Student device self-management guidance method and system based on achievement motivation

Abstract

This invention relates to the field of intelligent education technology, specifically to a method and system for guiding student device self-management based on achievement incentives. The method includes: performing differential privacy perturbation processing on raw data of student device usage behavior to form aggregated statistical behavior data; associating and mapping the aggregated statistical behavior data with a preset educational scenario template to generate an initial control strategy for student devices; deploying a process protection component according to the initial control strategy to generate process protection status data; merging the process protection status data and the aggregated statistical behavior data, and generating a self-management badge and matching corresponding self-management permissions when the behavior performance evaluation meets the continuous achievement conditions; after a student's temporary time extension request initiated by the permission is approved, optimizing the control strategy by coupling peripheral device status data, synchronizing it to the local policy engine, and supporting the independent adjustment of some parameters; this invention can improve the efficiency of student device self-management.

Description

Technical Field

[0001] This invention relates to the field of intelligent education technology, and in particular to a method and system for guiding student device self-management based on achievement incentives. Background Technology

[0002] Existing student device management technologies employ fixed rules, failing to dynamically adapt management strategies based on actual student usage behavior, resulting in a severe lack of targeted and flexible management. Furthermore, the collection and analysis of device usage data lack robust privacy protection measures, compromising data security and hindering accurate behavioral feature analysis while remaining compliant with regulations.

[0003] Current technologies lack an achievement-based incentive system to guide students in self-management of their devices. Relying solely on mandatory control measures to restrict device use fails to motivate students' initiative in self-management. Furthermore, the process monitoring mechanism suffers from poor stability, and control strategies cannot be dynamically optimized based on student behavior and peripheral usage status. Consequently, the guidance and iterative implementation of student self-management strategies are ineffective. Therefore, improving the efficiency of guiding students in self-management of their devices has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a student device self-management guidance method and system based on achievement incentives to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a student device self-management guidance method based on achievement incentives, comprising: A1. Perform differential privacy perturbation processing on the raw data of student device usage behavior to obtain aggregated statistical behavior data of student devices; A2. Associate and map the aggregated statistical behavioral data with the preset educational scenario template to obtain the initial control strategy for student devices; A3. Based on the process protection configuration information in the initial control strategy, deploy the process protection component on the student device, and lock the same file through the main process and monitoring process in the process protection component to generate process protection status data of the student device. A4. Integrate process guardian status data with aggregated statistical behavior data. When the evaluation result of the integrated behavior meets the preset continuous compliance conditions, generate a self-management badge for student devices and generate self-management permissions for student devices based on the level of the self-management badge. A5. When a student's temporary time extension request based on self-management permissions is approved, the collected peripheral connection status data and aggregated statistical behavior data are coupled and analyzed. Based on the analysis results, the initial control strategy is dynamically optimized to obtain an adaptive control strategy for student devices. A6. Synchronize the adaptive control strategy to the local policy engine of the student device, and adjust some control parameters in the adaptive control strategy according to the student's self-management permissions.

[0006] In a preferred embodiment, the differential privacy perturbation processing of the raw student device usage behavior data to obtain aggregated statistical behavior data of the student devices includes: Obtain raw data on student device usage behavior, and then anonymize the raw data to obtain an anonymized sequence of student device behavior data. Based on the preset privacy protection level, a corresponding privacy budget is allocated to the de-identified behavioral data sequence, and the noise perturbation intensity corresponding to the de-identified behavioral data sequence is calculated based on the privacy budget. Based on the noise perturbation intensity, the data points in the desensitized behavioral data sequence are subjected to Laplace perturbation to obtain the noisy behavioral data sequence of student equipment; According to the management group to which the student equipment belongs, the noisy behavior data sequence is hierarchically aggregated to obtain the aggregated statistical behavior data of the student equipment.

[0007] In a preferred embodiment, the formula for calculating the noise disturbance intensity is as follows: ; In the formula, The noise disturbance intensity, As a preset global noise benchmark, The sensitivity parameter is extracted from the desensitized behavioral data sequence. For privacy budget, This refers to the number of data points contained in the desensitized behavioral data sequence. To manage the number of student devices contained in a group, This is a logarithmic operation with the natural constant e as the base.

[0008] In a preferred embodiment, the step of associating and mapping aggregated statistical behavioral data with a preset educational scenario template to obtain an initial control strategy for student devices includes: Extract behavioral characteristic indicators of student devices from aggregated statistical behavioral data for each management period to generate behavioral characteristic vectors of student devices; The similarity between the behavioral feature vector and the matching rules in the preset educational scenario template is evaluated to obtain the matching score between the educational scenario template and the student's device. Based on the matching score, the target education scenario template with the highest matching degree is selected from the education scenario templates, and the control strategy template is extracted from the target education scenario template. The behavioral feature indicators in the behavioral feature vector are used as constraint parameters and filled into the policy configuration items in the control policy template to generate the initial control policy for student devices.

[0009] In a preferred embodiment, the step of deploying a process guardian component on the student device according to the process guardian configuration information in the initial control policy, and locking the same file through the main process and monitoring process in the process guardian component to generate process guardian status data for the student device, includes: The process protection configuration information in the initial control policy is decomposed to obtain the target process list, protection policy parameters and component identification code of the process protection configuration information; Based on the component identifier, retrieve the process guardian component installation package that matches the component identifier from the local secure storage area of ​​the student device; Based on the guardian policy parameters, the executable file in the process guardian component installation package is extracted to the target installation directory of the student device to complete the deployment of the process guardian component; The process guardian component is started, which creates a main process and a monitoring process. The main process is used to execute the guardian actions specified in the guardian policy parameters, and the monitoring process is used to monitor the running status of the main process. The main process and the monitoring process simultaneously initiate a lock request to the state lock file specified in the file system of the student device. When both the main process and the monitoring process successfully lock the state lock file, the monitoring process generates the process guardian state data of the student device.

[0010] In a preferred embodiment, the process guardian status data is fused with aggregated statistical behavior data. When the fused behavior performance evaluation result meets preset continuous compliance conditions, a self-management badge for the student device is generated. Based on the level of the self-management badge, self-management permissions for the student device are generated, including: The main process running status and locked file status in the process guardian status data are integrated into a guardian stability index for student devices. By comprehensively summarizing the behavioral characteristic indicators of student devices in each management period from the aggregated statistical behavioral data, behavioral compliance indicators are obtained. By weighting and integrating the stability indicators and the behavioral compliance indicators, the behavioral performance evaluation results of student devices are obtained. The behavioral performance evaluation results are compared with the target thresholds in the preset continuous target conditions. When the behavioral performance evaluation results reach the target thresholds in multiple consecutive evaluation periods, the target period count of the behavioral performance evaluation results is generated. When the number of compliance cycles reaches the number of compliance cycles in the continuous compliance condition, the numerical range corresponding to the compliance cycle count is mapped to a level to obtain the student equipment self-management badge. Based on the level of the self-management badge, determine the corresponding control parameter adjustment permissions and temporary time extension application permissions to generate self-management permissions for student devices.

[0011] In a preferred embodiment, when a student's temporary time extension request based on self-management permissions is approved, the collected peripheral connection status data and aggregated statistical behavior data are coupled and analyzed. Based on the analysis results, the initial control strategy is dynamically optimized to obtain an adaptive control strategy for the student's devices, including: Receive temporary overtime requests initiated by students based on their self-management permissions, verify the request time period and duration carried in the temporary overtime request against the permission parameters in the self-management permissions, and obtain the approval status of the temporary overtime request; In response to the application approval status, the peripheral connection status data of the student's device during the application period is collected. The peripheral connection status data includes peripheral type identifier, peripheral connection duration and peripheral usage frequency. By correlating and comparing the peripheral connection status data with the behavioral characteristic indicators of the corresponding time period in the aggregated statistical behavioral data, the behavioral deviation analysis results of student devices during the temporary time extension period are obtained. Based on the behavioral deviation analysis results, the control rules corresponding to the peripheral device type identifiers in the initial control strategy are identified, and the control intensity parameters in the control rules are adjusted to obtain the adaptive control strategy for student devices.

[0012] In a preferred embodiment, the step of correlating and comparing peripheral connection status data with behavioral characteristic indicators of the corresponding time period in aggregated statistical behavioral data to obtain behavioral deviation analysis results of student devices during temporary time extension includes: Feature anchoring is performed on peripheral connection status data to obtain the peripheral type identifier of the peripheral connection status data; Based on the peripheral device type identifier, tensor synthesis is performed on the behavioral feature indicators corresponding to the application period in the aggregated statistical behavioral data to obtain the benchmark behavioral feature vector within the application period. Based on the baseline behavioral feature vector, the deviation measure of peripheral connection duration and peripheral usage frequency in peripheral connection status data is quantified to obtain the behavioral deviation coefficient corresponding to the peripheral type identifier. By comprehensively analyzing the behavioral deviation coefficient and the application duration, the behavioral deviation analysis results of student devices during the temporary extension period were obtained.

[0013] In a preferred embodiment, the step of synchronizing the adaptive control strategy to the local policy engine of the student device, and autonomously adjusting some control parameters in the adaptive control strategy according to the student's self-management permissions, includes: The adaptive control strategy is encapsulated into a policy data package and sent to the local policy engine of the student's device. The local policy engine then loads and stores the adaptive control strategy. Read the current parameter configuration of the adaptive control policy from the local policy engine, and obtain the permission parameters in the self-management permissions. The permission parameters include a list of adjustable parameter identifiers and parameter adjustment ranges. By overlapping the list of adjustable parameter identifiers with the control parameter identifiers in the current parameter configuration, the adjustable parameters of the student equipment can be obtained. Based on the parameter adjustment range, the parameter values ​​of the adjustable parameters are reset, and the reset parameter configuration is written to the local policy engine.

[0014] To address the aforementioned problems, this invention also provides a student device self-management guidance system based on achievement incentives, the system comprising: The privacy perturbation module is used to perform differential privacy perturbation processing on the raw data of student device usage behavior to obtain aggregated statistical behavior data of student devices. The scene mapping module is used to associate and map aggregated statistical behavior data with preset educational scene templates to obtain the initial control strategy for student devices; The process protection module is used to deploy the process protection component on the student device according to the process protection configuration information in the initial control policy, and to lock the same file through the main process and monitoring process in the process protection component to generate process protection status data of the student device. The badge authorization module is used to integrate process protection status data with aggregated statistical behavior data. When the integrated behavior performance evaluation result meets the preset continuous achievement conditions, a self-management badge for the student device is generated, and self-management permissions for the student device are generated according to the level of the self-management badge. The dynamic optimization module is used to couple and analyze the collected peripheral connection status data and aggregated statistical behavior data when a student's temporary time extension request based on self-management permissions is approved. Based on the analysis results, the initial control strategy is dynamically optimized to obtain an adaptive control strategy for student devices. The strategy adjustment module is used to synchronize the adaptive control strategy to the local strategy engine of the student device, and to adjust some control parameters in the adaptive control strategy according to the student's self-management permissions.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention processes student device usage behavior data through differential privacy perturbation, achieving compliant data collection and hierarchical aggregation, and ensuring the privacy and security of student device behavior data. It leverages educational scenario templates to achieve precise mapping between data and policies, efficiently generating initial control policies adapted to student usage scenarios. Through a dual-process file locking protection mechanism, it stably generates device process protection status data, ensuring the reliability of control policy execution.

[0016] 2. This invention integrates process monitoring status and behavioral data to complete behavioral performance evaluation, constructs an achievement incentive system with self-management badges, matches tiered self-management permissions, and stimulates students' initiative in self-management. It dynamically optimizes control strategies based on peripheral device usage data, forming an adaptive control scheme that allows students to adjust parameters independently, comprehensively improving the efficiency and accuracy of students' self-management of devices. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a student device self-management guidance method based on achievement incentives according to an embodiment of the present invention; Figure 2 This is a functional block diagram of a student equipment self-management guidance system based on achievement incentives provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for guiding students to self-manage their devices based on achievement incentives. The executing entity of this method includes, but is not limited to, at least one electronic device that can be configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1The diagram shown is a flowchart illustrating a student device self-management guidance method based on achievement incentives according to an embodiment of the present invention. In this embodiment, the student device self-management guidance method based on achievement incentives includes: A1. Perform differential privacy perturbation processing on the raw data of student device usage behavior to obtain aggregated statistical behavior data of student devices; In this embodiment of the invention, the differential privacy perturbation processing of the raw data on student device usage behavior to obtain aggregated statistical behavior data of student devices includes: Obtain raw data on student device usage behavior, and then anonymize the raw data to obtain an anonymized sequence of student device behavior data. Based on the preset privacy protection level, a corresponding privacy budget is allocated to the de-identified behavioral data sequence, and the noise perturbation intensity corresponding to the de-identified behavioral data sequence is calculated based on the privacy budget. Based on the noise perturbation intensity, the data points in the desensitized behavioral data sequence are subjected to Laplace perturbation to obtain the noisy behavioral data sequence of student equipment; According to the management group to which the student equipment belongs, the noisy behavior data sequence is hierarchically aggregated to obtain the aggregated statistical behavior data of the student equipment.

[0021] The formula for calculating the noise disturbance intensity is as follows: ; In the formula, The noise disturbance intensity, As a preset global noise benchmark, The sensitivity parameter is extracted from the desensitized behavioral data sequence. For privacy budget, This refers to the number of data points contained in the desensitized behavioral data sequence. To manage the number of student devices contained in a group, This is a logarithmic operation with the natural constant e as the base.

[0022] The system collects behavioral data on student devices, including usage time, application status, operation period, and frequency of function use. It records the device usage behavior in real time to form raw data. Information that can be associated with a student's personal identity, such as name, student ID, and unique hardware identifier of the device, is removed from the raw data. Privacy content such as home address and contact information is hidden from the data. The characteristic information that can identify individual students is replaced with a general, non-discriminatory identifier. The data is arranged in chronological order of collection time to form a de-identified behavioral data sequence of student devices.

[0023] The system presets three privacy protection levels: basic, medium, and strict. The basic level is suitable for daily teaching data protection needs, the medium level is suitable for centralized management scenarios within the school, and the strict level is suitable for off-campus usage scenarios. After matching the de-identified behavioral data sequence of student devices with the corresponding scenario level, a fixed privacy budget value bound to the level is assigned to the sequence. The privacy budget value corresponding to the strict level is less than that of the medium level, and the privacy budget value corresponding to the medium level is less than that of the basic level.

[0024] The global noise benchmark is determined based on historical practice data on student device behavior data privacy protection and compliance experience in data perturbation in educational scenarios. Multiple sets of comparative tests on data anonymization perturbation of student devices covering different educational stages and different usage scenarios were conducted, and a fixed value that can simultaneously balance the effectiveness of data privacy protection and the usability of data analysis was selected as the global noise benchmark.

[0025] The sensitivity parameter is extracted from the desensitized behavioral data sequence of student devices. The difference between the data points of adjacent time nodes in the desensitized behavioral data sequence is calculated one by one. The maximum value of the difference between all adjacent data points is extracted as the change range of the data sequence. This change range is the value of the sensitivity parameter.

[0026] The number of data points is obtained by counting the number of independent data entries in the desensitized behavioral data sequence of student devices line by line. The counting process is free of data omissions and duplicates, and the final counting result is directly used as the determined value of this parameter.

[0027] The number of student devices is determined by counting all registered student devices within their respective management groups. The counting scope covers all registered devices within the group, ensuring no devices are missed. The counted value is directly used as the final value for this parameter.

[0028] Logarithmic operations with the natural constant e as the base require first calculating the ratio of the total number of managed group devices to the number of data points in the desensitized behavioral data sequence, then adding this ratio to the value 1, and finally performing logarithmic operations with the natural constant e as the base on the sum to obtain the corresponding logarithmic operation result.

[0029] By sequentially performing calculations on the global noise baseline, sensitivity parameters, privacy budget, number of data points, and number of management group devices according to a predetermined calculation logic, the noise disturbance intensity of the behavioral data sequence after anonymization adapted to student devices can be accurately obtained.

[0030] Based on the determined noise disturbance intensity, the values ​​of each independent data point in the desensitized behavioral data sequence of the student equipment are precisely adjusted. Random values ​​that perfectly match the noise disturbance intensity are added to the original values ​​of each data point. After the adjustment operation of all data points in the sequence is completed, the noise-induced behavioral data sequence of the student equipment is formed.

[0031] Based on the management groups assigned to student devices according to grade and class, the noise behavior data sequences of all student devices belonging to the same management group are aggregated and summarized. The data is then classified, organized, and statistically analyzed in sequence according to the management levels of school, grade, and class, and finally aggregated statistical behavior data of student devices is generated.

[0032] The beneficial effects are that this process, through multi-level data processing operations, comprehensively protects the privacy and security of student device behavior data, completes the compliant processing and accurate aggregation of data, provides a reliable and effective data foundation for the generation of subsequent student device management strategies, and the accurate calculation of noise disturbance intensity provides a stable numerical basis for Laplace disturbance, ensuring that both noisy data and aggregated statistical data simultaneously meet the dual requirements of privacy protection and data usability.

[0033] A2. Associate and map the aggregated statistical behavioral data with the preset educational scenario template to obtain the initial control strategy for student devices; In this embodiment of the invention, the step of associating aggregated statistical behavioral data with a preset educational scenario template to obtain an initial control strategy for student devices includes: Extract behavioral characteristic indicators of student devices from aggregated statistical behavioral data for each management period to generate behavioral characteristic vectors of student devices; The similarity between the behavioral feature vector and the matching rules in the preset educational scenario template is evaluated to obtain the matching score between the educational scenario template and the student's device. Based on the matching score, the target education scenario template with the highest matching degree is selected from the education scenario templates, and the control strategy template is extracted from the target education scenario template. The behavioral feature indicators in the behavioral feature vector are used as constraint parameters and filled into the policy configuration items in the control policy template to generate the initial control policy for student devices.

[0034] Behavioral data of student devices during pre-class, in-class, post-class, self-study, and after-class management periods are extracted from aggregated statistical behavioral data. Based on the preset usage standards for each management period, compliance indicators for usage duration are determined. Based on the list of applications allowed to run on the device, compliance indicators for application operation are determined. Based on the time range of each management period, operation period matching indicators are determined. Based on the list of functions allowed to use on the device, compliance indicators for function use are determined. The four behavioral characteristic indicators corresponding to the above five management periods are combined in a fixed dimension order of usage duration, application operation, operation period, and function use to form a complete and dimensionally unified behavioral characteristic vector of student devices.

[0035] Pre-defined indicator matching rules are established for four types of educational scenarios: classroom teaching, self-study, after-school leisure, and home-school collaboration. All behavioral feature indicators contained in the behavioral feature vector of the student device are compared with the indicator matching rules in the template of each pre-defined educational scenario item by item. It is determined whether the behavioral feature indicators and the matching rules are completely consistent. The total number of matching feature items is counted. The total number of matching feature items is divided by the total number of feature items in the behavioral feature vector. The calculated ratio is used as the matching degree score between the corresponding educational scenario template and the student device.

[0036] The matching scores of all preset education scenario templates are aggregated, and the scores are arranged in descending order of value. The education scenario template corresponding to the matching score with the highest value is determined and designated as the target education scenario template. The exclusive control strategy template is completely extracted from the built-in configuration content of the target education scenario template. This template includes the basic framework of time period control, application control, function control, and duration control.

[0037] The usage duration compliance indicators, application operation compliance indicators, operation time period matching indicators, and function usage compliance indicators contained in the behavioral feature vector of student devices are used as constraint parameters in sequence. According to the correspondence between parameter type and configuration item, they are filled into the usage duration configuration item, application control configuration item, function restriction configuration item, and time period control configuration item in the control strategy template, respectively, to ensure that each constraint parameter is completely matched with the corresponding configuration item. After completing the parameter filling of all policy configuration items, an initial control strategy adapted to the usage behavior of student devices is generated.

[0038] The beneficial effects are that this process, through refined extraction of multi-time-period and multi-dimensional behavioral features, standardized assessment of educational scenario similarity, precise selection of target scenario templates, and standardized filling of constraint parameters, can generate initial control strategies that are highly consistent with students' actual usage behavior and corresponding educational scenarios. This significantly improves the adaptation accuracy and implementation effectiveness of control strategies, and provides a stable and reliable strategy foundation for subsequent process protection component deployment, behavior performance evaluation, and dynamic strategy optimization.

[0039] A3. Based on the process protection configuration information in the initial control strategy, deploy the process protection component on the student device, and lock the same file through the main process and monitoring process in the process protection component to generate process protection status data of the student device. In this embodiment of the invention, the step of deploying a process guardian component on the student device according to the process guardian configuration information in the initial control strategy, and locking the same file through the main process and monitoring process in the process guardian component to generate process guardian status data for the student device, includes: The process protection configuration information in the initial control policy is decomposed to obtain the target process list, protection policy parameters and component identification code of the process protection configuration information; Based on the component identifier, retrieve the process guardian component installation package that matches the component identifier from the local secure storage area of ​​the student device; Based on the guardian policy parameters, the executable file in the process guardian component installation package is extracted to the target installation directory of the student device to complete the deployment of the process guardian component; The process guardian component is started, which creates a main process and a monitoring process. The main process is used to execute the guardian actions specified in the guardian policy parameters, and the monitoring process is used to monitor the running status of the main process. The main process and the monitoring process simultaneously initiate a lock request to the state lock file specified in the file system of the student device. When both the main process and the monitoring process successfully lock the state lock file, the monitoring process generates the process guardian state data of the student device.

[0040] The process protection configuration information in the initial control strategy is broken down and parsed layer by layer according to three configuration types: process object, execution rule, and component identifier. All application processes and system process entries that need to be continuously protected by the device system are separated from the configuration information and a complete list of target processes is formed. Specific execution rules such as process keep-alive method, abnormal restart conditions, and continuous protection duration are separated from the configuration information and integrated to form protection strategy parameters. A globally unique process protection component identification code is separated from the configuration information. This code corresponds one-to-one with the device's pre-stored installation package to form a component identification code.

[0041] The extracted component identifier code is compared character by character with the unique identifier code of all process guardian component installation packages pre-stored in the local secure storage area of ​​the student device. The unique process guardian component installation package with completely identical code combination is selected, and the installation package is located according to the fixed storage path preset in the local secure storage area of ​​the device. It is then completely retrieved to the dedicated temporary running area allocated by the device system to ensure that the installation package is not tampered with and can be read normally.

[0042] Based on the component installation path, file read / write permissions, and process execution permission requirements specified in the protection policy parameters, all executable files, configuration files, and dependency files contained in the process protection component installation package are released one by one to the target installation directory pre-set by the student device system. After the files are released, the integrity, storage location accuracy, and permission configuration compliance of all files are checked item by item. After all the checks meet the preset standards, the deployment of the process protection component is confirmed to be complete.

[0043] The system sends a command to the student device's operating system to start the process guardian component. After receiving the command, the operating system loads all the running files and configuration information of the process guardian component. After the component completes initialization and loading, it automatically creates an independently running main process and a monitoring process. The main process strictly follows the guardian actions specified in the guardian policy parameters, such as process keep-alive and prevention of abnormal termination, and continues to execute. The monitoring process continuously tracks the main process's startup status, running stability, interrupt triggering status, and abnormal error information in real time.

[0044] The main process and the monitoring process synchronously send file lock requests to a pre-defined dedicated state lock file in the student device's file system. The device file system verifies the identity, permissions, and access rights of the two processes item by item. When both the main process and the monitoring process pass all permission verifications and successfully monopolize the state lock file, the monitoring process records the main process's running status, file lock duration, lock stability, and other information in real time, integrating them to form the process protection status data of the student device.

[0045] The beneficial effects are that this process, through refined configuration decomposition, precise component retrieval, standardized file deployment, standardized dual-process creation, and strict dual locking operation of state lock files, can stably generate real, accurate, and traceable process protection status data, significantly improving the operational stability and anti-interference capability of the process protection mechanism, and providing comprehensive and reliable status data support for subsequent process protection status and behavior data fusion and student behavior performance evaluation.

[0046] A4. Integrate process guardian status data with aggregated statistical behavior data. When the evaluation result of the integrated behavior meets the preset continuous compliance conditions, generate a self-management badge for student devices and generate self-management permissions for student devices based on the level of the self-management badge. In this embodiment of the invention, the process guardian status data and aggregated statistical behavior data are fused. When the fused behavior performance evaluation result meets the preset continuous achievement conditions, a self-management badge for the student device is generated. Based on the level of the self-management badge, self-management permissions for the student device are generated, including: The main process running status and locked file status in the process guardian status data are integrated into a guardian stability index for student devices. By comprehensively summarizing the behavioral characteristic indicators of student devices in each management period from the aggregated statistical behavioral data, behavioral compliance indicators are obtained. By weighting and integrating the stability indicators and the behavioral compliance indicators, the behavioral performance evaluation results of student devices are obtained. The behavioral performance evaluation results are compared with the target thresholds in the preset continuous target conditions. When the behavioral performance evaluation results reach the target thresholds in multiple consecutive evaluation periods, the target period count of the behavioral performance evaluation results is generated. When the number of compliance cycles reaches the number of compliance cycles in the continuous compliance condition, the numerical range corresponding to the compliance cycle count is mapped to a level to obtain the student equipment self-management badge. Based on the level of the self-management badge, determine the corresponding control parameter adjustment permissions and temporary time extension application permissions to generate self-management permissions for student devices.

[0047] Extract the main process running status from the process protection status data, including specific running status content such as continuous normal execution, unexpected abnormal stop, and automatic recovery triggered by the mechanism. Extract the locked file status, including specific locking status content such as continuous locking throughout, temporary lock interruption, and lock recovery. Based on the preset judgment criteria for the effectiveness of device process protection, verify and integrate the duration, frequency of occurrence, and recovery efficiency of the two types of status item by item to form the protection stability index of student devices.

[0048] The system extracts compliance indicators for usage time, application operation, operation time matching, and function usage from the aggregated statistical behavioral data for pre-class, in-class, post-class, self-study, and after-class management periods. Based on the campus teaching management regulations and equipment usage rules corresponding to each management period, the system performs compliance judgment, classification, and overall summarization on all behavioral characteristic indicators to obtain the behavioral compliance indicators for student equipment.

[0049] According to the system's preset fixed weight allocation rules, the calculation proportions for the protection stability index and the behavior compliance index are determined. The actual judgment results of the two indicators are combined with their corresponding calculation proportions, and the numerical conversion and comprehensive summary calculations are completed in sequence. After the calculation process is completed, the behavioral performance evaluation results of the student's device are obtained.

[0050] The behavioral performance evaluation results are compared item by item with the fixed threshold values ​​set in the preset continuous compliance conditions. The comparison results are recorded in real time on a single independent evaluation cycle basis. When the behavioral performance evaluation results reach or exceed the preset compliance threshold values ​​in multiple consecutive fixed evaluation cycles, the total number of consecutive compliance cycles is accurately counted, and the compliance cycle count of the behavioral performance evaluation results is generated.

[0051] When the actual value of the compliance cycle count is exactly the same as the number of compliance cycles specified in the preset continuous compliance conditions, the value range corresponding to the compliance cycle count is matched one-to-one with the system's preset self-management badge level division range. After completing the level mapping operation, the student's self-management badge is obtained.

[0052] Based on the specific level corresponding to the self-management badge, the system accurately retrieves the control parameter adjustment range, parameter adjustment limit, single duration limit for temporary time extension application, and daily application frequency permission from the system's preset permission configuration table. All of the above permission contents are integrated and summarized to generate the self-management permissions for student devices.

[0053] The beneficial effects are that this process establishes a complete and implementable achievement incentive assessment system through multi-dimensional integrated evaluation of guardian status and behavioral compliance, accurate statistics of continuous achievement cycles, standardized mapping of badge levels, and precise matching of permissions. It can accurately generate self-management badges and corresponding permissions that match students' actual behavioral performance, fully stimulating students' initiative and awareness in participating in the self-management of equipment, and providing a clear and legal basis for subsequent temporary overtime application verification and dynamic optimization of control strategies.

[0054] A5. When a student's temporary time extension request based on self-management permissions is approved, the collected peripheral connection status data and aggregated statistical behavior data are coupled and analyzed. Based on the analysis results, the initial control strategy is dynamically optimized to obtain an adaptive control strategy for student devices. In this embodiment of the invention, when a student's temporary time extension request based on self-management permissions is approved, the collected peripheral connection status data and aggregated statistical behavior data are coupled and analyzed. Based on the analysis results, the initial control strategy is dynamically optimized to obtain an adaptive control strategy for the student's devices, including: Receive temporary overtime requests initiated by students based on their self-management permissions, verify the request time period and duration carried in the temporary overtime request against the permission parameters in the self-management permissions, and obtain the approval status of the temporary overtime request; In response to the application approval status, the peripheral connection status data of the student's device during the application period is collected. The peripheral connection status data includes peripheral type identifier, peripheral connection duration and peripheral usage frequency. By correlating and comparing the peripheral connection status data with the behavioral characteristic indicators of the corresponding time period in the aggregated statistical behavioral data, the behavioral deviation analysis results of student devices during the temporary time extension period are obtained. Based on the behavioral deviation analysis results, the control rules corresponding to the peripheral device type identifiers in the initial control strategy are identified, and the control intensity parameters in the control rules are adjusted to obtain the adaptive control strategy for student devices.

[0055] The step of correlating and comparing peripheral connection status data with behavioral characteristic indicators of the corresponding time period in aggregated statistical behavioral data to obtain behavioral deviation analysis results of student devices during temporary time extension includes: Feature anchoring is performed on peripheral connection status data to obtain the peripheral type identifier of the peripheral connection status data; Based on the peripheral device type identifier, tensor synthesis is performed on the behavioral feature indicators corresponding to the application period in the aggregated statistical behavioral data to obtain the benchmark behavioral feature vector within the application period. Based on the baseline behavioral feature vector, the deviation measure of peripheral connection duration and peripheral usage frequency in peripheral connection status data is quantified to obtain the behavioral deviation coefficient corresponding to the peripheral type identifier. By comprehensively analyzing the behavioral deviation coefficient and the application duration, the behavioral deviation analysis results of student devices during the temporary extension period were obtained.

[0056] The system receives temporary overtime requests initiated by students based on their self-management permissions. It extracts the start and end times of the requested time period and the total duration of the request from the request. It then checks the requested time period against the preset available time periods on teaching days and non-teaching days within the self-management permissions. It also checks the requested duration against the maximum duration limit for a single request and the maximum request frequency per unit time. If the requested time period is within the available range, the requested duration does not exceed the limit, and the requested frequency does not reach the limit, the request is marked as approved. If any of these conditions are not met, the request is marked as rejected. Finally, the system obtains the approval status of the temporary overtime request.

[0057] After receiving feedback that the temporary time extension request has been approved, the system immediately activates the real-time monitoring module for student peripheral devices. During the complete application period corresponding to the temporary time extension request, it continuously collects all information on peripheral device connections, accurately records the unique type identifier of each type of peripheral device, the start and end time of each peripheral device connection, calculates the duration of each connection, and counts the total number of times peripheral devices are used during the application period. All monitoring information is then compiled into student peripheral device connection status data, which includes peripheral device type identifier, peripheral device connection duration, and peripheral device usage frequency.

[0058] The core feature localization processing is carried out on the collected peripheral connection status data. The focus is on the unique identification information in the data used to distinguish different types of peripherals such as headphones, keyboards, mice, and external storage devices. Irrelevant auxiliary records such as data collection time and device hardware number are filtered out. Through feature matching and information extraction, the peripheral type identifier of the peripheral connection status data is accurately determined.

[0059] Using the extracted peripheral type identifier as the core screening criterion, compliance indicators for usage duration, application operation, operation time matching, and function usage are selected from the aggregated statistical behavior data that completely correspond to the temporary time extension application period. These indicators are then combined and constructed in a fixed dimension order of duration, application, time period, and function to obtain the baseline behavior feature vector within the application period.

[0060] Using the baseline behavior feature vector as a unified reference standard, the peripheral connection duration in the peripheral connection status data is compared with the duration index in the baseline behavior feature vector item by item, and the peripheral usage frequency is compared with the frequency index in the baseline behavior feature vector item by item. The actual difference between the two data and the baseline index is calculated and converted into a standardized quantitative value. This quantitative value is the behavior deviation coefficient corresponding to the peripheral type identifier.

[0061] The calculated behavioral deviation coefficient is combined with the application duration of temporary extension. Based on the preset behavioral deviation judgment rules, the impact of different deviation levels on the compliant use of student equipment within the corresponding extension duration is analyzed. After completing the multi-dimensional comprehensive judgment, the behavioral deviation analysis results of student equipment during the temporary extension period are formed.

[0062] Based on the degree of deviation in peripheral device usage and actual usage characteristics reflected in the behavioral deviation analysis results, the initial control strategy accurately identifies the control rules such as peripheral device access control, function usage restrictions, and usage time constraints that directly correspond to the peripheral device type identifier. The strictness of the control rules is adjusted up or down according to the degree of behavioral deviation. After completing all adaptation adjustments, an adaptive control strategy that fits the actual usage status of student devices is generated.

[0063] The beneficial effects of this process are that it achieves dynamic adaptive optimization of student device management strategies in temporary overtime scenarios through comprehensive verification of temporary overtime application permissions, full-process peripheral status data collection, high-precision peripheral feature anchoring, standardized baseline behavior vector synthesis, quantitative behavior deviation calculation, rigorous deviation result analysis, and targeted adjustment of control rules. This ensures that the control strategies are highly matched with students' actual peripheral usage behavior, significantly improving the accuracy and scenario adaptability of device management, while ensuring that students' self-management permissions are used reasonably within the compliant scope, balancing control constraints and self-management needs.

[0064] A6. Synchronize the adaptive control strategy to the local policy engine of the student device, and adjust some control parameters in the adaptive control strategy according to the student's self-management permissions.

[0065] In this embodiment of the invention, the step of synchronizing the adaptive control strategy to the local policy engine of the student device, and autonomously adjusting some control parameters in the adaptive control strategy according to the student's self-management permissions, includes: The adaptive control strategy is encapsulated into a policy data package and sent to the local policy engine of the student's device. The local policy engine then loads and stores the adaptive control strategy. Read the current parameter configuration of the adaptive control policy from the local policy engine, and obtain the permission parameters in the self-management permissions. The permission parameters include a list of adjustable parameter identifiers and parameter adjustment ranges. By overlapping the list of adjustable parameter identifiers with the control parameter identifiers in the current parameter configuration, the adjustable parameters of the student equipment can be obtained. Based on the parameter adjustment range, the parameter values ​​of the adjustable parameters are reset, and the reset parameter configuration is written to the local policy engine.

[0066] The adaptive control policy is packaged and encapsulated according to the student device's local policy engine's proprietary parsable data format. This encapsulates all control rules, time period configurations, application restrictions, functional constraints, duration parameters, and other content into a standard policy data package that carries a unique policy identifier, policy version number, and all control rules and configuration items. This policy data package is then stably distributed to the student device's built-in local policy engine via a dedicated encrypted data transmission channel between the student device and the control terminal. Upon receiving the policy data package, the local policy engine first verifies the package's integrity, format compliance, and content validity item by item. Once all verification results meet the preset standards, the engine fully loads all configurations and rules of the adaptive control policy and then stores the loaded policy data in a dedicated fixed storage area allocated by the local policy engine, ensuring the security of policy data storage and preventing arbitrary tampering.

[0067] From the dedicated fixed storage area of ​​the student device's local policy engine, all current parameter configurations of the adaptive control policy, including usage duration configuration, application control configuration, function restriction configuration, and time period control configuration, are fully read. At the same time, from the dedicated permission management module built into the student device system, all permission parameters corresponding to the self-management permissions bound to the student device are retrieved. The permission parameters clearly include a list of control parameters that students are allowed to adjust independently, as well as the fixed upper and lower limits of the minimum and maximum adjustment values ​​for each type of adjustable parameter.

[0068] Each independent parameter identifier in the adjustable parameter identifier list included in the self-management permissions is matched and compared one by one with all the control parameter identifiers in the current parameter configuration of the adaptive control strategy. Overlapping parameter items that exist in both the adjustable parameter identifier list and the control parameter identifiers in the current parameter configuration are accurately identified. After complete screening and double confirmation, these overlapping parameter items are the self-adjustable parameters that the student device has the legal right to adjust.

[0069] Strictly adhere to the fixed upper and lower limits of each adjustable parameter as specified in the self-management permissions, and compliantly reset the original operating parameter values ​​of the adjustable parameters to ensure that each reset parameter value is within the allowed adjustment range and there are no cases of exceeding the range. Write all the parameter configurations that have been compliantly reset back into the local policy engine of the student device, completely overwrite and replace the original parameter configuration content, and complete the real-time effective update and local synchronization of the parameter configuration.

[0070] The beneficial effects are that this process, through standardized strategy encapsulation, encrypted transmission, integrity verification, local loading and storage, precise parameter matching, compliant parameter reset and effective update, enables the stable implementation and secure execution of adaptive control strategies on student devices. Under the premise of strictly limiting the scope of self-adjustment permissions, students can reasonably adjust control parameters according to their own usage needs, which not only ensures the binding effectiveness of control strategies, but also enhances the personalized adaptability of strategies and students' initiative in participating in device self-management.

[0071] like Figure 2 The diagram shown is a functional block diagram of a student equipment self-management guidance system based on achievement incentives provided in an embodiment of the present invention.

[0072] The achievement-incentive-based student device self-management guidance system 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the achievement-incentive-based student device self-management guidance system 100 may include a privacy disturbance module 101, a scene mapping module 102, a process protection module 103, a badge authorization module 104, a dynamic optimization module 105, and a strategy adjustment module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0073] In this embodiment, the functions of each module / unit are as follows: The privacy perturbation module 101 is used to perform differential privacy perturbation processing on the raw data of student device usage behavior to obtain aggregated statistical behavior data of student devices. The scene mapping module 102 is used to associate and map aggregated statistical behavior data with preset educational scene templates to obtain the initial control strategy for student devices; The process protection module 103 is used to deploy a process protection component on the student device according to the process protection configuration information in the initial control policy, and to lock the same file through the main process and monitoring process in the process protection component to generate process protection status data of the student device. The badge authorization module 104 is used to integrate process protection status data with aggregated statistical behavior data. When the integrated behavior performance evaluation result meets the preset continuous compliance conditions, a self-management badge for the student device is generated, and self-management permissions for the student device are generated according to the level of the self-management badge. The dynamic optimization module 105 is used to couple and analyze the collected peripheral connection status data and aggregated statistical behavior data when the temporary time extension request initiated by the student terminal based on the self-management permission is approved, and dynamically optimize the initial control strategy based on the analysis results to obtain the adaptive control strategy of the student device. The strategy adjustment module 106 is used to synchronize the adaptive control strategy to the local strategy engine of the student device, and to adjust some control parameters in the adaptive control strategy according to the student's self-management permissions.

[0074] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0075] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0077] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0078] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A student-led device self-management guidance method based on achievement incentives, characterized in that: The method includes: A1. Perform differential privacy perturbation processing on the raw data of student device usage behavior to obtain aggregated statistical behavior data of student devices; A2. Associate and map the aggregated statistical behavioral data with the preset educational scenario template to obtain the initial control strategy for student devices; A3. Based on the process protection configuration information in the initial control strategy, deploy the process protection component on the student device, and lock the same file through the main process and monitoring process in the process protection component to generate process protection status data of the student device. A4. Integrate process guardian status data with aggregated statistical behavior data. When the evaluation result of the integrated behavior meets the preset continuous compliance conditions, generate a self-management badge for student devices and generate self-management permissions for student devices based on the level of the self-management badge. A5. When a student's temporary time extension request based on self-management permissions is approved, the collected peripheral connection status data and aggregated statistical behavior data are coupled and analyzed. Based on the analysis results, the initial control strategy is dynamically optimized to obtain an adaptive control strategy for student devices. A6. Synchronize the adaptive control strategy to the local policy engine of the student device, and adjust some control parameters in the adaptive control strategy according to the student's self-management permissions.

2. The student equipment self-management guidance method based on achievement incentives as described in claim 1, characterized in that, The raw data on student device usage behavior is processed with differential privacy perturbation to obtain aggregated statistical behavior data of student devices, including: Obtain raw data on student device usage behavior, and then anonymize the raw data to obtain an anonymized sequence of student device behavior data. Based on the preset privacy protection level, a corresponding privacy budget is allocated to the de-identified behavioral data sequence, and the noise perturbation intensity corresponding to the de-identified behavioral data sequence is calculated based on the privacy budget. Based on the noise perturbation intensity, the data points in the desensitized behavioral data sequence are subjected to Laplace perturbation to obtain the noisy behavioral data sequence of student equipment. According to the management group to which the student equipment belongs, the noisy behavior data sequence is hierarchically aggregated to obtain the aggregated statistical behavior data of the student equipment.

3. The student equipment self-management guidance method based on achievement incentives as described in claim 2, characterized in that, The formula for calculating the noise disturbance intensity is as follows: ； In the formula, The noise disturbance intensity, As a preset global noise benchmark, The sensitivity parameter is extracted from the desensitized behavioral data sequence. For privacy budget, The number of data points contained in the desensitized behavioral data sequence. To manage the number of student devices contained in a group, This is a logarithmic operation with the natural constant e as the base.

4. The student equipment self-management guidance method based on achievement incentives as described in claim 1, characterized in that, The step of associating aggregated statistical behavioral data with preset educational scenario templates to obtain the initial control strategy for student devices includes: Extract behavioral characteristic indicators of student devices from aggregated statistical behavioral data for each management period to generate behavioral characteristic vectors of student devices; The similarity between the behavioral feature vector and the matching rules in the preset educational scenario template is evaluated to obtain the matching score between the educational scenario template and the student's device. Based on the matching score, the target education scenario template with the highest matching degree is selected from the education scenario templates, and the control strategy template in the target education scenario template is extracted. The behavioral feature indicators in the behavioral feature vector are used as constraint parameters and filled into the policy configuration items in the control policy template to generate the initial control policy for student devices.

5. The student equipment self-management guidance method based on achievement incentives as described in claim 1, characterized in that, The process guardian component is deployed on the student device according to the process guardian configuration information in the initial control policy. The main process and monitoring process within the process guardian component lock the same file to generate process guardian status data for the student device, including: The process protection configuration information in the initial control policy is decomposed to obtain the target process list, protection policy parameters and component identification code of the process protection configuration information; Based on the component identifier, retrieve the process guardian component installation package that matches the component identifier from the local secure storage area of ​​the student device; Based on the guardian policy parameters, the executable file in the process guardian component installation package is extracted to the target installation directory of the student device to complete the deployment of the process guardian component; The process guardian component is started, which creates a main process and a monitoring process. The main process is used to execute the guardian actions specified in the guardian policy parameters, and the monitoring process is used to monitor the running status of the main process. The main process and the monitoring process simultaneously initiate a lock request to the state lock file specified in the file system of the student device. When both the main process and the monitoring process successfully lock the state lock file, the monitoring process generates the process guardian state data of the student device.

6. The student equipment self-management guidance method based on achievement incentives as described in claim 1, characterized in that, The process daemon status data is fused with aggregated statistical behavior data. When the fused behavior performance evaluation result meets the preset continuous achievement conditions, a self-management badge for the student device is generated. Based on the level of the self-management badge, self-management permissions for the student device are generated, including: The main process running status and locked file status in the process guardian status data are integrated into a guardian stability index for student devices. By comprehensively summarizing the behavioral characteristic indicators of student devices in each management period from the aggregated statistical behavioral data, behavioral compliance indicators are obtained. By weighting and integrating the stability indicators and the behavioral compliance indicators, the behavioral performance evaluation results of student devices are obtained. The behavioral performance evaluation results are compared with the target thresholds in the preset continuous target conditions. When the behavioral performance evaluation results reach the target thresholds in multiple consecutive evaluation periods, the target period count of the behavioral performance evaluation results is generated. When the number of compliance cycles reaches the number of compliance cycles in the continuous compliance condition, the numerical range corresponding to the compliance cycle count is mapped to a level to obtain the student equipment self-management badge. Based on the level of the self-management badge, determine the corresponding control parameter adjustment permissions and temporary time extension application permissions to generate self-management permissions for student devices.

7. The student equipment self-management guidance method based on achievement incentives as described in claim 1, characterized in that, When a student's temporary time extension request based on their self-management permissions is approved, the collected peripheral connection status data and aggregated statistical behavior data are coupled and analyzed. Based on the analysis results, the initial control strategy is dynamically optimized to obtain an adaptive control strategy for the student's devices, including: Receive temporary overtime requests initiated by students based on their self-management permissions, verify the request time period and duration carried in the temporary overtime request against the permission parameters in the self-management permissions, and obtain the approval status of the temporary overtime request; In response to the application approval status, the peripheral connection status data of the student's device during the application period is collected. The peripheral connection status data includes peripheral type identifier, peripheral connection duration and peripheral usage frequency. By correlating and comparing the peripheral connection status data with the behavioral characteristic indicators of the corresponding time period in the aggregated statistical behavioral data, the behavioral deviation analysis results of student devices during the temporary time extension period are obtained. Based on the behavioral deviation analysis results, the control rules corresponding to the peripheral device type identifiers in the initial control strategy are identified, and the control intensity parameters in the control rules are adjusted to obtain the adaptive control strategy for student devices.

8. The student equipment self-management guidance method based on achievement incentives as described in claim 7, characterized in that, The step of correlating and comparing peripheral connection status data with behavioral characteristic indicators of the corresponding time period in aggregated statistical behavioral data to obtain behavioral deviation analysis results of student devices during temporary time extension includes: Feature anchoring is performed on peripheral connection status data to obtain the peripheral type identifier of the peripheral connection status data; Based on the peripheral device type identifier, tensor synthesis is performed on the behavioral feature indicators corresponding to the application period in the aggregated statistical behavioral data to obtain the benchmark behavioral feature vector within the application period. Based on the baseline behavioral feature vector, the deviation measure of peripheral connection duration and peripheral usage frequency in peripheral connection status data is quantified to obtain the behavioral deviation coefficient corresponding to the peripheral type identifier. By comprehensively analyzing the behavioral deviation coefficient and the application duration, the behavioral deviation analysis results of student devices during the temporary extension period were obtained.

9. The student equipment self-management guidance method based on achievement incentives as described in claim 1, characterized in that, The process of synchronizing the adaptive control strategy to the local policy engine of student devices and, based on self-management permissions, autonomously adjusting some control parameters in the adaptive control strategy includes: The adaptive control strategy is encapsulated into a policy data package and sent to the local policy engine of the student device. The local policy engine then loads and stores the adaptive control strategy. Read the current parameter configuration of the adaptive control policy from the local policy engine, and obtain the permission parameters in the self-management permissions. The permission parameters include a list of adjustable parameter identifiers and parameter adjustment ranges. By overlapping the list of adjustable parameter identifiers with the control parameter identifiers in the current parameter configuration, the adjustable parameters of the student equipment can be obtained. Based on the parameter adjustment range, the parameter values ​​of the adjustable parameters are reset, and the reset parameter configuration is written to the local policy engine.

10. A student equipment self-management guidance system based on achievement incentives, characterized in that: The system for implementing the achievement-incentive-based student device self-management guidance method of claim 1, the system comprising: The privacy perturbation module is used to perform differential privacy perturbation processing on the raw data of student device usage behavior to obtain aggregated statistical behavior data of student devices. The scene mapping module is used to associate and map aggregated statistical behavior data with preset educational scene templates to obtain the initial control strategy for student devices; The process protection module is used to deploy the process protection component on the student device according to the process protection configuration information in the initial control policy, and to lock the same file through the main process and monitoring process in the process protection component to generate process protection status data of the student device. The badge authorization module is used to integrate process protection status data with aggregated statistical behavior data. When the integrated behavior performance evaluation result meets the preset continuous achievement conditions, a self-management badge for the student device is generated, and self-management permissions for the student device are generated according to the level of the self-management badge. The dynamic optimization module is used to couple and analyze the collected peripheral connection status data and aggregated statistical behavior data when a student's temporary time extension request based on self-management permissions is approved. Based on the analysis results, the initial control strategy is dynamically optimized to obtain an adaptive control strategy for student devices. The strategy adjustment module is used to synchronize the adaptive control strategy to the local strategy engine of the student device, and to adjust some control parameters in the adaptive control strategy according to the student's self-management permissions.

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