprehensive evaluation information to the terminal. Thus, the terminal can display the comprehensive evaluation information of the target service provider.

[0141] Specifically, the comprehensive evaluation information for the target service provider includes not only its overall service score, but also the evaluation score for each dimension and the corresponding evaluation score threshold. Please refer to [link / reference]. Figure 7 , Figure 7 This is a schematic diagram illustrating the various indicators of the target service provider for the comprehensive dimension in this embodiment of the application. As shown in the figure, 701 indicates the comprehensive service score. 702 indicates the evaluation information for the service dimension, where the service evaluation score is 47 points, and the corresponding evaluation score threshold is 50. 703 indicates the evaluation information for the security dimension, where the security evaluation score is 21 points, and the corresponding evaluation score threshold is 30. 704 indicates the evaluation information for the customer service dimension, where the customer service evaluation score is 18 points, and the corresponding evaluation score threshold is 20. 705 indicates the evaluation information for the event dimension, where the event evaluation score is 0 points, and the corresponding evaluation score threshold can be set to 50.

[0142] Understandably, for low ratings, relevant alerts can also be displayed. For example, if a rating is below 80% of a threshold, the font color of the rating score can be changed (e.g., to yellow) as a reminder. Alternatively, a threshold can be set based on the median ratings of various service providers; if a rating is below the median, not only can the font color of that rating score be changed (e.g., to red), but an alarm alert can also be displayed.

[0143] It should be noted that the term "in response to" in this application refers to the conditions or states upon which the execution of an operation depends, and one or more operations that can be executed when certain conditions or states are met. These operations can be real-time or have a certain delay.

[0144] This application provides a method for displaying evaluation information. Through this method, ride-hailing service providers can clearly understand their service level and competitiveness on the aggregation platform based on comprehensive evaluation information. Simultaneously, based on evaluation scores from different dimensions, specific problems can be quickly identified, providing strong support for the operational decisions of ride-hailing service providers and helping to improve their service quality.

[0145] Optionally, in the above Figure 6 Based on one or more corresponding embodiments, in another optional embodiment provided by this application, the conditions for satisfying the evaluation triggering condition include at least one of the following:

[0146] The data collection period has ended;

[0147] or,

[0148] Obtain evaluation statistics requests for the target service provider.

[0149] In one or more embodiments, two scenarios where the evaluation triggering conditions are met are described. As can be seen from the foregoing embodiments, when the evaluation triggering conditions are met, the aggregation platform will analyze and calculate the collected N-type parameter set and provide corresponding comprehensive evaluation information. The two scenarios where the evaluation triggering conditions are met will be described below.

[0150] Scenario 1: Automatic triggering;

[0151] Specifically, the platform's operators can pre-set data collection periods, for example, using one month as the data collection period, with the end time being 24:00 on the 10th of each month. When the data collection period ends, the platform automatically accesses data sources such as databases, logs, and APIs to collect N types of parameter sets, then analyzes these parameter sets to obtain comprehensive evaluation information. Finally, the comprehensive evaluation information is stored in the database and pushed to the terminal for display.

[0152] Scenario 2: Manual triggering;

[0153] Specifically, the target service provider's managers can click the "Evaluate and Rate Now" button through the relevant interface provided by the aggregation platform, thereby triggering an evaluation and statistical request for the target service provider. The aggregation platform then analyzes the N types of parameters collected during the data collection period to obtain comprehensive evaluation information. Finally, the comprehensive evaluation information is pushed to the target service provider's terminal for display.

[0154] Secondly, this application embodiment provides two scenarios that satisfy the evaluation triggering conditions. The advantage of automatically evaluating service providers for each data collection period using the above method is that data acquired at fixed intervals can form a stable data sequence, facilitating the analysis of data trends over time and the discovery of potential problems or patterns. The advantage of manually triggering service provider evaluations based on user needs is that it allows users to understand the situation promptly and adjust their strategies accordingly.

[0155] Optionally, in the above Figure 6Based on one or more corresponding embodiments, in another optional embodiment provided by the present application, the service parameter set includes at least one of non-passenger cancellation rate, average pick-up distance per ride, price overestimation rate, customer complaint rate, and negative review rate;

[0156] Based on each of the N parameter sets, N evaluation scores are determined, specifically including:

[0157] When the service parameter set includes the average pick-up distance, the average pick-up distance is standardized to obtain the pick-up distance standard rate.

[0158] The service evaluation score is obtained by weighting and summing at least one of the following: non-passenger cancellation rate, pick-up distance standard rate, price overestimation rate, customer complaint rate, and negative review rate, among N evaluation scores.

[0159] In one or more embodiments, a method for calculating service evaluation scores is described. As can be seen from the foregoing embodiments, the set of service parameters includes, but is not limited to, non-passenger cancellation rate, average pick-up distance per ride, price overestimation rate, customer complaint rate, and negative review rate. Among them, the value of average pick-up distance per ride needs to be mapped to a uniform interval. For example, a standardization method (e.g., max-min normalization) can be used to map the average pick-up distance per ride to the interval [0,1] to obtain the standard rate of pick-up distance.

[0160] Specifically, the following provides a method for calculating service evaluation scores:

[0161] S1 = S1_max * (Non-passenger cancellation rate * a_1 + Pickup distance standard rate * a_2 + Price overestimation rate * a_3 + Customer complaint rate * a_4 + Negative review rate * a_5); Formula (1)

[0162] Where S1 represents the service evaluation score. S1_max represents the evaluation score threshold corresponding to the service evaluation score. For example, the maximum value of the service evaluation score is 50, that is, its corresponding evaluation score threshold is 50. a_1, a_2, a_3, a_4 and a_5 are the weights corresponding to each service class parameter, and these weights can be adjusted according to the actual situation. The calculation parameters support flexible expansion.

[0163] It should be noted that the non-passenger cancellation rate represents the percentage of orders cancelled due to driver or platform reasons (i.e., not initiated by the passenger). Average pick-up distance per order represents the average distance a driver travels from their current location to the passenger's pick-up location after receiving an order. Price over-estimate rate represents the percentage of orders where the actual payment exceeded the estimated price. Customer complaint rate represents the percentage of orders with passenger complaints. Negative review rate represents the percentage of orders with negative reviews.

[0164] Secondly, this application provides a method for calculating service evaluation scores. By using this method, service evaluation scores are calculated based on different types of service parameters, which not only more comprehensively reflects the service provider's service situation but also quantifies the service level, facilitating a more intuitive understanding of service experience performance.

[0165] Optionally, in the above Figure 6 Based on one or more corresponding embodiments, in another optional embodiment provided by the present application, the set of security parameters includes at least one of order compliance rate, security-related customer complaint rate, resale order rate, and business license upload rate;

[0166] Based on each of the N parameter sets, N evaluation scores are determined, specifically including:

[0167] The safety evaluation score is obtained by weighting and summing at least one of the following: order compliance rate, security-related customer complaint rate, resale order rate, and business license upload rate, among N evaluation scores.

[0168] In one or more embodiments, a method for calculating a security evaluation score is described. As can be seen from the foregoing embodiments, the set of security parameters includes, but is not limited to, order compliance rate, security-related customer complaint rate, resale order rate, and business license upload rate.

[0169] Specifically, the following provides a method for calculating the safety evaluation score:

[0170] S2 = S2_max * (Order compliance rate * b_1 + Security-related customer complaint rate * b_2 + Resale order rate * b_3 + Business license upload rate * b_4); Formula (2)

[0171] Where S2 represents the safety evaluation score. S2_max represents the evaluation score threshold corresponding to the safety evaluation score. For example, the maximum safety evaluation score is 30, that is, the corresponding evaluation score threshold is 30. b_1, b_2, b_3, and b_4 are the weights corresponding to each safety category parameter, and these weights can be adjusted according to the actual situation. The calculation parameters support flexible expansion.

[0172] It should be noted that the order compliance rate represents the proportion of orders that comply with the platform's operating rules out of the total number of orders. The safety-related customer complaint rate represents the proportion of orders with passenger complaints arising from safety issues such as trip safety and vehicle equipment hazards. The resale order rate represents the proportion of orders that service providers have resold to other third parties for completion. The operating license upload rate represents the ratio of the number of operating license documents that service providers have uploaded to the total number of operating license documents that should be uploaded.

[0173] Secondly, this application provides a method for calculating a security evaluation score. By using this method, a security evaluation score is calculated based on different types of security parameters. This not only more comprehensively reflects the service provider's security situation but also quantifies the security level, facilitating a direct understanding of performance in security operations.

[0174] Optionally, in the above Figure 6 Based on one or more corresponding embodiments, in another optional embodiment provided by the present application, the customer service parameter set includes at least one of the case closure rate of a first time range, the case closure rate of a second time range, and the case closure rate of a third time range, wherein the first time range is less than the second time range, and the second time range is less than the third time range.

[0175] Based on each of the N parameter sets, N evaluation scores are determined, specifically including:

[0176] The customer service evaluation score is obtained by weighted summing at least one of the case closure rates for the first time period, the second time period, and the third time period.

[0177] In one or more embodiments, a method for calculating customer service evaluation scores is described. As can be seen from the foregoing embodiments, the set of security parameters includes, but is not limited to, the case closure rate for a first time period, the case closure rate for a second time period, and the case closure rate for a third time period. It is understood that the first time period < the second time period < the third time period. The following description will use an example where the first time period is 24 hours, the second time period is 48 hours, and the third time period is 72 hours.

[0178] Specifically, the following is a method for calculating customer service evaluation scores:

[0179] S3 = S3_max * (24-hour case closure rate * c_1 + 48-hour case closure rate * c_2 + 72-hour case closure rate * c_3); Formula (3)

[0180] Here, S3 represents the customer service evaluation score. S3_max represents the evaluation score threshold corresponding to the customer service evaluation score. For example, the maximum customer service evaluation score is 20, meaning its corresponding evaluation score threshold is 20. c_1, c_2, and c_3 are the weights corresponding to each customer service category parameter, and these weights can be adjusted according to the actual situation. The calculation parameters support flexible expansion.

[0181] It should be noted that the 24-hour case closure rate represents the percentage of customer complaints successfully processed within 24 hours out of the total number of customer complaints during that period. The 48-hour case closure rate represents the percentage of customer complaints successfully processed within 48 hours out of the total number of customer complaints during that period. The 72-hour case closure rate represents the percentage of customer complaints successfully processed within 72 hours out of the total number of customer complaints during that period.

[0182] Secondly, this application provides a method for calculating customer service evaluation scores. By calculating customer service evaluation scores based on different types of customer service parameters using the above method, not only can the service provider's customer service situation be more comprehensively reflected, but the customer service level can also be quantified, making it easier to intuitively understand the performance in terms of the timeliness of handling customer complaints.

[0183] Optionally, in the above Figure 6 Based on one or more corresponding embodiments, in another optional embodiment provided by the present application, the event class parameter set includes at least one of the occurrence count of first-level events, the occurrence count of second-level events, and the occurrence count of third-level events, wherein the importance of first-level events is higher than that of second-level events, and the importance of second-level events is higher than that of third-level events;

[0184] Based on each of the N parameter sets, N evaluation scores are determined, specifically including:

[0185] The event evaluation score is determined from N evaluation scores based on at least one of the occurrences of Level 1 events, Level 2 events, and Level 3 events.

[0186] In one or more embodiments, a method for calculating customer service evaluation scores is described. As can be seen from the foregoing embodiments, the event parameter set includes, but is not limited to, the number of occurrences of first-level events, the number of occurrences of second-level events, and the number of occurrences of third-level events.

[0187] Understandably, events can be categorized into different levels based on factors such as the sensitivity of the content and the risk of dissemination, with the severity ranking as Level 1 > Level 2 > Level 3. For example, Level 1 events typically involve public safety issues, Level 2 events are usually regional or industry-specific negative events, and Level 3 events are typically negative issues related to media coverage.

[0188] Specifically, a Level 1 event deducts 20 points (i.e., the event evaluation score is "20"). A Level 2 event deducts 10 points (i.e., the event evaluation score is "10"). A Level 3 event deducts 5 points (i.e., the event evaluation score is "5"). In the case of multiple events of the same level, points may be deducted only once, or deducted according to the number of occurrences. In the case of multiple events of different levels, the deductions for each level may be accumulated, or the points corresponding to the highest level event may be deducted.

[0189] Secondly, this application provides a method for calculating event evaluation scores. By using this method, event evaluation scores are determined based on different types of event parameters. These scores are then used as deductions in the overall service score, guiding service providers to pay attention to the occurrence of events.

[0190] Optionally, in the above Figure 6 Based on one or more corresponding embodiments, in another optional embodiment provided by this application, the comprehensive service score of the target service provider during the data collection period is determined according to N evaluation scores, specifically including:

[0191] Given N evaluation scores, including service evaluation score, security evaluation score, customer service evaluation score, and incident evaluation score, the initial service score is determined based on the service evaluation score, security evaluation score, and customer service evaluation score.

[0192] The event evaluation score is deducted from the initial service score to obtain the target service provider's comprehensive service score during the data collection period.

[0193] In one or more embodiments, a method for generating a comprehensive service score for a target service provider is described. As can be seen from the foregoing embodiments, the N evaluation scores may include a service evaluation score, a security evaluation score, a customer service evaluation score, and an incident evaluation score. The service evaluation score, security score, and customer service evaluation score constitute an initial service score, while the incident evaluation score is a deduction item. That is, by deducting the incident evaluation score from the initial service score, the comprehensive service score of the target service provider during the data collection period can be obtained.

[0194] Specifically, the following is a method for calculating the comprehensive service score:

[0195] S = α * Service Evaluation Score + β * Security Evaluation Score + γ * Customer Service Evaluation Score + δ * Incident Evaluation Score; Formula (4)

[0196] Where S represents the comprehensive service score. α, β, γ, and δ are the weights corresponding to each evaluation score, and these weights can be adjusted according to actual conditions. The calculation parameters support flexible expansion.

[0197] It should be noted that the aggregation platform adopts a modular and distributed design, supports horizontal scaling, and can cope with the ever-increasing data volume and business needs. The calculation formulas and weights of the comprehensive service score and various evaluation scores can be adjusted according to actual conditions, making the system highly flexible and adaptable to meet the needs of different business scenarios.

[0198] Furthermore, leveraging the decentralized (i.e., decentralized architecture reduces dependence on a single platform, improving system reliability and resistance to attacks), immutable (i.e., the immutability of blockchain ensures data integrity and security, preventing malicious modification of data), and transparent (i.e., all data is recorded on the blockchain, ensuring data transparency and credibility) characteristics of blockchain technology, a blockchain-based service quality evaluation system can be constructed, thereby storing various scores, service data, and user feedback in the blockchain system.

[0199] Secondly, this application provides a method for generating a comprehensive service score for a target service provider. Through this method, the aggregation platform collects and processes various types of data from service providers in real time, dynamically assesses service quality, reduces manual intervention, and improves assessment efficiency and accuracy. An accurate comprehensive service score not only helps service providers understand their service level and user satisfaction but also enables them to promptly identify and resolve problems, thereby improving overall service quality.

[0200] Optionally, in the above Figure 6 In addition to one or more corresponding embodiments, another optional embodiment provided in this application may further include:

[0201] Based on N evaluation scores, determine the quality level of the target service provider during the data collection period;

[0202] Following an information query operation targeting a service provider, it may also include:

[0203] Displays the quality level of the target service provider during the data collection period.

[0204] In one or more embodiments, a method for displaying the quality level of a target service provider is described. As can be seen from the foregoing embodiments, after obtaining N evaluation scores, the comprehensive service score of the target service provider during the data collection period can be calculated. Different comprehensive service scores correspond to different quality levels. The aggregation platform rewards and supports service providers with better quality levels, and manages and controls service providers with lower quality levels, providing an observation and rectification period. If a service provider continues to fail to meet the standards, the control measures will be strengthened.

[0205] Specifically, please refer to [the relevant document] again. Figure 7706 indicates the quality level of the target service provider during the data collection period. The following section uses quality levels—Excellent, Good, Pass, and Fail—as an example to illustrate the corresponding service intervals for each quality level. In practical applications, the number of quality levels and the range of service intervals can be adjusted. For clarity, please refer to Table 1, which illustrates the relationship between quality levels and their corresponding service intervals.

[0206] Table 1

[0207]

[0208] Assuming the target service provider's overall service score during the data collection period is 92, based on Table 1, the target service provider's quality level is excellent.

[0209] Secondly, this application embodiment provides a method for displaying the corresponding quality level of a target service provider. Through this method, service providers can accurately understand their own competitiveness, optimize resource allocation, reduce resource waste, and thus lower operating costs. Furthermore, it can also reduce complaints and disputes to a certain extent; that is, real-time evaluation of service quality allows for timely identification and resolution of problems, reducing user complaints and disputes and lowering processing costs. For the aggregation platform, it facilitates the allocation and adjustment of transportation resources based on the quality level of each service provider.

[0210] Optionally, in the above Figure 6 Based on one or more corresponding embodiments, in another optional embodiment provided by this application, the quality level of the target service provider during the data collection period is determined according to N evaluation scores, specifically including:

[0211] Starting from the root node of the decision tree, traverse the evaluation scores among the N evaluation scores. The decision tree includes a root node and at least two leaf nodes.

[0212] When traversing to the leaf nodes of the decision tree, the quality level indicated by the leaf node is taken as the quality level of the target service provider during the data collection period.

[0213] In one or more embodiments, a method for determining the quality level of a target service provider based on a decision tree is described. As can be seen from the foregoing embodiments, after obtaining N evaluation scores, in addition to determining the quality level based on the service score interval where the comprehensive service score falls, a decision tree method can also be used to determine the quality level.

[0214] Specifically, taking N evaluation scores—including service evaluation score, safety evaluation score, customer service evaluation score, and incident evaluation score—as an example, and assuming that the quality levels include excellent, good, and unsatisfactory. Please refer to [link / reference]. Figure 8 , Figure 8 This is a schematic diagram illustrating the acquisition of quality levels based on a decision tree in an embodiment of this application. As shown in the figure, the root node is the service evaluation score. If the target service provider's service evaluation score is greater than or equal to 40, the process proceeds to the left branch. If the target service provider's service evaluation score is greater than or equal to 20 and less than 40, the process proceeds to the middle score. If the target service provider's service evaluation score is less than 20, the process proceeds to the right branch.

[0215] The left branch node represents the security evaluation score. If the target service provider's security evaluation score is greater than or equal to 25, then if the target service provider's event evaluation score is less than or equal to 5, the leaf node will be set to "Excellent" as the target service provider's quality level; if the target service provider's event evaluation score is greater than 5, the leaf node will be set to "Good" as the target service provider's quality level. If the target service provider's security evaluation score is less than 25, then the leaf node will be set to "Good" as the target service provider's quality level.

[0216] The middle branch node represents the customer service evaluation score. If the target service provider's customer service evaluation score is greater than or equal to 15, then if the target service provider's incident evaluation score is less than or equal to 10, the leaf node will be set to "Good" as the target service provider's quality level; if the target service provider's incident evaluation score is greater than 10, the leaf node will be set to "Fail" as the target service provider's quality level. If the target service provider's customer service evaluation score is less than 15, then the leaf node will be set to "Fail" as the target service provider's quality level.

[0217] The right branch node is a leaf node, therefore, the "failure" of this leaf node is taken as the quality level of the target service provider.

[0218] It should be noted that in practical applications, random forests, support vector machines (SVMs), or logistic regression (LR) can also be used to determine the quality level. Random forests are ensembles of multiple decision trees and have high accuracy and robustness. SVMs classify data by finding the optimal splitting hyperplane and are suitable for high-dimensional data. LR is used for binary classification problems, outputting the probability of each class.

[0219] Furthermore, this application provides a method for determining the quality level of a target service provider based on a decision tree. This method, based on a tree-structured model, is easy to interpret and implement. Decision trees can classify multiple evaluation scores and the relationships between them, considering more comprehensive factors and thus obtaining a more accurate quality level.

[0220] Optionally, in the above Figure 6 Based on one or more corresponding embodiments, in another optional embodiment provided by the present application, the N-type parameter set includes a service parameter set, a security parameter set, a customer service parameter set, and an event parameter set;

[0221] It may also include:

[0222] Obtain the corresponding grading intervals for each service-related indicator, each security-related indicator, each customer service-related indicator, and each event-related indicator.

[0223] If there are abnormal parameters in the service parameter set based on the grade intervals corresponding to each service indicator, the service indicator corresponding to the abnormal parameter will be highlighted.

[0224] If there are abnormal parameters in the set of safety parameters according to the grading intervals corresponding to each safety indicator, the safety indicator corresponding to the abnormal parameter will be highlighted.

[0225] If there are abnormal parameters in the customer service parameter set based on the graded intervals corresponding to each customer service indicator, the customer service indicator corresponding to the abnormal parameter will be highlighted.

[0226] If abnormal parameters are found in the event parameter set based on the grading intervals corresponding to each event-type indicator, the event-type indicator corresponding to the abnormal parameter will be highlighted.

[0227] In one or more embodiments, a method for highlighting abnormal indicators is described. As can be seen from the foregoing embodiments, the N-type parameter set may include a service-type parameter set, a security-type parameter set, a customer service-type parameter set, and an event-type parameter set. Specifically, the service-type parameter set includes parameters corresponding to each service-type indicator, the security-type parameter set includes parameters corresponding to each security-type indicator, the customer service-type parameter set includes parameters corresponding to each customer service-type indicator, and the event-type parameter set includes parameters corresponding to each event-type indicator.

[0228] Specifically, each category of indicators has a corresponding grading range. Taking the negative review rate among service-related indicators as an example, a negative review rate of less than or equal to 10% is considered normal. If the negative review rate is greater than 10% but less than or equal to 30%, it is considered a level two abnormal situation. If the negative review rate is greater than 30%, it is considered a level one abnormal situation. For level two abnormal situations, the corresponding indicator can be highlighted in yellow, while for level one abnormal situations, the corresponding indicator can be highlighted in red.

[0229] For better understanding, please refer to the following again. Figure 7707 is used to indicate various service-related indicators (e.g., non-passenger cancellation rate, average pick-up distance per order, price overestimation rate, customer complaint rate, negative review rate). 708 is used to indicate various safety-related indicators (e.g., order compliance rate, customer complaint rate per million orders, resale order rate, business license upload rate). Among these, the parameters corresponding to the resale order rate and business license upload rate are considered Level 1 abnormal situations; therefore, these two safety-related indicators are highlighted. 709 is used to indicate various customer service-related indicators (e.g., 24-hour case closure rate, 48-hour case closure rate, 72-hour case closure rate). 710 is used to indicate various incident-related indicators (e.g., Level A incidents, Level B incidents, Level C incidents).

[0230] Secondly, this application embodiment provides a method for highlighting abnormal indicators. Through this method, a visual analysis dashboard can intuitively display the service provider's problems and improvement suggestions, helping the service provider quickly locate issues and take targeted measures, thereby contributing to improved operational efficiency.

[0231] Optionally, in the above Figure 6 In addition to one or more corresponding embodiments, another optional embodiment provided in this application may further include:

[0232] In response to a region query operation targeting a service provider, the system displays various parameters of the service provider for at least one region. Each parameter is derived from at least one of the following: service parameters, security parameters, customer service parameters, and event parameters.

[0233] In one or more embodiments, a method for displaying various parameters by region is described. As can be seen from the foregoing embodiments, since the operating scope of the target service provider may include different regions, the relevant parameters can be displayed by region. The region in this application can be a city, province, administrative region, etc., and is not limited thereto.

[0234] Specifically, please refer to Figure 9 , Figure 9 This is a schematic diagram illustrating various indicators of the target service provider at the regional level in this embodiment of the application. As shown in the figure, after triggering a regional query operation for the target service provider, various parameters corresponding to the target service provider in city A and city B can be displayed (e.g., non-passenger cancellation rate, average pick-up distance per order, price overestimation rate, customer complaint rate, negative review rate, 24-hour case closure rate, 48-hour case closure rate, 72-hour case closure rate, etc.). Based on this, further filtering by time and city is possible to view only the service data that needs improvement.

[0235] Secondly, this application provides a method for displaying various parameters by region. This method intuitively reflects the differences in parameters between different regions, facilitating service providers to formulate reasonable and feasible service strategies for different regions.

[0236] Optionally, in the above Figure 6 In addition to one or more corresponding embodiments, another optional embodiment provided in this application may further include:

[0237] If the target service provider meets the conditions for handling violations, generate the target service provider's pending approval information;

[0238] In response to the approval of pending information, a penalty notice is sent to the target service provider.

[0239] In one or more embodiments, a method for controlling violations is described. As can be seen from the foregoing embodiments, if a target service provider violates relevant regulations, the aggregation platform can implement corresponding control measures.

[0240] For easier understanding, please refer to Figure 10 , Figure 10 This is a schematic diagram of the service provider penalty process in an embodiment of this application, as shown in the figure. Specifically:

[0241] In step S1, a travel violation by the target service provider is detected, meaning the target service provider meets the conditions for violation handling. At this point, the target service provider's evaluation results and corresponding management and governance plan can be synchronized via email, service account, SMS, or other means.

[0242] In step S2, the operator determines whether the target service provider needs to be penalized. If so, step S3 is executed. If not, the process ends.

[0243] In step S3, the operator specifies the penalty according to the "Service Provider Management System".

[0244] In step S4, the operator configures the penalty content on the operation platform.

[0245] In step S5, the operator generates a penalty notice and initiates the penalty notice review process.

[0246] In step S6, the management approves the process.

[0247] In step S7, it is determined whether the management has approved the application. If yes, steps S8 and S9 are executed. If not, the process ends.

[0248] In step S8, the service provider receives the penalty notice and makes rectifications.

[0249] In step S9, after approval, a corresponding penalty strategy is generated through an automatic algorithm.

[0250] In step S10, the operator determines whether the penalty termination conditions are met; if so, the process ends.

[0251] In practical applications, an intelligent customer service system can be built. Through natural language processing and machine learning algorithms, it can automatically analyze user feedback and comprehensive evaluation information to generate service quality assessment reports and provide improvement suggestions to service providers. On the one hand, utilizing artificial intelligence technology to automatically analyze user feedback and service data improves the accuracy and efficiency of assessments. On the other hand, an intelligent customer service system can automatically answer user questions, provide personalized services, and enhance the user experience. Furthermore, machine learning algorithms can continuously learn and optimize, improving the system's intelligence and adaptability.

[0252] Secondly, this application provides a method for controlling violations. Through this method, the aggregation platform can also reasonably manage service providers who violate regulations, thereby maintaining a healthy competitive environment.

[0253] Optionally, in the above Figure 6 In addition to one or more corresponding embodiments, another optional embodiment provided in this application may further include:

[0254] In response to the graphical viewing operation for the target service provider, display at least one of the following: service score trend chart, order completion rate chart, user feedback chart, problem analysis chart, and customer service analysis chart corresponding to the target service provider;

[0255] The service score trend chart is used to reflect the changes in the overall service score of the target service provider within a preset time period.

[0256] The order completion rate chart is used to reflect the order completion status of the target service provider within a preset time period;

[0257] User feedback charts are used to reflect users' evaluation of the target service provider;

[0258] Problem analysis diagrams are used to reflect the target service provider's response to comprehensive problems;

[0259] Customer service analysis charts are used to reflect the target service provider's response to customer service-related questions.

[0260] In one or more embodiments, a method for displaying various analytical charts is described. As can be seen from the foregoing embodiments, the aggregation platform can provide detailed service quality reports and analysis results by analyzing and processing various service data of the target service provider over a period of time, helping the target service provider to make data-driven decisions.

[0261] For example, please refer to Figure 11 , Figure 11 This is a schematic diagram of a service score trend chart in an embodiment of this application. As shown in the figure, after triggering the chart viewing operation for a target service provider, the service score trend chart corresponding to the target service provider can be displayed. The service score trend chart reflects the changes in the target service provider's overall service score within a preset time period (e.g., within the past year).

[0262] For example, please refer to Figure 12 , Figure 12 This is a schematic diagram of an order completion rate chart in an embodiment of this application. As shown in the figure, after triggering the chart viewing operation for the target service provider, the order completion rate chart corresponding to the target service provider can be displayed. The order completion rate chart reflects the order completion status of the target service provider within a preset time period (e.g., within the past year).

[0263] For example, please refer to Figure 13 , Figure 13 This is a schematic diagram of a user feedback graph in an embodiment of this application. As shown in the figure, after triggering the graph viewing operation for the target service provider, the user feedback graph corresponding to the target service provider can be displayed. The user feedback graph reflects the user's evaluation of the target service provider over a period of time (e.g., within the past year). For example, 5 stars is the highest rating, decreasing sequentially downwards, with 1 star being the worst rating. Furthermore, the distribution of user reviews can also be displayed.

[0264] For example, please refer to Figure 14 , Figure 14 This is a schematic diagram of a problem analysis graph in an embodiment of this application. As shown in the figure, after triggering the graph viewing operation for the target service provider, the problem analysis graph corresponding to the target service provider can be displayed. The problem analysis graph reflects the target service provider's response to comprehensive problems. Furthermore, improvement suggestions for the problems can also be displayed.

[0265] For example, please refer to Figure 15 , Figure 15 This is a schematic diagram of a customer service analysis graph in an embodiment of this application. As shown in the figure, after triggering the graph viewing operation for the target service provider, the customer service analysis graph corresponding to the target service provider can be displayed. The customer service analysis graph reflects the target service provider's response to customer service-related questions. Furthermore, improvement suggestions for addressing these questions can also be displayed.

[0266] Secondly, this application provides a method for displaying various analytical charts. By displaying these charts, complex data becomes more intuitive and transparent, facilitating service providers' understanding and analysis. This helps service providers offer more personalized and high-quality services, thereby increasing user satisfaction and trust.

[0267] Optionally, in the above Figure 6 In addition to one or more corresponding embodiments, another optional embodiment provided in this application may further include:

[0268] Obtain T sets of historical parameters for the target service provider, where each set of historical parameters includes at least one of service-related parameters, security-related parameters, customer service-related parameters, and event-related parameters for a historical time period, and T is an integer greater than or equal to 1;

[0269] Construct a linear regression model based on T sets of historical parameters;

[0270] Obtain the current parameter set of the target service provider;

[0271] Based on the current parameter set, the predicted service score of the target service provider is obtained through a linear regression model.

[0272] In one or more embodiments, a method for predicting the future performance of a target service provider based on a linear regression algorithm is described. As can be seen from the foregoing embodiments, the aggregation platform can also obtain parameter sets corresponding to the target service provider in T historical time periods, i.e., obtain T historical parameter sets. Using the historical parameter sets as independent variables and the comprehensive service score corresponding to each historical time period as the dependent variable, a linear regression model is constructed.

[0273] Specifically, assuming the historical parameter set includes negative review rate, customer complaint rate, order compliance rate, and 24-hour case closure rate, the model expression can be constructed as follows:

[0274] Y=β0+β1*X1+β2*X2+β3*X3+β4*X4; Formula (5)

[0275] Where Y represents the overall service score. X1 represents the negative review rate, X2 represents the customer complaint rate, X3 represents the order compliance rate, and X4 represents the 24-hour case closure rate. β0, β1, β2, β3, and β4 are all parameters to be solved.

[0276] Based on this, using the collected T historical parameter sets, the parameter estimates of the model are obtained through the least squares method. Assuming we obtain β0 = 100, β1 = -20, β2 = 15, β3 = 30, and β4 = -10, the trained linear regression model is expressed as:

[0277] Y = 100 - 20*X1 + 15*X2 + 30*X3 - 10*X4; Formula (6)

[0278] Based on this, by substituting the target service provider’s current parameter set (e.g., negative review rate, customer complaint rate, order compliance rate and 24-hour case closure rate in the past month) into formula (6), the predicted service score of the target service provider can be obtained.

[0279] It should be noted that ridge regression, least absolute shrinkage and selection operator regression (LASSO regression), or gradient boosting regression can also be used to predict the future performance of the target service provider. Ridge regression adds a regularization term to linear regression to prevent overfitting. LASSO regression selects features using L1 regularization and is suitable for high-dimensional data. Gradient boosting regression uses an ensemble of multiple weak prediction models for prediction and has high accuracy.

[0280] Secondly, this application provides a method for predicting the future performance of a target service provider based on a linear regression algorithm. Through this method, the linear regression algorithm can make predictions by fitting a straight line. Based on this, by analyzing the historical parameter set of the target service provider, relationships between data can be uncovered, and trends can be presented in a simple and intuitive mathematical model. Furthermore, the linear regression algorithm has relatively low computational complexity, fast processing speed, and can efficiently process large amounts of data, outputting prediction results in a timely manner.

[0281] Optionally, in the above Figure 6 In addition to one or more corresponding embodiments, another optional embodiment provided in this application may further include:

[0282] Obtain T sets of historical parameters for the target service provider, where each set of historical parameters includes at least one of service-related parameters, security-related parameters, customer service-related parameters, and event-related parameters for a historical time period, and T is an integer greater than or equal to 1;

[0283] Generate T feature vectors based on T sets of historical parameters;

[0284] Select K feature vectors from T feature vectors as K cluster centers, where K is an integer greater than 1 and less than T;

[0285] For each of the T feature vectors, calculate the distance between the feature vector and the K cluster centers;

[0286] Each of the T feature vectors is assigned to the cluster corresponding to the shortest cluster center until the convergence condition is met, resulting in K clustering results.

[0287] In one or more embodiments, a method for analyzing target service providers based on the K-means clustering algorithm is introduced. As described in the foregoing embodiments, the aggregation platform can also obtain the parameter sets corresponding to the target service provider in T historical time periods, i.e., obtain T historical parameter sets. A corresponding feature vector is constructed for each historical parameter set, thus obtaining T feature vectors. Then, the K-means clustering algorithm is used to generate K clustering results. Finally, by analyzing the commonalities of each clustering result, a problem analysis is performed on the target service provider.

[0288] Specifically, the first step is to determine the number of clusters, i.e., the value of K. For example, the number of clusters can be customized based on the context and experience of the specific problem, or a value can be calculated using the silhouette coefficient method as the number of clusters. Then, K feature vectors are randomly selected from the T feature vectors as the K cluster centers. Next, the iterative calculation process begins. That is, for each of the T feature vectors, the distance between the feature vector and the K cluster centers (e.g., Euclidean distance or Manhattan distance) is calculated, and the feature vector is assigned to the cluster containing the nearest cluster center. After the assignment is complete, the mean of all feature vectors within each cluster is used as the new cluster center.

[0289] Repeat the steps of assigning feature vectors and updating cluster centers as described above until the convergence condition is met, thus obtaining K clustering results. The convergence condition includes, but is not limited to, the cluster centers no longer changing, or the change in the objective function (e.g., the sum of squared errors function) being less than a certain threshold, or reaching the preset maximum number of iterations.

[0290] It should be noted that clusters can also be constructed using hierarchical clustering or density-based spatial clustering of applications with noise (DBSCAN). Hierarchical clustering, which builds a hierarchical tree for clustering, is suitable for small-scale datasets. DBSCAN, a density-based clustering algorithm, is suitable for discovering clusters of arbitrary shapes.

[0291] Secondly, this application provides a method for analyzing target service providers based on the K-means clustering algorithm. Through this method, the K-means clustering algorithm iteratively optimizes and divides data into K clusters. It is suitable for large-scale data, easy to understand and implement, and has low computational complexity. Therefore, it can process large-scale data quickly and obtain clustering results rapidly. Furthermore, the K-means clustering algorithm can automatically discover the distribution patterns of the dataset, facilitating an intuitive understanding of the relationships between data and providing a clear basis for further analysis and decision-making regarding target service providers.

[0292] Optionally, in the above Figure 6 In addition to one or more corresponding embodiments, another optional embodiment provided in this application may further include:

[0293] Obtain the target service provider's target historical parameter set, wherein the target historical parameter set includes at least one of service-related parameters, security parameters, customer service parameters, and event-related parameters for the target historical time period;

[0294] Construct the first feature vector based on the target's historical parameter set;

[0295] Based on the first feature vector, a prediction evaluation score for at least one dimension is obtained through a time series model.

[0296] In one or more embodiments, a method for predicting future evaluation scores of service providers based on a time-series model is introduced. As described in the foregoing embodiments, after obtaining the target historical parameter set of the target service provider, each parameter in the target historical parameter set is first encoded (e.g., one-hot encoding). Then, the encoded results are standardized to construct a first feature vector corresponding to the target service provider. The target historical parameter set includes at least one of the following: service-related parameters, security-related parameters, customer service-related parameters, and event-related parameters of the target service provider during a target historical time period (e.g., the past year).

[0297] Specifically, the first feature vector corresponding to the target service provider is used as the input to the time series model, which outputs a predicted evaluation score in at least one dimension. For example, it can output predicted evaluation scores in four dimensions: service evaluation prediction score, security evaluation prediction score, customer service evaluation prediction score, and event evaluation prediction score. The time series model used in this application can be a long short-term memory network (LSTM), where LSTM is a special type of recurrent neural network (RNN) suitable for time series data with long time dependencies.

[0298] It should be noted that the prediction evaluation score can also be obtained using either the autoregressive integrated moving average model (ARIMA) or the seasonal autoregressive integrated moving average model (SARIMA). ARIMA is used for time series forecasting and is suitable for time series data with trends and seasonality. SARIMA adds a seasonal component to ARIMA and is suitable for time series data with significant seasonality.

[0299] Secondly, this application provides a method for predicting future evaluation scores of service providers based on a time series model. Through this method, the time series model can uncover the time-series characteristics and patterns in the data, capturing the trends of data changes over time and providing a strong basis for prediction. Therefore, based on the time series model, various evaluation scores of service providers can be predicted over a future period, helping service providers understand the dynamic changes in service quality and facilitating the formulation of long-term development strategies.

[0300] Optionally, in the above Figure 6 In addition to one or more corresponding embodiments, another optional embodiment provided in this application may further include:

[0301] Based on the set of N types of parameters, construct the second feature vector corresponding to the target service provider;

[0302] Based on the second feature vector, a predicted probability distribution is obtained through an anomaly detection model, where each element in the predicted probability distribution corresponds to an anomaly category;

[0303] The anomaly detection results of the target service provider are determined based on the predicted probability distribution.

[0304] In one or more embodiments, a model-based method for identifying service provider anomalies is described. As described in the foregoing embodiments, after obtaining the N-type parameter set of the target service provider, each parameter in the N-type parameter set is first encoded (e.g., one-hot encoding). Then, the encoded results are standardized to construct a second feature vector corresponding to the target service provider.

[0305] Specifically, the second feature vector corresponding to the target service provider is used as input to the anomaly detection model, which outputs a predicted probability distribution. Assume the predicted probability distribution is (0.2, 0.1, 0.6, 0.1), where each element corresponds to an anomaly category. For example, the first element "0.2" indicates a 20% probability of belonging to the fraudulent order category, the second element "0.1" indicates a 10% probability of belonging to the data anomaly category, the third element "0.6" indicates a 60% probability of belonging to the service violation category, and the fourth element "0.1" indicates a 10% probability of belonging to the system failure category. Here, fraudulent order order category, data anomaly category, service violation category, and system failure category are all anomaly categories.

[0306] Based on this, anomaly categories with probabilities greater than the anomaly probability threshold (e.g., 0.5) can be used as the anomaly detection result for the target service provider. That is, in the example above, the target service provider belongs to the service violation category. If the probabilities of all elements in the predicted probability distribution are less than the anomaly probability threshold, the anomaly detection result is no anomaly.

[0307] It should be noted that anomaly detection models can employ autoencoders, which can detect anomalies based on reconstruction errors and are suitable for high-dimensional data. Alternatively, isolation forests or local outlier factors (LOF) can also be used for anomaly detection. Isolation forests detect anomalies by constructing random trees and are suitable for large-scale data. LOF detects anomalies by comparing local densities and is suitable for discovering local anomalies.

[0308] Secondly, this application provides a method for identifying service provider anomalies based on a model. Using this method, anomaly detection models can quickly process large amounts of data and promptly identify subtle anomalies that are easily overlooked by humans. This not only reduces labor costs but also improves the accuracy of anomaly identification.

[0309] The evaluation information display device in this application is described in detail below. Please refer to [link / reference]. Figure 16 , Figure 16 This is a schematic diagram of one embodiment of the evaluation information display device in this application. The evaluation information display device 160 includes:

[0310] The acquisition module 1601 is used to acquire the set of N types of parameters of the target service provider for the data collection period when the evaluation triggering conditions are met. The set of N types of parameters includes at least one of the service parameter set, security parameter set, customer service parameter set and event parameter set, and N is an integer greater than or equal to 1.

[0311] The determination module 1602 is used to determine N evaluation scores based on each of the N parameter sets, wherein each evaluation score is calculated based on one set of parameters.

[0312] The determination module 1602 is also used to determine the comprehensive service score of the target service provider during the data collection period based on N evaluation scores;

[0313] Display module 1603 is used to respond to information query operations for the target service provider and display the comprehensive evaluation information of the target service provider. The comprehensive evaluation information includes a comprehensive service score and evaluation information in N dimensions. The evaluation information in each dimension includes the evaluation score and the evaluation score threshold for the corresponding dimension.

[0314] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application, the conditions for satisfying the evaluation triggering conditions include at least one of the following:

[0315] The data collection period has ended;

[0316] or,

[0317] Obtain evaluation statistics requests for the target service provider.

[0318] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application, the service parameter set includes at least one of the following: non-passenger cancellation rate, average pick-up distance per ride, price overestimation rate, customer complaint rate, and negative review rate.

[0319] Module 1602 is specifically used to standardize the average pick-up distance when the service parameter set includes the average pick-up distance, so as to obtain the pick-up distance standard rate.

[0320] The service evaluation score is obtained by weighting and summing at least one of the following: non-passenger cancellation rate, pick-up distance standard rate, price overestimation rate, customer complaint rate, and negative review rate, among N evaluation scores.

[0321] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application, the set of safety parameters includes at least one of order compliance rate, safety-related customer complaint rate, resale order rate, and business license upload rate;

[0322] Module 1602 is specifically used to perform a weighted summation of at least one of the following: order compliance rate, security-related customer complaint rate, resale order rate, and business license upload rate, to obtain a security evaluation score from N evaluation scores.

[0323] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application, the customer service parameter set includes at least one of the case closure rate of a first time range, the case closure rate of a second time range, and the case closure rate of a third time range, wherein the first time range is smaller than the second time range, and the second time range is smaller than the third time range.

[0324] The determination module 1602 is specifically used to perform a weighted summation of at least one of the case closure rates for the first time period, the second time period, and the third time period to obtain the customer service evaluation score among N evaluation scores.

[0325] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application, the event class parameter set includes at least one of the occurrence count of first-level events, the occurrence count of second-level events, and the occurrence count of third-level events, wherein the importance of first-level events is higher than the importance of second-level events, and the importance of second-level events is higher than the importance of third-level events.

[0326] The determination module 1602 is specifically used to determine the event evaluation score among N evaluation scores based on at least one of the occurrence times of first-level events, second-level events, and third-level events.

[0327] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application,

[0328] The determination module 1602 is specifically used to determine the initial service score based on the service evaluation score, security evaluation score, customer service evaluation score, and incident evaluation score when there are N evaluation scores, including service evaluation score, security evaluation score, customer service evaluation score, and incident evaluation score.

[0329] The event evaluation score is deducted from the initial service score to obtain the target service provider's comprehensive service score during the data collection period.

[0330] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application,

[0331] The determination module 1602 is also used to determine the quality level of the target service provider during the data collection period based on N evaluation scores;

[0332] The display module 1603 is also used to display the quality level of the target service provider during the data collection period after responding to an information query operation for the target service provider.

[0333] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application,

[0334] The determination module 1602 is specifically used to traverse the evaluation scores among N evaluation scores starting from the root node of the decision tree, wherein the decision tree includes a root node and at least two leaf nodes;

[0335] When traversing to the leaf nodes of the decision tree, the quality level indicated by the leaf node is taken as the quality level of the target service provider during the data collection period.

[0336] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application, the N-type parameter set includes a service parameter set, a security parameter set, a customer service parameter set, and an event parameter set;

[0337] The acquisition module 1601 is also used to acquire the graded intervals corresponding to various service indicators, various security indicators, various customer service indicators, and various event indicators.

[0338] The display module 1603 is also used to highlight the service index corresponding to the abnormal parameter if there is an abnormal parameter in the service parameter set according to the hierarchical interval corresponding to each service index.

[0339] The display module 1603 is also used to highlight the safety index corresponding to the abnormal parameter if there is an abnormal parameter in the set of safety parameters according to the grade intervals corresponding to each safety index.

[0340] The display module 1603 is also used to highlight the customer service indicators corresponding to the abnormal parameters if there are abnormal parameters in the customer service parameter set according to the grade intervals corresponding to each customer service indicator.

[0341] The display module 1603 is also used to highlight the event-type indicators corresponding to the abnormal parameters if there are abnormal parameters in the event-type parameter set according to the hierarchical intervals corresponding to each event-type indicator.

[0342] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application,

[0343] The display module 1603 is also used to respond to a region query operation for a target service provider and display various parameters of the target service provider for at least one region, wherein each parameter is derived from at least one of service parameters, security parameters, customer service parameters, and event parameters.

[0344] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application, the evaluation information display device 160 further includes a generation module 1604 and a push module 1605;

[0345] The generation module 1604 is used to generate the target service provider's pending approval information when the target service provider meets the conditions for handling violations.

[0346] The push module 1605 is used to push penalty notices to the target service provider in response to the approval operation of pending information.

[0347] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application,

[0348] The display module 1603 is also used to respond to the graphical viewing operation for the target service provider and display at least one of the following: service score trend chart, order completion rate chart, user feedback chart, problem analysis chart, and customer service analysis chart corresponding to the target service provider;

[0349] The service score trend chart is used to reflect the changes in the overall service score of the target service provider within a preset time period.

[0350] The order completion rate chart is used to reflect the order completion status of the target service provider within a preset time period;

[0351] User feedback charts are used to reflect users' evaluation of the target service provider;

[0352] Problem analysis diagrams are used to reflect the target service provider's response to comprehensive problems;

[0353] Customer service analysis charts are used to reflect the target service provider's response to customer service-related questions.

[0354] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application,

[0355] The acquisition module 1601 is also used to acquire T sets of historical parameters of the target service provider, wherein each set of historical parameters includes at least one of service parameters, security parameters, customer service parameters and event parameters for a historical time period, and T is an integer greater than or equal to 1;

[0356] The generation module 1604 is also used to construct a linear regression model based on T sets of historical parameters;

[0357] The acquisition module 1601 is also used to acquire the current parameter set of the target service provider;

[0358] The acquisition module 1601 is also used to obtain the predicted service score of the target service provider based on the current parameter set through a linear regression model.

[0359] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application,

[0360] The acquisition module 1601 is also used to acquire T sets of historical parameters of the target service provider, wherein each set of historical parameters includes at least one of service parameters, security parameters, customer service parameters and event parameters for a historical time period, and T is an integer greater than or equal to 1;

[0361] The generation module 1604 is also used to generate T feature vectors based on T sets of historical parameters;

[0362] The determination module 1602 is also used to select K feature vectors from T feature vectors as K cluster centers, where K is an integer greater than 1 and less than T;

[0363] The determination module 1602 is also used to calculate the distance between each of the T feature vectors and the K cluster centers respectively;

[0364] The generation module 1604 is also used to assign each of the T feature vectors to the cluster corresponding to the shortest cluster center until the convergence condition is met, and obtain K clustering results.

[0365] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application,

[0366] The acquisition module 1601 is also used to acquire the target historical parameter set of the target service provider, wherein the target historical parameter set includes at least one of service parameters, security parameters, customer service parameters and event parameters for the target historical time period;

[0367] The generation module 1604 is also used to construct a first feature vector based on the target historical parameter set;

[0368] The acquisition module 1601 is also used to obtain a predicted evaluation score of at least one dimension based on the first feature vector through a time series model.

[0369] Optionally, in the above Figure 16 Based on the corresponding embodiments, in another embodiment of the evaluation information display device 160 provided in this application,

[0370] The generation module 1604 is also used to construct the second feature vector corresponding to the target service provider based on the set of N types of parameters;

[0371] The acquisition module 1601 is also used to acquire a predicted probability distribution based on the second feature vector through an anomaly detection model, wherein each element in the predicted probability distribution corresponds to an anomaly category;

[0372] The determination module 1602 is also used to determine the anomaly detection results of the target service provider based on the predicted probability distribution.

[0373] This application also provides a terminal, such as... Figure 17 As shown, for ease of explanation, only the parts related to the embodiments of this application are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of this application. In the embodiments of this application, a mobile phone is used as an example for illustration:

[0374] Figure 17 This is a block diagram illustrating a portion of the structure of a mobile phone related to the terminal provided in the embodiments of this application. (Reference) Figure 17 The mobile phone includes components such as a radio frequency (RF) circuit 1710, a memory 1720, an input unit 1730, a display unit 1740, a sensor 1750, an audio circuit 1760, a wireless fidelity (WiFi) module 1770, a processor 1780, and a power supply 1790. Those skilled in the art will understand that... Figure 17 The mobile phone structure shown does not constitute a limitation on the mobile phone and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0375] The following is combined Figure 17 A detailed introduction to each component of a mobile phone:

[0376] RF circuit 1710 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink information from the base station and processes it with processor 1780; additionally, it transmits uplink data to the base station. Typically, RF circuit 1710 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier (LNA), and a duplexer. Furthermore, RF circuit 1710 can also communicate wirelessly with networks and other devices. The aforementioned wireless communication can use any communication standard or protocol, including but not limited to Global System for Mobile Communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, and Short Message Service (SMS).

[0377] The memory 1720 can be used to store software programs and modules. The processor 1780 executes various mobile phone functions and data processing by running the software programs and modules stored in the memory 1720. The memory 1720 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 1720 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0378] The input unit 1730 can be used to receive input numerical or character information, and to generate key signal inputs related to user settings and function control of the mobile phone. Specifically, the input unit 1730 may include a touch panel 1731 and other input devices 1732. The touch panel 1731, also known as a touch screen, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel 1731), and drive the corresponding connection devices according to a pre-set program. Optionally, the touch panel 1731 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 1780, and can also receive and execute commands sent by the processor 1780. In addition, the touch panel 1731 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 1731, the input unit 1730 may also include other input devices 1732. Specifically, other input devices 1732 may include, but are not limited to, one or more of the following: a physical keyboard, function keys (such as volume control buttons, power buttons, etc.), a mouse, and a joystick.

[0379] The display unit 1740 can be used to display information input by the user or information provided to the user, as well as various menus of the mobile phone. The display unit 1740 may include a display panel 1741, which may optionally be configured as a liquid crystal display (LCD), organic light-emitting diode (OLED), or similar form. Further, a touch panel 1731 may cover the display panel 1741. When the touch panel 1731 detects a touch operation on or near it, it transmits the information to the processor 1780 to determine the type of touch event. Subsequently, the processor 1780 provides corresponding visual output on the display panel 1741 based on the type of touch event. Although in Figure 17 In this embodiment, the touch panel 1731 and the display panel 1741 are two separate components to realize the input and output functions of the mobile phone. However, in some embodiments, the touch panel 1731 and the display panel 1741 can be integrated to realize the input and output functions of the mobile phone.

[0380] The mobile phone may also include at least one sensor 1750, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display panel 1741 according to the ambient light level, and the proximity sensor can turn off the display panel 1741 and / or the backlight when the phone is moved to the ear. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used for applications that recognize the phone's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition-related functions (such as pedometer, taps), etc. Other sensors that may be configured in the mobile phone, such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, will not be described in detail here.

[0381] Audio circuit 1760, speaker 1761, and microphone 1762 provide an audio interface between the user and the mobile phone. Audio circuit 1760 converts received audio data into electrical signals and transmits them to speaker 1761, where speaker 1761 converts them into sound signals for output. On the other hand, microphone 1762 converts collected sound signals into electrical signals, which are received by audio circuit 1760, converted into audio data, and then processed by processor 1780 before being transmitted via RF circuit 1710 to, for example, another mobile phone, or the audio data can be output to memory 1720 for further processing.

[0382] WiFi is a short-range wireless transmission technology. Mobile phones using the WiFi module 1770 can help users send and receive emails, browse web pages, and access streaming media, providing users with wireless broadband internet access. Although Figure 17 WiFi module 1770 is shown, but it is understood that it is not an essential component of a mobile phone and can be omitted as needed without changing the essence of the invention.

[0383] The processor 1780 is the control center of the mobile phone, connecting various parts of the phone through various interfaces and lines. It executes various functions and processes data by running or executing software programs and / or modules stored in the memory 1720 and calling data stored in the memory 1720. Optionally, the processor 1780 may include one or more processing units; optionally, the processor 1780 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the aforementioned modem processor may also not be integrated into the processor 1780.

[0384] The phone also includes a power supply 1790 (such as a battery) that supplies power to various components. Optionally, the power supply can be logically connected to the processor 1780 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system.

[0385] Although not shown, mobile phones may also include a camera, Bluetooth module, etc., which will not be described in detail here.

[0386] The steps performed by the terminal in the above embodiments can be based on this Figure 17 The terminal structure shown.

[0387] This application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the methods described in the foregoing embodiments.

[0388] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the methods described in the foregoing embodiments.

[0389] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described in the foregoing embodiments.

[0390] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0391] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0392] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0393] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0394] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0395] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a server or terminal device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0396] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for displaying evaluation information, characterized in that, include: When the evaluation triggering conditions are met, obtain the N-type parameter set of the target service provider for the data collection period, wherein the N-type parameter set includes at least one of the service parameter set, security parameter set, customer service parameter set and event parameter set, and N is an integer greater than or equal to 1; Based on each of the N parameter sets, N evaluation scores are determined, where each evaluation score is calculated based on one set of parameters. Based on the N evaluation scores, the comprehensive service score of the target service provider during the data collection period is determined; In response to an information query operation targeting the target service provider, the comprehensive evaluation information of the target service provider is displayed. The comprehensive evaluation information includes the comprehensive service score and evaluation information in N dimensions. The evaluation information in each dimension includes the evaluation score and the evaluation score threshold for the corresponding dimension.

2. The display method according to claim 1, characterized in that, The conditions for meeting the evaluation triggering conditions include at least one of the following: The data collection period has ended; or, A request for evaluation statistics for the target service provider was obtained.

3. The display method according to claim 1, characterized in that, The set of service parameters includes at least one of the following: non-passenger cancellation rate, average pick-up distance per ride, price overestimation rate, customer complaint rate, and negative review rate. The step of determining N evaluation scores based on each of the N parameter sets includes: If the service parameter set includes the average pick-up distance, the average pick-up distance is standardized to obtain the pick-up distance standard rate. The service evaluation score is obtained by weighting and summing at least one of the non-passenger cancellation rate, the standard pick-up distance rate, the price overestimation rate, the customer complaint rate, and the negative review rate, among the N evaluation scores.

4. The display method according to claim 1, characterized in that, The set of security parameters includes at least one of the following: order compliance rate, security-related customer complaint rate, resale order rate, and business license upload rate. The step of determining N evaluation scores based on each of the N parameter sets includes: The safety evaluation score is obtained by weighted summing of at least one of the following: order compliance rate, security-related customer complaint rate, resale order rate, and business license upload rate.

5. The display method according to claim 1, characterized in that, The customer service parameter set includes at least one of the following: a case closure rate for a first time range, a case closure rate for a second time range, and a case closure rate for a third time range, wherein the first time range is less than the second time range, and the second time range is less than the third time range. The step of determining N evaluation scores based on each of the N parameter sets includes: The customer service evaluation score is obtained by weighted summing of at least one of the case closure rates for the first time period, the second time period, and the third time period.

6. The display method according to claim 1, characterized in that, The event class parameter set includes at least one of the following: the number of occurrences of first-level events, the number of occurrences of second-level events, and the number of occurrences of third-level events. The importance of first-level events is higher than that of second-level events, and the importance of second-level events is higher than that of third-level events. The step of determining N evaluation scores based on each of the N parameter sets includes: The event evaluation score among the N evaluation scores is determined based on at least one of the occurrence counts of the first-level event, the occurrence counts of the second-level event, and the occurrence counts of the third-level event.

7. The display method according to any one of claims 1 to 6, wherein determining the comprehensive service score of the target service provider during the data collection period based on the N evaluation scores includes: When the N evaluation scores include service evaluation score, security evaluation score, customer service evaluation score and incident evaluation score, an initial service score is determined based on the service evaluation score, the security evaluation score and the customer service evaluation score; The event evaluation score is deducted from the initial service score to obtain the target service provider's comprehensive service score during the data collection period.

8. The display method according to any one of claims 1 to 7, characterized in that, The method further includes: Based on the N evaluation scores, the quality level of the target service provider during the data collection period is determined; Following the information query operation for the target service provider, the method further includes: This displays the quality level of the target service provider during the data collection period.

9. The display method according to claim 8, characterized in that, The step of determining the quality level of the target service provider during the data collection period based on the N evaluation scores includes: The decision tree is traversed from the root node of the decision tree, where the decision tree includes a root node and at least two leaf nodes. When traversing to a leaf node of the decision tree, the quality level indicated by the leaf node is taken as the quality level of the target service provider during the data collection period.

10. The display method according to any one of claims 1 to 9, characterized in that, The N-type parameter set includes the service parameter set, the security parameter set, the customer service parameter set, and the event parameter set; The method further includes: Obtain the corresponding grading intervals for each service-related indicator, each security-related indicator, each customer service-related indicator, and each event-related indicator. If there are abnormal parameters in the service parameter set according to the grade intervals corresponding to each service indicator, the service indicator corresponding to the abnormal parameter will be highlighted. If, based on the grading intervals corresponding to the aforementioned safety indicators, there are abnormal parameters in the set of safety parameters, then the safety indicator corresponding to the abnormal parameter will be highlighted. If there are abnormal parameters in the customer service parameter set according to the grade intervals corresponding to each of the customer service indicators, the customer service indicator corresponding to the abnormal parameter will be highlighted. If, based on the grading intervals corresponding to the various event-type indicators, there are abnormal parameters in the event-type parameter set, then the event-type indicator corresponding to the abnormal parameter will be highlighted.

11. The display method according to any one of claims 1 to 10, characterized in that, The method further includes: In response to a region query operation for the target service provider, various parameters of the target service provider for at least one region are displayed, wherein the various parameters are derived from at least one of service parameters, security parameters, customer service parameters, and event parameters.

12. The display method according to any one of claims 1 to 11, characterized in that, The method further includes: If the target service provider meets the conditions for handling violations, generate the target service provider's pending approval information; In response to the approval of the information pending approval, a penalty notice is sent to the target service provider.

13. The display method according to any one of claims 1 to 12, characterized in that, The method further includes: In response to the graphical viewing operation for the target service provider, at least one of the following is displayed: service score trend chart, order completion rate chart, user feedback chart, problem analysis chart, and customer service analysis chart corresponding to the target service provider; The service score trend chart is used to reflect the changes in the overall service score of the target service provider within a preset time period; The order completion rate chart is used to reflect the order completion status of the target service provider within a preset time period; The user feedback graph is used to reflect users' evaluation of the target service provider; The problem analysis diagram is used to reflect the target service provider's response to comprehensive problems; The customer service analysis chart is used to reflect the target service provider's response to customer service questions.

14. The display method according to any one of claims 1 to 13, characterized in that, The method further includes: Obtain T sets of historical parameters for the target service provider, wherein each set of historical parameters includes at least one of service-related parameters, security-related parameters, customer service-related parameters, and event-related parameters for a historical time period, and T is an integer greater than or equal to 1; Based on the T sets of historical parameters, construct a linear regression model; Obtain the current parameter set of the target service provider; Based on the current parameter set, the predicted service score of the target service provider is obtained through the linear regression model.

15. The display method according to any one of claims 1 to 14, characterized in that, The method further includes: Obtain T sets of historical parameters for the target service provider, wherein each set of historical parameters includes at least one of service-related parameters, security-related parameters, customer service-related parameters, and event-related parameters for a historical time period, and T is an integer greater than or equal to 1; Generate T feature vectors based on the T sets of historical parameters; Select K feature vectors from the T feature vectors as K cluster centers, where K is an integer greater than 1 and less than T; For each of the T feature vectors, calculate the distance between the feature vector and the K cluster centers; Each of the T feature vectors is assigned to the cluster corresponding to the shortest cluster center until the convergence condition is met, resulting in K clustering results.

16. The display method according to any one of claims 1 to 14, characterized in that, The method further includes: Obtain the target historical parameter set of the target service provider, wherein the target historical parameter set includes at least one of service-related parameters, security-related parameters, customer service-related parameters, and event-related parameters for the target historical time period; Construct a first feature vector based on the target historical parameter set; Based on the first feature vector, a predicted evaluation score for at least one dimension is obtained through a time series model.

17. The display method according to any one of claims 1 to 16, characterized in that, The method further includes: Based on the set of N types of parameters, construct the second feature vector corresponding to the target service provider; Based on the second feature vector, a predicted probability distribution is obtained through an anomaly detection model, wherein each element in the predicted probability distribution corresponds to an anomaly category; The anomaly detection result of the target service provider is determined based on the predicted probability distribution.

18. An evaluation information display device, characterized in that, include: The acquisition module is used to acquire a set of N types of parameters from the target service provider for the data collection period when the evaluation triggering conditions are met. The set of N types of parameters includes at least one of the following: a set of service parameters, a set of security parameters, a set of customer service parameters, and a set of event parameters. N is an integer greater than or equal to 1. The determining module is used to determine N evaluation scores based on each of the N parameter sets, wherein each evaluation score is calculated based on one set of parameters. The determining module is further configured to determine the comprehensive service score of the target service provider during the data collection period based on the N evaluation scores; The display module is used to respond to the information query operation for the target service provider and display the comprehensive evaluation information of the target service provider. The comprehensive evaluation information includes the comprehensive service score and evaluation information of N dimensions. The evaluation information of each dimension includes the evaluation score of the corresponding dimension and the evaluation score threshold.

19. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for displaying evaluation information according to any one of claims 1 to 17.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method for displaying evaluation information according to any one of claims 1 to 17.

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