Autistic child adolescent activity and behavior problem multi-dimensional correlation analysis system

Abstract

The invention discloses a multi-dimensional association analysis system for autistic child adolescent activity and behavior problems, and mainly relates to the technical field of child health behavior analysis. Comprising a data acquisition module, a data preprocessing module, a multi-dimensional association analysis module, a result visualization module and a report generation module, and is used for comprehensively analyzing the relationship between 24-hour activity modes and behavior problems of autistic children and adolescents. The system has the advantages that the limitation of traditional single-dimension analysis is broken through, three core dimensions of body movement, screen time and sleep in a 24-hour movement mode are comprehensively considered, and the complex relation between the movement and behavior problems of autistic children and adolescents can be more comprehensively revealed. The multi-dimensional analysis view angle is beneficial for deeply understanding the cause of the behavior problem, and a scientific basis is provided for formulating comprehensive intervention measures.

Description

Technical Field

[0001] The present invention relates to the technical field of children's health behavior analysis, and in particular to a multidimensional correlation analysis system for activities and behavioral problems of children and adolescents with autism. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Autism is a pervasive developmental disorder whose core symptoms include social impairment, language and communication difficulties, and repetitive and stereotyped behaviors. These symptoms not only severely impact the daily lives and learning of children and adolescents with autism, but are also often accompanied by complex behavioral issues such as aggression, self-injury, anxiety, depression, and social withdrawal. These problems not only cause immense distress to children and adolescents with autism themselves, but also place a heavy burden on their families and society.

[0004] Currently, research on behavioral problems in children and adolescents with autism has mostly focused on a single dimension, focusing solely on the impact of physical activity or screen time on behavior. Specifically, some studies have explored the potential benefits of physical activity for behavioral problems in children and adolescents with autism and found that moderate physical activity can alleviate autism symptoms, improve emotional regulation, and reduce anxiety and depression.

[0005] Screen time, an integral part of modern life, has a profound impact on the behavior and development of children and adolescents with autism. Prolonged screen time not only leads to reduced physical activity but can also trigger a range of behavioral problems, such as inattention, hyperactivity, and increased social difficulties. While previous studies have highlighted the negative impacts of screen time on children and adolescents with autism, most have examined the effects of screen time in isolation, failing to integrate this with physical activity and sleep patterns for a comprehensive analysis.

[0006] Sleep, as an important process for restoring physical strength and energy, has a crucial impact on the behavior and emotional state of children and adolescents with autism. Insufficient sleep or poor sleep quality may lead to more behavioral problems in children and adolescents with autism, such as irritability, increased aggression, and difficulty concentrating.

[0007] More importantly, the 24-hour activity patterns of children and adolescents with autism are an interconnected and mutually influential whole. Single-dimensional analysis methods often fail to fully reveal the causes of behavioral problems, nor can they provide sufficient basis for the development of effective intervention measures. Therefore, there is an urgent need for a system that can comprehensively and systematically analyze the multidimensional associations between the 24-hour activity patterns of children and adolescents with autism and behavioral problems. This system should be able to comprehensively consider multiple dimensions such as physical activity, screen time, and sleep, and deeply explore the complex relationship between them and behavioral problems, providing a scientific basis for the development of personalized intervention measures. Summary of the Invention

[0008] The purpose of this invention is to provide a multidimensional correlation analysis system for activities and behavioral problems in children and adolescents with autism. This system breaks the limitations of traditional single-dimensional analysis and comprehensively considers three core dimensions of 24-hour activity patterns: physical activity, screen time, and sleep. This system can more comprehensively reveal the complex relationship between activities and behavioral problems in children and adolescents with autism. This multidimensional analytical perspective helps to gain a deeper understanding of the causes of behavioral problems and provides a scientific basis for the development of comprehensive intervention measures.

[0009] To achieve the above-mentioned purpose, the present invention is implemented through the following technical solutions:

[0010] A multidimensional correlation analysis system for activities and behavioral problems of children and adolescents with autism, comprising a data acquisition module, a data preprocessing module, a multidimensional correlation analysis module, a result visualization module and a report generation module, for comprehensively analyzing the relationship between the 24-hour activity patterns and behavioral problems of children and adolescents with autism.

[0011] The data acquisition module includes a wearable device data acquisition submodule, a questionnaire data acquisition submodule and a behavior assessment scale data acquisition submodule; the wearable device data acquisition submodule is used to monitor and record the physical activity data of autistic children and adolescents in real time; the questionnaire data acquisition submodule is used to collect log questionnaire data on daily activities, screen time and sleep conditions; the behavior assessment scale data acquisition submodule is used to assess the behavioral problems of autistic children and adolescents using a standardized behavior assessment scale.

[0012] The wearable device used in the wearable device data acquisition submodule has long battery life, is comfortable to wear, and supports data synchronization function. It can monitor and record physical activity data such as step count, activity intensity, and activity time in real time.

[0013] The wearable device used in the wearable device data acquisition submodule has long battery life, is comfortable to wear, and supports data synchronization function. It can monitor and record physical activity data such as step count, activity intensity, and activity time in real time.

[0014] The behavior assessment scale data collection submodule uses a standardized behavior assessment scale that has been tested for reliability and validity to conduct behavioral assessments on autistic children and adolescents and record the assessment results.

[0015] The data preprocessing module includes a data cleaning submodule, a data integration submodule and a data standardization submodule; the data cleaning submodule is used to identify and process outliers, missing values ​​and duplicate values; the data integration submodule is used to match timestamps and unify data formats of data from different sources; the data standardization submodule is used to standardize continuous variables to eliminate dimensional effects.

[0016] The multidimensional association analysis module adopts methods such as descriptive statistical analysis, difference test, correlation analysis and complex relationship modeling. The result visualization module includes a visualization chart design submodule and an interactive interface design submodule; the visualization chart design submodule is used to design appropriate visualization charts based on the analysis results.

[0017] The report generation module includes a report writing submodule and a report output submodule; the report writing submodule is used to write a customized analysis report, organize the analysis results and make suggestions; the report output submodule is used to output the report.

[0018] Data Collection Module: Responsible for collecting 24-hour activity data and behavioral assessment data from children and adolescents with autism. Data collection is the foundation of the entire system, and its accuracy and completeness directly impact the reliability of subsequent analysis.

[0019] Collection Method: 24-hour activity data: Wearable devices (such as accelerometers and heart rate monitors) are used to monitor the physical activity levels, screen time, and sleep patterns of children and adolescents with autism in real time. This data is combined with diaries completed by parents or guardians to record the timing, type, and intensity of the child's daily activities.

[0020] Behavioral Assessment Data: Professionals conduct a comprehensive assessment of behavioral issues in children and adolescents with autism using standardized behavioral assessment scales, such as the Autism Behavior Checklist (ABC) and the Child Behavior Checklist (CBCL). This assessment covers a variety of areas, including aggressive behavior, self-injurious behavior, anxiety and depression, and social difficulties.

[0021] Data preprocessing module: cleans, integrates and standardizes the collected raw data to ensure data quality and consistency, and provides a reliable data foundation for subsequent multidimensional correlation analysis.

[0022] Data cleaning: remove outliers, missing values, and duplicate values, and deal with noise and interference in the data.

[0023] Data integration: Integrate data from different sources and formats into a unified dataset. For example, real-time activity data collected by wearable devices can be matched and integrated with parent-generated log data.

[0024] Data standardization: Standardize the data to eliminate the impact of different dimensions and units on the analysis results. For example, data such as physical activity levels, screen time, and sleep duration can be standardized to make them comparable.

[0025] Multidimensional Correlation Analysis Module: This core analysis module is responsible for in-depth analysis of the multidimensional correlation between 24-hour activity patterns and behavioral issues. This module contains multiple analysis submodules, each analyzing the relationship between activity and behavioral issues from different dimensions.

[0026] Physical Activity (MVPA) Analysis Submodule: Analyzes the association between moderate-to-vigorous physical activity (MVPA) achievement and behavioral problems in children and adolescents with autism. This submodule examines the impact of MVPA achievement on the severity of behavioral problems and explores the moderating effect of MVPA achievement on behavioral problems under different demographic variables.

[0027] Analytical Methods: Descriptive statistics, t-tests, and analysis of variance were used to compare behavioral problem scores across the MVPA achievement groups. Path analysis was also used to explore the direct and indirect impacts of MVPA achievement on behavioral problems.

[0028] Screen Time (ST) Analysis Submodule: This module explores the relationship between screen time (ST) compliance and behavioral problems in children and adolescents with autism. This module distinguishes between the impact of school days and holidays, analyzing the differential impact of screen time use on behavioral problems during different time periods. Furthermore, it examines the moderating effects of variables such as gender and BMI on the relationship between screen time and behavioral problems.

[0029] Analytical Methods: Chi-square tests and Pearson correlation analyses were used to analyze the correlation between screen time compliance and behavioral problem scores. Hierarchical regression analysis was also used to explore the moderating effects of different demographic variables on the relationship between screen time and behavioral problems.

[0030] Sleep (SD) Analysis Submodule: Analytical Content: This submodule examines the association between sleep (SD) compliance and behavioral problems in children and adolescents with autism. This submodule considers the differential impact of sleep compliance on behavioral problems during school days, holidays, and average weekly sleep, and analyzes the differential effects of sleep quality on behavioral problems during different time periods. Furthermore, this submodule explores the moderating effects of age, BMI, and autism severity on the relationship between sleep and behavioral problems.

[0031] Analytical Methods: Descriptive statistics, t-tests, and analysis of variance were used to compare behavioral problem scores across sleep achievement groups. Structural equation modeling and other methods were used to construct a complex model of the relationship between sleep and behavioral problems and to explore the influence of various moderating variables.

[0032] 24-Hour Activity Combination Attainment Analysis Submodule: This module comprehensively analyzes the relationship between achieving the combined target of physical activity, screen time, and sleep and behavioral problems in children and adolescents with autism. This module reveals the specific impact of different activity combination patterns (e.g., achieving MVPA+ST, achieving MVPA+SD, achieving ST+SD, achieving all three), on behavioral problems and explores the optimal activity combination to minimize behavioral problems.

[0033] Analytical Methods: Chi-square tests and multivariate analysis of variance were used to analyze the association between achievement patterns of different activity combinations and behavioral problem scores. Machine learning methods, such as decision trees and random forests, were used to construct a behavioral problem prediction model and identify the activity combination factors that most strongly influence behavioral problems.

[0034] Statistical Method Library: Provides a rich set of statistical methods to support the data analysis needs of the Multidimensional Association Analysis module. This library includes a variety of methods from basic descriptive statistics to advanced multivariate statistical analysis, ensuring the scientific and accurate analysis process.

[0035] Included methods: descriptive statistics, t-test, analysis of variance, chi-square test, Pearson correlation analysis, path analysis, structural equation model, decision tree, random forest, etc.

[0036] Function: The analysis results of the multidimensional correlation analysis module are intuitively displayed in the form of charts, reports, etc., making it easy for users to understand and apply. At the same time, customized analysis reports are generated according to user needs, providing a scientific basis for formulating personalized intervention measures.

[0037] Graphical Presentation: Utilize various chart formats, including bar charts, line charts, scatter plots, and heat maps, to visually demonstrate the correlation between different activity dimensions and behavioral problems. For example, a bar chart can be used to compare behavioral problem scores across different MVPA achievement groups, while a heat map can be used to present a correlation matrix between achievement patterns across different activity combinations and the severity of behavioral problems.

[0038] Report Generation: Generate customized reports based on user needs, including analysis results, conclusions, and recommendations. Report content may include: data overview, analysis methods, key findings, conclusions, and recommendations. Reports can also be exported in formats such as Word and PDF for easy access and sharing.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] Comprehensiveness: This system breaks the limitations of traditional single-dimensional analysis by comprehensively considering the three core dimensions of 24-hour activity patterns: physical activity, screen time, and sleep. This allows for a more comprehensive understanding of the complex relationship between activity and behavioral problems in children and adolescents with autism. This multidimensional analytical perspective facilitates a deeper understanding of the causes of behavioral problems and provides a scientific basis for developing comprehensive interventions.

[0041] Accuracy: Through multidimensional correlation analysis, the system can more accurately identify key factors influencing behavioral problems in children and adolescents with autism. For example, the system can analyze the independent and interactive effects of different activity dimensions on behavioral problems, revealing the moderating effects of different demographic variables on the relationship between activity and behavioral problems. This precise analysis helps develop more targeted intervention plans and improve intervention effectiveness.

[0042] Practicality: The data collection, preprocessing, analysis, and visualization capabilities provided by this system can significantly improve the efficiency and accuracy of research on behavioral problems in children and adolescents with autism. The system supports automated processing throughout the entire process, from data collection to output, reducing the impact of manual intervention and subjective judgment. Furthermore, the customized reports generated by the system provide researchers, clinicians, and parents with intuitive and easy-to-understand analysis results, helping them better understand and apply research findings, develop personalized interventions, improve behavioral problems in children and adolescents with autism, and enhance their quality of life.

[0043] Scalability: The system adopts a modular design, with each functional module working independently yet collaboratively, which provides good scalability. In the future, as research deepens and technology develops, new analysis modules, statistical methods, or data sources can be easily added to adapt to changing research needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Attachment Figure 1 This is a flow chart of a multidimensional correlation analysis system for activities and behavioral problems of autistic children and adolescents according to the present invention.

[0045] Attachment Figure 2 This is a flow chart of a multidimensional correlation analysis module of a multidimensional correlation analysis system for activities and behavioral problems of autistic children and adolescents in the present invention. DETAILED DESCRIPTION

[0046] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the application equally.

[0047] This invention describes a multidimensional correlation analysis system for activities and behavioral problems in children and adolescents with autism. Its main structure consists of the following core modules: a data acquisition module, a data preprocessing module, a multidimensional correlation analysis module, a results visualization module, and a report generation module. Each module has a specific function, and they work together to complete the entire process from data collection to result output.

[0048] Data collection is the foundation of the entire analysis process and determines the accuracy and reliability of subsequent analysis. This system uses multiple data collection methods to ensure comprehensive and diverse data. Select wearable devices suitable for children and adolescents with autism, such as smart bracelets or smartwatches. These devices should have long battery life to ensure 24 / 7 monitoring, be comfortable to wear, and avoid discomfort for children and adolescents with autism. They should also support data synchronization to facilitate real-time transmission of collected data to the system for analysis. The devices should be able to monitor and record physical activity data in real time. This data provides an important basis for subsequent physical activity analysis.

[0049] Design a diary questionnaire for parents or guardians to complete, covering their child's physical activity, screen time, and sleep during school days and holidays. The questionnaire should detail the type, duration, and intensity of activity during each time period. Also, include descriptions of screen use and subjective assessments of sleep quality. Distribute the questionnaire online or offline to ensure that parents or guardians can easily complete and submit it. Also, set reminders to encourage parents to complete the questionnaire regularly to ensure data continuity.

[0050] Use a standardized behavioral assessment scale with proven reliability and validity. This scale encompasses multiple dimensions and can comprehensively assess behavioral problems in children and adolescents with autism. Conduct behavioral assessments on children and adolescents with autism. The assessment process should follow standardized procedures to ensure objectivity and accuracy. Furthermore, regular training should be provided to assessors to enhance consistency and reliability.

[0051] Use statistical methods to identify and address outliers. Further verify or eliminate values ​​that significantly deviate from the normal range. Select an appropriate treatment method based on the data characteristics and missing values. For a small number of missing values, use methods such as mean, median, or mode interpolation. For a large number of missing values, consider multiple interpolation or sample deletion. Check and remove duplicate records to ensure data uniqueness.

[0052] Match the timestamps of real-time activity data collected by wearable devices, questionnaire data, and behavioral assessment scale data to form a complete time series dataset. Standardize data formats and coding standards to ensure data consistency and comparability. Convert all time data to a unified date and time format, and convert categorical variables to numeric codes. Perform Z-score or minimum-maximum normalization on continuous variables to eliminate dimensionality effects and facilitate subsequent analysis.

[0053] Multidimensional correlation analysis is the core of the system. Using a variety of statistical methods and machine learning algorithms, it deeply explores the complex relationships between physical activity, screen time, sleep duration, and behavioral issues. Statistical indicators such as the mean, standard deviation, and frequency distribution of each variable are calculated to understand the overall characteristics of the data. The distribution of each variable is displayed through charts such as bar charts and line graphs, visually presenting data characteristics and trends.

[0054] Methods such as t-tests and analysis of variance were used to compare differences between groups on various variables. By comparing intergroup differences, we could determine whether there were significant differences in behavioral problems among groups with different characteristics. Chi-square tests were used to analyze associations between categorical variables, such as the relationship between gender and behavioral problem type.

[0055] Pearson correlation analysis was used to explore the linear correlations between physical activity, screen time, sleep duration, and behavioral problems. Correlation coefficients were calculated to understand the degree and direction of associations between variables. For variables with potential nonlinear relationships, methods such as Spearman rank correlation analysis were used for further investigation. Correlations between variables were displayed using scatter plots and correlation coefficient matrices, visualizing the association patterns between variables.

[0056] Path analysis was used to construct a causal model between physical activity, screen time, sleep duration, and behavioral problems. Path analysis can help us understand the direct and indirect influence relationships between variables, providing a basis for developing intervention measures.

[0057] For more complex relationship patterns, structural equation modeling (SEM) is used. SEM can simultaneously consider the relationships between multiple independent and dependent variables, as well as the mediating and moderating effects between them. Model fit indices are used to assess the explanatory and predictive power of the model. Based on these assessment results, the model is optimized and adjusted to improve its accuracy and reliability.

[0058] Result visualization is a key step in presenting analysis results to users in an intuitive and easy-to-understand manner. This system helps users better understand the analysis results by designing rich visual charts and interactive interfaces. Select appropriate chart types based on the characteristics of the analysis results and user needs, such as bar charts, line charts, scatter plots, heat maps, etc. For example, use a bar chart to show the difference in scores between different groups on behavioral problems; use a line chart to show the trend relationship between physical activity time and behavioral problem scores; use a scatter plot to show the correlation between screen time and sleep time, etc. Beautify the chart, such as adjusting the color, font, legend, etc., to improve the visual effect and readability of the chart. At the same time, ensure that the information in the chart is accurate to avoid misleading users.

[0059] Provide an interactive visualization interface, allowing users to explore data and analysis results in depth through operations such as dragging and zooming. For example, users can drag a slider to select different time periods or groups for viewing; zoom in and out on a chart to view detailed information. For time series data or analysis results that experience dynamic changes, dynamic presentation is used. Use animation effects to show the changing trends of physical activity time over time; use heat maps to show the changing correlation between screen time and behavioral problems over different time periods.

[0060] Charts and graphs can be exported as images or vector graphics, making it easy to incorporate analysis results into reports or academic discussions. Online sharing allows users to share visualizations with other researchers, clinicians, or parents. This facilitates information exchange and collaborative research. The report generation module is crucial for organizing analysis results into customized reports. By creating reports with clear structure, concise language, and rich charts, this system helps users better understand and apply analysis results.

[0061] Write a customized analysis report based on user needs. The report content should include sections such as research background and purpose, data and methods, main results, conclusions and recommendations. Each section should have a clear theme and logical structure to ensure the overall coherence and readability of the report. Content writing: In the report writing process, pay attention to the accuracy and conciseness of the language. Avoid using overly complex or vague vocabulary and sentence structures; at the same time, ensure that the information in the report is accurate to avoid misleading users. Insert charts: Insert appropriate visual charts in the report to intuitively display the analysis results. Charts should be closely related to the content of the report and have clear titles and legends. By inserting charts, the readability and persuasiveness of the report can be improved.

[0062] To ensure smooth implementation and effective operation of the system, the following implementation steps need to be followed:

[0063] Conduct in-depth discussions with researchers, clinicians, and parents to clarify the system's functional requirements and usage scenarios. Design the system architecture and module functionality based on the needs research results. Develop a detailed technical implementation plan and timeline.

[0064] Device Procurement and Deployment: Select, procure, and deploy appropriate wearable devices. Ensure stable device operation and accurate data collection. Questionnaire Design and Distribution: Design a diary questionnaire for parents or guardians to complete and distribute it online or offline. Set up reminders to encourage parents to complete the questionnaire regularly. Behavioral Assessment: Professionals conduct behavioral assessments on children and adolescents with autism and record the results. Data Preprocessing: Clean, integrate, and standardize the collected data. Ensure data accuracy and consistency.

[0065] Analytical Method Selection: Select appropriate analytical methods based on the research question and data characteristics. These methods may include descriptive statistics, variance testing, correlation analysis, and complex relationship modeling. Analysis Implementation: Conduct in-depth data analysis using the selected analytical methods to explore the complex relationships between physical activity, screen time, sleep duration, and behavioral problems. Result Verification: Verify and evaluate the analytical results to ensure their accuracy and reliability.

[0066] Visualization Design: Design appropriate visualization charts and interactive interfaces based on the analysis results. Ensure that the charts are intuitive, easy to understand, and informative. Report Writing: Develop a customized analysis report, organizing the analysis results and providing recommendations. The report should have clear logic, concise language, and rich charts. Output and Sharing: Output the report in an appropriate format and share it with users. Provide online viewing and downloading capabilities for easy access to report content anytime, anywhere.

[0067] Functional testing: Comprehensively test all system functions to ensure they operate stably and meet user needs. Performance testing: Test system performance, including metrics like response time and throughput, to ensure the system maintains good performance even under high concurrency. User feedback collection: Gather user feedback and suggestions on the system. Understand the problems and needs users encounter during use. System optimization: Optimize and improve the system based on user feedback and test results to improve system usability, stability, and reliability.

[0068] Detailed instructions for use:

[0069] The system of the present invention breaks the limitations of traditional single-dimensional analysis and comprehensively considers the three core dimensions of physical activity, screen time, and sleep in the 24-hour activity pattern. It can more comprehensively reveal the complex relationship between activities and behavioral problems in children and adolescents with autism. This multi-dimensional analytical perspective helps to more deeply understand the causes of behavioral problems and provide a scientific basis for the formulation of comprehensive intervention measures. Through multidimensional correlation analysis, the system of the present invention can more accurately identify the key factors affecting behavioral problems in children and adolescents with autism. For example, the system can analyze the independent impact and interaction of different activity dimensions on behavioral problems, and reveal the regulatory effect of different demographic variables on the relationship between activities and behavioral problems. This precise analysis helps to formulate more targeted intervention plans and improve the effectiveness of intervention. The data collection, preprocessing, analysis, and visualization functions provided by the system of the present invention can greatly improve the efficiency and accuracy of research on behavioral problems in children and adolescents with autism. The system supports full-process automated processing from data collection to result output, reducing the impact of manual intervention and subjective judgment. At the same time, the customized reports generated by the system provide researchers, clinicians, and parents with intuitive and easy-to-understand analysis results, helping them better understand and apply research results, develop personalized intervention measures, improve behavioral problems in children and adolescents with autism, and enhance their quality of life. The system of the present invention adopts a modular design, with each functional module working independently yet collaboratively, and has good scalability. In the future, as research deepens and technology develops, new analysis modules, statistical methods, or data sources can be easily added to adapt to changing research needs.

Claims

1. A multidimensional correlation analysis system for activities and behavioral problems of children and adolescents with autism, characterized by: The system includes a data acquisition module, a data preprocessing module, a multidimensional correlation analysis module, a result visualization module and a report generation module, which are used to comprehensively analyze the relationship between the 24-hour activity patterns and behavioral problems of children and adolescents with autism.

2. The multidimensional correlation analysis system for activities and behavioral problems of children and adolescents with autism according to claim 1, characterized by: The data acquisition module includes a wearable device data acquisition submodule, a questionnaire data acquisition submodule and a behavior assessment scale data acquisition submodule; the wearable device data acquisition submodule is used to monitor and record the physical activity data of autistic children and adolescents in real time; the questionnaire data acquisition submodule is used to collect log questionnaire data on daily activities, screen time and sleep conditions; the behavior assessment scale data acquisition submodule is used to assess the behavioral problems of autistic children and adolescents using a standardized behavior assessment scale.

3. The multidimensional correlation analysis system for activities and behavioral problems of children and adolescents with autism according to claim 2 is characterized by: The wearable device used in the wearable device data acquisition submodule has long battery life, is comfortable to wear, and supports data synchronization function, and can monitor and record physical activity data such as step count, activity intensity, and activity time in real time.

4. The multidimensional correlation analysis system for activities and behavioral problems of children and adolescents with autism according to claim 3, characterized by: The wearable device used in the wearable device data acquisition submodule has long battery life, is comfortable to wear, and supports data synchronization function, and can monitor and record physical activity data such as step count, activity intensity, and activity time in real time.

5. The multidimensional correlation analysis system for activities and behavioral problems of children and adolescents with autism according to claim 4 is characterized by: The behavior assessment scale data collection submodule uses a standardized behavior assessment scale that has been tested for reliability and validity to conduct behavioral assessments on autistic children and adolescents and record the assessment results.

6. The multidimensional correlation analysis system for activities and behavioral problems of children and adolescents with autism according to claim 5, characterized by: The data preprocessing module includes a data cleaning submodule, a data integration submodule and a data standardization submodule; the data cleaning submodule is used to identify and process outliers, missing values ​​and duplicate values; the data integration submodule is used to match timestamps and unify data formats of data from different sources; the data standardization submodule is used to standardize continuous variables to eliminate dimensional effects.

7. The multidimensional correlation analysis system for activities and behavioral problems of children and adolescents with autism according to claim 6, characterized by: The multidimensional association analysis module adopts methods such as descriptive statistical analysis, difference test, correlation analysis and complex relationship modeling, and the result visualization module includes a visualization chart design submodule and an interactive interface design submodule; The visualization chart design submodule is used to design a suitable visualization chart according to the analysis results.

8. The multidimensional correlation analysis system for activities and behavioral problems of children and adolescents with autism according to claim 7, characterized by: The report generation module includes a report writing submodule and a report output submodule; the report writing submodule is used to write a customized analysis report, organize the analysis results and make suggestions; The report output submodule is used to output the report.

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