A human-computer interaction performance evaluation method and system based on virtual reality

Abstract

The present invention relates to the technical field of human-computer interaction performance data processing, and specifically discloses a human-computer interaction performance evaluation method and system based on virtual reality. The method collects and analyzes the operation behaviors of users in the entire task execution process, evaluates the response speed, accuracy and processing efficiency of the human-computer interaction system, and is helpful to improve the overall performance and efficiency of the human-computer interaction system; the human-computer interaction performance is evaluated, and problems and deficiencies in the design of the human-computer interaction system can be discovered in time, and difficulties and bottlenecks in the task execution process of users in a virtual reality environment can be identified, thereby optimizing the human-computer interaction interface design, interaction logic and operation process, and improving the user's interaction experience in the virtual environment; the human-computer interaction performance of the human-computer interaction device is optimized, and the erroneous interaction between the user and the device is reduced, thereby improving the user's satisfaction with the human-computer interaction.

Description

Technical Field

[0001] The present invention relates to the technical field of human-computer interaction performance data processing, and in particular to a human-computer interaction performance evaluation method and system based on virtual reality. Background Art

[0002] At present, the evaluation process of human-computer interaction performance needs to rely on the interactive performance in the human-computer interaction process. If the degree of analysis of the interactive performance in the human-computer interaction process is insufficient, the evaluation of the human-computer interaction performance will be affected, and the interactive response, human-computer interaction risks and other aspects will be delayed and restricted.

[0003] For example, the invention patent with announcement number CN113282475B relates to the field of human-computer interaction technology, and discloses a method and device for evaluating the interactive performance of an interactive system. The method for evaluating the interactive performance of an interactive system includes: obtaining the interactive status of each log in the interactive log of the interactive system, and marking each log according to the interactive status; according to the marking result of each log, determining the self-learning index of the interactive system to evaluate the interactive performance of the interactive system. The method for evaluating the interactive performance of an interactive system can be used to evaluate the dynamic process of the interactive system improving its own performance through self-learning, and also discloses a device for evaluating the interactive performance of an interactive system.

[0004] For example, the invention patent with publication number CN116361130A discloses an evaluation method based on a virtual reality human-computer interaction system, which involves the field of virtual reality technology. It obtains the degree of work completion to form an evaluation of the execution of work tasks, and establishes an interactive digital twin model; establishes a system response data set, and forms a functional ratio Gnb based on the proportion of the interaction efficiency coefficient Jxs that exceeds the corresponding threshold; constructs a performance detection data set, associates to form a performance evaluation coefficient Nxs, and forms a performance ratio Xnb based on the proportion of several interaction efficiency coefficients Jxs that are higher than the corresponding threshold; fits the change trend of the interaction coefficient jHxs, and outputs the formed jHxs fitting function; associates to obtain the interaction coefficient jHxs; judges the operation risk of the interactive system, issues an early warning to the outside, and outputs the risk nodes and response strategies; matches the corresponding response strategy from the response strategy library based on the abnormal parameters, and outputs the response strategy on the basis of evaluation and prediction, so that it can be handled in time when operation risks may occur.

[0005] Combined with the above technical solutions, it is found that in the human-computer interaction link, the reliability analysis of human-computer interaction performance has a direct impact on its performance evaluation. If only a single performance evaluation is performed for the complex human-computer interaction link, there will be a large error between the evaluated human-computer interaction performance results and the actual human-computer interaction performance results, and the operation strength of the human-computer interaction cannot be improved. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for evaluating human-computer interaction performance based on virtual reality, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a human-computer interaction performance evaluation method based on virtual reality, including: collecting and analyzing human-computer interaction data: based on a virtual reality environment, a human-computer interaction device controls a user to perform an interactive task, thereby collecting and analyzing the user's behavior data during the task execution process, collecting and analyzing the interaction data of the human-computer interaction device during the task control process, and obtaining a user behavior interaction index and a device interaction performance index.

[0008] Evaluate the human-computer interaction performance: Comprehensively analyze the user behavior interaction index and the device interaction performance index to obtain the human-computer interaction performance evaluation index.

[0009] Human-computer interaction optimization: Compare the human-computer interaction performance evaluation indicators with the preset human-computer interaction performance definition indicators, match the human-computer interaction performance optimization plan, and finally optimize the human-computer interaction performance of the human-computer interaction device.

[0010] As a further method, the user's behavioral data during the task execution process specifically includes the total user interaction time period during the task execution process, the number of user interactions during the task execution process, the user's maximum heart rate during the task execution process, and the number of user breaths during the task execution process.

[0011] As a further method, the user behavior interaction index, the specific analysis process is:

[0012] The user's interaction efficiency during task execution, the user's maximum heart rate during task execution, and the user's average breathing rate during task execution are comprehensively analyzed to obtain the user behavior interaction index.

[0013] As a further method, the interaction data of the human-computer interaction device during the task control process specifically includes: the task interaction response time period, the task interaction execution time period, the total number of interaction task executions, the number of interaction tasks correctly executed, and the interaction task correct execution time period during the task control process.

[0014] As a further method, the device interaction performance index, the specific analysis process is:

[0015] The interactive task execution completion time period, interactive task execution accuracy and interactive task execution time accuracy are comprehensively processed to obtain the device interactive performance index.

[0016] As a further method, the human-computer interaction performance evaluation index, the specific analysis process is:

[0017] The user behavior interaction index and the device interaction performance index are comprehensively analyzed to obtain the human-computer interaction performance evaluation index. The specific analysis formula is:

[0018]

[0019] In the formula, is the human-computer interaction performance evaluation index, e is a natural constant, ρ is the user behavior interaction index, m1 is the weight factor corresponding to the user behavior interaction index preset in the interaction database, di is the device interaction performance index, and m2 is the weight factor corresponding to the device interaction performance index preset in the interaction database.

[0020] As a further method, the human-computer interaction performance of the human-computer interaction device is optimized, and the specific analysis process is as follows:

[0021] The human-computer interaction performance evaluation index is compared with the preset human-computer interaction performance evaluation definition index. If the human-computer interaction performance evaluation index is less than or equal to the human-computer interaction performance evaluation definition index, there is no need to optimize the human-computer interaction performance of the human-computer interaction device.

[0022] If the human-computer interaction performance evaluation index is greater than the human-computer interaction performance evaluation definition index, the human-computer interaction performance evaluation index is matched with the human-computer interaction performance optimization scheme corresponding to each preset human-computer interaction performance evaluation index interval to obtain the human-computer interaction performance optimization scheme, and finally the human-computer interaction performance of the human-computer interaction device is optimized through the human-computer interaction performance optimization scheme.

[0023] The second aspect of the present invention provides a system for a human-computer interaction performance evaluation method based on virtual reality, including: a human-computer interaction data collection and analysis module, which is used for controlling a user to perform an interactive task based on a virtual reality environment, thereby collecting and analyzing the user's behavior data during the task execution process, and collecting and analyzing the interaction data of the human-computer interaction device during the task control process, to obtain a user behavior interaction index and a device interaction performance index.

[0024] The human-computer interaction performance evaluation module is used to comprehensively analyze the user behavior interaction index and the device interaction performance index to obtain the human-computer interaction performance evaluation index.

[0025] The human-computer interaction optimization module compares the human-computer interaction performance evaluation indicators with the preset human-computer interaction performance definition indicators, matches the human-computer interaction performance optimization plan, and finally optimizes the human-computer interaction performance of the human-computer interaction device.

[0026] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0027] (1) The present invention collects and analyzes the user's behavioral data during the task execution process and the interaction data of the human-computer interaction device during the task control process. It records the user's operating behavior in the entire task execution process in detail and conducts in-depth analysis of the user's operating behavior, thereby accurately identifying the advantages and disadvantages in the interaction design. By analyzing the interaction data of the human-computer interaction device, the response speed, accuracy and processing efficiency of the human-computer interaction system can be evaluated, so as to find potential performance bottlenecks and make improvements, which helps to improve the overall performance and efficiency of the human-computer interaction system and ensure that users can complete interaction tasks quickly and accurately.

[0028] (2) By evaluating the human-computer interaction performance, the present invention can timely discover problems and deficiencies in the design of the human-computer interaction system, and can identify the difficulties and bottlenecks encountered by users in the process of executing tasks in a virtual reality environment, thereby optimizing the human-computer interaction interface design, interaction logic and operation process, and improving the user's interaction experience in the virtual environment.

[0029] (3) The present invention optimizes the human-computer interaction performance of the human-computer interaction device through a human-computer interaction performance optimization scheme. The optimized human-computer interaction device can more accurately understand and execute user command tasks, reduce erroneous interactions between users and devices, and thus improve user satisfaction with human-computer interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0031] Figure 1 The figure is a schematic flow chart of the method steps of the present invention.

[0032] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0033] Figure 3 It is a simulation curve diagram between the interactive task execution completion time period and the device interactive performance index involved in the present invention. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] Reference Figure 1 As shown, the first aspect of the present invention provides a human-computer interaction performance evaluation method based on virtual reality, including: collecting and analyzing human-computer interaction data: based on a virtual reality environment, a human-computer interaction device controls a user to perform an interaction task, thereby collecting and analyzing the user's behavior data during the task execution process, collecting and analyzing the interaction data of the human-computer interaction device during the task control process, and obtaining a user behavior interaction index and a device interaction performance index.

[0036] Evaluate the human-computer interaction performance: Comprehensively analyze the user behavior interaction index and the device interaction performance index to obtain the human-computer interaction performance evaluation index.

[0037] Human-computer interaction optimization: Compare the human-computer interaction performance evaluation indicators with the preset human-computer interaction performance definition indicators, match the human-computer interaction performance optimization plan, and finally optimize the human-computer interaction performance of the human-computer interaction device.

[0038] Specifically, the user's behavioral data during the task execution process includes the total user interaction time period during the task execution process, the number of user interactions during the task execution process, the user's maximum heart rate during the task execution process, and the number of user breaths during the task execution process.

[0039] It should be explained that the total user interaction time period of the above-mentioned user during the task execution process can be obtained by directly observing and recording the time points when the user starts and ends the task when the user executes the task, and calculating the difference between the two time points. The number of user interactions during the task execution process can be obtained by counting the number of user interactions during the task execution process through statistical tools (for example: Umeng). The highest heart rate of the user during the task execution process is obtained by real-time monitoring of the user's heart rate value during the task execution by an electrocardiograph, and arranging the heart rate values ​​in order from large to small, and finally obtaining the highest heart rate of the user during the task execution. The number of user breathing times during the task execution process can be obtained by real-time measurement of the user's breathing times during the task execution process through a respiratory rate sensor.

[0040] Specifically, the analysis of the user's behavior data during the task execution process includes the following steps:

[0041] The number of user interactions during the task execution process is compared with the total user interaction time period during the task execution process to obtain the user interaction efficiency during the task execution process.

[0042] The number of user respirations during the task execution is divided by the total user interaction time period during the task execution to obtain the user's average respiration rate during the task execution.

[0043] Furthermore, the user behavior interaction index is specifically analyzed as follows:

[0044] The user's interaction efficiency during task execution, the user's maximum heart rate during task execution, and the user's average breathing rate during task execution are comprehensively analyzed to obtain the user behavior interaction index. The specific analysis formula is:

[0045]

[0046] Where ρ is the user behavior interaction index. If the user's interaction efficiency is low during task execution, it may take more time to complete the interactive task, which will reduce the user's satisfaction and trust in human-computer interaction, and then gradually reduce the user's willingness to interact with the human-computer interaction. At the same time, it will make the user's heart rate too high during task execution, which may mean that the user is under greater physiological pressure. This pressure may cause users to feel uncomfortable, tired or nervous, thereby reducing the user's interaction effect and satisfaction in the virtual environment. In addition, a high heart rate state may make it difficult for users to concentrate, resulting in an increase in the error rate during task execution, making the user's interactive behavior in the virtual environment become unsmooth. For example, users may find it difficult to operate devices such as handles or head-mounted displays due to sweaty hands or nervousness, which in turn affects their exploration and interaction in the virtual environment. When the user's average breathing rate during task execution exceeds the preset user average reference breathing rate, it may mean that the user is under greater physiological pressure. This pressure may cause the user to feel uncomfortable, which in turn affects the user's performance during task execution, for example, making it difficult for users to concentrate and increasing the user's operation error rate. Therefore, by analyzing the various data of the user behavior interaction index, we can find the difficulties and bottlenecks encountered by users when performing interactive tasks, and then optimize the interactive task process, reduce the difficulty of interactive operations, and identify the advantages and disadvantages in interactive design.

[0047] It should be explained that, in this embodiment, by expressing the user's interaction efficiency during task execution, the user's maximum heart rate during task execution, and the user's average breathing frequency during task execution in a nonlinear form, the user's interaction efficiency, physiological reactions (such as heart rate changes) and physiological states (such as breathing frequency) during task execution can be comprehensively evaluated. This comprehensive evaluation can more comprehensively reflect the user's overall experience quality when using a device or system, rather than a one-sided reflection of a single indicator. The use of nonlinear operations can capture the complex relationships and interactive effects that may exist between indicators. For example, the user's interaction efficiency, heart rate and breathing frequency may not be a simple linear relationship, but rather there may be a nonlinear complex pattern. Through nonlinear operations, these complex relationships can be more accurately described and quantified, thereby improving the precision and accuracy of the evaluation.

[0048] yt is the user's interaction efficiency during task execution, which refers to the relationship between the time consumed by the user to perform the interactive task and the number of interactions.

[0049] Δyt is the interaction reference efficiency preset by the interaction database, which refers to the reference efficiency of users performing interaction tasks.

[0050] x1 is the correction factor corresponding to the interaction efficiency preset in the interaction database, which refers to the parameter used to adjust the interaction efficiency when evaluating and optimizing the human-computer interaction system. The corresponding relationship between the historical human-computer interaction duration and the interaction efficiency can be used to fit the fitting curve corresponding to the interaction efficiency. The real-time human-computer interaction duration is brought into the fitting curve corresponding to the interaction efficiency, thereby obtaining the correction factor corresponding to the interaction efficiency in this embodiment, and the value range is (0, 1).

[0051] yo is the user's highest heart rate during task execution, which refers to the maximum value of the user's actual heart rate monitored during the interactive task.

[0052] Δyo is the reference heart rate preset in the interaction database, which refers to the heart rate reference value preset by the user when performing an interactive task.

[0053] x2 is the correction factor corresponding to the user's heart rate preset in the interaction database, which refers to the degree of influence of changes in the user's heart rate on the user behavior interaction index. The fitting curve corresponding to the user's heart rate can be fitted through the correspondence between the historical human-computer interaction time, the historical user age and the user's heart rate. The real-time human-computer interaction time and the user's age are brought into the fitting curve corresponding to the user's heart rate, thereby obtaining the correction factor corresponding to the user's heart rate in this embodiment, and the value range is (0, 1).

[0054] yh is the average breathing frequency of the user during the task execution, which refers to the average number of breaths per minute measured when the user performs the interactive task.

[0055] Δyh is the average reference breathing frequency of the user preset in the interaction database, which refers to the baseline value of the user's breathing frequency when the user performs the interaction task.

[0056] x3 is the correction factor corresponding to the user's breathing frequency preset in the interaction database, which refers to the degree of influence of changes in the user's breathing frequency on the user behavior interaction index. The corresponding relationship between the historical user interaction time and the user's breathing frequency can be used to fit the fitting curve corresponding to the user's breathing frequency. The real-time human-computer interaction time is brought into the fitting curve corresponding to the user's breathing frequency, thereby obtaining the correction factor corresponding to the user's breathing frequency in this embodiment, and the value range is (0, 1).

[0057] It should be explained that the changes in the user's maximum heart rate and the user's average breathing rate during the task execution are related to the specific interactive task. Table 1 below shows the user's maximum heart rate and average breathing rate collected during the execution of different virtual tasks.

[0058] Table 1 User heart rate and breathing rate

[0059]

[0060] By observing the data in Table 1 above, it is found that different types of tasks have a significant impact on the user's physiological response. For example, dynamic or stressful tasks such as shooting games, puzzle tasks and sports competitions lead to higher maximum heart rate and average breathing rate, while more static and relaxing tasks such as education and artistic creation are relatively low. Different users have different physiological responses to the same task, which may be related to the individual's physique, experience, age and personal background. From the data, it can be inferred that there may be a positive correlation between the maximum heart rate and the average breathing rate, that is, the higher the heart rate, the higher the breathing rate tends to increase, reflecting the body's physiological adjustment when coping with more tense or intense activities.

[0061] Specifically, the interaction data of the human-computer interaction device during the task control process specifically includes: the task interaction response time period, the task interaction execution time period, the total number of interactive task executions, the number of correctly executed interactive tasks, and the correctly executed time period of the interactive tasks during the task control process.

[0062] It needs to be explained that, during the task control process of the above-mentioned human-computer interaction equipment, the task interaction response time period, the task interaction execution time period, the total number of interaction task executions, the number of interaction tasks correctly executed, and the time period for correct execution of interaction tasks can all be obtained through relevant record data in the human-computer interaction log in the human-computer interaction system.

[0063] Specifically, the analysis of the interaction data of the human-computer interaction device during the task control process includes the following specific analysis process:

[0064] The task interaction response time period and the task interaction execution time period of the human-computer interaction device in the task control process are summed up to obtain the interactive task execution completion time period.

[0065] The correct execution rate of interactive tasks is obtained by ratioing the number of interactive tasks correctly executed with the total number of interactive tasks executed.

[0066] The correct execution time period of the interactive task is divided by the interactive task completion time period to obtain the interactive task execution time accuracy rate.

[0067] Furthermore, the specific analysis process of the device interaction performance index is as follows:

[0068] The interactive task execution completion time period, interactive task execution accuracy, and interactive task execution time accuracy are comprehensively processed to obtain the device interactive performance index. The specific analysis formula is:

[0069]

[0070] Where di is the device interaction performance index. A longer interactive task execution completion time period means that the time required for users to complete a specific interactive task is too long. When users are waiting for the task to be completed, the perceived time is often longer than the actual time, which will lead to user dissatisfaction and further reduce the correctness of interactive task execution. At the same time, if errors frequently occur in the device during the interaction process, they need to be corrected continuously, such as revocation and modification, which causes users to repeat interactive tasks, increasing the complexity of interactive operations. At the same time, in order to correct errors, more interactive operations and time are required. These operations will bring additional burden to users, thereby extending the execution time of a single task, which may lead to the accumulation of other tasks and affect the overall efficiency. When users are waiting for a task to be completed, it may be difficult to effectively switch to other tasks, increasing the time cost. Therefore, by analyzing the various data in the device interaction performance index, the response speed, accuracy and processing efficiency of the human-computer interaction system can be evaluated, which is helpful to improve the overall performance and efficiency of the human-computer interaction system and ensure that users can complete interactive tasks quickly and accurately.

[0071] It should be explained that in this embodiment, the nonlinear representation of the interactive task execution completion time period, the interactive task execution accuracy and the interactive task execution time accuracy can more comprehensively reflect the performance of the device in actual use. It not only focuses on the speed of task completion, but also takes into account the accuracy of task execution and the precision of time control. Through nonlinear expression, it can capture the complex relationship and mutual influence that may exist between task completion time, accuracy and time accuracy. For example, when the task completion time is short, the changing trend of the accuracy or time accuracy may not be a simple linear relationship. Nonlinear expression can more accurately describe and quantify these complex interaction characteristics.

[0072] dt is the interactive task execution completion time period, which refers to the total length of time from the start of the interactive task to the completion of the interactive task.

[0073] Δdt is the reference time period for completing the interactive task preset in the interactive database, which refers to the maximum allowed time period for completing the interactive task.

[0074] h1 is the correction factor corresponding to the interactive task execution completion time period preset in the interactive database, which refers to the degree of influence of the length of the interactive task execution completion time period on the device interaction performance index. The corresponding relationship between the historical number of interactive tasks and the interactive task execution completion time period can be used to fit the fitting curve corresponding to the interactive task execution completion time period. The real-time number of interactive tasks is brought into the fitting curve corresponding to the interactive task execution completion time period, thereby obtaining the correction factor corresponding to the interactive task execution completion time period in this embodiment, and the value range is (0, 1).

[0075] dr is the correct execution rate of the interactive task, which refers to the proportion of correct interactions in the completed interactive tasks.

[0076] Δdr is the reference accuracy of interactive task execution preset in the interactive database, which refers to the target accuracy of correctly executing interactive tasks. It is used to measure the operational accuracy of users in completing specific interactive tasks under normal circumstances.

[0077] h2 is the correction factor corresponding to the interactive task execution accuracy preset in the interactive database, which refers to the degree of influence of the interactive task execution accuracy on the device interactive performance index. The corresponding relationship between the historical number of interactive tasks correctly executed and the interactive task execution accuracy can be used to fit the fitting curve corresponding to the interactive task execution accuracy. The real-time number of interactive tasks correctly executed is brought into the fitting curve corresponding to the interactive task execution accuracy, thereby obtaining the correction factor corresponding to the interactive task execution accuracy in this embodiment, and the value range is (0, 1).

[0078] dp is the correctness of the interactive task execution time, which refers to the proportion of the time for correctly executing the interactive task to the total interactive time.

[0079] Δdp is the reference accuracy of the interaction task execution time preset in the interaction database, which refers to the reference ratio of the time for correctly executing the interaction task to the total interaction time.

[0080] h3 is the correction factor corresponding to the accuracy of the interactive task execution time preset in the interactive database, which refers to the degree of influence of the accuracy of the interactive task execution time on the device interactive performance index. The fitting curve corresponding to the accuracy of the interactive task execution time can be obtained by fitting the correspondence between the time period of historical correct execution of interactive tasks and the accuracy of interactive task execution time. The real-time correct execution time period of interactive tasks is brought into the fitting curve corresponding to the accuracy of interactive task execution time, thereby obtaining the correction factor corresponding to the accuracy of the interactive task execution time in this embodiment, and the value range is (0, 1).

[0081] In this embodiment, the simulation curve diagram consisting of the above interactive task execution completion time period and the device interaction performance index is as follows: Figure 3 As shown, Figure 3 The horizontal axis is the interactive task execution completion time period dt, in seconds, and the vertical axis is the device interactive performance index di, in %.

[0082] By observation Figure 3 The constructed simulation curve can intuitively observe the changing trend of di (device interaction performance index) with the increase of dt (interaction task execution completion time period). As the interaction task execution completion time period increases, the device interaction performance index shows a trend of first increasing and then decreasing. This is because the increase in the interaction task execution completion time period may lead to an extension of the task completion time, thereby affecting the overall interaction performance index.

[0083] Specifically, the human-computer interaction performance evaluation index has a specific analysis process as follows:

[0084] The user behavior interaction index and the device interaction performance index are comprehensively analyzed to obtain the human-computer interaction performance evaluation index. The specific analysis formula is:

[0085]

[0086] In the formula, As an evaluation index for human-computer interaction performance, a lower interaction index may mean that users are more likely to make misoperations or the probability of operating errors increases, which will not only increase the time cost for users to complete tasks, but may also cause some unnecessary problems or risks, such as an increased possibility of data input errors or equipment damage. A low device interaction performance index means that the device is slow in responding to user instructions and executing instructions, causing users to spend more time and energy to complete tasks, thereby reducing interaction efficiency. It may also mean that there are problems with the stability and reliability of the device. Therefore, by analyzing the data in the human-computer interaction performance evaluation index, problems and deficiencies in the design of the human-computer interaction system can be discovered in a timely manner, and difficulties and bottlenecks in the user's task execution process in a virtual reality environment can be identified, thereby optimizing the human-computer interaction interface design, interaction logic and operation process, and improving the user's interactive experience in the virtual environment.

[0087] e is a natural constant, ρ is the user behavior interaction index, and di is the device interaction performance index.

[0088] m1 is a weight factor corresponding to the user behavior interaction index preset in the interaction database, and m2 is a weight factor corresponding to the device interaction performance index preset in the interaction database.

[0089] It should be explained that the weight factor corresponding to the above-mentioned device user behavior interaction index refers to the influence of the change of the user behavior interaction index on the human-computer interaction performance evaluation index. The fitting curve corresponding to the user behavior interaction index can be fitted through the correspondence between the historical user interaction time, the user's historical heart rate, and the user's historical breathing frequency and the user behavior interaction index. At the same time, the real-time user interaction time, user heart rate, and user breathing frequency are brought into the fitting curve corresponding to the user behavior interaction index, thereby obtaining the weight factor corresponding to the user behavior interaction index in this embodiment, and the value range is (0, 1); the weight factor corresponding to the device interaction performance index refers to the change of the device interaction performance index. The influence of the historical interaction duration, the historical interaction accuracy and the device interaction performance index on the human-computer interaction performance evaluation index can be obtained by fitting the fitting curve corresponding to the device interaction performance index through the corresponding relationship between the historical interaction duration, the historical interaction accuracy and the device interaction performance index. The real-time interaction duration and the interaction accuracy are brought into the fitting curve corresponding to the device interaction performance index, thereby obtaining the weight factor corresponding to the device interaction performance index in this embodiment, and the value range is (0, 1). In this embodiment, the above m1+m2=1, and the weight factor m1 corresponding to the user behavior interaction index is set to 0.4 in this embodiment, and the weight factor corresponding to the device interaction performance index is set to 0.6. The values ​​of the human-computer interaction performance evaluation index in this embodiment are shown in Table 2:

[0090] Table 2 Human-computer interaction performance evaluation index table

[0091]

[0092] By observing the human-computer interaction performance evaluation index table in Table 2 above, it is found that the human-computer interaction performance evaluation index It has a positive proportional relationship with the user behavior interaction index ρ and the device interaction performance index di. It will change accordingly with the changes of user behavior interaction index ρ and device interaction performance index di. For example, when the user behavior interaction index ρ is 80 and the device interaction performance index di is 100, the human-computer interaction performance evaluation index is 118. When the user behavior interaction index ρ is 20 and the device interaction performance index di is 10, the human-computer interaction performance evaluation index is 78.9, that is, when the user behavior interaction index ρ and the device interaction performance index di are large, the corresponding human-computer interaction performance evaluation index When the user behavior interaction index ρ and the device interaction performance index di are small, the corresponding human-computer interaction performance evaluation index It will also be smaller.

[0093] Specifically, the human-computer interaction performance of the human-computer interaction device is optimized, and the specific analysis process is as follows:

[0094] The human-computer interaction performance evaluation index is compared with the preset human-computer interaction performance evaluation definition index. If the human-computer interaction performance evaluation index is less than or equal to the human-computer interaction performance evaluation definition index, there is no need to optimize the human-computer interaction performance of the human-computer interaction device.

[0095] It should be explained that the above-mentioned preset human-computer interaction performance evaluation definition index refers to the maximum value of the human-computer interaction performance evaluation index, which can be obtained by referring to historical human-computer interaction performance evaluation indicators and referring to references related to human-computer interaction performance evaluation. In this embodiment, the human-computer interaction performance evaluation definition index is set to 60.

[0096] If the human-computer interaction performance evaluation index is greater than the human-computer interaction performance evaluation definition index, the human-computer interaction performance evaluation index is matched with the human-computer interaction performance optimization scheme corresponding to each preset human-computer interaction performance evaluation index interval to obtain the human-computer interaction performance optimization scheme, and finally the human-computer interaction performance of the human-computer interaction device is optimized through the human-computer interaction performance optimization scheme.

[0097] It should be explained that the above-mentioned preset human-computer interaction performance evaluation index intervals are obtained by comprehensive analysis based on a large amount of historical manual interaction performance evaluation data and actual human-computer interaction performance.

[0098] The above human-computer interaction performance evaluation index is matched with the human-computer interaction performance optimization scheme corresponding to each preset human-computer interaction performance evaluation index interval. The specific matching method is as follows:

[0099] Among them, when the human-computer interaction performance evaluation index is in (60,80], it is an acceptable interval, and when the human-computer interaction performance evaluation index is greater than 80, it is a bad interval; when the interval corresponding to the human-computer interaction performance evaluation index is an acceptable interval, it means that there is a bottleneck in the current human-computer interaction performance, and targeted improvement measures are specified according to the performance bottleneck of human-computer interaction to improve the efficiency of human-computer interaction. When the interval corresponding to the human-computer interaction performance evaluation index is a bad interval, serious problems that affect the user experience should be repaired immediately to reduce the negative impact, and the human-computer interaction system should be comprehensively reviewed to find out the root cause of the poor performance, strengthen the human-computer interaction system test and user experience test, ensure that the problem is solved, and avoid similar problems from happening again.

[0100] Reference Figure 2 As shown, the second aspect of the present invention provides a system for a human-computer interaction performance evaluation method based on virtual reality, including: a module for collecting and analyzing human-computer interaction data, a module for evaluating human-computer interaction performance, and a human-computer interaction optimization module.

[0101] The module for collecting and analyzing human-computer interaction data is connected to the module for evaluating human-computer interaction performance, and the module for evaluating human-computer interaction performance is connected to the human-computer interaction optimization module. The module for collecting and analyzing human-computer interaction data, the module for evaluating human-computer interaction performance, and the human-computer interaction optimization module are all connected to the interaction database.

[0102] The module for collecting and analyzing human-computer interaction data is used to control the user to perform interactive tasks based on the virtual reality environment, thereby collecting and analyzing the user's behavior data during the task execution process, and collecting and analyzing the interaction data of the human-computer interaction device during the task control process, to obtain the user behavior interaction index and the device interaction performance index.

[0103] The human-computer interaction performance evaluation module is used to comprehensively analyze the user behavior interaction index and the device interaction performance index to obtain a human-computer interaction performance evaluation index.

[0104] The human-computer interaction optimization module compares the human-computer interaction performance evaluation index with the preset human-computer interaction performance definition index, matches the human-computer interaction performance optimization plan, and finally optimizes the human-computer interaction performance of the human-computer interaction device.

[0105] The second aspect of the present invention provides a system for a human-computer interaction performance evaluation method based on virtual reality, which also includes an interaction database for storing a preset preset interaction reference efficiency, a correction factor corresponding to the preset interaction efficiency, a preset reference heart rate, a correction factor corresponding to the preset user heart rate, a preset user average reference breathing frequency, a correction factor corresponding to the preset user breathing frequency, a preset reference time period for completing an interaction task, a correction factor corresponding to the interaction task execution completion time period preset in the database, a preset reference accuracy rate for executing an interaction task, a correction factor corresponding to the preset accuracy rate for executing an interaction task, a preset reference accuracy rate for executing an interaction task, a correction factor corresponding to the preset accuracy rate for executing an interaction task, a weight factor corresponding to a preset user behavior interaction index, a weight factor corresponding to a preset device interaction performance index, and a human-computer interaction performance optimization scheme corresponding to each preset human-computer interaction performance evaluation index interval.

[0106] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for evaluating human-computer interaction performance based on virtual reality, characterized in that: include: Collect and analyze human-computer interaction data: Based on the virtual reality environment, the human-computer interaction device controls the user to perform interactive tasks, thereby collecting and analyzing the user's behavior data during the task execution process, and collecting and analyzing the interaction data of the human-computer interaction device during the task control process, and obtaining the user behavior interaction index and the device interaction performance index; Evaluate the human-computer interaction performance: Comprehensively analyze the user behavior interaction index and the device interaction performance index to obtain the human-computer interaction performance evaluation index; Human-computer interaction optimization: Compare the human-computer interaction performance evaluation index with the preset human-computer interaction performance definition index, match the human-computer interaction performance optimization plan, and finally optimize the human-computer interaction performance of the human-computer interaction device; The specific analysis process of the user behavior interaction index is as follows: The user's interaction efficiency during task execution, the user's maximum heart rate during task execution, and the user's average breathing rate during task execution are comprehensively analyzed to obtain the user behavior interaction index. The specific analysis formula is: Wherein, ρ is the user behavior interaction index, yt is the user's interaction efficiency during task execution, Δyt is the interaction reference efficiency preset in the interaction database, x1 is the correction factor corresponding to the interaction efficiency preset in the interaction database, yo is the user's highest heart rate during task execution, Δyo is the reference heart rate preset in the interaction database, x2 is the correction factor corresponding to the user's heart rate preset in the interaction database, yh is the user's average breathing frequency during task execution, Δyh is the user's average reference breathing frequency preset in the interaction database, and x3 is the correction factor corresponding to the user's breathing frequency preset in the interaction database; The interactive task execution completion time period, interactive task execution accuracy, and interactive task execution time accuracy are comprehensively processed to obtain the device interactive performance index. The specific analysis formula is: Wherein, di is the device interaction performance index, dt is the interactive task execution completion time period, Δdt is the interactive task completion reference time period preset in the interactive database, h1 is the correction factor corresponding to the interactive task execution completion time period preset in the interactive database, dr is the interactive task execution accuracy, Δdr is the interactive task execution reference accuracy preset in the interactive database, h2 is the correction factor corresponding to the interactive task execution accuracy preset in the interactive database, dp is the interactive task execution time accuracy, Δdp is the interactive task execution time reference accuracy preset in the interactive database, and h3 is the correction factor corresponding to the interactive task execution time accuracy preset in the interactive database; The specific analysis process of the human-computer interaction performance evaluation index is as follows: The user behavior interaction index and the device interaction performance index are comprehensively analyzed to obtain the human-computer interaction performance evaluation index. The specific analysis formula is: In the formula, is the human-computer interaction performance evaluation index, e is a natural constant, ρ is the user behavior interaction index, m1 is the weight factor corresponding to the user behavior interaction index preset in the interaction database, di is the device interaction performance index, and m2 is the weight factor corresponding to the device interaction performance index preset in the interaction database.

2. The method for evaluating human-computer interaction performance based on virtual reality according to claim 1, characterized in that: The user's behavioral data during the task execution process specifically includes the user's total user interaction time period during the task execution process, the number of user interactions during the task execution process, the user's maximum heart rate during the task execution process, and the number of user breaths during the task execution process.

3. The method for evaluating human-computer interaction performance based on virtual reality according to claim 2, characterized in that: The analysis of the user's behavior data during the task execution process includes the following specific analysis processes: The number of user interactions during the task execution process is processed by ratio processing with the total user interaction time period during the task execution process to obtain the user interaction efficiency during the task execution process; The number of user respirations during the task execution is divided by the total user interaction time period during the task execution to obtain the user's average respiration rate during the task execution.

4. The method for evaluating human-computer interaction performance based on virtual reality according to claim 1, characterized in that: The interaction data of the human-computer interaction device during the task control process specifically includes: the task interaction response time period, the task interaction execution time period, the total number of interactive task executions, the number of correctly executed interactive tasks, and the correctly executed time period of interactive tasks during the task control process.

5. The method for evaluating human-computer interaction performance based on virtual reality according to claim 1, characterized in that: The analysis of the interaction data of the human-computer interaction device during the task control process includes the following specific analysis processes: The task interaction response time period and the task interaction execution time period of the human-computer interaction device in the task control process are summed to obtain the interactive task execution completion time period; The correct execution rate of interactive tasks is obtained by ratioing the number of interactive tasks correctly executed with the total number of interactive tasks executed; The correct execution time period of the interactive task is divided by the interactive task completion time period to obtain the interactive task execution time accuracy rate.

6. The method for evaluating human-computer interaction performance based on virtual reality according to claim 1, characterized in that: The human-computer interaction performance of the human-computer interaction device is optimized, and the specific analysis process is as follows: Compare the human-computer interaction performance evaluation index with the preset human-computer interaction performance evaluation definition index. If the human-computer interaction performance evaluation index is less than or equal to the human-computer interaction performance evaluation definition index, there is no need to optimize the human-computer interaction performance of the human-computer interaction device. If the human-computer interaction performance evaluation index is greater than the human-computer interaction performance evaluation definition index, the human-computer interaction performance evaluation index is matched with the human-computer interaction performance optimization scheme corresponding to each preset human-computer interaction performance evaluation index interval to obtain the human-computer interaction performance optimization scheme, and finally the human-computer interaction performance of the human-computer interaction device is optimized through the human-computer interaction performance optimization scheme.

7. A system using a method for evaluating human-computer interaction performance based on virtual reality as claimed in any one of claims 1 to 6, characterized in that: include: The module for collecting and analyzing human-computer interaction data is used to control the user to perform interactive tasks based on the virtual reality environment, thereby collecting and analyzing the user's behavior data during the task execution process, and collecting and analyzing the interaction data of the human-computer interaction device during the task control process, and obtaining the user behavior interaction index and the device interaction performance index; The human-computer interaction performance evaluation module is used to comprehensively analyze the user behavior interaction index and the device interaction performance index to obtain the human-computer interaction performance evaluation index; The human-computer interaction optimization module compares the human-computer interaction performance evaluation indicators with the preset human-computer interaction performance definition indicators, matches the human-computer interaction performance optimization plan, and finally optimizes the human-computer interaction performance of the human-computer interaction device.

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