Methods, devices, and equipment for adjusting a user's subjective willingness to accept a physical challenge task.

Abstract

This application relates to a method, apparatus, computer device, and storage medium for adjusting a user's subjective willingness to accept a physical challenge task. The method includes: obtaining expected task performance data using average performance data of the current physical challenge task and the user's self-efficacy scale score; calculating a subjective estimate of the user's success rate in completing the current physical challenge task using the expected task performance data; calculating an overall estimate of the user's ability to perform the current physical challenge task using the subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor; determining the user's subjective probability of accepting the current physical challenge task using the overall estimate; and adjusting the task success reward factor and task failure penalty factor of the current physical challenge task based on the user's subjective probability of accepting the current physical challenge task and the pre-set probability of the current physical challenge task. This method can dynamically adjust a user's subjective willingness to accept a physical challenge task.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, computer device, and storage medium for adjusting a user's subjective willingness to accept a physical challenge task. Background Technology

[0002] Many human-computer interaction systems or platforms, such as rehabilitation training systems for the elderly to improve their motor function, operation skills training platforms for workers, and open-ended exploration games that are popular among young people, design physical challenge tasks of varying difficulty for users and give users full freedom and authority to choose or accept the physical challenge tasks they face.

[0003] In such application systems or platforms, adjusting a user's subjective willingness to accept a particular physical challenge task can help meet the design intent of different tasks. For example, in rehabilitation training systems for the motor function of the elderly, older adults often refuse certain training tasks due to a lack of self-confidence, resulting in a lack of subjective initiative and affecting their rehabilitation outcomes. Therefore, adjusting the elderly's subjective willingness to perform training tasks can encourage them to be more proactive in accepting rehabilitation training.

[0004] Existing technologies primarily use Dynamic Difficulty Adjustment (DDA) to adjust the objective difficulty level of video game tasks, thereby maintaining user interest and playtime. However, this technology cannot be applied to scenarios where difficulty cannot or should not be adjusted, thus failing to dynamically adjust users' subjective willingness to accept physically challenging tasks and ultimately failing to achieve the task designers' intended goals. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, computer equipment, and storage medium for adjusting a user's subjective willingness to accept a physical challenge task, in order to address the aforementioned technical problems. This method or device can dynamically adjust a user's subjective willingness to accept a physical challenge task until the task designer's expected goal is achieved.

[0006] A method for adjusting a user's subjective willingness to accept a physical challenge task includes: obtaining the user's expected task performance data through average performance data of the current physical challenge task and the user's self-efficacy scale score; calculating the user's subjective estimate of the success rate of completing the current physical challenge task through the user's expected task performance data; calculating the user's overall estimate of performing the current physical challenge task through the user's subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor; determining the user's subjective probability of accepting the current physical challenge task through the user's overall estimate of performing the current physical challenge task; and adjusting the task success reward factor and task failure penalty factor of the current physical challenge task based on the user's subjective probability of accepting the current physical challenge task and the pre-set probability of the current physical challenge task, so as to dynamically adjust the user's subjective willingness to accept the physical challenge task.

[0007] In one embodiment, the average performance data of the current physical challenge task is an average performance parameter that follows a Gaussian distribution, and the user's expected task performance data is an expected task performance parameter that follows a Gaussian distribution. Obtaining the user's expected task performance data by combining the average performance data of the current physical challenge task and the user's self-efficacy scale score includes: obtaining a first expected value and a first standard deviation of the average performance parameter; obtaining the user's self-efficacy scale, and determining a first constant related to the expected value and a second constant related to the standard deviation based on the self-efficacy scale score; determining a second expected value of the expected task performance parameter based on the first expected value and the first constant, and determining a second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant.

[0008] In one embodiment, when a higher expected value of the average performance parameter indicates better user task performance, a higher self-efficacy scale score indicates a larger first constant.

[0009] In one embodiment, if a smaller first mathematical expectation of the average performance parameter indicates better user task performance, then a smaller first constant is determined if the self-efficacy scale score is higher.

[0010] In one embodiment, a smaller first standard deviation of the average performance parameter indicates better user task performance, and a higher score on the self-efficacy scale indicates a smaller determined second constant.

[0011] In one embodiment, determining a second mathematical expectation of the expected task performance parameters based on a first mathematical expectation and a first constant includes: based on μ p =k1μ m Calculate the second mathematical expectation of the expected task performance parameters; where μ pLet k1 represent the second expected value of the expected task performance parameters, and μ represent the first constant. m Represents the first mathematical expectation; and / or, determines the second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant, including: based on σ p =k2σ m Calculate the second standard deviation of the expected task performance parameters; where σ p σ represents the second standard deviation of the expected task performance parameters, k2 represents the second constant, and σ represents the second standard deviation. m This represents the first standard deviation.

[0012] In one embodiment, when the first expected value of the average performance parameter is closer to the center of the task-specified range, indicating better user task performance, a first constant is determined based on the self-efficacy scale score, including: based on... The first constant is calculated; where a and b are the two constant values ​​that the user needs to control the target within the specified range in the current physics challenge task.

[0013] In one embodiment, calculating a user's subjective estimate of their success rate in completing the current physical challenge task using the user's projected task performance data includes: based on... Calculate the user's subjective estimate of their success rate in completing the current physics challenge; where p s This represents the user's subjective estimate of the success rate of completing the current physics challenge, where x represents the position variable of the target manipulated by the user in the current physics challenge.

[0014] In one embodiment, calculating the user's overall estimate of performing the current physical challenge task using the user's subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor includes: calculating the user's subjective estimate, the pre-configured task success reward factor, and the pre-configured task failure penalty factor, and then applying V=p... s *Gain+(1-p s Loss calculates the user's overall estimate of performing the current physics challenge task; where V represents the parameter of the user's overall estimate of performing the current physics challenge task, Gain represents the parameter of the task success reward factor, and Loss represents the parameter of the task failure penalty factor.

[0015] In one embodiment, determining the probability of a user subjectively accepting the current physical challenge task based on the user's overall estimate of the task includes: determining the probability of a user subjectively accepting the current physical challenge task based on the user's overall estimate of the task and according to... Determine the probability that the user subjectively accepts the current physics challenge task; where p acceptThis parameter represents the probability that a user will subjectively accept the current physics challenge task.

[0016] In one embodiment, adjusting the success reward factor and failure penalty factor of the current physical challenge task based on the user's subjective acceptance probability and a preset probability of the current physical challenge task includes: adjusting the success reward factor and failure penalty factor of the current physical challenge task so that, according to V=p s *Gain+(1-p s Loss and The calculated probability that the user subjectively accepts the current physical challenge task is equal to the preset probability of the current physical challenge task, or the difference between the calculated probability that the user subjectively accepts the current physical challenge task and the preset probability of the current physical challenge task is within a set range.

[0017] A device for adjusting a user's subjective willingness to accept a physical challenge task includes: an acquisition module for obtaining the user's expected task performance data through average performance data of the current physical challenge task and the user's self-efficacy scale score; a first calculation module for calculating the user's subjective estimate of the success rate of completing the current physical challenge task based on the user's expected task performance data; a second calculation module for calculating the user's overall estimate of performing the current physical challenge task based on the user's subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor; a determination module for determining the user's subjective probability of accepting the current physical challenge task based on the user's overall estimate of performing the current physical challenge task; and an adjustment module for adjusting the task success reward factor and task failure penalty factor of the current physical challenge task based on the user's subjective probability of accepting the current physical challenge task and the pre-set probability of the current physical challenge task, so as to dynamically adjust the user's subjective willingness to accept the physical challenge task.

[0018] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0019] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0020] The aforementioned method, apparatus, computer device, and storage medium for adjusting a user's subjective willingness to accept a physical challenge task obtain the user's expected task performance data through average performance data of the current physical challenge task and the user's self-efficacy scale score; calculate the user's subjective estimate of the success rate of completing the current physical challenge task based on the user's expected task performance data; calculate the user's overall estimate of performing the current physical challenge task based on the user's subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor; determine the user's subjective probability of accepting the current physical challenge task based on the user's overall estimate of performing the current physical challenge task; and adjust the task success reward factor and task failure penalty factor of the current physical challenge task based on the user's subjective probability of accepting the current physical challenge task and the pre-set probability of the current physical challenge task, so as to dynamically adjust the user's subjective willingness to accept the physical challenge task.

[0021] Therefore, the system can predict the probability that a user will subjectively accept a current physical challenge task based on the user's self-expected performance on the task, the potential rewards for success, and the potential penalties for failure. Then, the system quantitatively adjusts the reward factor for success and the penalty factor for failure based on the subjective willingness the task designer hopes the user will have, thereby dynamically adjusting the degree of the user's subjective willingness until the task designer's expected goal is achieved. Attached Figure Description

[0022] Figure 1 This is an application environment diagram of a method for adjusting a user's subjective willingness to accept a physical challenge task, as described in one embodiment.

[0023] Figure 2 This is a flowchart illustrating a method for adjusting a user's subjective willingness to accept a physical challenge task in one embodiment.

[0024] Figure 3 This is a schematic diagram illustrating the conceptual logic of a method for adjusting a user's subjective willingness to accept a physical challenge task in one embodiment;

[0025] Figure 4 This is a structural block diagram of a device for adjusting a user's subjective willingness to accept a physical challenge task, as described in one embodiment.

[0026] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0028] This application provides a method for adjusting a user's subjective willingness to accept a physical challenge task, applicable to, for example... Figure 1 The application environment shown. For example... Figure 1 As shown, the task challenge platform is used to implement a method for adjusting a user's subjective willingness to accept a physical challenge task, as described in this application. Specifically, the front end is configured with multiple task challenge systems, each providing a physical challenge task to a different user. Through these front-end task challenge systems, the task challenge platform can collect user performance data for various physical challenge tasks within a population. For example, as... Figure 1 As shown, multiple task challenge systems include Task Challenge System 1, Task Challenge System 2, ..., Task Challenge System N. Each task challenge system provides the current physical challenge task to the user group and sends the user performance data of the current physical challenge task to the backend task challenge platform. Therefore, the task challenge platform can obtain the average performance data of the current physical challenge task per person.

[0029] like Figure 1 As shown, the task challenge system displays the current physical challenge task. When a user considers whether to accept the task, the platform obtains the user's self-efficacy scale. Using the average performance data of the current physical challenge task and the user's self-efficacy scale score, the platform obtains the user's expected task performance data. Based on this expected performance data, the platform calculates the user's subjective estimate of the success rate of completing the task. Using this subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor, the platform calculates the user's overall estimate of performing the task. This overall estimate determines the user's subjective probability of accepting the task. The platform then adjusts the task success reward factor and task failure penalty factor based on this probability, dynamically adjusting the user's subjective willingness to accept the task. Therefore, when a user decides whether to accept the task, the platform dynamically adjusts the task success reward factor and task failure penalty factor to adjust the user's subjective willingness to accept the task until the task designer's intended goal is achieved.

[0030] In one embodiment, such as Figure 2 As shown, a method for adjusting a user's subjective willingness to accept a physical challenge task is provided, and this method is applied to... Figure 1 Taking the task challenge platform in the middle as an example, the following steps are included:

[0031] S202 obtains the user's expected task performance data by using the average performance data of the current physical challenge task and the user's self-efficacy scale score.

[0032] In one embodiment, the average performance data of the current physical challenge task is an average performance parameter that follows a Gaussian distribution, and the user's expected task performance data is an expected task performance parameter that follows a Gaussian distribution. The method of obtaining the user's expected task performance data through the average performance data of the current physical challenge task and the user's self-efficacy scale score includes: obtaining a first expected value and a first standard deviation of the average performance parameter; obtaining the user's self-efficacy scale, and determining a first constant related to the expected value and a second constant related to the standard deviation based on the self-efficacy scale score; determining a second expected value of the expected task performance parameter based on the first expected value and the first constant, and determining a second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant.

[0033] Specifically, when performing the current physics challenge task, the user needs to manipulate the target and control it within the specified range. For example, if the target is controlled within the range [a, b], the current physics challenge task is successfully completed; otherwise, it fails. Correspondingly, the user's performance in the current physics challenge task is represented as a random variable conforming to a Gaussian distribution, i.e., X ~ N(μ, σ). 2 X represents a random variable, and N(μ,σ) 2 ) represents a Gaussian distribution, μ represents the expected value, and σ represents the standard deviation.

[0034] In one example of this embodiment, determining the second mathematical expectation of the expected task performance parameters based on the first mathematical expectation and the first constant includes: based on μ p =k1μ m Calculate the second mathematical expectation of the expected task performance parameters; where μ p Let k1 represent the second expected value of the expected task performance parameters, and μ represent the first constant. m Represents the first mathematical expectation; and / or, determines the second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant, including: based on σ p =k2σ m Calculate the second standard deviation of the expected task performance parameters; where σ p σ represents the second standard deviation of the expected task performance parameters, k2 represents the second constant, and σ represents the second standard deviation. m This represents the first standard deviation.

[0035] Specifically, through the average performance parameter N of the current physics challenge task. m (μ m ,σ m 2 The user's predicted task performance parameter N is calculated using the user's self-efficacy scale scores. p (μ p ,σp 2 ):

[0036] μ p =k1μ m ;

[0037] σ p =k2σ m .

[0038] The average performance parameter N for current physics challenge tasks m (μ m ,σ m 2 This was obtained through testing on a certain number of people.

[0039] Self-efficacy refers to an individual's prediction and judgment of their ability to perform a certain behavior. The General Self-Efficacy Scale (GSES) is a scale that measures an individual's self-efficacy, with scores ranging from [1, 4]. k1 and k2 are determined by the user's scores on the self-efficacy scale.

[0040] In one example of this embodiment, when a larger expected value of the average performance parameter indicates better user task performance, a larger first constant is determined if the self-efficacy scale score is higher.

[0041] Specifically, when a larger μ indicates better user task performance, k1 can be obtained from the self-efficacy scale rating. When the rating is less than 2.1, the value of k1 can be set to 0.5; when the rating is within [2.1, 3.1], the value of k1 can be set to 1; and when the rating exceeds 3.1, the value of k1 can be set to 2.

[0042] In one example of this embodiment, when a smaller expected value of the average performance parameter indicates better user task performance, a higher self-efficacy scale score indicates a smaller first constant.

[0043] Specifically, when a smaller μ indicates better user task performance, k1 can be obtained from the self-efficacy scale rating. When the rating is less than 2.1, the value of k1 can be set to 2; when the rating is within [2.1, 3.1], the value of k1 can be set to 1; and when the rating exceeds 3.1, the value of k1 can be set to 0.5.

[0044] In one example of this embodiment, when the first expected value of the average performance parameter is closer to the center of the task-specified range, indicating better user task performance, a first constant is determined based on the self-efficacy scale score, including: based on... The first constant is calculated; where a and b are the two constant values ​​that the user needs to control the target within the specified range in the current physics challenge task.

[0045] In one example of this embodiment, when a smaller first standard deviation of the average performance parameter indicates better user task performance, a higher self-efficacy scale score results in a smaller determined second constant.

[0046] Specifically, when σ is smaller, it means that the user performs better on the task. k2 is obtained by the self-efficacy scale score. When the score is less than 2.1, the value of k2 can be set to 2. When the score is within [2.1, 3.1], the value of k2 can be set to 1. When the score is greater than 3.1, the value of k2 can be set to 0.5.

[0047] S204, calculates the user's subjective estimate of the success rate of completing the current physical challenge task based on the user's expected task performance data.

[0048] In one example of this embodiment, calculating a user's subjective estimate of the success rate of completing the current physical challenge task based on the user's expected task performance data includes: according to Calculate the user's subjective estimate of their success rate in completing the current physics challenge; where p s This represents the user's subjective estimate of the success rate of completing the current physics challenge, where x represents the position variable of the target manipulated by the user in the current physics challenge.

[0049] Specifically, the subjective estimate p of the user's success rate in completing the current physical challenge task is calculated using the user's expected task performance data. s :

[0050]

[0051] Where a and b represent the two extreme values ​​that the user needs to manipulate and control within the specified range in the current physics challenge task, μ p and σ p These are the expected value and standard deviation of the user's projected task performance parameters, respectively.

[0052] S206 calculates the user's overall estimate of performing the current physical challenge task using the user's subjective estimate, the pre-configured task success reward factor, and the pre-configured task failure penalty factor.

[0053] In one example of this embodiment, the user's overall estimate of performing the current physical challenge task is calculated using the user's subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor. This includes: calculating the user's subjective estimate, the pre-configured task success reward factor, and the pre-configured task failure penalty factor, and then applying V=p... s *Gain+(1-p s Loss calculates the user's overall estimate of performing the current physics challenge task; where V represents the parameter of the user's overall estimate of performing the current physics challenge task, Gain represents the parameter of the task success reward factor, and Loss represents the parameter of the task failure penalty factor.

[0054] Specifically, the user's overall estimate of the task execution is calculated using the user's subjective estimate of the success rate, the task success reward factor, and the task failure penalty factor:

[0055] V = p s *Gain+(1-p s Loss;

[0056] Substitute the subjective estimate of user success rate into the formula for p. s By substituting the pre-configured task success reward factor into the parameter Gain of the task success reward factor in the formula, and substituting the pre-configured task failure penalty factor into the parameter of the task failure penalty factor in the formula, the specific value of the user's overall estimate of the task execution can be calculated.

[0057] When using the technology of this invention, it is necessary to define the constraint relationship between Gain and Loss in advance. This constraint relationship can be determined by the designer according to different application scenarios and design principles.

[0058] S208, determine the probability that the user subjectively accepts the current physical challenge task by using the user's overall estimate of the current physical challenge task.

[0059] In one example of this embodiment, determining the probability of a user subjectively accepting the current physical challenge task based on the user's overall estimate of the task includes: determining the probability of a user subjectively accepting the current physical challenge task based on the user's overall estimate of the task and according to... Determine the probability that the user subjectively accepts the current physics challenge task; where P accept This parameter represents the probability that a user will subjectively accept the current physics challenge task.

[0060] Specifically, by substituting the user's overall estimate of performing the current physical challenge task into the parameter V of the overall estimate in the formula, the specific value of the probability that the user subjectively accepts the current physical challenge task can be calculated.

[0061] S210 adjusts the success reward factor and failure penalty factor of the current physical challenge task by adjusting the probability of the user subjectively accepting the current physical challenge task and the preset probability of the current physical challenge task, so as to dynamically adjust the user's subjective willingness to accept the physical challenge task.

[0062] In one example of this embodiment, adjusting the success reward factor and failure penalty factor of the current physical challenge task based on the user's subjective acceptance probability and a preset probability of the current physical challenge task includes: adjusting the success reward factor and failure penalty factor of the current physical challenge task so that, according to V=p s *Gain+(1-p s Loss and The calculated probability that the user subjectively accepts the current physical challenge task is equal to the preset probability of the current physical challenge task, or the difference between the calculated probability that the user subjectively accepts the current physical challenge task and the preset probability of the current physical challenge task is within a set range.

[0063] Specifically, based on the subjective desires that the task designer hopes the user will have, that is, the designer's desired p accept This allows for the quantitative adjustment of the task success reward factor (Gain) and task failure penalty factor (Loss) for the current physical challenge task, thereby dynamically adjusting the user's subjective willingness to achieve the task designer's expected goals.

[0064] The aforementioned method for adjusting a user's subjective willingness to accept a physical challenge task allows the system to predict the probability of a user accepting the task based on their self-expected performance, the potential rewards for success, and the potential penalties for failure. Then, the system quantitatively adjusts the reward factor for success and the penalty factor for failure according to the subjective willingness the task designer hopes the user will have, thereby dynamically adjusting the degree of the user's subjective willingness until the task designer's expected goal is achieved.

[0065] The above-mentioned method for adjusting users' subjective willingness to accept physical challenge tasks proposes a quantitative adjustment method for users' subjective willingness to make decisions regarding physical challenge tasks in human-computer interaction systems. See details below. Figure 3 As shown, the quantitative adjustment of users' subjective decision-making intentions for physics challenge tasks mainly involves three aspects: users' self-estimated task performance, the benefits of task success, and the penalties for task failure. Specifically, the benefits of task success are adjusted using a task success reward factor, and the penalties for task failure are adjusted using a task failure penalty factor. By combining users' self-estimated task performance with the average performance of other users on the current physics challenge task, the user's subjective perception of the task completion success rate is determined, i.e., the aforementioned user's estimated task performance data. Furthermore, as... Figure 3As shown, the overall value of a user's task execution is determined based on their self-estimated task performance, the rewards for success, and the penalties for failure—that is, the overall estimate of the current physical challenge task. The probability of a user accepting the physical challenge task is then determined by this overall value. This probability represents the user's subjective willingness to accept the physical challenge task. Therefore, adjusting the user's subjective willingness to accept the physical challenge task can be achieved by adjusting the task success reward factor and the task failure penalty factor within certain rules, thereby realizing a quantitative adjustment of the user's subjective willingness.

[0066] In short, the system first predicts the probability that a user will subjectively accept the current physical challenge task based on their self-estimated task performance, the potential rewards for success, and the potential penalties for failure. Then, the system quantitatively adjusts the reward and penalty factors of the current task according to the subjective willingness the task designer hopes the user will have, thereby dynamically adjusting the user's subjective willingness until the task designer's expected goals are achieved.

[0067] This invention adjusts users' subjective willingness to perform physics challenge tasks by changing the reward and penalty factors. By guiding users' subjective will rather than forcing them to complete the tasks, it better stimulates user participation and improves the user experience.

[0068] It should be understood that although the steps in the flowchart are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0069] This application also provides a device for adjusting a user's subjective willingness to accept a physical challenge task. For example... Figure 4As shown, a device for adjusting a user's subjective willingness to accept a physical challenge task includes an acquisition module 402, a first calculation module 404, a second calculation module 406, a determination module 408, and an adjustment module 410. The acquisition module 402 is used to obtain the user's expected task performance data through average performance data of the current physical challenge task and the user's self-efficacy scale score; the first calculation module 404 is used to calculate the user's subjective estimate of the success rate of completing the current physical challenge task based on the user's expected task performance data; the second calculation module 406 is used to calculate the user's overall estimate of performing the current physical challenge task based on the user's subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor; the determination module 408 is used to determine the user's subjective probability of accepting the current physical challenge task based on the user's overall estimate of performing the current physical challenge task; the adjustment module 410 is used to adjust the task success reward factor and task failure penalty factor of the current physical challenge task based on the user's subjective probability of accepting the current physical challenge task and the pre-set probability of the current physical challenge task, so as to dynamically adjust the user's subjective willingness to accept the physical challenge task.

[0070] In one embodiment, the average performance data of the current physical challenge task is an average performance parameter that follows a Gaussian distribution, and the user's expected task performance data is an expected task performance parameter that follows a Gaussian distribution. Obtaining the user's expected task performance data by combining the average performance data of the current physical challenge task and the user's self-efficacy scale score includes: obtaining a first expected value and a first standard deviation of the average performance parameter; obtaining the user's self-efficacy scale, and determining a first constant related to the expected value and a second constant related to the standard deviation based on the self-efficacy scale score; determining a second expected value of the expected task performance parameter based on the first expected value and the first constant, and determining a second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant.

[0071] In one embodiment, when a higher expected value of the average performance parameter indicates better user task performance, a higher self-efficacy scale score indicates a larger first constant.

[0072] In one embodiment, if a smaller first mathematical expectation of the average performance parameter indicates better user task performance, then a smaller first constant is determined if the self-efficacy scale score is higher.

[0073] In one embodiment, a smaller first standard deviation of the average performance parameter indicates better user task performance, and a higher score on the self-efficacy scale indicates a smaller determined second constant.

[0074] In one embodiment, determining a second mathematical expectation of the expected task performance parameters based on a first mathematical expectation and a first constant includes: based on μ p =k1μ m Calculate the second mathematical expectation of the expected task performance parameters; where μ p Let k1 represent the second expected value of the expected task performance parameters, and μ represent the first constant. m Represents the first mathematical expectation; and / or, determines the second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant, including: based on σ p =k2σ m Calculate the second standard deviation of the expected task performance parameters; where σ p σ represents the second standard deviation of the expected task performance parameters, k2 represents the second constant, and σ represents the second standard deviation. m This represents the first standard deviation.

[0075] In one embodiment, when the first expected value of the average performance parameter is closer to the center of the task-specified range, indicating better user task performance, a first constant is determined based on the self-efficacy scale score, including: based on... The first constant is calculated; where a and b are the two constant values ​​that the user needs to control the target within the specified range in the current physics challenge task.

[0076] In one embodiment, calculating a user's subjective estimate of their success rate in completing the current physical challenge task using the user's projected task performance data includes: based on... Calculate the user's subjective estimate of their success rate in completing the current physics challenge; where p s This represents the user's subjective estimate of the success rate of completing the current physics challenge, where x represents the position variable of the target manipulated by the user in the current physics challenge.

[0077] In one embodiment, calculating the user's overall estimate of performing the current physical challenge task using the user's subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor includes: calculating the user's subjective estimate, the pre-configured task success reward factor, and the pre-configured task failure penalty factor, and then applying V=p... s *Gain+(1-p s Loss calculates the user's overall estimate of performing the current physics challenge task; where V represents the parameter of the user's overall estimate of performing the current physics challenge task, Gain represents the parameter of the task success reward factor, and Loss represents the parameter of the task failure penalty factor.

[0078] In one embodiment, determining the probability of a user subjectively accepting the current physical challenge task based on the user's overall estimate of the task includes: determining the probability of a user subjectively accepting the current physical challenge task based on the user's overall estimate of the task and according to... Determine the probability that the user subjectively accepts the current physics challenge task; where p accept This parameter represents the probability that a user will subjectively accept the current physics challenge task.

[0079] In one embodiment, adjusting the success reward factor and failure penalty factor of the current physical challenge task based on the user's subjective acceptance probability and a preset probability of the current physical challenge task includes: adjusting the success reward factor and failure penalty factor of the current physical challenge task so that, according to V=p s *Gain+(1-p s Loss and The calculated probability that the user subjectively accepts the current physical challenge task is equal to the preset probability of the current physical challenge task, or the difference between the calculated probability that the user subjectively accepts the current physical challenge task and the preset probability of the current physical challenge task is within a set range.

[0080] Specific limitations regarding a device for adjusting a user's subjective willingness to accept a physical challenge task can be found in the above-described limitations regarding a method for adjusting a user's subjective willingness to accept a physical challenge task, and will not be repeated here. Each module in the aforementioned device for adjusting a user's subjective willingness to accept a physical challenge task can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0081] In one embodiment, a computer device is provided, which may be a server supporting the operation of a task challenge platform, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for network communication with external devices. When the computer program is executed by the processor, it implements the aforementioned method for adjusting a user's subjective willingness to accept a physical challenge task.

[0082] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device on which the present application is intended to be applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0083] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: obtaining the user's expected task performance data through average performance data of the current physical challenge task and the user's self-efficacy scale score; calculating the user's subjective estimate of the success rate of completing the current physical challenge task through the user's expected task performance data; calculating the user's overall estimate of performing the current physical challenge task through the user's subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor; determining the user's subjective probability of accepting the current physical challenge task through the user's overall estimate of performing the current physical challenge task; and adjusting the task success reward factor and task failure penalty factor of the current physical challenge task based on the user's subjective probability of accepting the current physical challenge task and the pre-set probability of the current physical challenge task, so as to dynamically adjust the user's subjective willingness to accept the physical challenge task.

[0084] In one embodiment, the average performance data of the current physical challenge task is an average performance parameter that follows a Gaussian distribution, and the user's expected task performance data is an expected task performance parameter that follows a Gaussian distribution. When the processor executes the computer program to implement the above-mentioned step of obtaining the user's expected task performance data through the average performance data of the current physical challenge task and the user's self-efficacy scale score, the processor specifically implements the following steps: obtaining the first mathematical expectation and the first standard deviation of the average performance parameter; obtaining the user's self-efficacy scale, and determining a first constant related to the mathematical expectation and a second constant related to the standard deviation based on the self-efficacy scale score; determining the second mathematical expectation of the expected task performance parameter based on the first mathematical expectation and the first constant, and determining the second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant.

[0085] In one embodiment, when a higher expected value of the average performance parameter indicates better user task performance, a higher self-efficacy scale score indicates a larger first constant.

[0086] In one embodiment, if a smaller first mathematical expectation of the average performance parameter indicates better user task performance, then a smaller first constant is determined if the self-efficacy scale score is higher.

[0087] In one embodiment, a smaller first standard deviation of the average performance parameter indicates better user task performance, and a higher score on the self-efficacy scale indicates a smaller determined second constant.

[0088] In one embodiment, when the processor executes the computer program to implement the above-described step of determining the second mathematical expectation of the expected task performance parameters based on the first mathematical expectation and the first constant, it specifically implements the following steps: based on μ p =k1μ m Calculate the second mathematical expectation of the expected task performance parameters; where μ p Let k1 represent the second expected value of the expected task performance parameters, and μ represent the first constant. m Representing the first mathematical expectation; and / or, when the processor executes the computer program to implement the above-described step of determining the second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant, specifically implementing the following steps: based on σ p =k2σ m Calculate the second standard deviation of the expected task performance parameters; where σ p σ represents the second standard deviation of the expected task performance parameters, k2 represents the second constant, and σ represents the second standard deviation. m This represents the first standard deviation.

[0089] In one embodiment, when the first expected value of the average performance parameter is closer to the center of the task-specified range, indicating better user task performance, the processor executes the computer program to implement the above-mentioned step of determining the first constant based on the self-efficacy scale rating, specifically implementing the following steps: based on The first constant is calculated; where a and b are the two constant values ​​that the user needs to control the target within the specified range in the current physics challenge task.

[0090] In one embodiment, when the processor executes the computer program to implement the above-described step of calculating the user's subjective estimate of the success rate of completing the current physical challenge task based on the user's expected task performance data, it specifically implements the following steps: based on Calculate the user's subjective estimate of their success rate in completing the current physics challenge; where p s This represents the user's subjective estimate of the success rate of completing the current physics challenge, where x represents the position variable of the target manipulated by the user in the current physics challenge.

[0091] In one embodiment, when the processor executes the computer program to implement the above-mentioned step of calculating the user's overall estimate of performing the current physical challenge task using the user's subjective estimate, the pre-configured task success reward factor, and the pre-configured task failure penalty factor, the specific steps are as follows: using the user's subjective estimate, the pre-configured task success reward factor, and the pre-configured task failure penalty factor, and according to V=p s *Gain+(1-p s Loss calculates the user's overall estimate of performing the current physics challenge task; where V represents the parameter of the user's overall estimate of performing the current physics challenge task, Gain represents the parameter of the task success reward factor, and Loss represents the parameter of the task failure penalty factor.

[0092] In one embodiment, when the processor executes the computer program to implement the above-mentioned step of determining the probability of the user subjectively accepting the current physical challenge task based on the user's overall estimate of the current physical challenge task, it specifically implements the following steps: using the user's overall estimate of the current physical challenge task and based on... Determine the probability that the user subjectively accepts the current physics challenge task; where p accept This parameter represents the probability that a user will subjectively accept the current physics challenge task.

[0093] In one embodiment, when the processor executes the computer program to implement the above-mentioned step of adjusting the task success reward factor and task failure penalty factor of the current physical challenge task based on the probability of the user subjectively accepting the current physical challenge task and the preset probability of the current physical challenge task, the specific steps are as follows: Adjust the task success reward factor and task failure penalty factor of the current physical challenge task so that, according to V=p s *Gain+(1-p s Loss and The calculated probability that the user subjectively accepts the current physical challenge task is equal to the preset probability of the current physical challenge task, or the difference between the calculated probability that the user subjectively accepts the current physical challenge task and the preset probability of the current physical challenge task is within a set range.

[0094] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program performs the following steps: obtaining the user's expected task performance data using average performance data of the current physical challenge task and the user's self-efficacy scale score; calculating the user's subjective estimate of the success rate of completing the current physical challenge task using the user's expected task performance data; calculating the user's overall estimate of performing the current physical challenge task using the user's subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor; determining the user's subjective probability of accepting the current physical challenge task using the user's overall estimate of performing the current physical challenge task; and adjusting the task success reward factor and task failure penalty factor of the current physical challenge task using the user's subjective probability of accepting the current physical challenge task and the pre-set probability of the current physical challenge task, so as to dynamically adjust the user's subjective willingness to accept the physical challenge task.

[0095] In one embodiment, the average performance data of the current physical challenge task is an average performance parameter that follows a Gaussian distribution, and the user's expected task performance data is an expected task performance parameter that follows a Gaussian distribution. When the computer program is executed by the processor to implement the above-mentioned step of obtaining the user's expected task performance data through the average performance data of the current physical challenge task and the user's self-efficacy scale score, the following steps are specifically implemented: obtaining the first mathematical expectation and the first standard deviation of the average performance parameter; obtaining the user's self-efficacy scale, and determining a first constant related to the mathematical expectation and a second constant related to the standard deviation based on the self-efficacy scale score; determining the second mathematical expectation of the expected task performance parameter based on the first mathematical expectation and the first constant, and determining the second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant.

[0096] In one embodiment, when a higher expected value of the average performance parameter indicates better user task performance, a higher self-efficacy scale score indicates a larger first constant.

[0097] In one embodiment, if a smaller first mathematical expectation of the average performance parameter indicates better user task performance, then a smaller first constant is determined if the self-efficacy scale score is higher.

[0098] In one embodiment, a smaller first standard deviation of the average performance parameter indicates better user task performance, and a higher score on the self-efficacy scale indicates a smaller determined second constant.

[0099] In one embodiment, when the computer program is executed by the processor to implement the above-described step of determining the second mathematical expectation of the expected task performance parameters based on the first mathematical expectation and the first constant, the following steps are specifically implemented: based on μp =k1μ m Calculate the second mathematical expectation of the expected task performance parameters; where μ p Let k1 represent the second expected value of the expected task performance parameters, and μ represent the first constant. m This represents the first mathematical expectation; and / or, when the computer program is executed by the processor to implement the above-described step of determining the second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant, specifically the following steps are implemented: based on σ p =k2σ m Calculate the second standard deviation of the expected task performance parameters; where σ p σ represents the second standard deviation of the expected task performance parameters, k2 represents the second constant, and σ represents the second standard deviation. m This represents the first standard deviation.

[0100] In one embodiment, when the first expected value of the average performance parameter is closer to the center of the task-specified range, indicating better user task performance, the computer program, when executed by the processor, performs the step of determining the first constant based on the self-efficacy scale rating, specifically implementing the following steps: based on The first constant is calculated; where a and b are the two constant values ​​that the user needs to control the target within the specified range in the current physics challenge task.

[0101] In one embodiment, when the computer program is executed by the processor to perform the above-described step of calculating the user's subjective estimate of the success rate of completing the current physical challenge task based on the user's expected task performance data, the specific steps are as follows: Based on Calculate the user's subjective estimate of their success rate in completing the current physics challenge; where p s This represents the user's subjective estimate of the success rate of completing the current physics challenge, where x represents the position variable of the target manipulated by the user in the current physics challenge.

[0102] In one embodiment, when the computer program is executed by the processor to implement the above-described step of calculating the user's overall estimate of performing the current physical challenge task using the user's subjective estimate, the pre-configured task success reward factor, and the pre-configured task failure penalty factor, the specific steps are as follows: using the user's subjective estimate, the pre-configured task success reward factor, and the task failure penalty factor, and according to V=p s *Gain+(1-p s Loss calculates the user's overall estimate of performing the current physics challenge task; where V represents the parameter of the user's overall estimate of performing the current physics challenge task, Gain represents the parameter of the task success reward factor, and Loss represents the parameter of the task failure penalty factor.

[0103] In one embodiment, when the computer program is executed by the processor to implement the above-described step of determining the probability of the user subjectively accepting the current physical challenge task based on the user's overall estimate of the current physical challenge task, the specific steps are as follows: using the user's overall estimate of the current physical challenge task and based on... Determine the probability that the user subjectively accepts the current physics challenge task; where p accept This parameter represents the probability that a user will subjectively accept the current physics challenge task.

[0104] In one embodiment, when the computer program is executed by the processor to implement the above-described steps of adjusting the task success reward factor and task failure penalty factor of the current physical challenge task based on the probability of the user subjectively accepting the current physical challenge task and the preset probability of the current physical challenge task, the specific steps are as follows: Adjusting the task success reward factor and task failure penalty factor of the current physical challenge task so that, according to V=p s *Gain+(1-p s Loss and The calculated probability that the user subjectively accepts the current physical challenge task is equal to the preset probability of the current physical challenge task, or the difference between the calculated probability that the user subjectively accepts the current physical challenge task and the preset probability of the current physical challenge task is within a set range.

[0105] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0106] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method of adjusting a subjective willingness of a user to undertake a physical challenge task, characterized by, The physics challenge is provided through a human-computer interaction system, and the method includes: The expected task performance data of the user is obtained by using the average performance data of the current physical challenge task and the user's self-efficacy scale score; The user's subjective estimate of the success rate of completing the current physical challenge task is calculated based on the user's expected task performance data; The user's overall estimate of performing the current physical challenge task is calculated using the user's subjective estimate, the pre-configured task success reward factor, and the pre-configured task failure penalty factor. The probability that the user subjectively accepts the current physical challenge task is determined by the user's overall estimate of the current physical challenge task; The success reward factor and failure penalty factor of the current physical challenge task are adjusted by combining the probability of the user subjectively accepting the current physical challenge task with the preset probability of the current physical challenge task, so as to dynamically adjust the user's subjective willingness to accept the challenge task. Wherein, the average performance data of the current physical challenge task is the average performance parameter that follows a Gaussian distribution, and the user's expected task performance data is the expected task performance parameter that follows a Gaussian distribution. Obtaining the user's expected task performance data through the average performance data of the current physical challenge task and the user's self-efficacy scale score includes: Obtain the first expected value and first standard deviation of the average performance parameter; Obtain the user's self-efficacy scale, and determine a first constant related to mathematical expectation and a second constant related to standard deviation based on the score of the self-efficacy scale; According to calculating a second mathematical expectation of the predicted task performance parameter, denotes the second mathematical expectation of the predicted task performance parameter, denotes the first constant, denotes the first mathematical expectation; The second standard deviation of the expected task performance parameter is determined based on the first standard deviation and the second constant. Wherein, when the first mathematical expectation of the average performance parameter is closer to the center of the task-specified range, indicating better user task performance, the step of determining the first constant based on the self-efficacy scale score includes: according to The first constant is obtained through calculation; in, and These are two constant values ​​corresponding to the target control that the user needs to manipulate within the specified range of the current physics challenge task. The calculation of the user's subjective estimate of the success rate of completing the current physical challenge task based on the user's expected task performance data includes: according to Calculate the user's subjective estimate of the success rate of completing the current physical challenge task; in, This represents the user's subjective estimate of the success rate of completing the current physics challenge task. This represents the position variable of the target being manipulated by the user in the current physics challenge task.

2. The method for adjusting subjective will according to claim 1, characterized in that, When a higher expected value of the average performance parameter indicates better user task performance, a higher score on the self-efficacy scale indicates a larger first constant.

3. The method for adjusting subjective will according to claim 1, characterized in that, When a smaller expected value of the average performance parameter indicates better user task performance, a smaller first constant is determined if the self-efficacy scale score is higher.

4. The method for adjusting subjective will according to claim 1, characterized in that, When a smaller first standard deviation of the average performance parameter indicates better user task performance, a smaller second constant is determined if the self-efficacy scale score is higher.

5. The method for adjusting subjective will according to claim 1, characterized in that, The step of determining the second standard deviation of the expected task performance parameter based on the first standard deviation and the second constant includes: based on Calculate the second standard deviation of the predicted task performance parameters; wherein, This represents the second standard deviation of the predicted task performance parameter. This represents the second constant. This represents the first standard deviation.

6. The method for adjusting subjective will according to claim 1, characterized in that, The calculation of the user's overall estimate of performing the current physical challenge task using the user's subjective estimate, a pre-configured task success reward factor, and a pre-configured task failure penalty factor includes: Based on the user's subjective estimate, the pre-configured task success reward factor, and the pre-configured task failure penalty factor, and according to... Calculate the user's overall estimate of performing the current physics challenge task; in, The parameter represents the user's overall estimate of performing the current physics challenge task. The parameter representing the reward factor for successful task completion. The parameter represents the penalty factor for task failure.

7. The method for adjusting subjective will according to claim 6, characterized in that, The step of determining the probability that the user subjectively accepts the current physical challenge task based on the user's overall estimate of performing the current physical challenge task includes: Based on the user's overall estimate of performing the current physics challenge task and according to Determine the probability that the user subjectively accepts the current physical challenge task; in, The parameter represents the probability that the user subjectively accepts the current physical challenge task.

8. The method for adjusting subjective will according to claim 7, characterized in that, The adjustment of the task success reward factor and task failure penalty factor for the current physical challenge task based on the user's subjective acceptance probability and a preset probability of the current physical challenge task includes: Adjust the success reward factor and failure penalty factor for the current physics challenge task to make it more appropriate to... and stated The calculated probability that the user subjectively accepts the current physical challenge task is equal to the preset probability of the current physical challenge task, or the difference between the calculated probability that the user subjectively accepts the current physical challenge task and the preset probability of the current physical challenge task is within a set range.

9. A device for adjusting a user's subjective willingness to accept a physical challenge task, characterized in that, The physics challenge is provided through a human-computer interaction system, and the device includes: The acquisition module is used to obtain the user's expected task performance data through the average performance data of the current physical challenge task and the user's self-efficacy scale score; The first calculation module is used to calculate the user's subjective estimate of the success rate of completing the current physical challenge task based on the user's expected task performance data; The second calculation module is used to calculate the user's overall estimate of performing the current physical challenge task based on the user's subjective estimate, the pre-configured task success reward factor, and the pre-configured task failure penalty factor. The determination module is used to determine the probability that the user subjectively accepts the current physical challenge task based on the user's overall estimate of performing the current physical challenge task; The adjustment module is used to adjust the success reward factor and failure penalty factor of the current physical challenge task based on the probability of the user subjectively accepting the current physical challenge task and the preset probability of the current physical challenge task, so as to dynamically adjust the user's subjective willingness to accept the physical challenge task. Wherein, the average performance data of the current physical challenge task is the average performance parameter that follows a Gaussian distribution, and the user's expected task performance data is the expected task performance parameter that follows a Gaussian distribution. Obtaining the user's expected task performance data through the average performance data of the current physical challenge task and the user's self-efficacy scale score includes: Obtain the first expected value and first standard deviation of the average performance parameter; Obtain the user's self-efficacy scale, and determine a first constant related to mathematical expectation and a second constant related to standard deviation based on the score of the self-efficacy scale; according to Calculate the second mathematical expectation of the predicted task performance parameters, where, This represents the second mathematical expectation of the predicted task performance parameters. This represents the first constant. This represents the first mathematical expectation; The second standard deviation of the expected task performance parameter is determined based on the first standard deviation and the second constant. Wherein, when the first mathematical expectation of the average performance parameter is closer to the center of the task-specified range, indicating better user task performance, the step of determining the first constant based on the self-efficacy scale score includes: according to The first constant is obtained through calculation; in, and These are two constant values ​​corresponding to the target control that the user needs to manipulate within the specified range of the current physics challenge task. The calculation of the user's subjective estimate of the success rate of completing the current physical challenge task based on the user's expected task performance data includes: according to Calculate the user's subjective estimate of the success rate of completing the current physical challenge task; in, This represents the user's subjective estimate of the success rate of completing the current physics challenge task. This represents the position variable of the target being manipulated by the user in the current physics challenge task.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

Images