Special education resource management system

Abstract

The invention provides a special education resource management system. The special education resource management system comprises a data analysis and evaluation module, a learning support tool module, a teacher and assistant management module, a resource allocation and scheduling module, a student demand evaluation module, a resource database and a user interface module. The data analysis and evaluation module is responsible for monitoring and evaluating the use effect of resources in real time and providing accurate data support for decision makers, and the data analysis and evaluation module has a data acquisition and monitoring function, a data processing and cleaning function and a data analysis and evaluation function. Wherein a data acquisition part in the data acquisition and monitoring function is used for integrating a plurality of data sources. The special education resource management system provided by the invention has the advantages that various resources can be monitored and scheduled in real time, accurate resource configuration and evaluation analysis are provided, and then the problems existing in current special education resource management can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of educational resource management, and in particular relates to a special educational resource management system. Background Art

[0002] Special education is an educational service provided to students with special needs. Due to the different needs of students, the allocation of special education resources is relatively complex, involving teaching facilities, teaching aids, personalized education plans, counselors, professional support and other aspects. However, there are still some problems in the management and allocation of special education resources, such as uneven resource distribution, low level of informatization, and serious waste of resources.

[0003] Currently, most special education institutions rely on manual management and manual scheduling, lacking effective system support, which makes it difficult to optimally allocate resources.

[0004] Therefore, it is necessary to provide a new special education resource management system to solve the above technical problems. Summary of the invention

[0005] The technical problem solved by the present invention is to provide a special education resource management system that can monitor and schedule various resources in real time, provide accurate resource allocation and evaluation analysis, and thus effectively solve the problems existing in the current special education resource management.

[0006] In order to solve the above technical problems, the special education resource management system provided by the present invention includes: a data analysis and evaluation module, a learning support tool module, a teacher and assistant management module, a resource allocation and scheduling module, a student needs assessment module, a resource database and a user interface module;

[0007] The data analysis and evaluation module is responsible for real-time monitoring and evaluation of the use effect of resources, and provides accurate data support for decision makers. The data analysis and evaluation module includes data collection and monitoring functions, data processing and cleaning functions, and data analysis and evaluation functions. The data collection part of the data collection and monitoring function integrates multiple data sources, including but not limited to students' learning progress, teachers' teaching load and resource usage, so as to ensure that the system obtains comprehensive and timely feedback data, while the monitoring part can track the use of resources in real time, and can automatically collect resource consumption, usage frequency and student satisfaction information;

[0008] The data processing and cleaning function is used to clean and preprocess the collected data to ensure the accuracy and consistency of the data. The data cleaning operation includes:

[0009] 1). Deduplication: remove duplicate data to ensure the uniqueness of each data record;

[0010] 2) Filling missing values: Processing missing data. Common methods include mean filling, median filling or predicting missing values ​​through models.

[0011] 3) Outlier detection and elimination: Identify abnormal data points through statistical methods or machine learning algorithms, and eliminate or correct these abnormal data;

[0012] The analysis function in the data analysis and evaluation function is to extract effective information on resource use and conduct evaluation through different analysis methods. The main steps include:

[0013] 1) Resource utilization effect evaluation: evaluate the actual use effect of resources to identify whether the resources are fully utilized, whether there is waste or insufficient use;

[0014] 2) Student demand analysis: Combine students’ learning data to analyze students’ needs for different subjects and resources, and identify changing trends in resource needs;

[0015] 3) Teacher resource allocation assessment: assess the teacher resource allocation and analyze whether there are teachers who are overburdened or idle;

[0016] 4) Analysis of the matching degree between demand and supply: By comparing the matching degree between the supply of resources and the needs of students, the rationality of resource allocation can be evaluated;

[0017] The learning support tool module provides a personalized and flexible learning experience by integrating multiple digital learning tools to help students better master knowledge and skills. The module can provide diversified support for students with different learning needs to promote their learning effects. The learning support tool module includes tool classification function, user interface function, data analysis and feedback function and support and training function;

[0018] The tool classification functions are as follows:

[0019] 1). Speech recognition tool: used to convert speech into text to help students with dictation, recording and language learning;

[0020] 2) Image recognition tools: can be used to identify and analyze image content to help students understand visual information, such as charts and pictures;

[0021] 3). Supplementary teaching materials: including but not limited to e-books, interactive teaching materials and video tutorials, providing multimedia learning resources;

[0022] 4). Learning Management System (LMS): used to manage learning content, track learning progress and provide feedback;

[0023] 5). Collaboration tools: such as online discussion platforms and shared document tools to promote collaboration and communication among students;

[0024] 6). Assessment tools: used for online testing, quizzes and evaluation of students’ learning outcomes;

[0025] The user interface functions are as follows:

[0026] 1). User-friendly interface: Design a simple and intuitive interface to help students quickly find the tools and resources they need;

[0027] 2). Personalization: Allow students to customize the interface according to their needs and preferences, such as font size and color theme;

[0028] The data analysis and feedback functions are as follows:

[0029] 1). Learning data collection: record students’ usage, learning progress, grades and other data;

[0030] 2) Real-time feedback mechanism: Provide instant feedback based on students’ performance to help them adjust their learning strategies;

[0031] The support and training functions described are: User Guide: Provides detailed tool usage instructions and video tutorials to help students become familiar with each tool;

[0032] The teacher and assistant management module ensures that the work arrangements of teachers and assistants are reasonable, their training is appropriate, and their work performance is effectively evaluated through systematic management and analysis, thereby maximizing the quality of education. Through precise management and data analysis, this module can ensure that the roles and responsibilities of teachers and assistants in teaching can be fully utilized and matched with student needs. The teacher and assistant management module includes personnel file management functions, work arrangement system functions, training and development functions, performance evaluation and feedback functions, matching analysis functions, and communication and collaboration tool functions;

[0033] The personnel file management functions are as follows:

[0034] 1). Teacher files: record the teacher's basic information, academic background, teaching experience and subject data, providing a basis for arranging teaching tasks;

[0035] 2). Student assistant files: record the qualifications, work experience, task preferences, and professional expertise of student assistants to help match them with suitable auxiliary positions;

[0036] The functions of the work arrangement system are as follows:

[0037] 1). Teaching task allocation: assign courses to appropriate teachers based on their professional background, teaching experience and student needs;

[0038] 2). Arrangement of assistants: Reasonably arrange the support tasks of assistants according to the number of students, course progress and student needs;

[0039] 3) Scheduling management: Automated scheduling tools help teachers and teaching assistants schedule their work more efficiently and avoid resource conflicts;

[0040] The training and development functions are as follows:

[0041] 1). Teacher training plan: Regularly organize teacher training according to teaching needs and the development of new technologies, such as curriculum design, application of educational technology, and innovation of teaching methods;

[0042] 2) Training of teaching assistants: Provide training for teaching assistants, such as counseling skills, psychological counseling methods and communication skills, to ensure that they can effectively assist teachers and help students;

[0043] The performance evaluation and feedback functions are as follows:

[0044] 1). Teacher evaluation system: evaluate teachers’ teaching effectiveness through multi-dimensional data such as student feedback, teaching observation, and peer review;

[0045] 2) Student assistant evaluation system: Based on the feedback from students and teachers, the performance of student assistants is evaluated to ensure that they can effectively support students’ learning;

[0046] 3) Automated scoring and feedback: Through data collection and analysis tools, timely feedback on the work performance and problems of teachers and teaching assistants;

[0047] The matching degree analysis function is as follows:

[0048] 1). Personalized matching system: by analyzing the learning needs of teachers and students, and the working ability of assistants, we ensure that the matching degree between each teacher and student, and each assistant and task is optimized;

[0049] 2). Real-time adjustment mechanism: flexibly adjust staffing according to students’ learning feedback, teachers’ workload, and the working status of assistants to ensure continuous optimization of matching degree;

[0050] The communication and collaboration tool functions are as follows:

[0051] 1). Communication platform for teachers and teaching assistants: providing online discussion, information sharing and work task notification functions to ensure seamless collaboration within the team;

[0052] 2). Work progress tracking: Provide teachers and teaching assistants with progress tracking tools to ensure that teaching and tutoring tasks are completed on time;

[0053] 3) Student feedback channel: Establish a student feedback mechanism so that teachers and teaching assistants can understand students' needs and feedback in a timely manner and make adjustments;

[0054] The resource allocation and scheduling module scientifically and reasonably schedules and allocates teaching resources, including but not limited to teachers, teaching assistants, teaching equipment and classrooms, according to students' needs, the status of existing resources and allocation priorities. Through an automated and intelligent scheduling system, the optimal allocation of resources is achieved, the efficiency of education management is improved, and the smooth progress of teaching activities is ensured. The resource allocation and scheduling module includes resource archive management functions, demand forecasting and analysis systems, resource scheduling and allocation functions, real-time monitoring and adjustment functions, data analysis and reporting functions and resource sharing and collaboration platforms;

[0055] The resource archive management functions are as follows:

[0056] 1). Teacher and assistant files: record the professional background, work experience and teaching tasks of teachers and assistants, and provide basic information for resource allocation;

[0057] 2). Physical resource archives: including archive information of classrooms, teaching equipment and teaching materials, recording the usage and maintenance status of various resources;

[0058] 3) Time and space archive: including teaching timetable, classroom arrangement and equipment availability time to ensure that resources are not used in conflict;

[0059] The demand forecasting and analysis system is specifically as follows:

[0060] 1). Student demand analysis: Analyze the teaching resource demand for each semester or academic year based on the number of students, course content, learning progress, and subject demand data;

[0061] 2) Dynamic demand forecasting: Based on historical data, course schedules, and student feedback, predict future resource demand and prepare in advance;

[0062] 3) Matching courses with student needs: Automatically analyze the teachers, assistants and other resources required for each course and match them with the actual situation;

[0063] The resource scheduling and allocation functions are as follows:

[0064] 1). Automatic resource scheduling system: Based on demand forecasts and existing resources, the system automatically generates resource allocation plans and rationally allocates teachers, teaching assistants, classrooms, and equipment;

[0065] 2) Priority setting and dynamic adjustment: Set resource allocation priorities based on factors such as the importance of the course, the urgency of student needs, and the scarcity of resources to ensure that key tasks are given priority in resource allocation;

[0066] 3) Conflict detection and mediation: In the process of resource allocation, resource conflicts are automatically detected, such as teacher scheduling conflicts and classroom time conflicts, and mediation or adjustment is carried out through the system;

[0067] The real-time monitoring and adjustment functions are as follows:

[0068] 1) Resource usage monitoring: Real-time monitoring of the usage of various resources, including the workload of teachers, the workload of teaching assistants, and the usage status of classrooms and equipment, to ensure the rational use of resources;

[0069] 2) Dynamic adjustment mechanism: When resources are in short supply or are allocated irrationally, the system automatically adjusts the resource allocation plan, such as adjusting the course schedule and dispatching spare classrooms to ensure the smooth completion of teaching tasks;

[0070] 3) Feedback and optimization: Based on resource usage and actual feedback, continuously optimize resource allocation plans to improve the efficiency and rationality of resource allocation;

[0071] The data analysis and reporting functions are as follows:

[0072] 1). Resource allocation analysis report: The system generates a detailed resource allocation report to help managers understand the usage of various resources and the effect of resource allocation;

[0073] 2) Benefit evaluation: Evaluate the effectiveness of resource allocation plans, such as teaching quality, student satisfaction, and resource utilization, to help further adjust resource allocation strategies;

[0074] 3) Forecasting and trend analysis: Based on historical data and trends, predict possible changes in resource demand in the future, and make resource planning and scheduling in advance;

[0075] The resource sharing and collaboration platform is as follows:

[0076] 1) Resource Sharing Platform: Provide a platform where teachers, teaching assistants and students can share resources such as classroom arrangements, equipment usage, teaching materials and learning materials to promote collaboration;

[0077] 2) Cross-departmental collaboration: Provide resource scheduling and coordination tools for various departments of the school, such as the Academic Affairs Office, Logistics, and Technical Support, to ensure unified and efficient resource allocation;

[0078] The student needs assessment module is to develop a practical education plan and allocate appropriate resources for students by fully understanding their individual needs. This module can not only help teachers better understand students' academic, psychological, emotional and social needs, but also ensure the rational allocation of educational resources. The student needs assessment module includes data collection function, demand analysis function, resource planning and allocation function and implementation and tracking function;

[0079] The data collection functions are as follows:

[0080] 1) Academic performance data: including but not limited to students’ grades, homework completion, class performance and test scores, to help assess students’ academic needs;

[0081] 2) Mental health assessment: Use questionnaires and interviews to understand students’ mental state, stress level, and emotional management ability;

[0082] 3) Learning styles and preferences: students’ learning styles, such as visual, auditory, hands-on, learning interests and learning habits;

[0083] 4). Social skills and emotional needs: students’ interpersonal skills, emotional regulation and prosocial behavior;

[0084] 5) Family background and support resources: Family environment, parental involvement, and family economic status are critical factors in influencing student needs;

[0085] 6) Special needs: such as physical, cognitive or behavioral needs, such as special educational needs and language barriers;

[0086] The demand analysis functions are as follows:

[0087] 1). Data aggregation and analysis: Classify and aggregate the collected data, analyze students’ strengths and weaknesses, potential and challenges, and identify their specific needs;

[0088] 2) Multi-dimensional assessment: Comprehensively consider academic, psychological, emotional and social aspects to determine where students need support;

[0089] 3) Prioritization: Prioritize students based on their needs and urgency, helping teachers and schools to clarify intervention directions;

[0090] The resource planning and allocation functions are as follows:

[0091] 1). Personalized education plan: Develop a personalized education plan for each student, specifically describing the student’s academic goals, support measures, and learning strategies;

[0092] 2) Resource allocation: According to demand analysis, allocate corresponding teaching resources, such as tutors, learning aids and psychological counseling;

[0093] 3) Curriculum design: adjust the teaching content, format and pace according to students’ needs, for example, setting up additional tutoring classes and special classroom activities;

[0094] 4). Support system: Establish a support network, including teachers, counselors, parents and peer support;

[0095] The implementation and tracking functions are as follows:

[0096] 1). Plan implementation: Carry out teaching activities according to the formulated personalized education plan and provide corresponding support services;

[0097] 2) Follow-up evaluation: Regularly evaluate students’ progress, monitor their academic performance, mental health and social performance, and adjust educational strategies and resource allocation in a timely manner;

[0098] 3) Feedback and adjustment: Through feedback from students, parents and teachers, we continuously adjust and optimize the education plan to ensure that it can meet the development needs of students;

[0099] The resource database is used to store all special education resource information, including but not limited to teachers, students, teaching materials, teaching aids and auxiliary equipment;

[0100] The user interface module can provide a simple and easy-to-use operation interface to facilitate their information query, resource application and progress tracking operations.

[0101] As a further solution of the present invention, the data source further includes the following:

[0102] Student behavior data: including but not limited to students’ online time, course participation and homework completion;

[0103] Teacher work data: including but not limited to teacher teaching time, course load and student feedback;

[0104] Resource usage data: including but not limited to utilization of classroom, laboratory, computer room and library resources;

[0105] Demand forecast data: Provides demand forecasts for various resources based on historical data and forecasting models.

[0106] As a further solution of the present invention, the data analysis and evaluation module needs to build a technical architecture, which is as follows:

[0107] 1) Data layer: Use distributed storage systems such as Hadoop, Spark, and AWS to store large amounts of educational data;

[0108] 2) Computing layer: Use big data processing frameworks such as Spark and Flink to process and analyze data;

[0109] 3) Analysis layer: Use machine learning platforms such as TensorFlow and Scikit-learn to calculate demand forecasting and optimization algorithms;

[0110] 4). Visualization layer: Data visualization and report generation functions are realized through BI tools such as Power BI and Tableau.

[0111] As a further solution of the present invention, the student needs assessment module uses structured questionnaires and semi-structured interviews to collect students' needs data, wherein the questionnaires can be designed into multiple dimensions, such as academic, psychological, emotional and social, so as to have a comprehensive understanding of the students' comprehensive needs.

[0112] As a further solution of the present invention, the tool classification function is to integrate various tools into a learning platform through platform integration to ensure the compatibility and interoperability between the tools. Before the tool integration, suitable digital learning tools can be selected for integration based on the results of demand analysis.

[0113] As a further solution of the present invention, the teacher and teaching assistant management module matches teachers with course content, teaching assistants with student needs based on a data matching system and a data analysis system to ensure that each task is completed by the most suitable person.

[0114] As a further solution of the present invention, the teacher and teaching assistant management module can provide customized training and career development suggestions based on the personal development needs of teachers and teaching assistants, and then formulate annual or semester training plans, including the improvement of teachers' teaching skills and the application of educational technology, and the psychological counseling skills and communication skills of teaching assistants.

[0115] As a further solution of the present invention, the teacher and teaching assistant management module can continuously optimize the matching degree of personnel by analyzing the learning progress of students, the work pressure of teachers and the work performance data of teaching assistants.

[0116] As a further solution of the present invention, the data analysis and evaluation module can also enable the system to dynamically adjust resource allocation according to resource usage, student needs and resource surplus through big data analysis and artificial intelligence algorithms to ensure optimal resource utilization. The dynamic adjustment of resource allocation needs to refer to various relevant information and collect a large amount of historical data and real-time data as a reference, so as to ensure the rationality of resource allocation. When collecting data as a reference, the following should be noted:

[0117] 1). Data cleaning: pre-process the collected data by removing redundancy, filling missing values ​​and eliminating outliers. This step can ensure the quality of the data;

[0118] 2) Data integration and storage: Data from different sources are integrated, formatted in a unified way, and stored on a suitable big data platform;

[0119] 3) Data analysis and modeling: Use statistical analysis and machine learning methods to analyze data and extract valuable information. For example, cluster analysis can be used to identify students' demand patterns and time series analysis can be used to predict resource usage.

[0120] 4) Demand forecasting model: By analyzing historical data, students’ future needs can be predicted. Common methods include time series analysis (such as ARIMA), regression analysis, and deep learning.

[0121] 5) Resource utilization optimization model: Use optimization algorithms such as linear programming or integer programming to solve resource allocation problems. For example, a linear programming (LP) model can be used to establish the optimization problem of resource allocation. The formula is as follows to optimize the configuration of resources to minimize idle resources or maximize resource utilization benefits:

[0122] Objective function:

[0123]

[0124] Constraints:

[0125]

[0126] Among them, c i is the unit value of the i-th resource, x is the amount allocated to the i-th resource, a ij is the relationship between the i-th resource and the j-th constraint, b j is the upper limit of the j-th constraint.

[0127] As a further solution of the present invention, the dynamic adjustment of resource allocation can also be based on a machine learning algorithm - reinforcement learning. The formula is as follows: the optimal strategy is learned through interaction with the environment. The system tries different resource allocation strategies, obtains feedback, and adjusts the strategy according to the feedback to optimize long-term rewards:

[0128] Q(δ,a)←Q(δ,a)+a[r+γmaxQ(δ ′ , a ′ )-Q(δ,a)]

[0129] Among them, Q(δ, a) is the value of taking action a in state δ, a is the learning rate, r is the current reward, γ is the discount factor, δ ′ is the next state, a ′ It’s the next move.

[0130] Compared with the related art, the special education resource management system provided by the present invention has the following beneficial effects:

[0131] 1. The present invention can monitor and dispatch various resources in real time, provide accurate resource allocation and evaluation analysis, and thus effectively solve the problems existing in the current special education resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0132] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0133] Figure 1 It is a schematic diagram of the present invention;

[0134] Figure 2 It is a schematic diagram of the process of the present invention;

[0135] Figure 3 Schematic diagram of the data analysis and evaluation module in the present invention;

[0136] Figure 4 This is a schematic diagram of a learning support tool module in the present invention;

[0137] Figure 5 This is a schematic diagram of the teacher and student assistant management module in the present invention;

[0138] Figure 6 It is a schematic diagram of the resource allocation and scheduling module in the present invention;

[0139] Figure 7 It is a schematic diagram of the student needs assessment module of the present invention. DETAILED DESCRIPTION

[0140] Please refer to Figures 1 to 7 ,in, Figure 1 It is a schematic diagram of the present invention; Figure 2 It is a schematic diagram of the process of the present invention; Figure 3 Schematic diagram of the data analysis and evaluation module in the present invention; Figure 4 This is a schematic diagram of a learning support tool module in the present invention; Figure 5 This is a schematic diagram of the teacher and student assistant management module in the present invention; Figure 6 It is a schematic diagram of the resource allocation and scheduling module in the present invention; Figure 7The diagram of the student needs assessment module of the present invention is shown in FIG. The special education resource management system includes: a data analysis and assessment module, a learning support tool module, a teacher and assistant management module, a resource allocation and scheduling module, a student needs assessment module, a resource database and a user interface module;

[0141] The data analysis and evaluation module is responsible for real-time monitoring and evaluation of the use effect of resources, and provides accurate data support for decision makers. The data analysis and evaluation module includes data collection and monitoring functions, data processing and cleaning functions, and data analysis and evaluation functions. The data collection part of the data collection and monitoring function integrates multiple data sources, including but not limited to students' learning progress, teachers' teaching load and resource usage, so as to ensure that the system obtains comprehensive and timely feedback data, while the monitoring part can track the use of resources in real time, and can automatically collect resource consumption, usage frequency and student satisfaction information;

[0142] The data processing and cleaning function is used to clean and preprocess the collected data to ensure the accuracy and consistency of the data. The data cleaning operation includes:

[0143] 1). Deduplication: remove duplicate data to ensure the uniqueness of each data record;

[0144] 2) Filling missing values: Processing missing data. Common methods include mean filling, median filling or predicting missing values ​​through models.

[0145] 3) Outlier detection and elimination: Identify abnormal data points through statistical methods or machine learning algorithms, and eliminate or correct these abnormal data;

[0146] The analysis function in the data analysis and evaluation function is to extract effective information on resource use and conduct evaluation through different analysis methods. The main steps include:

[0147] 1) Resource utilization effect evaluation: evaluate the actual use effect of resources to identify whether the resources are fully utilized, whether there is waste or insufficient use;

[0148] 2) Student demand analysis: Combine students’ learning data to analyze students’ needs for different subjects and resources, and identify changing trends in resource needs;

[0149] 3) Teacher resource allocation assessment: assess the teacher resource allocation and analyze whether there are teachers who are overburdened or idle;

[0150] 4) Analysis of the matching degree between demand and supply: By comparing the matching degree between the supply of resources and the needs of students, the rationality of resource allocation can be evaluated;

[0151] The system evaluates students' classroom participation, equipment usage, etc. through data analysis, provides personalized learning resources for teachers and students, and optimizes the allocation of classroom resources (such as classrooms and experimental equipment) to ensure maximum resource utilization. By analyzing teachers' teaching time, student feedback and teaching effectiveness, the system can optimize teachers' work arrangements according to needs to avoid excessive workload or waste of resources. Based on students' course demand forecasts, the system can adjust the course schedule to avoid course scheduling conflicts and ensure that resources for different courses and subjects are reasonably allocated.

[0152] The learning support tool module provides a personalized and flexible learning experience by integrating multiple digital learning tools to help students better master knowledge and skills. The module can provide diversified support for students with different learning needs to promote their learning effects. The learning support tool module includes tool classification function, user interface function, data analysis and feedback function and support and training function;

[0153] The tool classification functions are as follows:

[0154] 1). Speech recognition tool: used to convert speech into text to help students with dictation, recording and language learning;

[0155] 2) Image recognition tools: can be used to identify and analyze image content to help students understand visual information, such as charts and pictures;

[0156] 3). Supplementary teaching materials: including but not limited to e-books, interactive teaching materials and video tutorials, providing multimedia learning resources;

[0157] 4). Learning Management System (LMS): used to manage learning content, track learning progress and provide feedback;

[0158] 5). Collaboration tools: such as online discussion platforms and shared document tools to promote collaboration and communication among students;

[0159] 6). Assessment tools: used for online testing, quizzes and evaluation of students’ learning outcomes;

[0160] The user interface functions are as follows:

[0161] 1). User-friendly interface: Design a simple and intuitive interface to help students quickly find the tools and resources they need;

[0162] 2). Personalization: Allow students to customize the interface according to their needs and preferences, such as font size and color theme;

[0163] The data analysis and feedback functions are as follows:

[0164] 1). Learning data collection: record students’ usage, learning progress, grades and other data;

[0165] 2) Real-time feedback mechanism: Provide instant feedback based on students’ performance to help them adjust their learning strategies;

[0166] The support and training functions described are: User Guide: Provides detailed tool usage instructions and video tutorials to help students become familiar with each tool;

[0167] The teacher and assistant management module ensures that the work arrangements of teachers and assistants are reasonable, their training is appropriate, and their work performance is effectively evaluated through systematic management and analysis, thereby maximizing the quality of education. Through precise management and data analysis, this module can ensure that the roles and responsibilities of teachers and assistants in teaching can be fully utilized and matched with student needs. The teacher and assistant management module includes personnel file management functions, work arrangement system functions, training and development functions, performance evaluation and feedback functions, matching analysis functions, and communication and collaboration tool functions;

[0168] The personnel file management functions are as follows:

[0169] 1). Teacher files: record the teacher's basic information, academic background, teaching experience and subject data, providing a basis for arranging teaching tasks;

[0170] 2). Student assistant files: record the qualifications, work experience, task preferences, and professional expertise of student assistants to help match them with suitable auxiliary positions;

[0171] The functions of the work arrangement system are as follows:

[0172] 1). Teaching task allocation: assign courses to appropriate teachers based on their professional background, teaching experience and student needs;

[0173] 2). Arrangement of assistants: Reasonably arrange the support tasks of assistants according to the number of students, course progress and student needs;

[0174] 3) Scheduling management: Automated scheduling tools help teachers and teaching assistants schedule their work more efficiently and avoid resource conflicts;

[0175] The training and development functions are as follows:

[0176] 1). Teacher training plan: Regularly organize teacher training according to teaching needs and the development of new technologies, such as curriculum design, application of educational technology, and innovation of teaching methods;

[0177] 2) Training of teaching assistants: Provide training for teaching assistants, such as counseling skills, psychological counseling methods and communication skills, to ensure that they can effectively assist teachers and help students;

[0178] The performance evaluation and feedback functions are as follows:

[0179] 1). Teacher evaluation system: evaluate teachers’ teaching effectiveness through multi-dimensional data such as student feedback, teaching observation, and peer review;

[0180] 2) Assistant evaluation system: Based on the feedback from students and teachers, the performance of assistants is evaluated to ensure that they can effectively support students' learning;

[0181] 3) Automated scoring and feedback: Through data collection and analysis tools, timely feedback on the work performance and problems of teachers and teaching assistants;

[0182] The matching degree analysis function is as follows:

[0183] 1). Personalized matching system: by analyzing the learning needs of teachers and students, and the working ability of assistants, we ensure that the matching degree between each teacher and student, and each assistant and task is optimized;

[0184] 2). Real-time adjustment mechanism: flexibly adjust staffing according to students’ learning feedback, teachers’ workload, and the working status of assistants to ensure continuous optimization of matching degree;

[0185] The communication and collaboration tool functions are as follows:

[0186] 1). Communication platform for teachers and teaching assistants: providing online discussion, information sharing and work task notification functions to ensure seamless collaboration within the team;

[0187] 2). Work progress tracking: Provide teachers and teaching assistants with progress tracking tools to ensure that teaching and tutoring tasks are completed on time;

[0188] 3) Student feedback channel: Establish a student feedback mechanism so that teachers and teaching assistants can understand students' needs and feedback in a timely manner and make adjustments;

[0189] The resource allocation and scheduling module scientifically and reasonably schedules and allocates teaching resources, including but not limited to teachers, teaching assistants, teaching equipment and classrooms, according to students' needs, the status of existing resources and allocation priorities. Through an automated and intelligent scheduling system, the optimal allocation of resources is achieved, the efficiency of education management is improved, and the smooth progress of teaching activities is ensured. The resource allocation and scheduling module includes resource archive management functions, demand forecasting and analysis systems, resource scheduling and allocation functions, real-time monitoring and adjustment functions, data analysis and reporting functions and resource sharing and collaboration platforms;

[0190] The resource archive management functions are as follows:

[0191] 1). Teacher and assistant files: record the professional background, work experience and teaching tasks of teachers and assistants, and provide basic information for resource allocation;

[0192] 2). Physical resource archives: including archive information of classrooms, teaching equipment and teaching materials, recording the usage and maintenance status of various resources;

[0193] 3) Time and space archive: including teaching timetable, classroom arrangement and equipment availability time to ensure that resources are not used in conflict;

[0194] The demand forecasting and analysis system is specifically as follows:

[0195] 1). Student demand analysis: Analyze the teaching resource demand for each semester or academic year based on the number of students, course content, learning progress, and subject demand data;

[0196] 2) Dynamic demand forecasting: Based on historical data, course schedules, and student feedback, predict future resource demand and prepare in advance;

[0197] 3) Matching courses with student needs: Automatically analyze the teachers, assistants and other resources required for each course and match them with the actual situation;

[0198] The resource scheduling and allocation functions are as follows:

[0199] 1). Automatic resource scheduling system: Based on demand forecasts and existing resources, the system automatically generates resource allocation plans and rationally allocates teachers, teaching assistants, classrooms, and equipment;

[0200] 2) Priority setting and dynamic adjustment: Set resource allocation priorities based on factors such as the importance of the course, the urgency of student needs, and the scarcity of resources to ensure that key tasks are given priority in resource allocation;

[0201] 3) Conflict detection and mediation: In the process of resource allocation, resource conflicts are automatically detected, such as teacher scheduling conflicts and classroom time conflicts, and mediation or adjustment is carried out through the system;

[0202] The real-time monitoring and adjustment functions are as follows:

[0203] 1) Resource usage monitoring: Real-time monitoring of the usage of various resources, including the workload of teachers, the workload of teaching assistants, and the usage status of classrooms and equipment, to ensure the rational use of resources;

[0204] 2) Dynamic adjustment mechanism: When resources are in short supply or are allocated irrationally, the system automatically adjusts the resource allocation plan, such as adjusting the course schedule and dispatching spare classrooms to ensure the smooth completion of teaching tasks;

[0205] 3) Feedback and optimization: Based on resource usage and actual feedback, continuously optimize resource allocation plans to improve the efficiency and rationality of resource allocation;

[0206] The data analysis and reporting functions are as follows:

[0207] 1). Resource allocation analysis report: The system generates a detailed resource allocation report to help managers understand the usage of various resources and the effect of resource allocation;

[0208] 2) Benefit evaluation: Evaluate the effectiveness of resource allocation plans, such as teaching quality, student satisfaction, and resource utilization, to help further adjust resource allocation strategies;

[0209] 3) Forecasting and trend analysis: Based on historical data and trends, predict possible changes in resource demand in the future, and make resource planning and scheduling in advance;

[0210] The resource sharing and collaboration platform is as follows:

[0211] 1) Resource Sharing Platform: Provide a platform where teachers, teaching assistants and students can share resources such as classroom arrangements, equipment usage, teaching materials and learning materials to promote collaboration;

[0212] 2) Cross-departmental collaboration: Provide resource scheduling and coordination tools for various departments of the school, such as the Academic Affairs Office, Logistics, and Technical Support, to ensure unified and efficient resource allocation;

[0213] The student needs assessment module is to develop a practical education plan and allocate appropriate resources for students by fully understanding their individual needs. This module can not only help teachers better understand students' academic, psychological, emotional and social needs, but also ensure the rational allocation of educational resources. The student needs assessment module includes data collection function, demand analysis function, resource planning and allocation function and implementation and tracking function;

[0214] The data collection functions are as follows:

[0215] 1) Academic performance data: including but not limited to students’ grades, homework completion, class performance and test scores, to help assess students’ academic needs;

[0216] 2) Mental health assessment: Use questionnaires and interviews to understand students’ mental state, stress level, and emotional management ability;

[0217] 3) Learning styles and preferences: students’ learning styles, such as visual, auditory, hands-on, learning interests and learning habits;

[0218] 4). Social skills and emotional needs: students’ interpersonal skills, emotional regulation and prosocial behavior;

[0219] 5) Family background and support resources: Family environment, parental involvement, and family economic status are critical factors in influencing student needs;

[0220] 6) Special needs: such as physical, cognitive or behavioral needs, such as special educational needs and language barriers;

[0221] The demand analysis functions are as follows:

[0222] 1). Data aggregation and analysis: Classify and aggregate the collected data, analyze students’ strengths and weaknesses, potential and challenges, and identify their specific needs;

[0223] 2) Multi-dimensional assessment: Comprehensively consider academic, psychological, emotional and social aspects to determine where students need support;

[0224] 3) Prioritization: Prioritize students based on their needs and urgency, helping teachers and schools to clarify intervention directions;

[0225] The resource planning and allocation functions are as follows:

[0226] 1). Personalized education plan: Develop a personalized education plan for each student, specifically describing the student’s academic goals, support measures, and learning strategies;

[0227] 2) Resource allocation: According to demand analysis, allocate corresponding teaching resources, such as tutors, learning aids and psychological counseling;

[0228] 3) Curriculum design: adjust the teaching content, format and pace according to students’ needs, for example, setting up additional tutoring classes and special classroom activities;

[0229] 4). Support system: Establish a support network, including teachers, counselors, parents and peer support;

[0230] The implementation and tracking functions are as follows:

[0231] 1). Plan implementation: Carry out teaching activities according to the formulated personalized education plan and provide corresponding support services;

[0232] 2) Follow-up evaluation: Regularly evaluate students’ progress, monitor their academic performance, mental health and social performance, and adjust educational strategies and resource allocation in a timely manner;

[0233] 3) Feedback and adjustment: Through feedback from students, parents and teachers, we continuously adjust and optimize the education plan to ensure that it can meet the development needs of students;

[0234] The resource database is used to store all special education resource information, including but not limited to teachers, students, teaching materials, teaching aids and auxiliary equipment;

[0235] The user interface module can provide a simple and easy-to-use operation interface to facilitate their information query, resource application and progress tracking operations.

[0236] The system uses cloud computing technology, and all data storage and processing are concentrated in the cloud to ensure high availability and easy maintainability of data. By analyzing a large amount of historical data, the system can identify potential problems in students' learning and development, and provide predictive support for future resource allocation. In terms of resource allocation and scheduling, the system introduces machine learning algorithms to optimize resource allocation in an intelligent way to improve efficiency and effectiveness. In special education scenarios, IoT technology can be used to intelligently monitor the use of teaching aids and equipment, provide real-time feedback on equipment status, and avoid equipment damage or idleness. The system supports PC and mobile devices, and users can access the system through mobile phones, tablets and other devices anytime and anywhere to apply for resources, query and provide feedback;

[0237] The data sources also include the following:

[0238] Student behavior data: including but not limited to students’ online time, course participation and homework completion;

[0239] Teacher work data: including but not limited to teacher teaching time, course load and student feedback;

[0240] Resource usage data: including but not limited to utilization of classroom, laboratory, computer room and library resources;

[0241] Demand forecast data: Provides demand forecasts for various resources based on historical data and forecasting models.

[0242] The data analysis and evaluation module requires the construction of a technical architecture, as follows:

[0243] 1) Data layer: Use distributed storage systems such as Hadoop, Spark, and AWS to store large amounts of educational data;

[0244] 2) Computing layer: Use big data processing frameworks such as Spark and Flink to process and analyze data;

[0245] 3) Analysis layer: Use machine learning platforms such as TensorFlow and Scikit-learn to calculate demand forecasting and optimization algorithms;

[0246] 4). Visualization layer: Data visualization and report generation functions are realized through BI tools such as Power BI and Tableau.

[0247] The student needs assessment module uses structured questionnaires and semi-structured interviews to collect student needs data, where the questionnaires can be designed into multiple dimensions, such as academic, psychological, emotional and social, so as to have a comprehensive understanding of the students' comprehensive needs.

[0248] Let students conduct self-assessment of their own needs, and at the same time conduct peer evaluation through teachers, parents, classmates, etc., to form a comprehensive portrait of needs. Needs assessment should be a dynamic process, with regular evaluations and adjustments based on students' growth and learning.

[0249] Develop specific individualized education plans (IEPs) based on the assessment results, clarify students' short-term and long-term goals, as well as the resources, support and teaching methods needed to achieve these goals, and encourage teachers to work closely with parents to discuss students' needs and goals. Parent participation can provide more support for students, especially in terms of mental health and emotional needs. Regular parent meetings and teacher seminars are held to ensure that parents understand students' needs and education plans, and provide family support for students.

[0250] A professional team is formed by experts in education, psychology, sociology and other fields to provide students with all-round support. For students with special needs (such as autism, language barriers, etc.), consideration can be given to introducing specialized counselors, such as special education teachers, psychological counselors, etc.

[0251] The tool classification function is to integrate various tools into a learning platform through platform integration to ensure the compatibility and interoperability between tools. Before the tool integration, suitable digital learning tools can be selected for integration based on the results of demand analysis.

[0252] Train teachers so that they are familiar with the use of tools and can effectively guide students in using them. Provide students with training courses on the use of tools to ensure that they can use various learning support tools proficiently. Regularly monitor the use and effectiveness of tools, collect feedback from students, and understand the impact of tools on learning.

[0253] The teacher and assistant management module matches teachers with course content, assistants with student needs based on a data matching system and a data analysis system, ensuring that each task is completed by the most appropriate person.

[0254] The teacher and assistant management module can provide customized training and career development suggestions based on the personal development needs of teachers and assistants, and then formulate annual or semester training plans, including the improvement of teachers' teaching skills and the application of educational technology, and the psychological counseling skills and communication skills of assistants.

[0255] The teacher and assistant management module can continuously optimize the matching of personnel by analyzing students' learning progress, teachers' work pressure and assistants' work performance data.

[0256] The data analysis and evaluation module can also enable the system to dynamically adjust resource allocation according to resource usage, student needs and remaining resources through big data analysis and artificial intelligence algorithms to ensure optimal resource utilization. The dynamic adjustment of resource allocation needs to refer to various relevant information and collect a large amount of historical data and real-time data as a reference, so as to ensure the rationality of resource allocation. When collecting data as a reference, the following should be noted:

[0257] 1). Data cleaning: pre-process the collected data by removing redundancy, filling missing values ​​and eliminating outliers. This step can ensure the quality of the data;

[0258] 2) Data integration and storage: Data from different sources are integrated, formatted in a unified way, and stored on a suitable big data platform;

[0259] 3) Data analysis and modeling: Use statistical analysis and machine learning methods to analyze data and extract valuable information. For example, cluster analysis can be used to identify students' demand patterns and time series analysis can be used to predict resource usage.

[0260] 4) Demand forecasting model: By analyzing historical data, students’ future needs can be predicted. Common methods include time series analysis (such as ARIMA), regression analysis, and deep learning.

[0261] 5) Resource utilization optimization model: Use optimization algorithms such as linear programming or integer programming to solve resource allocation problems. For example, a linear programming (LP) model can be used to establish the optimization problem of resource allocation. The formula is as follows to optimize the configuration of resources to minimize idle resources or maximize resource utilization benefits:

[0262] Objective function:

[0263]

[0264] Constraints:

[0265]

[0266] Among them, c i is the unit value of the i-th resource, x i is the amount allocated to the i-th resource, a ij is the relationship between the i-th resource and the j-th constraint, b j is the upper limit of the j-th constraint.

[0267] The dynamic adjustment of resource allocation can also be based on a machine learning algorithm - reinforcement learning. The formula is as follows. The optimal strategy is learned through interaction with the environment. The system tries different resource allocation strategies, obtains feedback, and adjusts the strategy based on the feedback to optimize long-term rewards:

[0268] Q(δ,a)←Q(δ,a)+a[r+γmaxQ(δ ′ , a ′ )-Q(δ,a)]

[0269] Among them, Q(δ, a) is the value of taking action a in state δ, a is the learning rate, r is the current reward, γ is the discount factor, δ ′ is the next state, a ′ It’s the next move.

[0270] The system can generate personalized resource allocation plans based on the specific needs of each student, such as learning disabilities, physical disabilities, etc., to ensure accurate matching and efficient use of resources. Through big data analysis and artificial intelligence algorithms, the system can dynamically adjust resource allocation based on resource usage, student needs and remaining resources to ensure optimal resource utilization. The system can monitor resource usage in real time, generate real-time reports, promptly identify problems in resource usage and provide feedback to ensure the quality of teaching and the rationality of resource allocation. The system supports the collaborative work of multiple users such as schools, parents, and educational institutions, shares information and resources through the platform, and promotes educational collaboration and resource sharing. The system can intelligently predict future resource demand trends through analysis of historical data, and provide data support for resource planning of schools and education departments. To ensure that all special education groups can use it conveniently, the system provides barrier-free design, such as voice recognition, text-to-speech, screen magnification and other functions. The system uses advanced encryption technology to ensure the security and privacy of user data and prevent the leakage of sensitive information.

[0271] Here are the steps:

[0272] Educators enter students' personalized needs information into the system based on their specific circumstances, such as intellectual disabilities, language barriers, motor disabilities, etc.

[0273] The system automatically analyzes demand information, performs intelligent matching based on existing resources, generates resource allocation plans, and provides them to administrators for approval;

[0274] After the administrator approves the application, the system will automatically schedule resources and allocate them to each student and teacher. The system will also automatically generate an execution plan and notify relevant personnel;

[0275] The system monitors resource usage in real time. When problems are found in resource allocation, the system will provide feedback and make dynamic adjustments.

[0276] At the end of each semester, the system will generate a detailed resource usage report to help schools and education departments evaluate the effectiveness of resource allocation and improve subsequent resource planning.

[0277] Through intelligent resource allocation and scheduling, the efficiency of resource use is greatly improved and waste is reduced. The system tailors education plans according to the special needs of each student to ensure that each student can get appropriate educational resources and improve educational equity. The automated management function reduces the need for manual intervention, reduces management costs, and improves management efficiency. The system can track resource usage in real time, identify problems in a timely manner and provide solutions to ensure the smooth progress of special education work;

[0278] Through big data analysis, the system can provide a scientific basis for educational decision-making and help educational institutions formulate more accurate resource plans. Parents, teachers, schools and education departments can share information through the platform, strengthen communication and cooperation, and form synergy. Accurate resource allocation and optimized educational support can improve students' learning outcomes and promote their physical and mental health development.

[0279] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A special education resource management system, characterized in that: include: Data analysis and evaluation module, learning support tool module, teacher and assistant management module, resource allocation and scheduling module, student needs assessment module, resource database and user interface module; The data analysis and evaluation module is responsible for real-time monitoring and evaluation of the use effect of resources, and provides accurate data support for decision makers. The data analysis and evaluation module includes data collection and monitoring functions, data processing and cleaning functions, and data analysis and evaluation functions. The data collection part of the data collection and monitoring function integrates multiple data sources, including but not limited to students' learning progress, teachers' teaching load and resource usage, so as to ensure that the system obtains comprehensive and timely feedback data, while the monitoring part can track the use of resources in real time, and can automatically collect resource consumption, usage frequency and student satisfaction information; The data processing and cleaning function is used to clean and preprocess the collected data to ensure the accuracy and consistency of the data. The data cleaning operation includes: 1). Deduplication: remove duplicate data to ensure the uniqueness of each data record; 2) Filling missing values: Processing missing data. Common methods include mean filling, median filling or predicting missing values ​​through models. 3) Outlier detection and elimination: Identify abnormal data points through statistical methods or machine learning algorithms, and eliminate or correct these abnormal data; The analysis function in the data analysis and evaluation function is to extract effective information on resource use and conduct evaluation through different analysis methods. The main steps include: 1) Resource utilization effect evaluation: evaluate the actual use effect of resources to identify whether the resources are fully utilized, whether there is waste or insufficient use; 2) Student demand analysis: Combine students’ learning data to analyze students’ needs for different subjects and resources, and identify changing trends in resource needs; 3) Teacher resource allocation assessment: assess the teacher resource allocation and analyze whether there are teachers who are overburdened or idle; 4) Analysis of the matching degree between demand and supply: By comparing the matching degree between the supply of resources and the needs of students, the rationality of resource allocation can be evaluated; The learning support tool module provides a personalized and flexible learning experience by integrating multiple digital learning tools to help students better master knowledge and skills. The module can provide diversified support for students with different learning needs to promote their learning effects. The learning support tool module includes tool classification function, user interface function, data analysis and feedback function and support and training function; The tool classification functions are as follows: 1). Speech recognition tool: used to convert speech into text to help students with dictation, recording and language learning; 2) Image recognition tools: can be used to identify and analyze image content to help students understand visual information, such as charts and pictures; 3). Supplementary teaching materials: including but not limited to e-books, interactive teaching materials and video tutorials, providing multimedia learning resources; 4). Learning Management System (LMS): used to manage learning content, track learning progress and provide feedback; 5). Collaboration tools: such as online discussion platforms and shared document tools to promote collaboration and communication among students; 6). Assessment tools: used for online testing, quizzes and evaluation of students’ learning outcomes; The user interface functions are as follows: 1). User-friendly interface: Design a simple and intuitive interface to help students quickly find the tools and resources they need; 2). Personalization: Allow students to customize the interface according to their needs and preferences, such as font size and color theme; The data analysis and feedback functions are as follows: 1). Learning data collection: record students’ usage, learning progress, grades and other data; 2) Real-time feedback mechanism: Provide instant feedback based on students’ performance to help them adjust their learning strategies; The support and training functions described are: User Guide: Provides detailed tool usage instructions and video tutorials to help students become familiar with each tool; The teacher and assistant management module ensures that the work arrangements of teachers and assistants are reasonable, their training is appropriate, and their work performance is effectively evaluated through systematic management and analysis, thereby maximizing the quality of education. Through precise management and data analysis, this module can ensure that the roles and responsibilities of teachers and assistants in teaching can be fully utilized and matched with student needs. The teacher and assistant management module includes personnel file management functions, work arrangement system functions, training and development functions, performance evaluation and feedback functions, matching analysis functions, and communication and collaboration tool functions; The personnel file management functions are as follows: 1). Teacher files: record the teacher's basic information, academic background, teaching experience and subject data, providing a basis for arranging teaching tasks; 2). Student assistant files: record the qualifications, work experience, task preferences, and professional expertise of student assistants to help match them with suitable auxiliary positions; The functions of the work arrangement system are as follows: 1). Teaching task allocation: assign courses to appropriate teachers based on their professional background, teaching experience and student needs; 2). Arrangement of assistants: Reasonably arrange the support tasks of assistants according to the number of students, course progress and student needs; 3) Scheduling management: Automated scheduling tools help teachers and teaching assistants schedule their work more efficiently and avoid resource conflicts; The training and development functions are as follows: 1). Teacher training plan: Regularly organize teacher training according to teaching needs and the development of new technologies, such as curriculum design, application of educational technology, and innovation of teaching methods; 2) Training of teaching assistants: Provide training for teaching assistants, such as counseling skills, psychological counseling methods and communication skills, to ensure that they can effectively assist teachers and help students; The performance evaluation and feedback functions are as follows: 1). Teacher evaluation system: evaluate teachers’ teaching effectiveness through multi-dimensional data such as student feedback, teaching observation, and peer review; 2) Student assistant evaluation system: Based on the feedback from students and teachers, the performance of student assistants is evaluated to ensure that they can effectively support students’ learning; 3) Automated scoring and feedback: Through data collection and analysis tools, timely feedback on the work performance and problems of teachers and teaching assistants; The matching degree analysis function is as follows: 1). Personalized matching system: by analyzing the learning needs of teachers and students, and the working ability of assistants, we ensure that the matching degree between each teacher and student, and each assistant and task is optimized; 2). Real-time adjustment mechanism: flexibly adjust staffing according to students’ learning feedback, teachers’ workload, and the working status of assistants to ensure continuous optimization of matching degree; The communication and collaboration tool functions are as follows: 1). Communication platform for teachers and teaching assistants: providing online discussion, information sharing and work task notification functions to ensure seamless collaboration within the team; 2). Work progress tracking: Provide teachers and teaching assistants with progress tracking tools to ensure that teaching and tutoring tasks are completed on time; 3) Student feedback channel: Establish a student feedback mechanism so that teachers and teaching assistants can understand students' needs and feedback in a timely manner and make adjustments; The resource allocation and scheduling module scientifically and reasonably schedules and allocates teaching resources, including but not limited to teachers, teaching assistants, teaching equipment and classrooms, according to students' needs, the status of existing resources and allocation priorities. Through an automated and intelligent scheduling system, the optimal allocation of resources is achieved, the efficiency of education management is improved, and the smooth progress of teaching activities is ensured. The resource allocation and scheduling module includes resource archive management functions, demand forecasting and analysis systems, resource scheduling and allocation functions, real-time monitoring and adjustment functions, data analysis and reporting functions and resource sharing and collaboration platforms; The resource archive management functions are as follows: 1). Teacher and assistant files: record the professional background, work experience and teaching tasks of teachers and assistants, and provide basic information for resource allocation; 2). Physical resource archives: including archive information of classrooms, teaching equipment and teaching materials, recording the usage and maintenance status of various resources; 3) Time and space archive: including teaching timetable, classroom arrangement and equipment availability time to ensure that resources are not used in conflict; The demand forecasting and analysis system is specifically as follows: 1). Student demand analysis: Analyze the teaching resource demand for each semester or academic year based on the number of students, course content, learning progress, and subject demand data; 2) Dynamic demand forecasting: Based on historical data, course schedules, and student feedback, predict future resource demand and prepare in advance; 3) Matching courses with student needs: Automatically analyze the teachers, assistants and other resources required for each course and match them with the actual situation; The resource scheduling and allocation functions are as follows: 1). Automatic resource scheduling system: Based on demand forecasts and existing resources, the system automatically generates resource allocation plans and rationally allocates teachers, teaching assistants, classrooms, and equipment; 2) Priority setting and dynamic adjustment: Set resource allocation priorities based on factors such as the importance of the course, the urgency of student needs, and the scarcity of resources to ensure that key tasks are given priority in resource allocation; 3) Conflict detection and mediation: In the process of resource allocation, resource conflicts are automatically detected, such as teacher scheduling conflicts and classroom time conflicts, and mediation or adjustment is carried out through the system; The real-time monitoring and adjustment functions are as follows: 1) Resource usage monitoring: Real-time monitoring of the usage of various resources, including the workload of teachers, the workload of teaching assistants, and the usage status of classrooms and equipment, to ensure the rational use of resources; 2) Dynamic adjustment mechanism: When resources are in short supply or are allocated irrationally, the system automatically adjusts the resource allocation plan, such as adjusting the course schedule and dispatching spare classrooms to ensure the smooth completion of teaching tasks; 3) Feedback and optimization: Based on resource usage and actual feedback, continuously optimize resource allocation plans to improve the efficiency and rationality of resource allocation; The data analysis and reporting functions are as follows: 1). Resource allocation analysis report: The system generates a detailed resource allocation report to help managers understand the usage of various resources and the effect of resource allocation; 2) Benefit evaluation: Evaluate the effectiveness of resource allocation plans, such as teaching quality, student satisfaction, and resource utilization, to help further adjust resource allocation strategies; 3) Forecasting and trend analysis: Based on historical data and trends, predict possible changes in resource demand in the future, and make resource planning and scheduling in advance; The resource sharing and collaboration platform is as follows: 1) Resource Sharing Platform: Provide a platform where teachers, teaching assistants and students can share resources such as classroom arrangements, equipment usage, teaching materials and learning materials to promote collaboration; 2) Cross-departmental collaboration: Provide resource scheduling and coordination tools for various departments of the school, such as the Academic Affairs Office, Logistics, and Technical Support, to ensure unified and efficient resource allocation; The student needs assessment module is to develop a practical education plan and allocate appropriate resources for students by fully understanding their individual needs. This module can not only help teachers better understand students' academic, psychological, emotional and social needs, but also ensure the rational allocation of educational resources. The student needs assessment module includes data collection function, demand analysis function, resource planning and allocation function and implementation and tracking function; The data collection functions are as follows: 1) Academic performance data: including but not limited to students’ grades, homework completion, class performance and test scores, to help assess students’ academic needs; 2) Mental health assessment: Use questionnaires and interviews to understand students’ mental state, stress level, and emotional management ability; 3) Learning styles and preferences: students’ learning styles, such as visual, auditory, hands-on, learning interests and learning habits; 4). Social skills and emotional needs: students’ interpersonal skills, emotional regulation and prosocial behavior; 5) Family background and support resources: Family environment, parental involvement, and family economic status are critical factors in influencing student needs; 6) Special needs: such as physical, cognitive or behavioral needs, such as special educational needs and language barriers; The demand analysis functions are as follows: 1). Data aggregation and analysis: Classify and aggregate the collected data, analyze students’ strengths and weaknesses, potential and challenges, and identify their specific needs; 2) Multi-dimensional assessment: Comprehensively consider academic, psychological, emotional and social aspects to determine where students need support; 3) Prioritization: Prioritize students based on their needs and urgency, helping teachers and schools to clarify intervention directions; The resource planning and allocation functions are as follows: 1). Personalized education plan: Develop a personalized education plan for each student, specifically describing the student’s academic goals, support measures, and learning strategies; 2) Resource allocation: According to demand analysis, allocate corresponding teaching resources, such as tutors, learning aids and psychological counseling; 3) Curriculum design: adjust the teaching content, format and pace according to students’ needs, for example, setting up additional tutoring classes and special classroom activities; 4). Support system: Establish a support network, including teachers, counselors, parents and peer support; The implementation and tracking functions are as follows: 1). Plan implementation: Carry out teaching activities according to the formulated personalized education plan and provide corresponding support services; 2) Follow-up evaluation: Regularly evaluate students’ progress, monitor their academic performance, mental health and social performance, and adjust educational strategies and resource allocation in a timely manner; 3) Feedback and adjustment: Through feedback from students, parents and teachers, we continuously adjust and optimize the education plan to ensure that it can meet the development needs of students; The resource database is used to store all special education resource information, including but not limited to teachers, students, teaching materials, teaching aids and auxiliary equipment; The user interface module can provide a simple and easy-to-use operation interface to facilitate their information query, resource application and progress tracking operations.

2. The special education resource management system according to claim 1, characterized in that: The data sources also include the following: Student behavior data: including but not limited to students’ online time, course participation and homework completion; Teacher work data: including but not limited to teacher teaching time, course load and student feedback; Resource usage data: including but not limited to utilization of classroom, laboratory, computer room and library resources; Demand forecast data: Provides demand forecasts for various resources based on historical data and forecasting models.

3. The special education resource management system according to claim 1, characterized in that: The data analysis and evaluation module requires the construction of a technical architecture, as follows: 1) Data layer: Use distributed storage systems such as Hadoop, Spark, and AWS to store large amounts of educational data; 2) Computing layer: Use big data processing frameworks such as Spark and Flink to process and analyze data; 3) Analysis layer: Use machine learning platforms such as TensorFlow and Scikit-learn to calculate demand forecasting and optimization algorithms; 4). Visualization layer: Data visualization and report generation functions are realized through BI tools such as Power BI and Tableau.

4. The special education resource management system according to claim 1, characterized in that: The student needs assessment module uses structured questionnaires and semi-structured interviews to collect student needs data, where the questionnaires can be designed into multiple dimensions, such as academic, psychological, emotional and social, so as to have a comprehensive understanding of the students' comprehensive needs.

5. The special education resource management system according to claim 1, characterized in that: The tool classification function is to integrate various tools into a learning platform through platform integration to ensure the compatibility and interoperability between tools. Before the tool integration, suitable digital learning tools can be selected for integration based on the results of demand analysis.

6. The special education resource management system according to claim 1, characterized in that: The teacher and assistant management module matches teachers with course content, assistants with student needs based on a data matching system and a data analysis system, ensuring that each task is completed by the most appropriate person.

7. The special education resource management system according to claim 1, characterized in that: The teacher and assistant management module can provide customized training and career development suggestions based on the personal development needs of teachers and assistants, and then formulate annual or semester training plans, including the improvement of teachers' teaching skills and the application of educational technology, and the psychological counseling skills and communication skills of assistants.

8. The special education resource management system according to claim 1, characterized in that: The teacher and assistant management module can continuously optimize the matching of personnel by analyzing students' learning progress, teachers' work pressure and assistants' work performance data.

9. The special education resource management system according to claim 1, characterized in that: The data analysis and evaluation module can also enable the system to dynamically adjust resource allocation according to resource usage, student needs and remaining resources through big data analysis and artificial intelligence algorithms to ensure optimal resource utilization. The dynamic adjustment of resource allocation needs to refer to various relevant information and collect a large amount of historical data and real-time data as a reference, so as to ensure the rationality of resource allocation. When collecting data as a reference, the following should be noted: 1). Data cleaning: pre-process the collected data by removing redundancy, filling missing values ​​and eliminating outliers. This step can ensure the quality of the data; 2) Data integration and storage: Data from different sources are integrated, formatted in a unified way, and stored on a suitable big data platform; 3) Data analysis and modeling: Use statistical analysis and machine learning methods to analyze data and extract valuable information. For example, cluster analysis can be used to identify students' demand patterns and time series analysis can be used to predict resource usage. 4) Demand forecasting model: By analyzing historical data, students’ future needs can be predicted. Common methods include time series analysis (such as ARIMA), regression analysis, and deep learning. 5) Resource utilization optimization model: Use optimization algorithms such as linear programming or integer programming to solve resource allocation problems. For example, a linear programming (LP) model can be used to establish the optimization problem of resource allocation. The formula is as follows to optimize the configuration of resources to minimize idle resources or maximize resource utilization benefits: Objective function: Constraints: Among them, c i is the unit value of the i-th resource, x i is the amount allocated to the i-th resource, a ij is the relationship between the i-th resource and the j-th constraint, b j is the upper limit of the j-th constraint.

10. The special education resource management system according to claim 9, characterized in that: The dynamic adjustment of resource allocation can also be based on a machine learning algorithm - reinforcement learning. The formula is as follows. The optimal strategy is learned through interaction with the environment. The system tries different resource allocation strategies, obtains feedback, and adjusts the strategy based on the feedback to optimize long-term rewards: Q(δ,a)←Q(δ,a)+a[r+γmaxQ(δ′,a′)-Q(δ,a)] Where Q(δ, a) is the value of taking action a in state δ, a is the learning rate, r is the current reward, γ is the discount factor, δ′ is the next state, and a′ is the next action.

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