Method and apparatus for controlling device operating state, and electronic device

Abstract

This application discloses a method, apparatus, and electronic device for controlling the operating status of a device. The method includes: receiving interactive behavior data of a target object; determining an evaluation index value corresponding to the interactive behavior data based on the response time and response result; determining a target duration based on the evaluation index value; and controlling the device operating status of the terminal device based on the target duration and the operating status of the target software during a second time period. This application solves the technical problem that the game duration allocation method used in related technologies is linearly correlated with the number of correct answers, resulting in the inability to accurately quantify the quality of answers and making it difficult to precisely control the usage time of device software.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to a method, apparatus, and electronic device for controlling the operating status of a device. Background Technology

[0002] With the popularization of online games, the game time allocation method used in anti-addiction technology, which involves exchanging game time for answers, is evaluated based solely on the accuracy of answers or simple points. When users quickly guess the answers, the system cannot distinguish between this behavior and actual mastery, making it impossible to accurately quantify the quality of answers and to precisely control the usage time of the device software.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method, apparatus, and electronic device for controlling the operating status of a device, in order to at least solve the technical problem that the game time allocation method used in related technologies is linearly related to the number of correct answers, which makes it impossible to accurately quantify the quality of answers and makes it difficult to accurately control the usage time of the device software.

[0005] According to one aspect of the embodiments of this application, a method for controlling the operating state of a device is provided, comprising: receiving interactive behavior data of a target object, wherein the interactive behavior data is triggered when the cumulative usage time of the target object on the target software within a first time period meets a preset duration, and the interactive behavior data is used to reflect the response time and response result of the target object to interactive information; determining an evaluation index value corresponding to the interactive behavior data based on the response time and response result reflected in the interactive behavior data; determining a target duration based on the evaluation index value, wherein the target duration is the allowed running time of the target software within a second time period, and the magnitude of the evaluation index value is positively correlated with the length of the target duration; and controlling the device operating state of a terminal device based on the target duration and the operating status of the target software within the second time period, wherein the device operating state includes the types of software allowed to run on the terminal device.

[0006] In some embodiments of this application, the evaluation index value corresponding to the interactive behavior data is determined based on the response time and response result reflected in the interactive behavior data, including: obtaining the difficulty coefficient and preset response time corresponding to the interactive information; determining the time efficiency factor based on the response time and preset response time in the interactive behavior data, wherein the time efficiency factor is used to quantitatively represent the target object's proficiency with the interactive information; and determining the evaluation index value based on the difficulty coefficient, the time efficiency factor and the response result.

[0007] In some embodiments of this application, determining a time efficiency factor based on the response time in the interaction behavior data and a preset response time includes: obtaining a time threshold, wherein the time threshold is determined based on the historical response time corresponding to the interaction between the target object and historical interaction information, and the historical interaction information and the interaction information have the same difficulty coefficient; comparing the response time with the time threshold and the preset response time respectively to obtain a comparison result, wherein the preset response time is greater than the time threshold; and determining a time efficiency factor based on the comparison result, wherein the time efficiency factor is positively correlated with the evaluation index value.

[0008] In some embodiments of this application, determining a time efficiency factor based on a comparison result includes: determining a first penalty coefficient as a time efficiency factor when the comparison result indicates that the response time is less than a time threshold; determining a time efficiency factor based on a reward coefficient when the comparison result indicates that the response time is not less than a time threshold and not greater than a preset response time; and determining a time efficiency factor based on a second penalty coefficient when the comparison result indicates that the response time is greater than a preset response time, wherein the first penalty coefficient is greater than or equal to the second penalty coefficient.

[0009] In some embodiments of this application, the evaluation index value includes sub-evaluation values ​​corresponding to multiple sub-interaction information; determining the target duration based on the evaluation index value includes: obtaining the age information of the target object; determining the duration conversion coefficient based on the age information, wherein the duration conversion coefficient is used to quantify the intensity of converting the evaluation index value into the available duration of the target software; determining the initial duration based on the duration conversion coefficient and the sub-evaluation values, wherein the initial duration increases with the accumulation of sub-evaluation values, and the rate of increase decreases with the increase of sub-evaluation values; and determining the target duration based on the initial duration.

[0010] In some embodiments of this application, determining the target duration based on the initial duration includes: determining the maximum allowed duration corresponding to age information and the runtime of the target software within a preset time period, wherein the end time of the preset time period is earlier than the start time of the second time period, and the runtime is the cumulative usage time of the target software by the target object within the preset time period; determining the maximum available duration based on the maximum allowed duration and the runtime; and taking the minimum value between the maximum available duration and the initial duration as the target duration.

[0011] In some embodiments of this application, the method further includes: obtaining historical evaluation index values ​​corresponding to the interaction information, and time information corresponding to the historical evaluation index values; determining the forgetting coefficient corresponding to the interaction information, wherein the forgetting coefficient is used to quantify the speed at which the interaction information is forgotten by the target object; updating the capability profile of the target object based on the evaluation index values, historical evaluation index values, time information, and forgetting coefficient, wherein the capability profile is used to reflect the correctness of the target object's responses in the preset interaction information database.

[0012] In some embodiments of this application, before receiving the interaction behavior data of the target object, the method further includes: in response to the interaction request of the target software, obtaining a capability profile of the target object; determining target interaction information based on the capability profile; and sending the target interaction information to the target software.

[0013] In some embodiments of this application, after determining the target duration based on the evaluation index value, the method further includes: determining an authorization token corresponding to the target duration; and issuing the authorization token to the game terminal or the target gateway, wherein the authorization token is used to instruct the game terminal or the target gateway to perform duration control.

[0014] According to another aspect of the embodiments of this application, a device for controlling the operating state of a device is also provided, comprising: a receiving module, configured to receive interactive behavior data of a target object, wherein the interactive behavior data is triggered when the cumulative usage time of the target object on the target software within a first time period meets a preset duration, and the interactive behavior data is used to reflect the response time and response result of the target object to interactive information; an evaluation module, configured to determine an evaluation index value corresponding to the interactive behavior data based on the response time and response result reflected in the interactive behavior data; a determining module, configured to determine a target duration based on the evaluation index value, wherein the target duration is the allowed running time of the target software within a second time period, and the magnitude of the evaluation index value is positively correlated with the length of the target duration; and a control module, configured to control the device operating state of a terminal device based on the target duration and the operating status of the target software within the second time period, wherein the device operating state includes the types of software allowed to run on the terminal device.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute a control method for implementing the above-described device operating state.

[0016] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored computer program, wherein the device on which the non-volatile storage medium is located executes the above-mentioned device operation state control method by running the computer program.

[0017] According to another aspect of the embodiments of this application, a computer program product is also provided, including computer instructions that, when executed by a processor, implement the control method for the operating state of the aforementioned device.

[0018] In this embodiment, a multi-dimensional behavioral feature fusion evaluation method is adopted. By collecting the response time and response result of the target object when the interaction mechanism is triggered, a comprehensive evaluation index value reflecting its true cognition and operation quality is constructed. Based on the evaluation index value, the runtime of the target software in the next time period is dynamically calculated to control the device operation status. This achieves the purpose of accurately quantifying the quality of user learning behavior, thereby realizing the technical effect of dynamically adjusting the access permissions of the device software according to the user's actual ability performance. This solves the technical problem that the game time allocation method used in related technologies is linearly related to the number of correct answers, which makes it impossible to accurately quantify the quality of answers and make it difficult to accurately control the usage time of the device software. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 This is a hardware structure block diagram of a computer terminal for a device operation state control method according to an embodiment of this application;

[0021] Figure 2 This is a flowchart of a method for controlling the operating state of a device according to an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of the overall process of a device operation status control method according to an embodiment of this application;

[0023] Figure 4 This is a system architecture diagram of a device operation state control method according to an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of the structure of a device for controlling the operating state of an equipment according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] The information collected in this application embodiment is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant regions, and necessary confidentiality measures have been taken. It does not violate public order and good morals, and provides corresponding operation entry points for users to choose to authorize or reject the automated decision results. If the user chooses to reject, the process will proceed to the expert decision-making process.

[0028] To better understand the embodiments of this application, the technical terms involved in the embodiments of this application are explained below:

[0029] Time Efficiency Factor: In this embodiment, it is a mathematical function parameter used to quantify the relative efficiency between a user's actual answering time and a standard reference time when completing a specific task, reflecting the user's proficiency and focus during the answering process. For example, the time efficiency factor can be used in the form of a piecewise function, combined with a rapid answering threshold, standard time, and preset penalty / reward coefficients, to correct the quality of user answering behavior and effectively distinguish between three behavioral patterns: "guessing answers," "proficient answers," and "exceeding the time limit."

[0030] Single-Item Effective Score: A weighted score that reflects the true learning quality of a single test, calculated based on a combination of factors including the question's difficulty level, the correctness of the answer, and time efficiency.

[0031] Time-Accuracy Coupled Evaluation Model: In this embodiment, it refers to a multivariate evaluation algorithm framework that integrates the answering time dimension and the answering accuracy dimension. It establishes the synergistic relationship between the two through a nonlinear function. The model takes the time efficiency factor as the core mechanism and incorporates the user's answering speed and answer accuracy into a unified evaluation system.

[0032] Game Access Token: A digital credential generated by the system that contains the validity period, authorization duration, and user identity information, used to implement access control at the terminal or gateway level.

[0033] The anti-addiction technologies employed can be broadly categorized into two types: The first type is mandatory time-limited technology based on identity authentication, such as the "anti-addiction system," which uniformly restricts the gaming time of minors through real-name authentication (e.g., no more than 3 hours per day on statutory holidays). While this type of technology complies with regulatory requirements, it lacks flexibility, employs a "one-size-fits-all" approach, and is prone to triggering rebellious behavior among minors, failing to address the fundamental issue of "balancing learning and play." The second type is "answer-for-time" technology based on incentive mechanisms, where users unlock phone usage time by answering questions correctly. This achieves a certain degree of integration between education and anti-addiction measures; however, this type of technology has the following technical drawbacks:

[0034] (1) The evaluation model is singular: it only evaluates based on the correct answer rate or simple points, without considering the key dimension of answering time. When users quickly guess the answer, the system cannot identify the difference between this behavior and true mastery, which allows users to cheat time by "answering in seconds" and weakens the incentive effect of the system.

[0035] (2) Lack of dynamic ability modeling: The static question bank is used, and the user's mastery of knowledge points is not dynamically modeled and updated. The difficulty of the questions cannot be adaptively adjusted according to changes in the user's ability, resulting in low learning efficiency.

[0036] (3) The time allocation logic is simple and linear: the time and the number of questions answered are in a simple linear relationship. There is a lack of deep integration of the anti-addiction hard constraint mechanism, and there is a risk of addiction caused by "exchanging time for questions".

[0037] To address the aforementioned technical problems, this application provides corresponding solutions, which are detailed below.

[0038] The device operation status control method provided in this application can be executed in a mobile terminal, computer terminal or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a method to control the operating status of a device is shown. Figure 1As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication functions connected via wired and / or wireless networks. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0039] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0040] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the device operation state control method in this embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned device operation state control method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0041] The transmission module 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission module 106 may be a radio frequency (RF) module, used for wireless communication with the Internet.

[0042] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0043] It should be noted here that, in some optional embodiments, the above... Figure 1 The computer terminal shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned computer terminal.

[0044] In the above operating environment, this application provides an embodiment of a method for controlling the operating state of a device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0045] Figure 2 This is a flowchart of a device operation state control method according to an embodiment of this application, such as... Figure 2 As shown, the method includes the following steps:

[0046] Step S202: Receive the interaction behavior data of the target object. The interaction behavior data is triggered when the cumulative usage time of the target software by the target object in the first time period meets the preset time. The interaction behavior data is used to reflect the response time and response result of the target object to the interaction information.

[0047] In step S202 above, interactive behavior data refers to structured behavioral records actively triggered and collected by the system to evaluate the quality of a target's (e.g., a minor) learning behavior when the cumulative time a target user (e.g., a minor) continuously uses the target software (e.g., a game application) within a first time period reaches the system's preset anti-addiction trigger threshold. In some embodiments of this application, interactive behavior data includes, but is not limited to, response time and response results. Response time is the objective time elapsed from when a user receives an interactive task (i.e., interactive information, such as a knowledge quiz question) to when they complete and submit their answer. Response results indicate whether the user's answer to the interactive task meets the correctness standard (correct or incorrect).

[0048] In some embodiments of this application, during the operation of the target software, the effective usage time of the application can be continuously accumulated in the background. When the accumulated time reaches a preset anti-addiction trigger point, the entertainment function is paused, the interactive interface is forcibly popped up, and a high-precision timer is started simultaneously to accurately record the time interval between the presentation of interactive information (such as the question) and the clicking of the submit button. At the same time, the answer result (correct / incorrect) is bound and encapsulated to form a structured interactive behavior record. It should be noted that during the operation of the target software, the accumulated usage time can be ensured to belong only to the target user by binding the user's real-name authentication identity and the unique identifier of the terminal device.

[0049] To minimize intrusiveness to user devices, some embodiments of this application may not directly participate in time monitoring on the terminal device. Instead, they may collaborate with the terminal operating system, smart router anti-addiction gateway, or the child mode system built into the smart terminal through standardized interfaces. These terminal-side systems have more direct and accurate device-level usage time collection capabilities. Specifically, when the terminal-side system detects that the cumulative usage time of the target object in the target software reaches a statutory or custom trigger threshold, it proactively initiates a standardized interaction request message (which can record user identity, cumulative usage time, and other information) to the system of this application. Upon receiving this request, it calls the user's historical learning data to generate target interaction information and returns it to the terminal device for display through a secure channel, subsequently receiving interaction behavior data.

[0050] Step S204: Determine the evaluation index value corresponding to the interaction behavior data based on the response time and response result reflected in the interaction behavior data.

[0051] In step S204 above, the evaluation index value refers to the numerical result that quantifies the quality of the user's learning behavior in this session, calculated by the system based on interactive behavior data through a time-accuracy coupled evaluation model. This index value is not a direct output of the original data, but a comprehensive ability score after multi-factor weighting correction.

[0052] In some embodiments of this application, the evaluation index value corresponding to the interactive behavior data can be determined in the following ways: obtaining the difficulty coefficient and preset response time corresponding to the interactive information; determining the time efficiency factor based on the response time and preset response time in the interactive behavior data, wherein the time efficiency factor is used to quantify the target object's proficiency with the interactive information; and determining the evaluation index value based on the difficulty coefficient, the time efficiency factor, and the response result.

[0053] It should be noted that the difficulty coefficient refers to a quantitative parameter pre-assigned by the system to each interactive message, reflecting its cognitive load and knowledge complexity. Its value is calculated based on the average accuracy and average response time of that interactive message among historical user groups, ranging from 0 to 1. A higher value indicates a stronger cognitive requirement for the question. The preset response time refers to a reasonable baseline response time set by the system for each interactive message, calculated based on the typical mastery level of the corresponding knowledge point within the target age group.

[0054] Specifically, taking the question bank as an example, during the question bank construction phase, education experts and data analysts jointly graded the difficulty of tens of thousands of questions. Based on the average accuracy and average time taken by historical user groups on the same knowledge points, statistical regression analysis was used to assign an initial difficulty coefficient to each question. Furthermore, an independent response time history file was established for each user and each knowledge point. Specifically, when a user first encounters a question on a certain knowledge point, the system uses the general benchmark value for that knowledge point among peers. Thereafter, the time taken for each answer is recorded and included in a weighted average calculation, forming an individualized answering habit model. For example, if user A's average time for the first five attempts at "decimal multiplication" questions is 48 seconds, the system dynamically updates the preset response time for that knowledge point to 48 seconds, and all subsequent related questions are judged based on this benchmark.

[0055] The time efficiency factor is used to objectively measure the degree of matching between the response efficiency of the target object and the expected standard when completing interactive information. In some embodiments of this application, the factor divides the response behavior into three quality levels: "quick guessing", "proficient mastery" and "time-consuming and inefficient" by combining the ratio of response time to preset response time and piecewise function design, and assigns different weight values ​​to each level.

[0056] In some embodiments of this application, the time efficiency factor can be determined in the following ways: obtaining a time threshold, wherein the time threshold is determined based on the historical response time corresponding to the interaction between the target object and historical interaction information, and the historical interaction information and the interaction information have the same difficulty coefficient; comparing the response time with the time threshold and a preset response time respectively to obtain a comparison result, wherein the preset response time is greater than the time threshold; determining the time efficiency factor based on the comparison result, wherein the time efficiency factor is positively correlated with the evaluation index value.

[0057] It should be noted that the time threshold is a personalized time boundary dynamically calculated based on statistical analysis of the target user's historical response behavior data accumulated in past learning activities for knowledge points of the same difficulty level. This threshold is not a fixed value, but rather selects the lower percentile value (such as the 20th percentile) as the boundary for "rapid response" behavior, based on the user's historical response time distribution in similar questions, to distinguish between guessing behavior and genuine cognitive response.

[0058] Specifically, before each interactive task is generated, the system automatically retrieves all interactive records of the user in the past three months that have the same difficulty level as the current task, extracts all historical response times, sorts them in ascending order, and then selects the 20th percentile value in the sequence as the current time threshold.

[0059] Furthermore, a three-interval logic judgment mechanism is adopted to divide the response timeline into three mutually exclusive intervals: the rapid guessing interval (response time ≤ time threshold), the proficiency interval (time threshold < response time ≤ preset response time), and the timeout inefficiency interval (response time > preset response time). The interval in which the response is located is determined step by step through the three-level logic judgment, and the corresponding comparison result code (such as 0, 1, 2) is output.

[0060] Further, based on the above-mentioned comparison result encoding, a predefined nonlinear mapping rule is used to determine the time efficiency factor value. In some embodiments of this application, the time efficiency factor can be determined in the following ways: when the comparison result indicates that the response time is less than a time threshold, a first penalty coefficient is determined as the time efficiency factor; when the comparison result indicates that the response time is not less than the time threshold and not greater than a preset response time, a time efficiency factor is determined based on a reward coefficient; when the comparison result indicates that the response time is greater than a preset response time, a time efficiency factor is determined based on a second penalty coefficient, wherein the first penalty coefficient is greater than or equal to the second penalty coefficient.

[0061] It should be noted that the first penalty coefficient is a punitive factor imposed when the user's response time is below the time threshold, used to compress the evaluation indicator value, reflecting a strict inhibition of thoughtless responses. The second penalty coefficient is a factor imposed when the user's response time exceeds the preset response time, used to moderately punish inefficient and procrastinating behavior, reflecting a tolerant constraint on slow thinking. The reward coefficient is a positive incentive factor used to enhance the evaluation indicator value when the user's response time is between the time threshold and the preset response time, reflecting precise encouragement for proficient behavior.

[0062] Specifically, when the response time is determined to be below the time threshold, no complex calculations are required; the time efficiency factor can be directly set to a preset fixed penalty value α (e.g., 0.4), regardless of the difficulty of the question or the user's historical performance. Furthermore, to achieve precise and personalized penalties, the first penalty coefficient can be dynamically adjusted based on the frequency of the user's historical "suspected guessing" behavior on that knowledge point. For example, if a user has 12 answers "below the time threshold" in the past three months, with 9 of them being incorrect, the system judges them as a "high-risk speculator" and increases α from 0.4 to 0.5; if only 2 such answers occur and both are correct, the system reduces α to 0.35, reflecting a moderate degree of leniency towards users who "occasionally answer quickly but have genuine abilities."

[0063] When the response time is determined to be neither less than the time threshold nor greater than the preset response time, a linear growth incentive mechanism can be adopted. For example, the formula can be used: Time Efficiency Factor = 1 + β × (1 – Actual Time / Preset Response Time), where β is the reward coefficient (e.g., 0.3). The faster the user answers within the standard time, the higher the factor value. Furthermore, the reward coefficient β can be dynamically adjusted based on the difficulty level of the question. For example, for high-difficulty questions (e.g., 0.9), the system increases β to 0.5 to provide a stronger incentive for "mastery"; for low-difficulty questions (e.g., 0.4), β decreases to 0.2 to prevent users from focusing on easy questions.

[0064] When the response time exceeds the preset response time, a linearly decreasing penalty mechanism can be used. For example, the formula can be: Time Efficiency Factor = 1 – γ × (Actual Time / Preset Response Time – 1), where γ is the second penalty coefficient (e.g., 0.2). When the user answers after the timeout, the factor decreases linearly with the proportion of timeout. Furthermore, in cases where the answer is "correct" despite the timeout, a "cognitive resilience compensation factor" (e.g., 0.85–0.95) can be automatically added to implicitly reward the behavior of persisting in reasoning and not giving up thinking.

[0065] The above mechanism effectively curbs cheating behavior by quickly answering questions through the first penalty coefficient, making it unprofitable for speculators. It also accurately incentivizes efficient learners through the reward coefficient, guiding users to actively improve the quality of their answers. Furthermore, the second penalty coefficient avoids wrongly penalizing slow and steady learners, ensuring that the evaluation criteria always follow the user's actual cognitive development trajectory.

[0066] After obtaining the time efficiency factor, a multiplicative coupled linear synthesis mechanism can be adopted. Specifically, it follows the formula: Evaluation index value = Response result × Difficulty coefficient × Time efficiency factor, where the response result is 0 or 1 (incorrect or correct). This model ensures that an incorrect answer, regardless of how short the time or how difficult the question, will always have an evaluation index value of 0, completely eliminating the possibility of getting a high score by luck. Correct answers, on the other hand, receive differentiated scores under the dual weighting of difficulty and efficiency. Furthermore, positive incentive reinforcement strategies can be introduced. For example, when a user answers a difficult question correctly even after the time limit has expired, the system still assigns a compensation coefficient of no less than a preset factor (such as 0.8) to encourage the learning qualities of persisting in thinking and not giving up easily.

[0067] The above embodiments integrate difficulty, efficiency, and correctness into a single evaluation index value, enabling accurate identification and differentiated treatment of behaviors such as "true mastery," "mechanical guessing," "slow thinking," and "ineffective procrastination." If users attempt to cheat time by answering quickly, they will suffer losses due to the significant compression of the time efficiency factor; if they answer steadily and efficiently, they will receive reasonable incentives for high evaluation values; even if they answer slowly, they can still receive appropriate rewards as long as they persist in thinking and answer correctly.

[0068] In some embodiments of this application, the following steps may also be performed: obtaining historical evaluation index values ​​corresponding to the interaction information, and time information corresponding to the historical evaluation index values; determining the forgetting coefficient corresponding to the interaction information, wherein the forgetting coefficient is used to quantify the speed at which the interaction information is forgotten by the target object; updating the capability profile of the target object based on the evaluation index values, historical evaluation index values, time information, and forgetting coefficient, wherein the capability profile is used to reflect the correctness of the target object's responses in the preset interaction information database.

[0069] It should be noted that the historical evaluation index value refers to the comprehensive score calculated and stored by the system when the target object participated in the same or similar knowledge point interaction tasks in the past, which represents the learning quality at that time and is used to carry the user's long-term mastery trace of specific knowledge content.

[0070] Time information refers to the objective time span between the current interaction and the most recent interaction on the same knowledge point, as recorded by the system. The unit is days. This information is not actively entered by the user, but is obtained by the system automatically tracking the difference in timestamps between the two interaction events.

[0071] The forgetting coefficient refers to a decay parameter that a system presets or dynamically adjusts for different knowledge point categories to quantify their natural decay rate in the user's cognition. This coefficient is based on the forgetting curve theory in educational psychology and is configured in combination with the characteristics of the subject and cognitive complexity. For example, rote memorization knowledge points (such as multiplication tables) have a low forgetting coefficient (e.g., 0.1) because they are easy to solidify; while abstract reasoning knowledge points (such as geometric proof logic) have a high forgetting coefficient (e.g., 0.4) because they rely on continuous application.

[0072] A competency profile is a dynamic digital model that reflects the target object's mastery of various knowledge points in a pre-set interactive information database. It is constructed by the system based on historical evaluation index values, current evaluation index values, forgetting coefficients, and time information through a fusion calculation model.

[0073] Specifically, when a user initiates a new interactive task, the system first extracts the knowledge point tags associated with the task (such as "linear equation in one variable" or "fraction finding a common denominator"). Then, it performs a precise search in the user's historical behavior database based on the knowledge point tag to extract the evaluation index value corresponding to the most recent knowledge point task and its occurrence time. In some embodiments of this application, when a user has only a small amount of historical data on a certain knowledge point, a nearest neighbor knowledge point migration strategy can be enabled: if the historical data for that knowledge point is less than three times, the system will select the three most recent evaluation index values ​​of other knowledge points with similar cognitive difficulty and high content relevance (such as "fraction addition and subtraction" and "fraction multiplication") and calculate a weighted average as the current estimated "historical evaluation index value". At the same time, the time information is taken as the average interval. For example, if a user has only one historical record of "fraction multiplication" (0.6, 15 days ago), the system will combine the historical data of "fraction addition and subtraction" (0.7, 10 days ago) and "decimal operations" (0.65, 12 days ago) to calculate a weighted average of 0.67, with the time taken as 12 days, as the input for this update.

[0074] Furthermore, during the question bank construction phase, educational experts can categorize all questions according to cognitive levels, such as "memory-based," "calculation-based," "reasoning-based," and "comprehensive application-based," and assign a fixed forgetting coefficient to each category. In addition, to ensure the forgetting coefficient truly reflects users' cognitive habits, a dynamic forgetting coefficient self-calibration mechanism based on user behavior feedback can be implemented. This involves continuously tracking the retest performance of the same knowledge point at different time intervals during long-term user use. For example, if a user scored 0.8 on a "percentage application" question 10 days ago and scores 0.5 on a subsequent attempt, the system estimates their true forgetting rate to be approximately 0.4. If the user scores 0.7 on the same question 5 days later, the system determines that their memory of the knowledge point is stable and lowers the forgetting coefficient to 0.2.

[0075] Furthermore, the user capability profile can be updated using the following formula:

[0076]

[0077] in, For the first The cumulative effective score for this knowledge point during each learning session. For the valid score obtained in this learning session, This represents the number of days since the last learning session. This is the forgetting factor, which is dynamically configured based on the type of knowledge point, and its value ranges from 0.1 to 0.5.

[0078] To ensure that the interactive information matches the capabilities of the target object, the following steps can be performed before receiving the interactive behavior data of the target object: in response to the interactive request of the target software, obtain the capability profile of the target object; determine the target interactive information based on the capability profile; and send the target interactive information to the target software.

[0079] It should be noted that target interaction information refers to the interactive task content that the system selects and generates from preset interaction information (such as preset question bank) based on the target object's current ability profile, and that is highly matched with the target's ability level. This includes the question content, difficulty level, and expected answering time range.

[0080] Specifically, when a user triggers a request to exchange learning time (interaction request) on their terminal, an identity verification request is immediately sent to the cloud-based capability profile service. The server then retrieves the user's most recently updated capability profile data from its memory cache and returns it in real time. If the user is a newly registered user or has not participated in learning recently, their capability profile may not yet be established or may be sparse. In such cases, an initial capability profile can be generated based on information such as the user's registration age, device usage time, historical gaming behavior (e.g., difficulty of game levels) from the interaction request, as well as the average capability model of the same age group.

[0081] Furthermore, all questions in the preset interactive information database are categorized by knowledge points and difficulty coefficients to form a multi-level question bank structure (such as three levels: "basic - advanced - challenge"). Based on the user's ability profile value, the system automatically selects questions within the "ability profile ± 0.1" range as target interactive information under the corresponding knowledge point. For example, if the user's "algebraic simplification" ability profile is 0.74, the system will randomly select a question from the question bank within the difficulty coefficient range of 0.65–0.85.

[0082] Furthermore, the selected target interaction information, along with metadata such as the unique identifier of this learning task, validity period, difficulty level, and expected response time range, are encapsulated into a digitally signed encrypted token and sent to the target software through a secure communication channel.

[0083] In related technologies, users are often faced with random questions from a fixed question bank. Regardless of their ability level, they are required to complete questions of the same difficulty, which makes the strong feel bored and the weak feel frustrated. However, the embodiments of this application use accurate identification of ability profiles to tailor the interactive tasks for each user to achieve intelligent guidance closed loop.

[0084] Step S206: Determine the target duration based on the evaluation index value, wherein the target duration is the allowed running time of the target software in the second time period, and the magnitude of the evaluation index value is positively correlated with the length of the target duration.

[0085] In step S206 above, the target duration refers to the total amount of time that the system dynamically grants to the target software (such as a game or entertainment application) to legally run in the next specific time period after the user completes a learning interaction task, based on the evaluation index value of the current task. This duration is not a fixed value, but changes non-linearly and positively correlated with the magnitude of the evaluation index value. For example, the higher the evaluation index value, the longer the game duration is granted, and the increase decreases marginally as the value increases.

[0086] In some embodiments of this application, the evaluation index value comprises sub-evaluation values ​​corresponding to multiple sub-interaction information. Based on this, the target duration can be determined as follows: obtaining the age information of the target object; determining a duration conversion coefficient based on the age information, wherein the duration conversion coefficient is used to quantify the intensity of converting the evaluation index value into the available duration of the target software; determining an initial duration based on the duration conversion coefficient and the sub-evaluation values, wherein the initial duration increases with the accumulation of sub-evaluation values, and the rate of increase decreases with the increase of sub-evaluation values; and determining the target duration based on the initial duration.

[0087] It should be noted that the time conversion coefficient refers to a dynamic proportional factor preset by the system for different age groups based on the target audience's legal age range. This factor is used to convert the evaluation index value into usable game time. For example, for children aged 6-8, the system sets the time conversion coefficient to K=2.5, focusing on encouraging the accumulation of basic cognitive knowledge; for teenagers aged 12-15, the coefficient is increased to K=4.0 to match their stronger logical thinking ability and learning potential; and for minors aged 16 and above who are close to adulthood, it is set to K=5.0, reflecting a higher level of trust in their self-learning ability.

[0088] The initial duration refers to the theoretically granted duration, which is calculated by the system based on the sum of all sub-evaluations and the duration conversion coefficient using a nonlinear function, and is not yet subject to the legally mandated upper limit of duration.

[0089] Specifically, age information can be obtained through interactive requests sent by terminal devices, and corresponding time conversion coefficient ranges are set for the legal upper limit of daily game time for each age group (e.g., under 8 years old: 0 minutes, 8-16 years old: 1 hour, 16-18 years old: 3 hours). For example, for users with a daily upper limit of 1 hour, the coefficient is set to K=3.5; for users with a daily upper limit of 3 hours, the coefficient is set to K=5.0.

[0090] Furthermore, the initial duration can be calculated using the following formula: Initial duration = duration conversion coefficient × ln(∑ sub-evaluation value + 1). This formula adopts a logarithmic smooth growth model mechanism, which has the characteristic of diminishing marginal returns. This allows the system to continue to reward users when they make continuous efforts, but no longer encourages endless practice, thus achieving the sustainability of incentives.

[0091] After obtaining the initial duration, the target duration can be determined in the following ways: determine the maximum allowed duration corresponding to the age information, and the running duration of the target software within the preset time period, wherein the end time of the preset time period is earlier than the start time of the second time period, and the running duration is the cumulative usage time of the target software by the target object within the preset time period; determine the maximum available duration based on the maximum allowed duration and the running duration; and take the minimum value between the maximum available duration and the initial duration as the target duration.

[0092] It should be noted that the runtime refers to the total time the target object has been used in the target software (such as a game or entertainment application) from 00:00 to the current time point in the current calendar day (i.e., the preset time period), which can be obtained through terminal reporting.

[0093] Specifically, during the initialization phase, the maximum allowed duration for each age group (e.g., under 8 years old: 0 minutes, 8–16 years old: 60 minutes, 16–18 years old: 180 minutes) is written as a fixed constant into the system configuration table. When the target software starts up, it reports its local system clock startup time and cumulative running time to the system. Then, it calculates the maximum available duration using the following formula: Maximum available duration = Maximum allowed duration – Runtime. After calculating the initial duration and the maximum available duration, it directly performs the minimum value operation. For example, if the initial duration is 20 minutes and the maximum available duration is 12 minutes, then the target duration is forced to be 12 minutes.

[0094] In some embodiments of this application, after determining the target duration based on the evaluation index value, the following steps may also be performed: determining the permission token corresponding to the target duration; and issuing the permission token to the game terminal or target gateway corresponding to the target software, wherein the permission token is used to instruct the game terminal or target gateway to perform duration control.

[0095] Specifically, after determining the target duration, the built-in asymmetric encryption module is invoked to sign key fields such as "target duration + user ID + timestamp + device fingerprint" using the server's private key. The signature result and plaintext data are then encapsulated into a JSON-structured token. Subsequently, the authorization token can be directly pushed to the user's logged-in game terminal in encrypted message form. Upon receiving the message, the terminal immediately invokes the local security module to verify the token. If the verification is successful, the game application is automatically unlocked and a countdown timer is started. Alternatively, the authorization token can be sent to the target gateway, which intercepts and identifies all traffic pointing to the game server at the packet level. Based on the user identifier and time window in the token, the gateway dynamically generates an access control list (ACL), allowing only the corresponding device to access the game domain name or IP address during the authorized period. That is, after receiving the authorization token, the target gateway establishes a "whitelist session" between the user's IP and the game service, allowing data interaction only within the token's validity period. When the token expires, the gateway immediately sends a "session termination" command to all target game servers, forcibly disconnecting the TCP connection and returning an "anti-addiction restriction" error code.

[0096] Step S208: Based on the target duration and the operation of the target software during the second time period, control the device operation status of the terminal device, wherein the device operation status includes the types of software allowed to run on the terminal device.

[0097] In step S208 above, the second time period refers to the next specific time window after the user completes the learning behavior, during which the system allows the user to use the target software. The device running state refers to the set of software categories that the terminal device is allowed to run at a specific time. Its control granularity can be refined to modes such as "only educational applications allowed", "only game applications allowed", or "all entertainment applications prohibited". This state is dynamically switched by the system according to the authorization logic of the target duration and the second time period. For example, when the target duration is 0, the device running state automatically switches to "only educational software allowed"; when the target duration is 10 minutes, the device running state switches to "game software allowed, other entertainment applications disabled"; when the target duration is exhausted, the device running state is forcibly rolled back to "educational applications only".

[0098] In some embodiments of this application, the terminal device runs an environment management service deeply integrated into the system. This service runs in the operating system kernel layer or system-level permission management module, unaffected by ordinary applications. After granting the target duration, the system sends a "whitelist update instruction" to this service, which includes: the allowed package names (e.g., "game_v1.2.3"), the allowed duration, and the allowed number of launches. For example, when the target duration is 8 minutes, the system issues the instruction: "Only software with the package name com.game.coolkids is allowed to run, valid until 20:03, and only allowed to launch once." Once the game application starts, the system starts a countdown. When the countdown ends, it automatically calls the system API to forcibly close the application and immediately restores the whitelist to "Only educational applications (such as learning apps, e-books)."

[0099] Traditional anti-addiction solutions only set countdowns within the game app, and users can easily bypass the restrictions by closing the application, restarting the device, or switching accounts, rendering the anti-addiction system ineffective. However, the embodiments of this application bind the granting of game permissions to the overall operating environment of the terminal through the system-level control dimension of "standby running status", so that "learning-authorization-game" forms an inseparable physical closed loop.

[0100] To achieve precise control over runtime, in some embodiments of this application, an application behavior monitoring agent module can be deployed in the terminal operating system kernel or system service layer to capture the target software's entire lifecycle status in real time, including startup, running, pause, and background persistence. After the system grants the target runtime, the agent module begins to track the target software's process activity with high precision, recording key performance indicators such as actual runtime, memory usage, and graphics rendering frequency. When the runtime reaches a threshold set by the system, the agent module, regardless of whether the application itself responds to a "shutdown request," directly calls the application lifecycle management interface provided by the operating system to forcibly terminate the target software's main process and clear all its cached data and background services. Simultaneously, the system locks the application's startup entry point, rejecting any form of restart request within a set time. Even if the user manually clears the cache, restarts the device, or switches accounts, the system still binds the device's unique identifier to the user's identity, preventing it from running again. Furthermore, an application environment isolation strategy can be introduced: during the target software's operation, the system only grants it the necessary system permissions (such as network access and input response) and blocks its ability to call potential bypass mechanisms such as background wake-up, multi-instance services, and cloud synchronization. Once the target time limit is exhausted, the system will not only shut down the software, but also mark the application as "today's usage limit has been met" and prohibit any launch behavior for the rest of the day until the user completes the next effective learning session and re-obtains authorization.

[0101] In some embodiments of this application, the approach can also be approached from the hardware resource scheduling level. Utilizing the dynamic power management systems commonly found in modern smart terminals (such as CPU frequency adjustment, GPU downclocking, intelligent screen brightness control, and wireless module sleep mechanisms), the system can proactively intervene by downgrading the device's power consumption level before the target software's runtime is nearing its end. It should be noted that the target software typically requires high computing power (such as high frame rate rendering, real-time physics calculations, and network synchronization), and its normal operation depends on the device being in a "high-performance" or "balanced" power consumption mode. When the system detects that the target runtime is about to expire, it can automatically switch the device's power consumption mode from "high-performance" to "power-saving mode" a certain period in advance (e.g., 5 minutes), and forcibly reduce the maximum operating frequency of the central processing unit, disable the graphics acceleration engine, restrict background network wake-up, and reduce the screen refresh rate to the minimum value. Upon reaching the target duration, the system further locks the device's power consumption level to an "ultra-low power" state. In this state, all high-load applications (including but not limited to games, videos, and live streaming) will fail to start or crash immediately due to insufficient computing resources. The device retains only the minimum operational requirements, allowing basic communication, voice calls, and lightweight educational applications (such as e-textbooks and text-to-speech tools) to continue running. Even if the user attempts to forcibly improve performance by restarting the device, switching system modes, or using third-party tools, the system still rejects any unauthorized power mode changes through firmware-level power consumption policy signature verification until the user completes the next effective learning session, at which point the system will unlock high-performance operation permissions again.

[0102] The control mechanism described in this application embodiment has robust characteristics of "anti-cracking, anti-bypass, and anti-tampering". No matter if the user tries to close the application, restart the device, change the account, or modify system permissions or improve hardware performance, the system can always ensure that the core concept of "learning quality determines entertainment rights" is unconditionally executed on the terminal device through the dual mechanisms of "application state lock" and "power consumption level lock", thereby truly realizing the deep integration of education and guidance empowered by technology and the protection of minors.

[0103] Through steps S202 to S208 above, a multi-dimensional behavioral feature fusion evaluation method is adopted. By collecting the response time and response result of the target object when the interaction mechanism is triggered, a comprehensive evaluation index value reflecting its true cognition and operation quality is constructed. Based on the evaluation index value, the runtime of the target software in the next time period is dynamically calculated to control the device operation status. This achieves the goal of accurately quantifying the quality of user learning behavior, thereby realizing the technical effect of dynamically adjusting the access permissions of the device software according to the user's actual ability performance. This solves the technical problem that the game time allocation method used in related technologies is linearly related to the number of correct answers, which makes it impossible to accurately quantify the quality of answers and make it difficult to accurately control the usage time of the device software.

[0104] Figure 3 This is an overall flowchart of a device operation state control method according to an embodiment of this application, such as... Figure 3 As shown, in some embodiments of this application, the method includes:

[0105] S302, Data Acquisition Layer: Acquires user answer behavior data.

[0106] This step is the starting point for the entire system operation. When the user completes each exercise question, the system automatically and seamlessly collects the user's behavior trajectory and captures the question's difficulty level (based on the standardized difficulty level preset in the question bank), the actual time taken to answer the question (precise millisecond-level timing from the time the question is displayed to the time the user submits the answer), the standard reference time (based on the median answering time of questions of this difficulty in the historical user group), and the correctness of the answer (Boolean value, 0 or 1) in real time through the underlying interface.

[0107] S304, Data collection: Question difficulty level, actual time taken to answer the questions, standard reference time, and correctness of the answers.

[0108] This is a detailed implementation of S302. The system assigns a fixed difficulty coefficient to each question to reflect its cognitive load; the standard reference time is a dynamically updated group statistical value, reflecting the time required to normally master the knowledge point; and the actual time reflects the individual's behavioral efficiency. Through the synchronous collection of these four types of data, the system breaks through the traditional single evaluation mode of "only looking at right or wrong" and constructs a multi-dimensional behavioral feature base.

[0109] S306, Single Question Effective Score Calculation Layer (Time-Accuracy Coupled Evaluation Model).

[0110] This step introduces a non-linear time efficiency factor, integrating the correctness of the answer with the efficiency of answering into a comprehensive quality score. The system uses a preset piecewise function to determine the range of the ratio of the actual time to the standard time: if the time is too short (below the fast answering threshold), it is judged as "guessing behavior" and multiplied by a penalty coefficient α (0.3–0.5), significantly reducing the score; if the time is within a reasonable range (close to the standard time), a proficiency reward is given to improve the score; if the time is exceeded, a moderate penalty is imposed.

[0111] S308, calculate the effective score for a single question.

[0112] Based on the piecewise function defined by S306, the system performs specific numerical calculations: multiplying the correctness of the answer, the difficulty of the question, and the time efficiency factor to obtain the effective score for a single question. This calculation process is completed in real time on the server or edge device, ensuring that the evaluation results are responded to immediately and providing accurate input for subsequent capability updates.

[0113] S310, Capability Model Update Layer, introduces a forgetting curve correction model.

[0114] This step introduces a forgetting curve correction mechanism based on exponential decay. The system establishes an independent mastery history for each knowledge point. When a user completes a new question, the score is not directly added; instead, the historical score is first processed by time decay. This model simulates the physiological law of human memory naturally decaying over time, allowing the system to dynamically perceive whether the user "truly understands" rather than "has reviewed" the material. For example, if a knowledge point learned three days ago is not reviewed, its historical score will be decayed to 55% of its original value. Only by re-mastering it can its weight be restored, thus guiding the user to engage in spaced repetitive learning.

[0115] S312, Update knowledge point mastery.

[0116] Driven by the S310 mathematical model, the system adds the current effective score to the decayed historical mastery level to generate the latest mastery status of the knowledge point. This update process is global and continuous. The system builds a personalized knowledge graph for each user. The answer to each question affects the weight of multiple related knowledge points. This mechanism enables the system to have long-term learning profile capabilities, no longer viewing users as one-time test takers, but as "cognitive subjects with memories, forgetting, and need for consolidation", thereby achieving personalized and dynamic learning guidance.

[0117] S314, Duration Decision Control Layer (Resource Allocation Engine and Anti-Addiction Constraints).

[0118] This step serves as a hub for integrating incentive mechanisms and regulatory compliance. Its principle is to couple a logarithmic smoothing allocation engine with rigid legal constraints. Instead of using a linear exchange of "answering one question earns one minute," the system takes the user's accumulated valid scores as input and substitutes them into a logarithmic function for calculation. Because it inherently possesses the characteristic of diminishing marginal returns, that is, the more questions answered, the smaller the increase in time gained per point, thus eliminating the risk of addiction to "exchanging endless questions for unlimited time."

[0119] S316, calculate the final game duration.

[0120] Based on S314, the system calculates the original game time, which is the theoretically granted quota based on learning performance.

[0121] S318, conduct compliance verification: whether the statutory limit has been exceeded.

[0122] This step calculates the remaining available credit by obtaining the legal maximum daily game time for the user's age group (e.g., 3 hours for minors on holidays) and combining it with the time already used that day.

[0123] S320, take the legally defined remaining time; S322, take the calculation time.

[0124] If the calculated original duration does not exceed the legally mandated remaining limit, the calculated duration will be executed to fully incentivize users. If the calculated value exceeds the legal limit (e.g., if a user's learning performance is excellent and the system intends to grant 50 minutes, but the legal limit is only 15 minutes remaining), the duration will be forcibly truncated to the legally mandated remaining duration to ensure that no technical incentives exceed the prescribed limits.

[0125] S324, execute the feedback layer, and generate a game permission token.

[0126] This step transforms the abstract time limit into an executable digital credential. The system generates an encrypted game permission token containing: authorized time (e.g., 15 minutes), precise effective timestamp, user identity identifier, device binding fingerprint, digital signature, and validity period. This token uses asymmetric encryption and time binding technology, and has anti-tampering, anti-replay, and anti-transfer characteristics to ensure its uniqueness and security.

[0127] S326, Output: Permission token (including expiration date); sent to game terminal / anti-addiction gateway; forced offline when time expires.

[0128] This step represents the final implementation of the closed-loop technology. The permission token is sent to the game terminal or carrier-grade anti-addiction gateway via a secure channel (such as HTTPS or a dedicated API). Upon receiving the token, the terminal starts a local timer and locks game access permissions. The gateway then establishes an access whitelist at the data flow level, allowing game traffic only during the token's validity period. When the authorization time expires, the system immediately triggers a forced shutdown: the terminal automatically closes the game process, the gateway disconnects all associated connections, and the user cannot continue using the game by restarting, switching accounts, or modifying the time.

[0129] It should be noted that, Figure 3 Preferred embodiments of the shown examples can be found in [reference needed]. Figure 2 The corresponding solutions in the illustrated embodiments will not be described in detail here.

[0130] To facilitate understanding of the above-mentioned control method for the operating status of the equipment, the following explanation is provided in conjunction with some specific embodiments.

[0131] S1. Data Acquisition Layer: Acquires user answering behavior data, the data of which at least includes the question difficulty coefficient. Actual time spent answering questions Standard reference time and the correctness of the answers ;

[0132] S2. Single-question effective score calculation layer: The data is input into a preset "time-accuracy coupling ability evaluation model" to calculate the single-question effective score. The calculation formula is as follows:

[0133]

[0134] in, This is a time efficiency factor used to quantify the quality of answers. The specific mathematical form of the function is a piecewise function, defined as follows:

[0135]

[0136] in:

[0137] The threshold for quick response is dynamically calculated by the system based on the user's historical answer time distribution on questions of this difficulty, for example, taking the 20th percentile of the historical average time.

[0138] To estimate the behavioral punishment coefficient, extensive experimental testing was conducted. Below 0.3, guessing behavior cannot be effectively suppressed, while above 0.5, user enthusiasm is severely dampened. Therefore, the preferred range is 0.3 to 0.5.

[0139] The proficiency reward coefficient is preferably between 0.2 and 0.5.

[0140] The timeout penalty coefficient is preferably 0.1 to 0.3.

[0141] S3. Ability Model Update Layer: This layer updates the user's knowledge mastery model based on individual question scores, and introduces a forgetting curve function based on exponential decay to correct the user's long-term ability profile. The update formula is as follows:

[0142]

[0143] in:

[0144] For the first The cumulative effective score for this knowledge point during each learning session;

[0145] The valid score obtained in this learning session;

[0146] This represents the number of days since the last learning session;

[0147] This is the forgetting factor, which is dynamically configured based on the type of knowledge point, and its value ranges from 0.1 to 0.5.

[0148] S4. Duration Decision Control Layer: Constructs a resource allocation engine to calculate the original game time based on the accumulated ability assessment value, and adjusts it in conjunction with anti-addiction regulations to generate the final game time limit. The calculation formula is as follows:

[0149]

[0150] in:

[0151] This represents the cumulative valid score within the current learning cycle.

[0152] This is a duration conversion factor, configured according to age group;

[0153] The legally mandated maximum playtime is determined based on the user's age group;

[0154] This represents the game time used that day.

[0155] The function implements dual constraints of incentives and compliance.

[0156] S5. Execution Feedback Layer: Generates a game permission token with an expiration date, sends it to the game terminal or anti-addiction gateway to implement time control, and triggers forced offline or lockout when the time expires.

[0157] Figure 4 This is a system architecture diagram of a device operation status control method according to an embodiment of this application. The system includes: a terminal device 402, a target gateway 404, and a control system 406. The control system includes a data acquisition module, a capability assessment engine, a resource allocation engine, and an access control module.

[0158] The terminal device 402 serves as the direct user interface, typically a smart terminal with network connectivity such as a smartphone, tablet, or smart TV. Its primary function is to provide the operating environment for learning and entertainment applications and act as the final executor of permission commands. During the learning phase, the terminal device collects user answer behavior data (such as answer time and accuracy) through a built-in educational app and uploads it to the control system 406 in real time. During the game authorization phase, the terminal receives a game permission token issued by the control system and, based on the validity period and device binding information carried in the token, starts or locks the game application. In some embodiments of this application, the terminal device has a built-in local timing and forced offline module. This module runs independently of the application process at the system's underlying layer. Even if the user attempts to close the game, restart the device, or clear the cache, the system can still execute a precise countdown based on the timestamp in the token and forcibly terminate the operation of all game applications upon expiration. Essentially, this module translates cloud-based decisions into hardware control at the terminal, ensuring that users cannot circumvent time limits through conventional means.

[0159] The target gateway 404 refers to a network node with traffic identification and access control capabilities deployed at the user's home network exit or in the operator's backbone network, such as a smart home router or an anti-addiction gateway device deployed by the operator. Its core function is to achieve unified access control across terminals and applications at the network layer of the system architecture, forming a "safety net" for terminal devices. When the control system 406 generates an access token, this token can be issued not only to the terminal but also pushed to the target gateway. Based on the user's identity identifier and device fingerprint in the token, the gateway dynamically generates access control rules, allowing only the user to access the IP address or domain name of the specified game server within the authorized time. Once the authorized time expires, the gateway immediately blocks all related network requests. Even if the user changes terminals, uninstalls apps, uses emulators, or switches networks, as long as they still access the Internet through this gateway, their game access permissions are automatically cut off. This mechanism overcomes the shortcomings of traditional "in-application restrictions" which are easily bypassed, constructs "end-to-network" dual protection, and achieves highly robust control that ensures "devices can be changed, identities remain the same, and control is not lost." It is a key infrastructure to ensure that the system's anti-addiction effect covers all scenarios.

[0160] Control system 406 is the "brain" of the entire system. It can be deployed on a cloud server cluster and includes four core subsystems: data acquisition module, capability assessment engine, resource allocation engine, and access control module. It undertakes all the responsibilities of intelligent analysis and strategy generation.

[0161] (1) The data acquisition module is responsible for receiving the raw answer behavior data uploaded from the terminal device, completing data cleaning, format standardization and encrypted storage, and providing high-quality input for subsequent modeling;

[0162] (2) The ability assessment engine runs the "time-accuracy coupled assessment model" and the "forgetting curve correction model" to scientifically score the quality of each user's answer to each question and dynamically update the knowledge mastery profile to achieve long-term, accurate and personalized modeling of the user's learning ability.

[0163] (3) The resource allocation engine is based on the cumulative effective score, uses the logarithmic function to calculate the game time granted, and combines legal parameters such as user age group and time used on the day to intelligently cut out the final compliant time, so as to achieve the dual goals of "maximizing incentives" and "zero regulatory overreach";

[0164] (4) The permission control module transforms the final decision into a secure and verifiable game permission token, and sends it to the terminal device and the target gateway through a secure channel to complete the distribution and synchronization of the instruction.

[0165] It should be noted that, Figure 4 The system shown is used to execute Figure 2 The control method for the operating status of the equipment shown is therefore Figure 2 The relevant explanations and descriptions in the equipment operation status control methods also apply to Figure 4 The system shown will not be described in detail here.

[0166] This application embodiment introduces a time efficiency factor. Its piecewise function definition quantifies answer quality at the algorithmic level, effectively distinguishing between "true mastery" and "guessing." When users guess quickly, The penalty coefficient reduces the score; when users are proficient at answering questions... The reward coefficient increases the score, thus solving the technical blind spot of "guessing to cheat for time duration" in existing technologies.

[0167] Furthermore, a forgetting curve function based on exponential decay is introduced. This enables the system to dynamically adjust the weight of historical scores based on time intervals, achieving long-term tracking and accurate profiling of users' knowledge mastery, thus solving the technical deficiency of existing static question banks that cannot adaptively match user abilities.

[0168] In addition, logarithmic functions are used. Time allocation is implemented to achieve diminishing marginal returns and avoid the risk of users gaining unlimited time through endless practice problems; simultaneously, through... The function couples the incentive algorithm with hard constraints on preventing addiction (age group limits, daily usage time), achieving an automatic balance between compliance and incentives from a technical perspective.

[0169] Finally, a complete technical closed loop is established, encompassing "learning behavior data collection → ability model update → game duration decision-making → terminal execution control," enabling the system to have self-optimization capabilities and achieving deep integration of education and anti-addiction measures at the architectural level, thereby motivating users to proactively improve their learning quality.

[0170] Figure 5 This is a structural diagram of a device for controlling the operating state of an equipment according to an embodiment of this application, such as... Figure 5 As shown, the device includes:

[0171] The receiving module 502 is used to receive the interaction behavior data of the target object. The interaction behavior data is triggered when the cumulative usage time of the target object on the target software in the first time period meets the preset time. The interaction behavior data is used to reflect the response time and response result of the target object to the interaction information.

[0172] Evaluation module 504 is used to determine the evaluation index value corresponding to the interaction behavior data based on the response time and response result reflected in the interaction behavior data.

[0173] The determination module 506 is used to determine the target duration based on the evaluation index value, wherein the target duration is the allowed running time of the target software in the second time period, and the magnitude of the evaluation index value is positively correlated with the length of the target duration;

[0174] The control module 508 is used to control the device operation status of the terminal device based on the target duration and the operation status of the target software in the second time period. The device operation status includes the types of software that are allowed to run on the terminal device.

[0175] It should be noted that, Figure 5 The control device shown is used to execute the equipment operation status control device. Figure 2 The control method for the operating status of the equipment shown is therefore Figure 2 The relevant explanations and descriptions in the equipment operation status control methods also apply to Figure 5 The control device for the equipment's operating status shown will not be described in detail here.

[0176] This application also provides an electronic device, which includes a memory and a processor. The memory is used to store program instructions, and the processor is connected to the memory to execute the steps of the control method for the device operating state implemented in the various embodiments of this application.

[0177] This application also provides a non-volatile storage medium including a stored computer program, wherein the device containing the non-volatile storage medium executes the steps of the device operation state control method in various embodiments of this application by running the computer program.

[0178] This application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the device operation state control method in various embodiments of this application.

[0179] This application also provides a computer program that, when executed by a processor, implements the steps of the device operation state control method in various embodiments of this application.

[0180] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0181] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0182] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0183] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0185] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0186] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method of controlling the operating state of a device, characterized by, include: Receive interactive behavior data of the target object, wherein the interactive behavior data is triggered when the cumulative usage time of the target object on the target software within a first time period meets a preset duration, and the interactive behavior data is used to reflect the response time and response result of the target object to the interactive information; Based on the response time and response result reflected in the interaction behavior data, the evaluation index value corresponding to the interaction behavior data is determined; The target duration is determined based on the evaluation index value, wherein the target duration is the allowed runtime of the target software within the second time period, and the magnitude of the evaluation index value is positively correlated with the length of the target duration; Based on the target duration and the operation of the target software during the second time period, the device operation status of the terminal device is controlled, wherein the device operation status includes the types of software allowed to run on the terminal device.

2. The method of claim 1, wherein, Based on the response time and response result reflected in the interaction behavior data, the evaluation index value corresponding to the interaction behavior data is determined, including: Obtain the difficulty level and preset response time corresponding to the interaction information; A time efficiency factor is determined based on the response time in the interaction behavior data and the preset response time, wherein the time efficiency factor is used to quantitatively represent the target object's proficiency with the interaction information; The evaluation index value is determined based on the difficulty coefficient, the time efficiency factor, and the response result.

3. The method of claim 2, wherein, Determining a time efficiency factor based on the response time in the interaction behavior data and the preset response time includes: Obtain a time threshold, wherein the time threshold is determined based on the historical response time corresponding to the interaction between the target object and the historical interaction information, and the historical interaction information has the same difficulty coefficient as the interaction information; The response time is compared with the time threshold and the preset response time to obtain a comparison result, wherein the preset response time is greater than the time threshold. The time efficiency factor is determined based on the comparison results, wherein the time efficiency factor is positively correlated with the evaluation index value.

4. The method of claim 3, wherein, Determining the time efficiency factor based on the comparison results includes: If the comparison result indicates that the response time is less than the time threshold, the first penalty coefficient is determined as the time efficiency factor; If the comparison result indicates that the response time is not less than the time threshold and not greater than the preset response time, the time efficiency factor is determined based on the reward coefficient. If the comparison result indicates that the response time is greater than the preset response time, the time efficiency factor is determined based on the second penalty coefficient, wherein the first penalty coefficient is greater than or equal to the second penalty coefficient.

5. The method of claim 1, wherein, The evaluation index value comprises sub-evaluation values ​​corresponding to multiple sub-interaction information; determining the target duration based on the evaluation index value includes: Obtain the age information of the target object; The duration conversion coefficient is determined based on the age information, wherein the duration conversion coefficient is used to quantitatively represent the strength of converting the evaluation index value into the available duration of the target software; An initial duration is determined based on the duration conversion coefficient and the sub-evaluation value, wherein the initial duration increases as the sub-evaluation value accumulates, and the rate of increase decreases as the sub-evaluation value increases; The target duration is determined based on the initial duration.

6. The method according to claim 5, characterized in that, Determining the target duration based on the initial duration includes: Determine the maximum allowed duration corresponding to the age information, and the runtime of the target software within a preset time period, wherein the end time of the preset time period is earlier than the start time of the second time period, and the runtime is the cumulative usage time of the target software by the target object within the preset time period; The maximum available duration is determined based on the maximum allowed duration and the already run duration; The minimum value between the maximum available time and the initial time is taken as the target time.

7. The method according to claim 1, characterized in that, The method further includes: Obtain the historical evaluation index values ​​corresponding to the interaction information, and the time information corresponding to the historical evaluation index values; Determine the forgetting coefficient corresponding to the interaction information, wherein the forgetting coefficient is used to quantify the speed at which the interaction information is forgotten by the target object; The target object's capability profile is updated based on the evaluation index value, the historical evaluation index value, the time information, and the forgetting coefficient. The capability profile is used to reflect the target object's response accuracy in a preset interaction information database.

8. The method according to claim 7, characterized in that, Before receiving the interaction behavior data of the target object, the method further includes: In response to the interaction request of the target software, obtain the capability profile of the target object; Target interaction information is determined based on the aforementioned capability profile; The target interaction information is sent to the target software.

9. The method according to claim 1, characterized in that, After determining the target duration based on the evaluation index values, the method further includes: Determine the permission token corresponding to the target duration; The permission token is sent to the game terminal or target gateway corresponding to the target software, wherein the permission token is used to instruct the game terminal or the target gateway to perform duration control.

10. A control device for the operating status of equipment, characterized in that, include: A receiving module is used to receive interactive behavior data of a target object, wherein the interactive behavior data is triggered when the cumulative usage time of the target object on the target software within a first time period meets a preset duration, and the interactive behavior data is used to reflect the response time and response result of the target object to the interactive information; The evaluation module is used to determine the evaluation index value corresponding to the interaction behavior data based on the response time and response result reflected in the interaction behavior data. The determination module is used to determine the target duration based on the evaluation index value, wherein the target duration is the allowed runtime of the target software within the second time period, and the magnitude of the evaluation index value is positively correlated with the length of the target duration; The control module is used to control the device operation status of the terminal device based on the target duration and the operation status of the target software during the second time period, wherein the device operation status includes the types of software allowed to run on the terminal device.

11. An electronic device, characterized in that, include: A memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute a control method for implementing the device operating state according to any one of claims 1 to 9.

12. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored computer program, wherein the device containing the non-volatile storage medium executes the device operation state control method according to any one of claims 1 to 9 by running the computer program.

13. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the device operation state control method according to any one of claims 1 to 9.

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