System user experience evaluation method and device, storage medium and electronic equipment

Abstract

The invention discloses a system user experience evaluation method and device, a storage medium and electronic equipment, and relates to the technical field of operating systems, and the method comprises the steps: obtaining application scene data of a system in an actual scene, and obtaining an abnormal type monitored for a subsystem in the system; carrying out weight adjustment according to the exception type by adopting a user experience scoring model to obtain a multi-dimensional weight coefficient; according to the application scene data and the multi-dimensional weight coefficient, user experience influence evaluation is carried out, and a multi-dimensional experience influence score is obtained; and performing calculation according to the multi-dimensional experience influence score to obtain a user experience score of the system in the actual scene. According to the method, the user experience of the system in an actual scene under the system abnormality can be accurately evaluated, and the improvement effect of system optimization on the user experience is improved.

Description

Technical Field

[0001] This application relates to the technical field of operating systems, and particularly to a system user experience evaluation method, device, storage medium, and electronic device. Background Art

[0002] In an operating system such as the Android system, full-system life cycle anomaly monitoring is usually performed, that is, life cycle anomaly monitoring is performed on all subsystems in the system, and the anomaly status of each subsystem can be obtained through the anomaly monitoring.

[0003] Currently, system anomaly monitoring and system optimization often detect system anomaly risks and perform system optimization based on the detected anomaly risks. Although the current system optimization can avoid some system anomalies, since the user experience of system anomalies cannot be effectively evaluated, the effect of system optimization on improving the user experience is still limited. Summary of the Invention

[0004] Embodiments of this application provide a solution that can accurately evaluate the user experience of subsystems in an actual scenario under system anomalies and improve the effect of system optimization on improving the user experience.

[0005] Embodiments of this application provide the following technical solutions:

[0006] According to an embodiment of this application, a system user experience evaluation method includes: obtaining application scenario data of the system in an actual scenario, and obtaining the anomaly types monitored for subsystems in the system; using a user experience scoring model to adjust weights according to the anomaly types to obtain multi-dimensional weight coefficients; and performing a user experience impact evaluation according to the application scenario data and the multi-dimensional weight coefficients to obtain multi-dimensional experience impact scores; and calculating according to the multi-dimensional experience impact scores to obtain the user experience score of the system in the actual scenario.

[0007] In some embodiments of this application, after calculating according to the multi-dimensional experience impact scores to obtain the user experience score of the system in the actual scenario, the method further includes: uploading the user experience score to the cloud; receiving model update parameters sent by the cloud, where the model update parameters are obtained by proofreading and analyzing customer complaint satisfaction data and the user experience score in the cloud; and updating the user experience scoring model according to the model update parameters.

[0008] In some embodiments of the present application, the weight adjustment according to the exception type to obtain multi-dimensional weight coefficients includes: determining the corresponding basic weight, mood coefficient, and function coefficient according to the exception type; multiplying the basic weight by the mood coefficient to obtain the first weight coefficient in the dimension of user emotion influence; multiplying the basic weight by the function coefficient to obtain the second weight coefficient in the dimension of function usage influence.

[0009] In some embodiments of the present application, the application scenario data includes load status data, usage habit data, application behavior data, and user popularity data; the evaluation of the user experience impact according to the application scenario data and the multi-dimensional weight coefficients to obtain multi-dimensional experience impact scores includes: calculating respectively according to the load status data, the usage habit data, the application behavior data, and the user popularity data to obtain the system load factor, usage habit coefficient, application behavior coefficient, and user popularity index; performing impact evaluation calculation according to the system load factor, the usage habit coefficient, the application behavior coefficient, the user popularity index, and the multi-dimensional weight coefficients to obtain multi-dimensional experience impact scores.

[0010] In some embodiments of the present application, the evaluation of the impact according to the system load factor, the usage habit coefficient, the application behavior coefficient, the user popularity index, and the multi-dimensional weight coefficients to obtain multi-dimensional experience impact scores includes: calculating according to the emotion impact calculation parameters, the first weight coefficient in the dimension of user emotion influence, the system load factor, the usage habit coefficient, the application behavior coefficient, and the user popularity index to obtain the first experience impact score in the dimension of user emotion influence; calculating according to the function impact calculation parameters, the second weight coefficient in the dimension of function usage influence, the system load factor, the usage habit coefficient, the application behavior coefficient, and the user popularity index to obtain the second experience impact score in the dimension of function usage influence.

[0011] In some embodiments of the present application, calculating the system load factor, usage habit coefficient, application behavior coefficient, and user popularity index respectively based on the load status data, usage habit data, application behavior data, and user popularity data includes: calculating the system load factor according to the formula S = X1*C_u + X2*M_u + X3*IO_w + X4*T_s, where S refers to the system load factor, X1, X2, X3, and X4 are load calculation parameters respectively, and C_u, M_u, IO_w, and T_s are the CPU occupancy rate, memory occupancy rate, input / output wait duration, and device temperature in the load status data respectively; calculating the usage habit coefficient according to the formula H = 1 - |c_h - p_h| / 12, where H refers to the usage habit coefficient, and c_h and p_h refer to the system current time and user preference time in the usage habit data respectively; calculating the application behavior coefficient according to the formula A = B1*F_i + B2*C_o, where A refers to the application behavior coefficient, B1 and B2 are behavior calculation parameters, and F_i and C_o refer to the weights corresponding to the application running status and key operations in the application behavior data respectively; calculating the user popularity index according to the formula U = log(C1 + C2*D + C3*T) / C4, where U refers to the user popularity index, and C1, C2, C3, and C4 are popularity calculation parameters, and D and T are the average daily usage times and average daily usage durations in the user popularity data respectively.

[0012] In some embodiments of the present application, the multi-dimensional experience impact scores include a first experience impact score in the user emotion impact dimension and a second experience impact score in the function usage impact dimension; calculating the user experience score of the system in the actual scenario according to the multi-dimensional experience impact scores includes: calculating the user experience score according to the formula Impact = √(M 2 + F2)*100, where Impact refers to the user experience score, M refers to the first experience impact score, and F refers to the second experience impact score.

[0013] According to an embodiment of the present application, a system user experience evaluation device includes: an acquisition unit configured to acquire application scenario data of the system in an actual scenario and acquire the abnormal types monitored for subsystems in the system; an evaluation unit configured to use a user experience score model to evaluate the user experience impact according to the application scenario data to obtain multi-dimensional experience impact scores; and assign weights according to the abnormal types to obtain multi-dimensional weight coefficients; and perform weighted calculation according to the multi-dimensional experience impact scores and the multi-dimensional weight coefficients to obtain the user experience score of the system in the actual scenario.

[0014] According to another embodiment of the present application, a storage medium stores a computer program, and when the computer program is executed by a processor of a computer, the computer is caused to execute the method described in the embodiments of the present application.

[0015] According to another embodiment of the present application, an electronic device may include: a memory storing a computer program; and a processor reading the computer program stored in the memory to execute the method described in the embodiments of the present application.

[0016] According to another embodiment of the present application, a computer program product or a computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method provided in various alternative implementations described in the embodiments of the present application.

[0017] In the embodiments of the present application, application scenario data of a system in an actual scenario is obtained, and an abnormal type monitored for a subsystem in the system is obtained; a user experience scoring model is adopted to adjust weights according to the abnormal type to obtain multi-dimensional weight coefficients; and, user experience impact evaluation is performed according to the application scenario data and the multi-dimensional weight coefficients to obtain multi-dimensional experience impact scores; and, a user experience score of the system in the actual scenario is obtained by calculating according to the multi-dimensional experience impact scores.

[0018] In this way of the embodiments of the present application, by obtaining the application scenario data and the abnormal type of the system in the actual scenario, and adopting the user experience scoring model to perform weight adjustment, user experience impact evaluation, and calculation of the user experience score, the user experience of the system in the actual scenario under system anomalies can be accurately evaluated, and the improvement effect of system optimization on the user experience can be enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings without creative efforts based on these drawings.

[0020] Figure 1 The flowchart of the system user experience evaluation method according to an embodiment of the present application is shown.

[0021] Figure 2 The system framework diagram of the system user experience evaluation according to an embodiment of the present application is shown.

[0022] Figure 3 shows a flowchart of model update according to an embodiment of the present application.

[0023] Figure 4 shows a block diagram of a system user experience evaluation device according to an embodiment of the present application.

[0024] Figure 5 shows a block diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0025] The present disclosure will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments provided herein are only used to explain the present disclosure and are not used to limit the present disclosure. In addition, the embodiments provided below are partial embodiments for implementing the present disclosure, rather than all embodiments for implementing the present disclosure. Without conflict, the technical solutions described in the embodiments of the present disclosure can be implemented in any combination.

[0026] It should be noted that, in the embodiments of the present disclosure, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a method or device including a series of elements not only includes the elements clearly recited, but also includes other elements not explicitly listed, or further includes elements inherent to the implementation of the method or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of other related elements in the method or device including the element (for example, steps in the method or units in the device, and the units can be partial circuits, partial processors, partial programs or software, etc.).

[0027] For example, the system user experience evaluation method provided in the embodiments of the present disclosure includes a series of steps, but the system user experience evaluation method provided in the embodiments of the present disclosure is not limited to the recited steps. Similarly, the system user experience evaluation device provided in the embodiments of the present disclosure includes a series of units, but the device provided in the embodiments of the present disclosure is not limited to including the explicitly recited units, and may further include units required for obtaining relevant information or processing based on the information.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present disclosure belongs. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0029] It can be understood that in the specific embodiments of the present application, when it comes to relevant data, when the embodiments in the present application are applied to specific products or technologies, user permission or consent needs to be obtained, and the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards in the relevant regions.

[0030] Figure 1 The flowchart of the system user experience evaluation method according to an embodiment of the present application is schematically shown. The execution subject of the system user experience evaluation method can be a device or server with processing capabilities, such as a TV, computer, mobile phone, smart watch, and home appliance device, etc. The server can be a cloud server or a physical server, etc.

[0031] The execution subject can execute each step of the system user experience evaluation method as Figure 1 shown. The system user experience evaluation method can include step S110 to step S140.

[0032] Step S110, obtain the application scenario data of the system in the actual scenario, and obtain the abnormal types monitored for the subsystems in the system;

[0033] Step S120, adopt a user experience scoring model, adjust the weights according to the abnormal types, and obtain multi-dimensional weight coefficients; and,

[0034] Step S130, conduct a user experience impact evaluation according to the application scenario data and the multi-dimensional weight coefficients, and obtain multi-dimensional experience impact scores; and,

[0035] Step S140, calculate according to the multi-dimensional experience impact scores to obtain the user experience score of the system in the actual scenario.

[0036] The system can include subsystems such as network, Bluetooth, multimedia, audio, etc. that implement various functions in the system, as well as subsystems that implement other custom functions, etc. Refer to Figure 2In one example, when the system detects an anomaly, the anomaly joint diagnosis module 210 in the execution entity (which can also be referred to as the anomaly monitoring center) can obtain application scenario data of the system in the actual scenario (i.e., the actual usage scenario where the device is currently located, such as scenarios like listening to music and playing games) from the scenario recognition module 220. The application scenario data can include, but is not limited to, load status data, usage habit data, application behavior data, and user popularity data, etc., applied in the actual scenario. At the same time, through the anomaly joint diagnosis module 210 in the execution entity, it is possible to obtain the anomaly information of each subsystem obtained by monitoring the operating status of each subsystem in the system from the anomaly information module 230, and jointly diagnose the anomaly type of the actual scenario based on the anomaly information of each subsystem (for example, Crash, Freeze, non-blocking Error, etc.).

[0037] Refer to Figure 2 In the execution entity (which can also be referred to as the anomaly monitoring center), the user experience scoring model 240 is configured, and the user experience scoring model is used to perform user experience scoring. Inputting the application scenario data and the anomaly type into the user experience scoring model, the user experience scoring model can further execute steps S120 to S140.

[0038] The user experience scoring model first adjusts the weights according to the anomaly type to obtain multi-dimensional weight coefficients, with each dimension corresponding to a weight coefficient. Then, it evaluates the impact on the user experience based on the application scenario data and the multi-dimensional weight coefficients to obtain multi-dimensional experience impact scores. The multi-dimensions can include, but are not limited to, the user emotion impact dimension and the function usage impact dimension, etc., with each dimension corresponding to an experience impact score.

[0039] Finally, the user experience scoring model calculates based on the multi-dimensional experience impact scores, and can accurately evaluate the user experience score of the system in the actual scenario when an anomaly occurs. This user experience score can accurately reflect the user's experience in the actual scenario. Optimizing the system based on this user experience score combined with the anomaly type can further effectively improve the user experience.

[0040] In summary, in the way of this embodiment of the present application, by obtaining the application scenario data and the anomaly type of the system in the actual scenario, and using the user experience scoring model to perform weight adjustment, user experience impact evaluation, and calculation of the user experience score, it is possible to accurately evaluate the user experience of the system in the actual scenario under system anomalies, and improve the improvement effect of system optimization on the user experience.

[0041] The following describes Figure 1 Specific optional embodiments for each step when performing system user experience evaluation in the embodiment.

[0042] In one embodiment, the weight adjustment according to the exception type to obtain multi-dimensional weight coefficients includes: determining a corresponding basic weight, mood coefficient, and function coefficient according to the exception type; multiplying the basic weight by the mood coefficient to obtain a first weight coefficient for the user emotion influence dimension; multiplying the basic weight by the function coefficient to obtain a second weight coefficient for the function usage influence dimension.

[0043] In this embodiment, each exception type has a corresponding basic weight, mood coefficient, and function coefficient. In a specific example, the basic weight, mood coefficient, and function coefficient corresponding to a crash are 1.0, 0.9, and 0.8 respectively, the basic weight, mood coefficient, and function coefficient corresponding to a freeze are 0.8, 0.7, and 0.9 respectively, and the basic weight, mood coefficient, and function coefficient corresponding to a non-blocking error are 0.5, 0.5, and 0.3 respectively.

[0044] Furthermore, multiply the basic weight by the mood coefficient to obtain a first weight coefficient for the user emotion influence dimension; multiply the basic weight by the function coefficient to obtain a second weight coefficient for the function usage influence dimension. By obtaining the first weight coefficient and the second weight coefficient in this way of weight adjustment, the user experience score can be accurately calculated by combining the user emotion influence dimension and the function usage influence dimension.

[0045] In one embodiment, the application scenario data includes load status data, usage habit data, application behavior data, and user popularity data; the user experience impact evaluation according to the application scenario data and the multi-dimensional weight coefficients to obtain multi-dimensional experience impact scores may include:

[0046] Calculate respectively according to the load status data, the usage habit data, the application behavior data, and the user popularity data to obtain a system load factor, a usage habit coefficient, an application behavior coefficient, and a user popularity index; perform an impact evaluation calculation according to the system load factor, the usage habit coefficient, the application behavior coefficient, the user popularity index, and the multi-dimensional weight coefficients to obtain multi-dimensional experience impact scores.

[0047] The load status data may include, but is not limited to, CPU occupancy, memory occupancy, input / output wait duration, and device temperature; the usage habit data may include, but is not limited to, user preferred time; the application behavior coefficient may include, but is not limited to, application running status and key operations; the user popularity data may include, but is not limited to, daily average usage times and daily average usage duration.

[0048] Calculate respectively according to the load status data, the usage habit data, the application behavior data, and the user popularity data to obtain a system load factor, a usage habit coefficient, an application behavior coefficient, and a user popularity index; then, perform an impact evaluation calculation according to the system load factor, the usage habit coefficient, the application behavior coefficient, the user popularity index, and the multi-dimensional weight coefficients, and the experience impact score reflecting the user experience in multiple dimensions can be accurately calculated by integrating the data in these aspects.

[0049] Further, in an embodiment, the performing an impact evaluation calculation according to the system load factor, the usage habit coefficient, the application behavior coefficient, the user popularity index, and the multi-dimensional weight coefficients to obtain a multi-dimensional experience impact score includes:

[0050] Calculate according to the emotion impact calculation parameter, the first weight coefficient of the user emotion impact dimension, the system load factor, the usage habit coefficient, the application behavior coefficient, and the user popularity index to obtain the first experience impact score of the user emotion impact dimension;

[0051] Calculate according to the function impact calculation parameter, the second weight coefficient of the function usage impact dimension, the system load factor, the usage habit coefficient, the application behavior coefficient, and the user popularity index to obtain the second experience impact score of the function usage impact dimension.

[0052] The emotion impact calculation parameter and the function impact calculation parameter are partial model parameters of the user experience scoring model. Calculating according to the emotion impact calculation parameter, the first weight coefficient of the user emotion impact dimension, the system load factor, the usage habit coefficient, the application behavior coefficient, and the user popularity index can accurately calculate the first experience impact score reflecting the user experience in the user emotion impact dimension. Calculating according to the function impact calculation parameter, the second weight coefficient of the function usage impact dimension, the system load factor, the usage habit coefficient, the application behavior coefficient, and the user popularity index can accurately calculate the second experience impact score reflecting the user experience in the function usage impact dimension.

[0053] Further, in a specific embodiment, the calculating respectively according to the load status data, the usage habit data, the application behavior data, and the user popularity data to obtain a system load factor, a usage habit coefficient, an application behavior coefficient, and a user popularity index includes:

[0054] The system load factor is calculated according to the formula S = X1*C_u + X2*M_u + X3*IO_w + X4*T_s, where S refers to the system load factor, X1, X2, X3, and X4 are load calculation parameters respectively, and C_u, M_u, IO_w, and T_s are the CPU occupancy rate, memory occupancy rate, input / output wait duration, and device temperature in the load status data respectively;

[0055] The usage habit coefficient is calculated according to the formula H = A1 - |c_h - p_h| / A2, where H refers to the usage habit coefficient, c_h and p_h refer to the system current time and user preference time in the usage habit data respectively, and A1 and A2 are habit calculation parameters;

[0056] The application behavior coefficient is calculated according to the formula A = B1*F_i + B2*C_o, where A refers to the application behavior coefficient, B1 and B2 are behavior calculation parameters, and F_i and C_o are the weights corresponding to the application running status and key operations in the application behavior data respectively;

[0057] The user popularity index is calculated according to the formula U = log(C1 + C2*D + C3*T) / C4, where U refers to the user popularity index, C1, C2, C3, and C4 are popularity calculation parameters, and D and T are the average daily usage times and average daily usage durations in the user popularity data respectively.

[0058] In the above several formulas, X1, X2, X3, and X4 are load calculation parameters respectively, A1 and A2 are habit calculation parameters, B1 and B2 are behavior calculation parameters, and C1, C2, C3, and C4 are popularity calculation parameters. These parameters are all partial model parameters of the user experience scoring model. Based on these model parameters and the above formulas, the system load factor, usage habit coefficient, application behavior coefficient, and user popularity index can be accurately obtained, which can be accurately used to calculate the first experience impact score and the second experience impact score.

[0059] Among them, in one example, X1, X2, X3, and X4 are 0.4, 0.3, 0.2, and 0.1 respectively, (S)[0-1]: S = 0.4*C_u + 0.3*M_u + 0.2*IO_w + 0.1*T_s; in one example, A1 and A2 are 1 and 12 respectively, (H)[0-1]: H = 1 - |c_h - p_h| / 12; in one example, B1 and B2 are 0.6 and 0.4 respectively, (A)[0-1]: A = 0.6*F_i + 0.4*C_o; in one example, C1, C2, C3, and C4 are 1, 3, 2, and 5 respectively, (U)[0-1]: U = log(1 + 3*D + 2*T) / 5. Based on the formulas of these several examples, the system load factor, usage habit coefficient, application behavior coefficient, and user popularity index can be calculated extremely accurately.

[0060] Among them, F_i and C_o respectively refer to the weights corresponding to the application running state and key operations in the application behavior data. The weights corresponding to different application running states are different. For example, the weight corresponding to the foreground core process of the application running state is 1.0, the weight corresponding to the foreground regular interface of the application running state is 0.7, the weight corresponding to the background service running of the application running state is 0.4, and the weight corresponding to the background silent running of the application running state is 0.1. The weight corresponding to the critical operation (Critical_operation) being the payment processing is 1, the weight corresponding to the critical operation being the data saving task is 0.8, the weight corresponding to the critical operation being the media playback task is 0.6, and the weight corresponding to other operations is 0.3.

[0061] Further, in one embodiment, the multi-dimensional experience impact score includes a first experience impact score in the user emotion impact dimension and a second experience impact score in the function usage impact dimension; the calculation based on the multi-dimensional experience impact score to obtain the user experience score of the system in the actual scenario includes: calculating according to the formula Impact = √(M 2 +F2)*100 to obtain the user experience score, where Impact refers to the user experience score, M refers to the first experience impact score, and F refers to the second experience impact score.

[0062] The multi-dimensional experience impact score includes a first experience impact score in the user emotion impact dimension and a second experience impact score in the function usage impact dimension. At this time, according to the formula Impact = √(M 2 +F2)*100 for calculation, the applicant finds that the user experience score reflecting the user experience can be calculated extremely accurately.

[0063] In one embodiment, referring to Figure 3 , after calculating according to the multi-dimensional experience impact score to obtain the user experience score of the system in the actual scenario, the method may further include:

[0064] Step S310, uploading the user experience score to the cloud;

[0065] Step S320, receiving the model update parameters sent by the cloud, where the model update parameters are obtained by proofreading and analyzing the customer complaint satisfaction data and the user experience score in the cloud;

[0066] Step S330, updating the user experience scoring model according to the model update parameters.

[0067] Combined with referring to Figure 2 , the user experience score evaluated by the user experience scoring model can be uploaded to the scoring error correction module in the cloud. The scoring error correction module can proofread and analyze the corresponding user experience score according to the customer complaint satisfaction data (i.e., customer complaint and satisfaction and other customer complaint data), and adjust the parameters of the user experience scoring model according to the matching degree of the user experience score and the customer complaint satisfaction data to obtain the model update parameters. The cloud further sends the model update parameters to the scoring model update module 250 of the terminal, and the scoring model update module 250 can update the user experience scoring model according to the model update parameters, thereby improving the scoring accuracy of the user experience scoring model.

[0068] Among them, the model update parameters may include, but are not limited to, the update parameters of the model parameters in the foregoing embodiments. The model parameters such as emotion impact calculation parameters, function impact calculation parameters, load calculation parameters, habit calculation parameters, behavior calculation parameters, heat calculation parameters, each abnormal type has corresponding basic weights, mood coefficients, and function coefficients, etc.

[0069] To facilitate better implementation of the system user experience evaluation method provided by the embodiments of the present application, the embodiments of the present application also provide a system user experience evaluation device based on the above system user experience evaluation method. The meanings of the nouns are the same as those in the above system user experience evaluation method, and the specific implementation details can refer to the description in the method embodiments. Figure 4 The block diagram of a system user experience evaluation device according to an embodiment of the present application is shown.

[0070] As Figure 4As shown, the system user experience evaluation device 400 may include: The acquisition unit 410 may be used to: acquire the application scenario data of the system in the actual scenario, and acquire the abnormal types monitored for the subsystems in the system; The evaluation unit 420 may be used to: adopt a user experience scoring model to perform user experience impact evaluation according to the application scenario data, and obtain multi-dimensional experience impact scores; and, perform weight allocation according to the abnormal types to obtain multi-dimensional weight coefficients; and, perform weighted calculation according to the multi-dimensional experience impact scores and the multi-dimensional weight coefficients to obtain the user experience score of the system in the actual scenario.

[0071] In some embodiments of the present application, after calculating according to the multi-dimensional experience impact scores to obtain the user experience score of the system in the actual scenario, the device further includes an update unit that may be used to: upload the user experience score to the cloud; receive the model update parameters sent by the cloud, where the model update parameters are obtained by the cloud through calibration and analysis based on customer complaint satisfaction data and the user experience score; update the user experience scoring model according to the model update parameters.

[0072] In some embodiments of the present application, the evaluation unit 420 may be used to: determine the corresponding basic weight, mood coefficient and function coefficient according to the abnormal type; multiply the basic weight by the mood coefficient to obtain the first weight coefficient in the dimension of user emotion impact; multiply the basic weight by the function coefficient to obtain the second weight coefficient in the dimension of function usage impact.

[0073] In some embodiments of the present application, the application scenario data includes load status data, usage habit data, application behavior data and user popularity data; the evaluation unit 420 may be used to: calculate respectively according to the load status data, the usage habit data, the application behavior data and the user popularity data to obtain a system load factor, a usage habit coefficient, an application behavior coefficient and a user popularity index; perform impact evaluation calculation according to the system load factor, the usage habit coefficient, the application behavior coefficient, the user popularity index and the multi-dimensional weight coefficients to obtain multi-dimensional experience impact scores.

[0074] In some embodiments of the present application, the evaluation unit 420 may be configured to: calculate, according to the emotion influence calculation parameter, the first weight coefficient of the user emotion influence dimension, the system load factor, the usage habit coefficient, the application behavior coefficient, and the user popularity index, to obtain the first experience influence score of the user emotion influence dimension; calculate, according to the function influence calculation parameter, the second weight coefficient of the function usage influence dimension, the system load factor, the usage habit coefficient, the application behavior coefficient, and the user popularity index, to obtain the second experience influence score of the function usage influence dimension.

[0075] In some embodiments of the present application, the evaluation unit 420 may be configured to: calculate the system load factor according to the formula S = X1*C_u + X2*M_u + X3*IO_w + X4*T_s, where S refers to the system load factor, and X1, X2, X3, and X4 are load calculation parameters respectively, and C_u, M_u, IO_w, and T_s are the CPU occupancy rate, memory occupancy rate, input / output waiting duration, and device temperature in the load status data respectively; calculate the usage habit coefficient according to the formula H = 1 - |c_h - p_h| / 12, where H refers to the usage habit coefficient, and c_h and p_h refer to the system current time and the user preference time in the usage habit data respectively; calculate the application behavior coefficient according to the formula A = B1*F_i + B2*C_o, where A refers to the application behavior coefficient, and B1 and B2 are behavior calculation parameters, and F_i and C_o are the weights corresponding to the application running status and the key operations in the application behavior data respectively; calculate the user popularity index according to the formula U = log(C1 + C2*D + C3*T) / C4, where U refers to the user popularity index, and C1, C2, C3, and C4 are popularity calculation parameters, and D and T are the average daily usage times and the average daily usage durations in the user popularity data respectively.

[0076] In some embodiments of the present application, the multi-dimensional experience influence scores include the first experience influence score of the user emotion influence dimension and the second experience influence score of the function usage influence dimension; the evaluation unit 420 may be configured to: calculate the user experience score according to the formula Impact = √(M 2 + F2)*100, where Impact refers to the user experience score, M refers to the first experience influence score, and F refers to the second experience influence score.

[0077] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0078] In addition, an embodiment of the present application also provides an electronic device, as Figure 5 shown, Figure 5 The block diagram of the electronic device according to an embodiment of the present application is shown. Specifically:

[0079] The electronic device may include components such as a processor 501 with one or more processing cores, a memory 502 with one or more computer-readable storage media, a power supply 503, and an input unit 504. Those skilled in the art can understand that Figure 5 the structure of the electronic device shown in

[0080] does not constitute a limitation on the electronic device, and it may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0081] The processor 501 is the control center of the electronic device, connecting various parts of the entire computer device through various interfaces and lines, and by running or executing software programs and / or modules stored in the memory 502, and calling data stored in the memory 502, it executes various functions of the computer device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interfaces, and application programs, etc., and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor 501.

[0082] The memory 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the memory 502. The memory 502 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the computer device. In addition, the memory 502 can include high-speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory 502 can also include a memory controller to provide the processor 501 with access to the memory 502.

[0083] The electronic device further includes a power supply 503 for powering each component. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 503 can also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0084] The electronic device may further include an input unit 504, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0085] Although not shown, the electronic device may further include a display unit, etc., which will not be elaborated here. Specifically, in this embodiment, the processor 501 in the electronic device will load the executable files corresponding to the processes of one or more computer programs into the memory 502 according to the following instructions, and the processor 501 will run the computer programs stored in the memory 502 to implement various functions in the foregoing embodiments of the present application. For example, the processor 501 can execute the following steps:

[0086] Obtain the application scenario data of the system in the actual scenario, and obtain the abnormal types monitored for the subsystems in the system; use the user experience scoring model to adjust the weights according to the abnormal types to obtain multi-dimensional weight coefficients; and, perform a user experience impact assessment according to the application scenario data and the multi-dimensional weight coefficients to obtain multi-dimensional experience impact scores; and, calculate according to the multi-dimensional experience impact scores to obtain the user experience score of the system in the actual scenario.

[0087] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by a computer program or by controlling related hardware through a computer program. The computer program can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0088] For this reason, the embodiments of the present application further provide a storage medium, in which a computer program is stored. The computer program can be loaded by a processor to execute the steps in any one of the methods provided by the embodiments of the present application.

[0089] Among them, the storage medium can be a computer-readable storage medium, and the storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0090] Since the computer program stored in the storage medium can execute the steps in any one of the methods provided by the embodiments of the present application, the beneficial effects that can be achieved by the methods provided by the embodiments of the present application can be realized. For details, see the previous embodiments and will not be repeated here.

[0091] After considering the specification and practicing the disclosed embodiments herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application.

[0092] It should be understood that the present application is not limited to the embodiments described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for evaluating system user experience, characterized in that: include: Obtain application scenario data of the system in actual scenarios, and obtain the types of anomalies monitored by subsystems in the system; Using the user experience scoring model, weights are adjusted according to the abnormality types to obtain multi-dimensional weight coefficients; as well as, Performing a user experience impact assessment based on the application scenario data and the multi-dimensional weight coefficients to obtain a multi-dimensional experience impact score; as well as, A calculation is performed based on the multi-dimensional experience impact scores to obtain a user experience score of the system in the actual scenario.

2. The method according to claim 1, characterized in that After calculating according to the multi-dimensional experience impact scores to obtain a user experience score of the system in the actual scenario, the method further includes: Uploading the user experience score to the cloud; Receiving the model update parameters sent by the cloud, where the model update parameters are obtained by proofreading and analyzing the customer complaint satisfaction data and the user experience score in the cloud; The user experience scoring model is updated according to the model update parameters.

3. The method according to claim 1, characterized in that The weight adjustment is performed according to the abnormality type to obtain multi-dimensional weight coefficients, including: Determine the corresponding basic weight, mood coefficient and function coefficient according to the abnormal type; Multiplying the basic weight by the mood coefficient to obtain a first weight coefficient of the user emotion impact dimension; The basic weight is multiplied by the functional coefficient to obtain a second weight coefficient of the functional usage impact dimension.

4. The method according to claim 1, characterized in that: The application scenario data includes load status data, usage habit data, application behavior data, and user popularity data; the user experience impact assessment is performed based on the application scenario data and the multi-dimensional weight coefficients to obtain a multi-dimensional experience impact score, including: Calculating the load state data, the usage habit data, the application behavior data, and the user heat data to obtain a system load factor, a usage habit coefficient, an application behavior coefficient, and a user heat index; An impact assessment calculation is performed based on the system load factor, the usage habit coefficient, the application behavior coefficient, the user popularity index, and the multi-dimensional weight coefficients to obtain a multi-dimensional experience impact score.

5. The method according to claim 4, characterized in that The impact assessment calculation is performed according to the system load factor, the usage habit coefficient, the application behavior coefficient, the user heat index and the multi-dimensional weight coefficients to obtain a multi-dimensional experience impact score, including: Calculate the first experience impact score of the user emotion impact dimension based on the emotion impact calculation parameter, the first weight coefficient of the user emotion impact dimension, the system load factor, the usage habit coefficient, the application behavior coefficient, and the user heat index; The second experience impact score of the function usage impact dimension is obtained by calculation based on the function impact calculation parameter, the second weight coefficient of the function usage impact dimension, the system load factor, the usage habit coefficient, the application behavior coefficient and the user popularity index.

6. The method according to claim 4, characterized in that The calculation is performed according to the load status data, the usage habit data, the application behavior data and the user heat data to obtain the system load factor, the usage habit coefficient, the application behavior coefficient and the user heat index, including: The system load factor is calculated according to the formula S=X1*C_u+X2*M_u+X3*IO_w+X4*T_s, wherein S refers to the system load factor, X1, X2, X3 and X4 are load calculation parameters, C_u, M_u, IO_w, T_s are CPU occupancy, memory occupancy, input and output waiting time, and device temperature in the load status data; The usage habit coefficient is calculated according to the formula H=A1-|c_h-p_h| / A2, wherein H refers to the usage habit coefficient, c_h and p_h refer to the system current time and the user preferred time in the usage habit data, respectively, and A1 and A2 are habit calculation parameters; The application behavior coefficient is calculated according to the formula A=B1*F_i+B2*C_o, wherein A refers to the application behavior coefficient, B1 and B2 are behavior calculation parameters, and F_i and C_o refer to the weights corresponding to the application running state and key operations in the application behavior data respectively; The user heat index is calculated according to the formula U=log(C1+C2*D+C3*T) / C4, where U refers to the user heat index, C1, C2, C3 and C4 are heat calculation parameters, and D and T are the average daily usage times and average daily usage time in the user heat data, respectively.

7. The method according to claim 1, characterized in that The multi-dimensional experience impact scores include a first experience impact score of a user emotion impact dimension and a second experience impact score of a function usage impact dimension; The calculating according to the multi-dimensional experience impact scores to obtain the user experience score of the system in the actual scenario includes: According to the formula Impact = √(M 2 +F2)*100 to calculate the user experience score, where Impact refers to the user experience score, M refers to the first experience impact score, and F refers to the second experience impact score.

8. A system user experience evaluation device, characterized in that: include: An acquisition unit is used to: acquire application scenario data of the system in an actual scenario, and acquire an abnormality type monitored by a subsystem in the system; An evaluation unit, configured to: use a user experience scoring model to perform a user experience impact evaluation based on the application scenario data to obtain a multi-dimensional experience impact score; And, weights are assigned according to the abnormality types to obtain multi-dimensional weight coefficients; And, a weighted calculation is performed according to the multi-dimensional experience impact scores and the multi-dimensional weight coefficients to obtain a user experience score of the system in the actual scenario.

9. A storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: a memory storing a computer program; A processor reads a computer program stored in a memory to execute the method according to any one of claims 1 to 7.

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