User perception index evaluation method, device, equipment and medium

Abstract

This application provides a method, apparatus, device, and medium for evaluating user perception indicators. The method includes: acquiring subscription data from the same user's business front-end and business back-end, and determining whether the subscription data from the business front-end and business back-end meets the data consistency requirement; if the subscription data from the business front-end and business back-end meets the data consistency requirement, acquiring the user's complaint data; if the complaint data is unrelated to the subscription data, then acquiring a user perception indicator evaluation value based on the user's experience data, wherein the experience data includes at least one of the following service data: call connection rate, MOS value, voice call drop rate, or data service download speed. This application's method combines subscription data with user complaint data, selecting objective parameters to evaluate user perception indicators, thus facilitating improved user experience.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method, apparatus, device, and medium for evaluating user perception indicators. Background Technology

[0002] User perception metrics are used to evaluate users' satisfaction with the quality and level of network services. Obtaining user perception metrics can help prevent customer complaints in advance, or analyze areas for improvement on the network service after a customer complaint has been filed, thereby improving service quality.

[0003] Current technologies typically use two data points to obtain user perception metric evaluation values: user complaint rate and user satisfaction. The user complaint rate, obtained from customer service platforms, contains many subjective parameters. User satisfaction, on the other hand, is based on satisfaction ratings from these platforms. However, because customer service platforms rarely follow up on sample ratings, misunderstandings about services cannot be addressed promptly, leading to less fair and objective ratings. In summary, current technologies result in user perception metric evaluations that do not accurately reflect the actual situation, hindering timely problem-solving. Therefore, a more objective and reasonable method is needed for user perception metric evaluation. Summary of the Invention

[0004] This application provides a method, apparatus, device, and medium for evaluating user perception indicators, in order to address the problems of insufficient and unobjective data utilization in the prior art.

[0005] Firstly, this application provides a method for evaluating user perception metrics, including:

[0006] Obtain the contract data of the business front-end and business back-end of the same user, and determine whether the contract data of the business front-end and the business back-end meet the data consistency requirement;

[0007] When the contract data between the business front-end and the business back-end meets the data consistency requirement, the user's complaint data is obtained;

[0008] If the complaint data is unrelated to the contract data, then the user perception index evaluation value is obtained based on the user's experience data, wherein the experience data includes at least one of the following service data: call connection rate, MOS value, voice service call drop rate, or data service download speed.

[0009] In one possible implementation, the subscription data in the business backend includes subscription data from multiple network elements, and determining whether the subscription data between the business frontend and the business backend satisfies data consistency includes:

[0010] Determine whether the contract data of the multiple network elements meets the data consistency requirement;

[0011] If so, determine whether the contract data in the business front end and the contract data in the business back end meet the data consistency requirement.

[0012] In one possible implementation, determining whether the subscription data of the multiple network elements satisfies data consistency includes:

[0013] Determine whether the services activated for the user by the multiple network elements are consistent. If they are consistent, the subscription data satisfies the data consistency requirement.

[0014] The step of determining whether the contract data in the business front-end and the contract data in the business back-end meet the data consistency requirement includes:

[0015] For each service type, determine whether the subscription status of the service type in the service front end matches the actual activation status of each network element. If they match, confirm that the subscription data in the service front end and the subscription data in the service back end meet the data consistency requirements.

[0016] In one possible implementation, obtaining the user perception index evaluation value based on the user's experience data includes:

[0017] The user perception index evaluation value is obtained based on the score of each business data of each user and the weight value corresponding to each business data. The score of each business data is obtained according to the mapping relationship between business data and score, and different business data ranges correspond to different scores.

[0018] In one possible implementation, before obtaining the user perception index evaluation value based on the score of each business data point for each evaluated user and the weight value corresponding to each business data point, the method further includes:

[0019] Obtain the service usage of each user's target service, wherein the service usage includes the number of calls for the call service and the data usage for the data service;

[0020] Users who make more than a preset number of calls or use more than a preset amount of data will be considered as the evaluated users.

[0021] In one possible implementation, after obtaining the user perception index evaluation value based on the user's experience data, the method further includes:

[0022] Based on the score of each service data of each evaluator, the data is categorized according to the target category to obtain the service data set corresponding to each of the multiple evaluation objects of the target category. The evaluation object of the target category is one of the following: communication cell, service data network element, service platform or terminal type. Each service data set corresponds to the service data of multiple users of each service type.

[0023] Based on the business data sets corresponding to the multiple participating objects in each target category, the evaluation indicators for the participating objects under each target category are obtained.

[0024] In one possible implementation, obtaining the evaluation indicators for each target category's participating objects based on their respective business data sets includes:

[0025] Based on the business data sets corresponding to multiple participating objects in each target category, obtain the percentage of users who meet the preset conditions for each participating object under each business type;

[0026] Based on the user percentage and preset threshold, determine whether the evaluation indicators of the participating object are abnormal.

[0027] Secondly, this application provides a user perception index evaluation device, comprising:

[0028] The acquisition module is used to acquire the contract data of the business front-end and business back-end of the same user, and to determine whether the contract data of the business front-end and the business back-end meet the data consistency requirement.

[0029] The judgment module is used to obtain the user's complaint data when the contract data in the business front end and the business back end meet the data consistency requirement;

[0030] The processing module is used to obtain a user perception index evaluation value based on the user's experience data if the complaint data is unrelated to the contract data. The experience data includes at least one of the following service data: call connection rate, MOS value, voice service call drop rate, or data service download rate.

[0031] Thirdly, this application provides a user perception index evaluation device, comprising: at least one processor and a memory;

[0032] The memory stores computer-executed instructions;

[0033] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the user perception index evaluation method as described above.

[0034] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the user perception index evaluation method as described above.

[0035] This application provides a method, apparatus, device, and medium for evaluating user perception indicators. The method acquires subscription data from the same user's business front-end and back-end, and determines whether the subscription data from the business front-end and back-end meets the data consistency requirement. If the subscription data from the business front-end and back-end meets the data consistency requirement, the method acquires the user's complaint data. If the complaint data is unrelated to the subscription data, then a user perception indicator evaluation value is obtained based on the user's experience data. The experience data includes at least one of the following service data: call connection rate, MOS value, voice call drop rate, or data download rate. In the above method, by combining subscription data and user complaint data, objective parameters are selected to evaluate the user perception indicator, which facilitates the improvement of user experience. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of a user perception index evaluation system proposed in this application;

[0038] Figure 2 This application proposes a user perception index evaluation method and process. Figure 1 ;

[0039] Figure 3 This application proposes a user perception index evaluation method and process. Figure 2 ;

[0040] Figure 4 This application proposes a user perception index evaluation method and process. Figure 3 ;

[0041] Figure 5 This application proposes a user perception index evaluation method and process. Figure 4 ;

[0042] Figure 6A A schematic diagram illustrating an anomaly localization method for user perception index evaluation proposed in this application;

[0043] Figure 6BA schematic diagram illustrating the call drop rate evaluation at the cell level provided in this application;

[0044] Figure 6C A diagram illustrating the call drop rate evaluation at the terminal level provided in this application;

[0045] Figure 7 This is a distribution diagram of the evaluation results of a user perception index evaluation method proposed in this application.

[0046] Figure 8 A diagram of a user perception index evaluation device provided in an embodiment of the present invention;

[0047] Figure 9 This is a hardware schematic diagram of a user perception index evaluation device provided in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] In the telecommunications market, in order to improve network quality and service levels, user perceptions of network and service usage are assessed.

[0050] Currently, commonly used methods for evaluating network quality and service levels include collecting user complaint rates and user satisfaction data, and then using these data to estimate user perception metrics. However, collecting user complaint rates typically involves gathering weekly complaint or appeal data from online customer service platforms, which presents a challenge in objectivity. Similarly, user satisfaction is usually measured through ratings obtained from operator platforms. Since customer service platforms generally don't follow up on data collection, any discrepancies in understanding of services cannot be addressed promptly or prevented in advance, leading to a lack of objectivity in this data collection method. Therefore, the user perception metrics calculated using these parameters are not entirely accurate and will hinder further improvements in network quality and service levels.

[0051] While collected network metrics data are generally relatively objective, current technologies do not fully utilize them. Therefore, this application proposes a more objective and data-driven method for evaluating user perception metrics by fully leveraging the experience data corresponding to contract signing data and network metrics data. The specific implementation process of this application will be illustrated below through concrete examples.

[0052] Figure 1 This is a schematic diagram of a user perception index evaluation system proposed in this application. Figure 1 As shown, the system includes: a business platform, XDR network elements, and a complaint platform;

[0053] The service platform is used to collect contracted data. Optionally, the network elements corresponding to the contracted data include: Unified Data Management (UDM), which is a 4G network element indicating 4G type services; IP Multimedia Subsystem (IMS), which has storage and session management functions; and Ethereum Name Service (ENS), which provides secure and decentralized address names.

[0054] The business platform includes a front-end and a back-end. The contract signing data between the front-end and the back-end needs to be confirmed to be consistent in the back-end, and then confirmed to be consistent in both the front-end and the back-end together to ensure that there are no network element anomalies or data record anomalies in the contract signing data.

[0055] The XDR network element, or X application detail record, is used to collect experience data, including detailed records of any application, including signaling information and experience information.

[0056] The experience data collected in this embodiment is based on network data that may cause a bad user experience, rather than subjective user data (such as excessive charges for a certain service). It includes at least one of the following service data: call connection rate, MOS value, voice call drop rate, or data download speed. Objectivity and rigor are ensured in the data selection.

[0057] The complaint platform is used to collect user complaint data; it is also used to filter user complaint information from the complaint platform based on the data collected from the business platform and XDR network elements, retaining only complaints related to contract data and experience data to ensure the objectivity of the data.

[0058] In the event of complaints regarding contract signing data and user experience data, operators need to handle these complaints promptly. In cases where contract signing data and user experience data are related but no complaints have been filed, the user perception index evaluation value can be calculated based on the contract signing data and user experience data through the complaint platform. This allows for cluster analysis to identify areas where telecommunications services are not performing well, enabling timely adjustments to ensure good network quality and service levels.

[0059] The following is combined with Figure 1 and Figure 2Please provide a detailed description of the evaluation process for the user perception index evaluation method in this application.

[0060] Figure 2 This application proposes a user perception index evaluation method and process. Figure 1 .like Figure 2 As shown, the method includes:

[0061] S201. Obtain the contract data of the business front-end and business back-end of the same user, and determine whether the contract data of the business front-end and the business back-end meet the data consistency requirement.

[0062] The business front-end is the terminal that can sign up services with users and record users' signing-up data. The implementation of signed-up services depends on the network elements in the business back-end. The network elements in the business back-end react differently to different signing-up data. That is, the business back-end can be used to indicate the actual effectiveness of the services signed up by users. When the signing-up data in the business front-end and business back-end are consistent, it means that the user's signing-up data is working normally. Conversely, if the user's signing-up data is found to be abnormal, the abnormality can be investigated and handled in a timely manner to ensure the user's service experience.

[0063] S202. When the contract data in the business front-end and the business back-end meet the data consistency requirement, obtain the user's complaint data.

[0064] When the contract data between the business front-end and business back-end meets the data consistency requirement, if a user files a complaint (for example, the reason selected by the user on the complaint platform is that the contracted service has not taken effect), it indicates that the complaint data is inaccurate. The complaint should be resolved by providing feedback to the user on the complaint result or by other means. If the content of the complaint is unrelated to the contract data, then further analysis is required.

[0065] S203. If the complaint data is unrelated to the contract data, then based on the user's experience data, obtain the user perception index evaluation value, wherein the experience data includes at least one of the following service data: call connection rate, MOS value, voice service call drop rate, or data service download speed.

[0066] If the complaint data is unrelated to the contract data, the reasons for poor user experience can be found in the user's experience data on the network. The experience data selected for calculating the user perception index evaluation value is objective data, including at least one of the following service data: call connection rate, voice quality evaluation (Mean Opinion Score, MOS) value, voice service call drop rate, or data service download rate.

[0067] Among them, the call connection rate indicates the connection status of outgoing and incoming calls; the MOS value is a voice quality assessment value, used to indicate the voice quality after the call is connected, including whether there is noise, etc.; the voice service call drop rate indicates the call stability after the call is connected, including whether the call is dropped; and the data service download rate indicates the usage of data traffic, and whether network instability causes slow data download.

[0068] The selected experience data are all objective data. These objective data are used as actual data generated during user interaction as evaluation elements for user perception indicators, and user perception indicator evaluation values ​​are calculated. For example, when a user is communicating, a higher call connection rate (e.g., 99.5%) means a lower probability of complaints due to the call connection rate, resulting in better user perception and a higher user perception indicator evaluation value. Call connection rate, MOS value, and data service download speed are all positive evaluation elements, so their actual data are directly proportional to the user perception indicator evaluation value. Voice call drop rate is a negative evaluation element, so its actual data is inversely proportional to the user perception indicator evaluation value.

[0069] In fact, the embodiments of this application are not only used for user complaint data analysis, but also for analyzing whether users who have not filed complaints may file complaints in the future by obtaining user perception index evaluation values.

[0070] In this embodiment, the contract data of the same user's business front-end and business back-end are obtained, and it is determined whether the contract data of the business front-end and the business back-end meet the data consistency requirement. If the contract data of the business front-end and the business back-end meet the data consistency requirement, the user's complaint data is obtained. If the complaint data is unrelated to the contract data, a user perception index evaluation value is obtained based on the user's experience data. The experience data includes at least one of the following service data: call connection rate, MOS value, voice call drop rate, or data download rate. In the above method, combining contract data with user complaint data and selecting objective parameters for user perception index evaluation facilitates the improvement of user experience.

[0071] The following is combined with Figure 3 This application provides a detailed explanation of the process for confirming the consistency of contracted data in the user perception indicator evaluation method.

[0072] Figure 3 This application proposes a user perception index evaluation method and process. Figure 2 .like Figure 3 As shown, the method includes:

[0073] S301. Determine whether the services activated for the user by the multiple network elements are consistent. If they are consistent, proceed to S302. If they are inconsistent, confirm that the subscription data is abnormal.

[0074] The services activated by users are also services provided by operators. These services have corresponding implementation network elements when they are set up. That is, after a certain service is activated, the service will be implemented through multiple network elements, but the status values ​​corresponding to different network elements may be displayed differently. For example, the status values ​​of the activation status of different services are displayed in different status positions.

[0075] For example, if the service is a 4G call service, when the service is actually active, the status value of the 4G call service status bit in the corresponding UDM network element and ENS network element in the service backend will both show as active. If the display of one of the network elements is inconsistent with the actual activation setting of the service, it indicates that there is an anomaly in the subscription data at the network element end.

[0076] Since each network element has its own execution order when performing services, if the execution order is inconsistent with the settings, it also indicates that there is an anomaly in the contract data at the network element end.

[0077] If all activated services are consistent, then the contracted data is confirmed to meet the data consistency requirement; if inconsistencies occur, the service provider can address the issues accordingly.

[0078] S302. Confirm that the signed data meets the data consistency requirements.

[0079] S303. For each service type, determine whether the subscription status of the service type in the service front end matches the actual activation status of each network element. If they match, execute S304; if they do not match, confirm that the subscription data is abnormal.

[0080] Users activate different types of services. The specific services activated by a user are recorded on the service front-end. However, the actual effectiveness of these services after activation needs to be determined through the network element status in the service back-end. If a user subscribes to a service on the front-end, but the network element status corresponding to that service in the service back-end is inconsistent with the actual activation status (e.g., activating service A but not responding to any other services or responding to service B), this inconsistency indicates an anomaly in the subscription data, requiring resolution. If the data is consistent, it means the subscription data on the service front-end and the subscription data in the service back-end meet the data consistency requirement, and further analysis is needed.

[0081] S304. Confirm that the contract data in the business front end and the contract data in the business back end are consistent.

[0082] In this embodiment, when determining the consistency of contracted data, it is first determined whether the services activated for the user by the multiple network elements are consistent. If consistent, then for each service type, it is determined whether the contracted status of the service type on the service front end matches the actual activation status of each network element. If matched, it is confirmed that the contracted data on the service front end and the contracted data on the service back end satisfy data consistency. In the above method, by first confirming the network element status on the service back end, some contracted anomalies can be identified in advance, reducing the amount of subsequent data processing. Then, based on the matching of contracted services on the front end and back end, it is confirmed whether contracted data anomalies have occurred, so as to avoid adverse effects such as user losses due to the user's contracted services not taking effect, and to avoid poor user experience.

[0083] The following is combined with Figure 4 Please provide a detailed explanation of the user selection process and business data evaluation process in the user perception index evaluation method of this application.

[0084] Figure 4 This application proposes a user perception index evaluation method and process. Figure 3 .like Figure 4 As shown, the method includes:

[0085] S401. Obtain the service usage of each user's target service, wherein the service usage includes the number of calls for the call service and the data usage for the data service.

[0086] Before obtaining user perception index evaluation values, user data needs to be screened. Only when a user's business usage reaches a certain standard will the user's relevant business data be included in the evaluation. This avoids biased judgments about the business experience due to low user usage. For example, if a user experiences network lag only once on their first use of data, including that user in the evaluation may result in randomness in the actual analysis results, which could interfere with the overall user perception index evaluation.

[0087] The usage data used for filtering can be the number of calls for call services and the amount of data used for data services.

[0088] S402. Users who make more than a preset number of calls and use more than a preset amount of data are designated as the evaluated users.

[0089] After determining the service usage volume to be screened, preset thresholds for the user's service usage volume should be set, including lower limits for the number of calls and the amount of data used. When a user's number of calls and data usage both exceed the lower limits, the user is considered an evaluation user, and the corresponding user data is used to obtain user perception indicator evaluation values ​​in the future.

[0090] S403. Based on the score of each business data of each evaluated user and the weight value corresponding to each business data, obtain the evaluation value of the user perception index, wherein the score of each business data is obtained according to the mapping relationship between business data and score, and different business data ranges correspond to different scores.

[0091] Each service data point for each evaluated user includes caller ID connection rate, MOS value, voice call drop rate, and data download speed. The score for each service data point is related to its actual value. For example, if the caller ID connection rate is 50%, the score is 50, and the higher the connection rate, the higher the score. If the MOS value is 3.5, the score is 60. If the MOS value ranges from 0 to 3.4, the score is 0 to 59. If the MOS value ranges from 3.6 to 6.5, the score is 61 to 100. If the voice call drop rate is 2%, the score is 60, and the higher the drop rate, the lower the score. If the data download speed is 50 Mbit / s, the score is 60, and the higher the download speed, the higher the score.

[0092] Correspondingly, the weights for the call connection rate, MOS value, voice call drop rate, and data download rate can be 0.3, 0.2, 0.2, and 0.3, respectively.

[0093] The user perception index evaluation value is obtained by multiplying the scores of each business data point by their respective weight values ​​and then summing them up.

[0094] In this embodiment, the usage of the target service for each user is obtained; users whose number of calls exceeds a preset number and whose data usage exceeds a preset number are designated as the evaluated users; and the user perception index evaluation value is obtained based on the score of each service data point for each evaluated user and the weight value corresponding to each service data point. In the above method, by setting data thresholds, including the number of calls and data usage, data generated by users after meeting certain experience requirements is considered valid, avoiding excessive randomness in the data and preventing inaccurate final analysis.

[0095] The following is combined with Figure 5 , Figure 6A , Figure 6B , Figure 6C and Figure 7 Specifically, this application describes how, after evaluating the user perception indicators using this method, the participating objects corresponding to abnormal indicators are categorized and screened.

[0096] Figure 5 This application proposes a user perception index evaluation method and process. Figure 4 . Figure 6A This diagram illustrates an anomaly localization method for evaluating user perception metrics proposed in this application. Figure 5 and Figure 6A As shown, the method includes:

[0097] S501. Based on the score of each service data of each evaluated user, classify and process according to the target category to obtain the service data set corresponding to each of the multiple evaluation objects of the target category. The evaluation object of the target category is one of the following: communication cell, service data network element, service platform or terminal type. Each service data set corresponds to the service data of multiple users of each service type.

[0098] Target categories can be categories based on business data, including call connection rate, MOS value, voice call drop rate, and data download rate (corresponding to...). Figure 6A The data (dashed box) represents the business data, which may have different scores for different users. The values ​​of multiple users are combined to evaluate the various business data. Judgment thresholds are set for different business data (the judgment thresholds for each business data can be the same or different). Business data that is below the judgment threshold is used for cluster analysis to select possible anomaly locations among different evaluation objects. The anomaly location is then selected from the selected evaluation objects to complete the anomaly location.

[0099] The evaluation objects for the target category are one of the following: communication cell, service data network element, service platform or terminal type. Each of the service data sets corresponds to the service data of multiple users for each service type.

[0100] S502. Based on the business data sets corresponding to the multiple participating objects in each target category, obtain the percentage of users who meet the preset conditions for each participating object under each business type.

[0101] As mentioned above, the preset conditions can be to set a judgment threshold, and then use the business data that is lower than the judgment threshold to select from the participating objects, and the proportion of users in different participating objects under this business data.

[0102] For example, if the sample size of the business data set is 100, and 30 of them have a call connection rate lower than the judgment threshold, then among these 30 users with low call connection rates, 26 are in cell A, the remaining 4 are in other cells, 22 are in the same business data network element B, the remaining 8 are in other business data network elements, 18 are in the same business platform C, the remaining 12 are in other business platforms, 16 are in the same terminal D, and the remaining 14 are in other terminals, then it is confirmed that the base station corresponding to cell A is abnormal, the business data network element B is abnormal, the business platform C is abnormal, and the terminal D is abnormal. Among these, terminal abnormalities are not handled by the operator, while the operator can handle other issues accordingly.

[0103] S503. Based on the user ratio and the preset threshold, determine whether the evaluation indicators of the participating object are abnormal.

[0104] For a given geographical area, regarding the call connection rate, if the percentage of users with a connection rate of 60% is greater than the preset threshold of 70%, then the call connection rate evaluation index of the participating object is normal; if it is lower than the preset threshold of 70%, then the call connection rate evaluation index of the participating object is abnormal.

[0105] For a given geographical area, if the percentage of users with a call drop rate score below 60 is greater than the preset threshold of 20%, then the call drop rate evaluation index of the evaluated object is abnormal; if it is lower than the preset threshold of 20%, then the call drop rate evaluation index of the evaluated object is normal.

[0106] by Figure 6B and Figure 6C Two specific embodiments will be used as examples to illustrate the details. Figure 6B This is a schematic diagram illustrating the call drop rate evaluation at the cell level provided in this application. Figure 6C This is a diagram illustrating the call drop rate evaluation at the terminal level provided in this application. Figure 6B and Figure 6C As shown, this embodiment uses call drop rate as an example for explanation. Other service data are similar and will not be described in detail here.

[0107] Please continue to refer to Figure 6B As shown, this embodiment uses communication cells as the evaluation object for the target category for explanation. Based on the call drop rate score of each evaluated user, the data is categorized according to cell category to obtain call drop rate service data sets corresponding to multiple communication cells within each cell category. Each communication cell's call drop rate service data set includes the call drop rate scores of all users within that cell.

[0108] Based on the call drop rate service data set corresponding to multiple communication cells in each cell category, the percentage of users with a call drop rate score below 60 in each communication cell is obtained. Cells with a user percentage greater than a preset threshold of 15% are identified as abnormal communication cells.

[0109] Please continue to refer to Figure 6C As shown, this embodiment uses terminal type as an example to illustrate the evaluation of the target category. Based on the call drop rate score of each evaluating user, the data is categorized according to terminal type to obtain the call drop rate service data set corresponding to each terminal type.

[0110] Based on the call drop rate service data sets corresponding to different terminal types, the proportion of users with call drop rate scores below 60 for different types of terminals is obtained, and cells with a user proportion greater than a preset threshold of 16% are identified as abnormal communication cells.

[0111] Figure 7 This is a distribution chart of the evaluation results of the user perception index evaluation method proposed in this application. Figure 7 As shown in the figure, this graph displays the evaluation results of a certain user, which can intuitively reflect the user's perception.

[0112] Figure 7 The display shows the distribution of user perception index evaluation values ​​for a certain user. The higher the score, the more positive the situation. Since this embodiment analyzes the situation of normal contract signing and irrelevant complaints, the evaluation value corresponding to the complaint value and contract signing value of all users is 100. Figure 7 The outer dashed ring represents the 100-point ring (i.e., the perfect score ring), and the inner dashed ring represents the 60-point ring (i.e., the passing score ring). The user's evaluation score falls between the two dashed rings, indicating that the user's perception is good and the possibility of subsequent complaints is relatively low.

[0113] In this embodiment, based on the score of each business data point for each evaluator, the data is categorized according to a target category to obtain a set of business data corresponding to each of the multiple evaluators in the target category. Based on the set of business data corresponding to each of the multiple evaluators in each target category, the percentage of users meeting preset conditions for each evaluator under each business type is obtained. Based on the user percentage and a preset threshold, it is determined whether the evaluation indicators of the evaluator are abnormal. In the above method, by classifying the scores of different business data and identifying evaluators with low scores corresponding to business types among different evaluators, potential problematic evaluators are identified to facilitate subsequent adaptive problem-solving.

[0114] Figure 8 A diagram of a user perception index evaluation device provided in an embodiment of the present invention is shown, such as... Figure 7 As shown, the device includes: an acquisition module 801, a judgment module 802, and a processing module 803;

[0115] The acquisition module 801 is used to acquire the contract data of the business front-end and business back-end of the same user, and to determine whether the contract data of the business front-end and the business back-end meet the data consistency requirement.

[0116] The acquisition module 801 is also used to determine whether the contract data of the multiple network elements meets the data consistency requirement;

[0117] If so, determine whether the contract data in the business front end and the contract data in the business back end meet the data consistency requirement.

[0118] The acquisition module 801 is also used to determine whether the services activated by the multiple network elements for the user are consistent. If they are consistent, the subscription data satisfies the data consistency requirement.

[0119] The step of determining whether the contract data in the business front-end and the contract data in the business back-end meet the data consistency requirement includes:

[0120] For each service type, determine whether the subscription status of the service type in the service front end matches the actual activation status of each network element. If they match, confirm that the subscription data in the service front end and the subscription data in the service back end meet the data consistency requirements.

[0121] The judgment module 802 is used to obtain the user's complaint data when the contract data in the business front end and the business back end meet the data consistency requirement.

[0122] The processing module 803 is used to obtain a user perception index evaluation value based on the user's experience data if the complaint data is unrelated to the contract data. The experience data includes at least one of the following service data: call connection rate, MOS value, voice service call drop rate, or data service download rate.

[0123] The processing module 803 is further configured to obtain the user perception index evaluation value based on the score of each business data of each evaluated user and the weight value corresponding to each business data, wherein the score of each business data is obtained according to the mapping relationship between business data and score, and different business data ranges correspond to different scores.

[0124] The processing module 803 is also used to obtain the service usage of each user's target service, wherein the service usage includes the number of calls for the call service and the data usage for the data service.

[0125] Users who make more than a preset number of calls or use more than a preset amount of data will be considered as the evaluated users.

[0126] The processing module 803 is also used to classify and process the data according to the target category based on the score of each service data of each evaluation user, so as to obtain the service data set corresponding to each of the multiple evaluation objects of the target category. The evaluation object of the target category is one of the communication cell, service data network element, service platform or terminal type, and each service data set corresponds to the service data of multiple users of each service type.

[0127] Based on the business data sets corresponding to the multiple participating objects in each target category, the evaluation indicators for the participating objects under each target category are obtained.

[0128] The processing module 803 is also used to obtain the percentage of users who meet the preset conditions for each evaluation object under each business type based on the business data set corresponding to each of the multiple evaluation objects in each target category.

[0129] Based on the user percentage and preset threshold, determine whether the evaluation indicators of the participating object are abnormal.

[0130] This application also provides a user perception index evaluation device, comprising: at least one processor and a memory;

[0131] The memory stores computer-executed instructions;

[0132] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to execute a user perception index evaluation method.

[0133] Figure 9 This is a hardware schematic diagram of a user perception index evaluation device provided in an embodiment of the present invention. Figure 9 As shown, the user perception index evaluation device 90 provided in this embodiment includes at least one processor 901 and a memory 902. The device 90 also includes a communication component 903. The processor 901, memory 902, and communication component 903 are connected via a bus 904.

[0134] In the specific implementation process, at least one processor 901 executes the computer execution instructions stored in the memory 902, causing at least one processor 901 to execute the user perception index evaluation method as described above.

[0135] The specific implementation process of processor 901 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0136] In the above Figure 9 In the illustrated embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0137] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0138] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0139] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the user perception index evaluation method described above.

[0140] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0141] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0142] The division of units described herein is merely a logical functional division. 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 indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0143] 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.

[0144] In addition, the functional units in the various embodiments of the present invention 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.

[0145] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion 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 personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0146] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0147] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for evaluating user perception metrics, characterized in that, include: Obtain the contract data of the same user's business front-end and business back-end; The contract data in the business backend includes contract data from multiple network elements; Determine whether the services activated for the user by the multiple network elements are consistent. If they are consistent, the subscription data satisfies the data consistency requirement. If so, for each service type, determine whether the contract status of the service type in the service front end matches the actual activation status of each network element. If they match, confirm that the contract data in the service front end and the contract data in the service back end meet the data consistency requirement. When the contract data between the business front-end and the business back-end meets the data consistency requirement, the user's complaint data is obtained; If the complaint data is unrelated to the contract data, then the user perception index evaluation value is obtained based on the user's experience data, wherein the experience data includes at least one of the following service data: call connection rate, MOS value, voice service call drop rate, or data service download speed.

2. The method according to claim 1, characterized in that, The step of obtaining user perception index evaluation values ​​based on the user's experience data includes: The user perception index evaluation value is obtained based on the score of each business data of each user and the weight value corresponding to each business data. The score of each business data is obtained according to the mapping relationship between business data and score, and different business data ranges correspond to different scores.

3. The method according to claim 2, characterized in that, Before obtaining the user perception index evaluation value based on the score of each business data point for each evaluated user and the weight value corresponding to each business data point, the method further includes: Obtain the service usage of each user's target service, wherein the service usage includes the number of calls for the call service and the data usage for the data service; Users who make more than a preset number of calls or use more than a preset amount of data will be considered as the evaluated users.

4. The method according to claim 2, characterized in that, After obtaining the user perception index evaluation value based on the user's experience data, the method further includes: Based on the score of each service data of each evaluator, the data is categorized according to the target category to obtain the service data set corresponding to each of the multiple evaluation objects of the target category. The evaluation object of the target category is one of the following: communication cell, service data network element, service platform or terminal type. Each service data set corresponds to the service data of multiple users of each service type. Based on the business data sets corresponding to the multiple participating objects in each target category, the evaluation indicators for the participating objects under each target category are obtained.

5. The method according to claim 4, characterized in that, The step of obtaining evaluation indicators for each target category's participating objects based on their respective business data sets includes: Based on the business data sets corresponding to multiple participating objects in each target category, obtain the percentage of users who meet the preset conditions for each participating object under each business type; Based on the user percentage and preset threshold, determine whether the evaluation indicators of the participating object are abnormal.

6. A user perception index evaluation device, characterized in that, include: The acquisition module is used to acquire the contract data of the business front-end and business back-end of the same user. The contract data of the business back-end includes the contract data of multiple network elements. Determine whether the services activated for the user by the multiple network elements are consistent. If they are consistent, the subscription data satisfies the data consistency requirement. If so, for each service type, determine whether the contract status of the service type in the service front end matches the actual activation status of each network element. If they match, confirm that the contract data in the service front end and the contract data in the service back end meet the data consistency requirement. The judgment module is used to obtain the user's complaint data when the contract data in the business front end and the business back end meet the data consistency requirement; The processing module is used to obtain a user perception index evaluation value based on the user's experience data if the complaint data is unrelated to the contract data. The experience data includes at least one of the following service data: call connection rate, MOS value, voice service call drop rate, or data service download rate.

7. A user perception index evaluation device, characterized in that, include: At least one processor and memory; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the user perception index evaluation method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the user perception index evaluation method as described in any one of claims 1-5.

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