Intelligent correction management method and system based on big data

Abstract

The invention discloses an intelligent correction management method and system based on big data, and relates to the technical field of correction management systems, and the system comprises a data collection module, a data processing module, an intelligent evaluation module, a personalized correction module, a monitoring management module, a report generation module, a user interaction module and a system security module. According to the intelligent correction management method and system based on the big data, a data acquisition module comprehensively collects personal, behavior, psychological and social data of a correction object through a sensor unit, a questionnaire system and a historical record database interface, and the comprehensiveness and accuracy of the data are ensured through the multi-dimensional data acquisition method; and an original material is provided for subsequent analysis and evaluation, so that the problem that data acquisition in an existing system is incomplete and inaccurate is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of correction management systems, and specifically to a correction management method and system based on big data intelligence. Background Art

[0002] In the modern criminal justice system, community corrections, as a non-custodial form of punishment, are increasingly valued. Community corrections aim to help criminals reform and reintegrate into society in the community through the participation of social forces. With the rapid development of information technology such as big data and artificial intelligence, smart correction management systems have emerged. They provide personalized correction plans to improve correction results by collecting and analyzing large amounts of data on correction subjects.

[0003] However, the existing intelligent correction management system still faces some challenges and limitations. First, the comprehensiveness and accuracy of data collection are insufficient, resulting in an inability to fully understand the actual situation of the correction subjects. Second, the data processing capacity is limited, making it difficult to extract valuable information from massive data.

[0004] In view of the shortcomings of the existing technology, the present invention proposes a smart correction management system based on big data, which realizes the all-round management of correction objects by integrating advanced data collection, processing, and analysis technologies, as well as intelligent evaluation and personalized correction modules, thereby providing strong technical support for community correction work. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a smart correction management method and system based on big data, which solves the problems that the data collection of the existing management system proposed in the background technology is not comprehensive and accurate enough, resulting in the inability to fully understand the actual situation of the correction object; secondly, the data processing capability is limited, and it is difficult to extract valuable information from massive data.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a big data-based intelligent correction management method and system, including a data acquisition module, a data processing module, an intelligent evaluation module, a personalized correction module, a monitoring management module, a report generation module, a user interaction module and a system security module;

[0007] The data collection module is responsible for collecting all-round data of the correction object, including basic personal information, behavioral data, psychological assessment data, and social interaction data; this data provides raw material for subsequent analysis and evaluation;

[0008] The data processing module is used to clean, convert, integrate and analyze the collected data; this module uses statistical analysis and machine learning techniques to extract valuable information from the data to provide support for correctional decisions;

[0009] The intelligent assessment module is used to comprehensively assess the risk level, behavioral trends and psychological state of the correction subject, providing a scientific basis for formulating a personalized correction plan;

[0010] The personalized correction module is used to formulate and adjust personalized correction plans according to the evaluation results, optimize resource allocation, and recommend appropriate intervention measures to improve the correction effect;

[0011] The monitoring management module is used to monitor the behavior and location of the correction subject in real time, ensure the implementation of the correction plan and compliance with the correction regulations, and promptly warn of potential violations;

[0012] The report generation module is used to generate reports on the progress and effect of corrections, and through data visualization, helps managers to intuitively understand the correction situation and provide support for decision-making;

[0013] The user interaction module is used to provide a user interface for administrators and correction subjects, which facilitates system settings, viewing reports, receiving correction plans and participating in educational activities, and enhances the interactivity and ease of use of the system;

[0014] The system security module is used to protect the security and integrity of system data, ensure that only authorized users can access sensitive information, prevent unauthorized access and data leakage, and ensure the compliance of system operations;

[0015] Preferably, the data acquisition module includes:

[0016] Sensor units: including GPS locators, biometric devices (such as fingerprint scanners, facial recognition cameras), and wearable devices (such as health monitoring bracelets) to collect real-time location, physiological, and behavioral data of the correction subject;

[0017] Questionnaire system: collect social, psychological and behavioral information of correctional subjects through online or offline methods;

[0018] Historical record database interface: connect with existing judicial, medical and social service databases to import historical data of correction subjects;

[0019] Preferably, the data processing module includes:

[0020] Data cleaning unit: removes erroneous, duplicate or incomplete data records;

[0021] Data Integration Unit: Formats data from different sources in a unified manner and builds a comprehensive data profile of the correctional subject;

[0022] Data Analysis Unit: Use statistical analysis, machine learning and data mining techniques to conduct in-depth analysis of data and extract valuable information;

[0023] Among them, the data is uniformly formatted: x is the original data value, min(D) and max(D) are the minimum and maximum values ​​in the data set, respectively, which are used to normalize the data to the range of 0 to 1;

[0024] Machine learning uses linear regression (prediction model): y = β0 + β1x1 + β2x2 + ... + β n x n +∈where y is the dependent variable x1,x2,…,x n are independent variables, β0,β1,…,β n are the model coefficients and ∈ is the error term.

[0025] Preferably, the intelligent assessment module includes:

[0026] Risk Assessment Engine: Assess the recidivism risk of the correctional subject based on historical data and current behavior;

[0027] Behavior prediction model: predict the possible behavioral trends of the correction object to provide support for management decisions;

[0028] Psychological status analysis tool: Analyze the mental health status of the correction object and identify potential psychological problems;

[0029] Among them, the recidivism risk score is: R = w1·H+w2·C+w3·B+w4·S, where R is the recidivism risk score, H is the influence of historical criminal records, C is the current behavior, B is the social background factor, S is the psychological state score, and w1, w2, w3, w4 are the weights of the corresponding factors, which are adjusted according to historical data and expert experience;

[0030] The behavior trend prediction of the behavior prediction model 302 (using time series analysis): t =α+βX t-1 +∈ t Among them, X t is the behavior indicator predicted at a certain moment in the future, X t -1 is the behavior indicator of the previous moment, α is the intercept term, β is the autoregressive coefficient, ∈ t is the error term, representing the random disturbance at time t.

[0031] Preferably, the personalized correction module includes:

[0032] Correction plan generator: automatically generates personalized correction plans based on the evaluation results;

[0033] Resource allocation unit: Determine the optimal allocation of correctional resources to maximize the correctional effect;

[0034] Intervention recommendation system: recommends interventions that are appropriate for the current status of the correctional subject;

[0035] The resource allocation unit determines the optimal allocation of resources based on an optimization algorithm, such as linear programming. The resource allocation model can be expressed as: Subject to the following constraints: Among them, c i is the benefit of the ith correction resource, x i is the amount of resources allocated to the i-th correction object, a ij is the contribution of the ith resource to the jth constraint, b j is the upper limit of the jth constraint.

[0036] Preferably, the monitoring management module includes:

[0037] Real-time monitoring system: monitors the location and behavior of the correctional subject to ensure that he complies with the correctional regulations;

[0038] Violation warning system: Issue warnings in a timely manner when the correction subject may violate regulations;

[0039] Correction plan execution tracker: Track the execution of the correction plan to ensure the effective implementation of correction activities;

[0040] Among them, a GPS locator sensor unit is used to collect the position data of the correction object in real time, and determine whether the correction object is within the specified activity range by calculating the distance between two points.

[0041] Preferably, the report generating module comprises:

[0042] Progress report generator: Generate correction progress reports regularly to show the improvement of the correction object;

[0043] Effectiveness evaluation tool: Evaluate the effectiveness of correctional programs and provide suggestions for improvement;

[0044] Data visualization platform: presents complex data in the form of charts and dashboards to facilitate managers’ understanding and analysis;

[0045] Among them, the effectiveness evaluation tool uses statistical methods and analytical models to evaluate the effectiveness of the correctional program, including the recidivism rate, social integration and satisfaction of the correctional subjects.

[0046] Preferably, the user interaction module includes:

[0047] Administrator interface: used by judicial staff to manage system settings, view reports and intervention measures;

[0048] Correction subject interface: used by correction subjects to receive correction plans, report progress, and participate in online education;

[0049] Preferably, the system security module includes:

[0050] Data encryption unit: ensure the security of sensitive data;

[0051] User authentication unit: verifies user identity and controls access to different parts of the system;

[0052] Audit tracking unit: records system operation logs for monitoring and auditing;

[0053] Preferably, the steps are as follows:

[0054] S01. Data Collection: This phase collects the personal, behavioral, psychological and social data of the correctional subjects through the integrated sensor unit, questionnaire system and historical record database interface to ensure the multi-dimensional and comprehensive nature of the data;

[0055] S02. Data processing: In this stage, the collected raw data will be cleaned and integrated to form a comprehensive data file of the correction object, and the data analysis unit will apply statistical analysis and machine learning techniques to further explore the data value;

[0056] S03. Intelligent Assessment: Use risk assessment engines, behavior prediction models and psychological status analysis tools to conduct multi-angle assessments of correction subjects and provide scientific assessment results for their risk levels, behavior trends and psychological conditions;

[0057] S04. Personalized correction: Based on the results of the intelligent assessment module, the personalized correction module formulates a personalized correction plan and achieves optimal allocation of correction resources through the correction plan generator, resource allocation unit and intervention measure recommendation system;

[0058] S05. Monitoring and management: The real-time monitoring system tracks the location and behavior of the correction subjects to ensure that the correction regulations are followed, the violation warning system issues warnings in a timely manner, and the correction plan execution tracker ensures the smooth progress of correction activities;

[0059] S06. Report generation: The progress report generator regularly outputs correction progress reports, the effect evaluation tool provides evaluation and suggestions on the effect of the correction plan, and the data visualization platform converts data into intuitive charts and dashboards for managers to understand;

[0060] S07. User interaction and system security: The user interaction module provides customized interfaces for different users. The system security module ensures data security and system compliance through data encryption, user authentication and audit tracking to prevent unauthorized access and data leakage.

[0061] The present invention provides a big data-based intelligent correction management method and system. It has the following beneficial effects:

[0062] (1) In this intelligent correction management method and system based on big data, the data collection module comprehensively collects the personal, behavioral, psychological and social data of the correction subjects through the sensor unit, questionnaire system and historical record database interface. This multi-dimensional data collection method ensures the comprehensiveness and accuracy of the data, provides raw materials for subsequent analysis and evaluation, and thus solves the problem of incomplete and inaccurate data collection in the existing system.

[0063] (2) In view of the limited data processing capability in the prior art, the data processing module of the present invention uses a data cleaning, integration and analysis unit to extract valuable information from the data using statistical analysis and machine learning techniques. The capability of this module improves the efficiency and accuracy of data processing, making it more efficient to extract useful information from massive data, and solving the problems of low data processing efficiency and insufficient analysis capability in the prior art.

[0064] (3) The intelligent assessment module of the present invention uses a risk assessment engine, a behavior prediction model, and a psychological state analysis tool to comprehensively assess the risk level, behavior trend, and psychological state of the correction subject. Based on these assessment results, the personalized correction module can formulate a more accurate and personalized correction plan, solving the problem of the lack of scientific basis for the formulation of personalized correction plans in existing systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the module of the present invention;

[0066] Figure 2 It is a schematic diagram of the process of the present invention.

[0067] In the figure, 1, data acquisition module; 101, sensor unit; 102, questionnaire system; 103, historical record database interface;

[0068] 2. Data processing module; 201. Data cleaning unit; 202. Data integration unit; 203. Data analysis unit;

[0069] 3. Intelligent assessment module; 301. Risk assessment engine; 302. Behavior prediction model; 303. Psychological state analysis tool;

[0070] 4. Personalized correction module; 401. Correction plan generator; 402. Resource allocation unit; 403. Intervention measure recommendation system;

[0071] 5. Monitoring and management module; 501. Real-time monitoring system; 502. Violation warning system; 503. Correction plan execution tracker;

[0072] 6. Report generation module; 601. Progress report generator; 602. Effect evaluation tool; 603. Data visualization platform;

[0073] 7. User interaction module; 701. Administrator interface; 702. Correction object interface;

[0074] 8. System security module; 801. Data encryption unit; 802. User authentication unit; 803. Audit tracking unit. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] See also Figure 1-2 The embodiment of the present invention provides a technical solution: a method and system for intelligent correction management based on big data, including a data acquisition module 1, a data processing module 2, an intelligent evaluation module 3, a personalized correction module 4, a monitoring management module 5, a report generation module 6, a user interaction module 7 and a system security module 8; the data acquisition module 1 is responsible for collecting all-round data of the correction object, including personal basic information, behavioral data, psychological evaluation data, and social interaction data; the data provides raw materials for subsequent analysis and evaluation; the data processing module 2 is used to clean, convert, integrate and analyze the collected data; the module uses statistical analysis and machine learning technology to extract valuable information from the data to provide support for correction decision-making; the intelligent evaluation module 3 is used to comprehensively evaluate the risk level, behavioral trend and psychological state of the correction object, and provide a scientific basis for formulating personalized correction plans; personalized The personalized correction module 4 is used to formulate and adjust personalized correction plans, optimize resource allocation, and recommend appropriate intervention measures based on the evaluation results to improve the correction effect; the monitoring management module 5 is used to monitor the behavior and location of the correction object in real time, ensure the implementation of the correction plan and compliance with the correction regulations, and promptly warn of potential violations; the report generation module 6 is used to generate reports on the progress and effects of the correction, and through data visualization, help managers to intuitively understand the correction situation and provide support for decision-making; the user interaction module 7 is used to provide a user interface for administrators and correction objects, which is convenient for managing system settings, viewing reports, receiving correction plans and participating in educational activities, and enhancing the interactivity and ease of use of the system; the system security module 8 is used to protect the security and integrity of system data, ensure that only authorized users can access sensitive information, prevent unauthorized access and data leakage, and ensure the compliance of system operations;

[0077] The data acquisition module 1 includes:

[0078] Sensor unit 101: including a GPS locator, a biometric device (such as a fingerprint scanner, a facial recognition camera), and a wearable device (such as a health monitoring bracelet) for collecting the position, physiological, and behavioral data of the correction subject in real time;

[0079] Questionnaire system 102: collect social, psychological and behavioral information of correction subjects through online or offline methods;

[0080] Historical record database interface 103: connects with existing judicial, medical and social service databases to import historical data of correction subjects;

[0081] Data processing module 2 includes:

[0082] Data cleaning unit 201: removes erroneous, duplicate or incomplete data records;

[0083] Data integration unit 202: unifies and formats data from different sources and builds a comprehensive data archive of the correction object; Data analysis unit 203: uses statistical analysis, machine learning and data mining techniques to conduct in-depth analysis of data and extract valuable information;

[0084] Among them, the data is uniformly formatted: x is the original data value, min(D) and max(D) are the minimum and maximum values ​​in the data set, respectively, which are used to normalize the data to the range of 0 to 1;

[0085] Machine learning uses linear regression (prediction model): y = β0 + β1x1 + β2x2 + ... + β n x n +∈where y is the dependent variable x1,x2,…,x n are independent variables, β0,β1,…,β n are the model coefficients and ∈ is the error term.

[0086] Smart Assessment Module 3 includes:

[0087] Risk assessment engine 301: assessing the recidivism risk of the correction subject based on historical data and current behavior;

[0088] Behavior prediction model 302: predicts the possible behavior trend of the correction object and provides support for management decision-making;

[0089] Psychological Status Analysis Tool 303: Analyze the mental health status of the correction subject and identify potential psychological problems;

[0090] Among them, the recidivism risk score is: R = w1·H+w2·C+w3·B+w4·S, where R is the recidivism risk score, H is the influence of historical criminal records, C is the current behavior, B is the social background factor, S is the psychological state score, and w1, w2, w3, w4 are the weights of the corresponding factors, which are adjusted according to historical data and expert experience;

[0091] Behavior trend prediction of behavior prediction model 302 (using time series analysis): X t =α+βX t-1 +∈ t Among them, X t is the behavior indicator predicted at a certain moment in the future, X t -1 is the behavior indicator of the previous moment, α is the intercept term, β is the autoregressive coefficient, ∈ t is the error term, representing the random disturbance at time t.

[0092] The personalized correction module 4 includes:

[0093] Correction plan generator 401: automatically generates a personalized correction plan based on the evaluation results;

[0094] Resource allocation unit 402: determine the optimal allocation of correction resources to maximize the correction effect;

[0095] Intervention recommendation system 403: recommends intervention measures suitable for the current status of the correction subject;

[0096] The resource allocation unit 402 determines the optimal allocation of resources based on an optimization algorithm, such as linear programming. The resource allocation model can be expressed as: Subject to the following constraints: Among them, c i is the benefit of the ith correction resource, x i is the amount of resources allocated to the i-th correction object, a ij is the contribution of the ith resource to the jth constraint, b j is the upper limit of the jth constraint.

[0097] The monitoring management module 5 includes:

[0098] Real-time monitoring system 501: monitors the location and behavior of the correction subject to ensure that he complies with the correction regulations;

[0099] Violation warning system 502: Issue warnings in a timely manner when the correction subject may violate regulations;

[0100] Correction plan execution tracker 503: Track the execution of the correction plan to ensure the effective implementation of the correction activities;

[0101] The GPS locator sensor unit 101 is used to collect the position data of the correction object in real time, and to determine whether the correction object is within the prescribed activity range by calculating the distance between two points.

[0102] The report generation module 6 includes:

[0103] Progress report generator 601: regularly generates correction progress reports to show the improvement of the correction object;

[0104] Effectiveness Evaluation Tool 602: Evaluate the effectiveness of the correctional program and provide suggestions for improvement;

[0105] Data visualization platform 603: presents complex data in the form of charts and dashboards to facilitate managers’ understanding and analysis;

[0106] Among them, the effectiveness evaluation tool 602 uses statistical methods and analytical models to evaluate the effectiveness of the correctional program, including the recidivism rate, social integration and satisfaction of the correctional subjects.

[0107] The user interaction module 7 includes:

[0108] Administrator interface 701: for use by judicial staff to manage system settings, view reports and intervention measures; Correction subject interface 702: for use by correction subjects to receive correction plans, report progress and participate in online education;

[0109] The system security module 8 includes:

[0110] Data encryption unit 801: ensures the security of sensitive data;

[0111] User authentication unit 802: verifies user identity and controls access rights to different parts of the system;

[0112] Audit tracking unit 803: records system operation logs for monitoring and auditing;

[0113] Based on big data intelligent correction management method, the steps are as follows:

[0114] S01. Data collection: In this stage, the personal, behavioral, psychological and social data of the correction object are comprehensively collected through the integrated sensor unit 101, the questionnaire system 102 and the historical record database interface 103 to ensure the multi-dimensionality and comprehensiveness of the data;

[0115] S02. Data processing: In this stage, the collected raw data will be cleaned and integrated to form a comprehensive data file of the correction object, and the data value will be further mined by applying statistical analysis and machine learning techniques through the data analysis unit 203;

[0116] S03. Intelligent assessment: Using the risk assessment engine 301, the behavior prediction model 302 and the psychological state analysis tool 303, the correction object is evaluated from multiple angles to provide scientific assessment results for its risk level, behavior trend and psychological condition;

[0117] S04. Personalized correction: According to the results of the intelligent evaluation module 3, the personalized correction module 4 formulates a personalized correction plan, and realizes the optimal allocation of correction resources through the correction plan generator 401, the resource allocation unit 402 and the intervention measure recommendation system 403;

[0118] S05. Monitoring and management: The real-time monitoring system 501 tracks the location and behavior of the correction object to ensure that the correction regulations are followed, the violation warning system 502 issues warnings in a timely manner, and the correction plan execution tracker 503 ensures the smooth progress of the correction activities;

[0119] S06. Report generation: The progress report generator 601 regularly outputs correction progress reports, the effect evaluation tool 602 provides evaluation and suggestions on the effect of the correction plan, and the data visualization platform 603 converts data into intuitive charts and dashboards for easy understanding by managers;

[0120] S07. User interaction and system security: The user interaction module 7 provides customized interfaces for different users. The system security module 8 ensures data security and system compliance through data encryption, user authentication and audit tracking to prevent unauthorized access and data leakage.

[0121] The following is a table of experimental data of three correction subjects included in this embodiment, which is used to demonstrate the data-based effects of the system of the present invention in data collection, processing, intelligent evaluation and personalized correction scheme:

[0122]

[0123] According to the above experimental data table, the following conclusions can be drawn:

[0124] Improved comprehensiveness and accuracy of data collection: After the experiment, the amount of data (number of entries) for all correction subjects increased, indicating that data collection was more comprehensive.

[0125] The data accuracy increased from 80%-88% before the experiment to 92%-98% after the experiment, indicating that the accuracy of data collection has been significantly improved.

[0126] Improved data processing efficiency: The data processing time was reduced from an average of 150 seconds before the experiment to an average of 90 seconds after the experiment, indicating that the data processing module has significantly improved in processing speed and efficiency.

[0127] Improved accuracy of risk assessment: The accuracy of risk assessment increased from 68%-72% before the experiment to 82%-88% after the experiment, showing that the accuracy of the intelligent assessment module in risk assessment has been significantly improved.

[0128] The matching degree of personalized correction scheme is improved: the matching degree of personalized correction scheme is improved from 55%-65% before the experiment to 80%-90% after the experiment, which shows that the personalized correction module can formulate the scheme more accurately according to the specific situation of the correction object.

[0129] Improved accuracy of violation warning: The accuracy of violation warning increased from 50%-60% before the experiment to 70%-80% after the experiment, indicating that the monitoring management module is more accurate and reliable in warning of potential violations.

[0130] Improved overall system performance: From data accuracy, processing efficiency to accuracy of assessment and early warning, the system has shown improved performance in all aspects, which shows that the system of the present invention can more effectively support community correction work.

[0131] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0132] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. Based on big data intelligent correction management system, it is characterized by: It includes a data acquisition module (1), a data processing module (2), an intelligent evaluation module (3), a personalized correction module (4), a monitoring management module (5), a report generation module (6), a user interaction module (7) and a system security module (8); The data collection module (1) is responsible for collecting all-round data of the correction object, including basic personal information, behavioral data, psychological assessment data, and social interaction data; this data provides raw materials for subsequent analysis and evaluation; The data processing module (2) is used to clean, convert, integrate and analyze the collected data; this module uses statistical analysis and machine learning techniques to extract valuable information from the data to provide support for correctional decisions; The intelligent assessment module (3) is used to comprehensively assess the risk level, behavioral trends and psychological state of the correction subject, providing a scientific basis for formulating a personalized correction plan; The personalized correction module (4) is used to formulate and adjust a personalized correction plan based on the evaluation results, optimize resource allocation, and recommend appropriate intervention measures to improve the correction effect; The monitoring management module (5) is used to monitor the behavior and location of the correction subject in real time, ensure the implementation of the correction plan and compliance with the correction regulations, and promptly warn of potential violations; The report generation module (6) is used to generate a report on the progress and effect of the correction, and through data visualization, it helps managers to intuitively understand the correction situation and provide support for decision-making; The user interaction module 7 is used to provide a user interface for administrators and correction subjects, which facilitates system settings, viewing reports, receiving correction plans and participating in educational activities, and enhances the interactivity and ease of use of the system; The system security module (8) is used to protect the security and integrity of system data, ensure that only authorized users can access sensitive information, prevent unauthorized access and data leakage, and ensure the compliance of system operations.

2. According to claim 1, the big data-based intelligent correction management system is characterized by: The data acquisition module (1) comprises: The sensor unit (101) includes a GPS locator, a biometric device (such as a fingerprint scanner, a facial recognition camera), and a wearable device (such as a health monitoring bracelet) for collecting the position, physiological, and behavioral data of the correction subject in real time; Questionnaire system (102): collects social, psychological and behavioral information of correctional subjects through online or offline methods; Historical record database interface (103): connects with existing judicial, medical and social service databases to import historical data of correctional subjects.

3. According to claim 1, the big data-based intelligent correction management system is characterized by: The data processing module (2) comprises: Data cleaning unit (201): removes erroneous, duplicate or incomplete data records; Data integration unit (202): formats data from different sources in a unified manner and constructs a comprehensive data profile of the correctional subject; Data Analysis Unit (203): Use statistical analysis, machine learning and data mining techniques to conduct in-depth analysis of data and extract valuable information; Among them, the data is uniformly formatted: Normalized x is the original data value, min(D) and max(D) are the minimum and maximum values ​​in the data set, respectively, which are used to normalize the data to the range of 0 to 1; Machine learning uses linear regression (prediction model): y = β0 + β1x1 + β2x2 + ... + β n x n +∈where y is the dependent variable x1,x2,…,x n are independent variables, β0,β1,…,β n are the model coefficients and ∈ is the error term.

4. The big data-based intelligent correction management system according to claim 1 is characterized by: The intelligent evaluation module (3) comprises: Risk Assessment Engine (301): assesses the recidivism risk of the correction subject based on historical data and current behavior; Behavior prediction model (302): predicts the possible behavioral trends of the correction object and provides support for management decisions; Psychological status analysis tool (303): Analyze the mental health status of the correction subject and identify potential psychological problems; Among them, the recidivism risk score is: R = w1·H+w2·C+w3·B+w4·S, where R is the recidivism risk score, H is the influence of historical criminal records, C is the current behavior, B is the social background factor, S is the psychological state score, and w1, w2, w3, w4 are the weights of the corresponding factors, which are adjusted according to historical data and expert experience; The behavior trend prediction of the behavior prediction model 302 (using time series analysis): t =α+βX t-1 +∈ t Among them, X t is the behavior indicator predicted at a certain moment in the future, X t -1 is the behavior indicator of the previous moment, α is the intercept term, β is the autoregressive coefficient, ∈ t is the error term, representing the random disturbance at time t.

5. The big data-based intelligent correction management system according to claim 1 is characterized by: The personalized correction module (4) comprises: Correction plan generator (401): automatically generates a personalized correction plan based on the evaluation results; Resource allocation unit (402): determines the optimal allocation of correction resources to maximize the correction effect; Intervention measure recommendation system (403): recommends intervention measures suitable for the current status of the correction subject; The resource allocation unit (402) determines the optimal allocation of resources based on an optimization algorithm, such as linear programming. The resource allocation model can be expressed as: Subject to the following constraints: Among them, c i is the benefit of the ith correction resource, x i is the amount of resources allocated to the i-th correction object, a ij is the contribution of the ith resource to the jth constraint, b j is the upper limit of the jth constraint.

6. The big data-based intelligent correction management system according to claim 1 is characterized by: The monitoring management module (5) comprises: Real-time monitoring system (501): monitors the location and behavior of the correction subject to ensure that he complies with the correction regulations; Violation warning system (502): Issues warnings in a timely manner when the correctional subject may violate regulations; Correction plan execution tracker (503): tracks the execution of the correction plan to ensure the effective implementation of correction activities; A GPS locator sensor unit (101) is used to collect the position data of the correction object in real time, and to determine whether the correction object is within the prescribed activity range by calculating the distance between two points.

7. The big data-based intelligent correction management system according to claim 1 is characterized by: The report generation module (6) comprises: Progress report generator (601): regularly generates correction progress reports to show the improvement of the correction object; Effectiveness evaluation tool (602): Evaluate the effectiveness of the correctional program and provide suggestions for improvement; Data visualization platform (603): presents complex data in the form of charts and dashboards to facilitate management’s understanding and analysis; Among them, the effectiveness evaluation tool (602) uses statistical methods and analytical models to evaluate the effectiveness of the correctional program, including the recidivism rate, social integration and satisfaction of the correctional subjects.

8. The big data-based intelligent correction management system according to claim 1 is characterized by: The user interaction module (7) comprises: Administrator interface (701): used by judicial staff to manage system settings, view reports and intervention measures; Correction subject interface (702): used by correction subjects to receive correction plans, report progress and participate in online education.

9. The big data-based intelligent correction management system according to claim 1 is characterized by: The system security module (8) comprises: Data encryption unit (801): ensures the security of sensitive data; User authentication unit (802): verifies the user's identity and controls access rights to different parts of the system; Audit tracking unit (803): records system operation logs for monitoring and auditing.

10. The intelligent correction management method based on big data according to any one of claims 1 to 9, characterized in that: The steps are as follows: S01. Data collection: In this stage, the personal, behavioral, psychological and social data of the correctional subject are comprehensively collected through the integrated sensor unit (101), the questionnaire system (102) and the historical record database interface (103), ensuring the multi-dimensionality and comprehensiveness of the data; S02. Data processing: In this stage, the collected raw data will be cleaned and integrated to form a comprehensive data file of the correction object, and statistical analysis and machine learning techniques will be applied through the data analysis unit (203) to further explore the value of the data; S03. Intelligent assessment: Using the risk assessment engine (301), behavior prediction model (302) and psychological state analysis tool (303), the correction object is evaluated from multiple angles to provide scientific assessment results for its risk level, behavior trend and psychological state; S04. Personalized correction: Based on the results of the intelligent assessment module (3), the personalized correction module (4) formulates a personalized correction plan, and realizes the optimal allocation of correction resources through the correction plan generator (401), the resource allocation unit (402) and the intervention measure recommendation system (403); S05. Monitoring and management: The real-time monitoring system (501) tracks the location and behavior of the correction subject to ensure that the correction regulations are followed, the violation warning system (502) issues warnings in a timely manner, and the correction plan execution tracker (503) ensures the smooth progress of correction activities; S06. Report generation: The progress report generator (601) regularly outputs correction progress reports, the effect evaluation tool (602) provides evaluation and suggestions on the effect of the correction plan, and the data visualization platform (603) converts data into intuitive charts and dashboards for easy understanding by managers; S07. User interaction and system security: The user interaction module (7) provides customized interfaces for different users. The system security module (8) ensures data security and system compliance through data encryption, user authentication and audit tracking, preventing unauthorized access and data leakage.

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