Intelligent teaching management method and system based on big data

Abstract

The invention discloses an intelligent teaching management method and system based on big data, and relates to the technical field of data processing, and the method comprises the following steps: distributing different classes at the same time into a plurality of use areas of a stadium according to a teaching demand principle; judging whether the area where the stadium is located has a rainwater feature or not, and if yes, dividing the indoor space of the stadium into a plurality of teaching areas according to the exit route; according to the invention, a complete and scientific obstacle influence assessment system is constructed, the static size and dynamic motion characteristics of the obstacle are comprehensively considered, and the safety of each teaching area is accurately assessed through the total obstacle coefficient of each teaching area; dangerous areas are excluded, safety risks caused by obstacles are effectively reduced, a safe and reliable space environment is created for teaching activities, and the safety and rationality of indoor space use of the stadium are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a teaching intelligent management method and system based on big data. Background Art

[0002] With the rapid development of educational informatization and the continuous expansion of school size, traditional stadium teaching management methods have gradually exposed many problems. In terms of teaching resource allocation, the previous reliance on manual experience to allocate stadium usage areas made it difficult to accurately consider the course types, teaching equipment requirements, and differences in student age and physical fitness across different classes. Equipment conflicts and venue mismatches often occurred, resulting in low teaching efficiency and inadequate utilization of professional stadium resources.

[0003] Traditional management practices lack effective weather monitoring and indoor space utilization strategies when responding to environmental changes. During inclement weather, such as rain, it's impossible to determine whether the stadium is suitable for use, and there's no scientific indoor zoning plan. Consequently, teaching activities are forced to be interrupted or conducted in environments with potential safety risks.

[0004] Furthermore, obstacles within the stadium are complexly distributed, and traditional management practices lack a systematic assessment. This inadequate consideration of the static dimensions and dynamic motion characteristics of obstacles prevents accurate assessment of the safety of teaching areas, posing significant safety risks. Furthermore, traditional approaches lack scientific calculations and prioritization mechanisms for matching the space requirements of different classes with the available teaching areas, making it difficult to optimize the allocation of teaching space resources. When the class activity area is larger than the actual area of a single safety zone, the normal conduct of teaching activities is difficult to ensure. Therefore, ensuring the rational use of the stadium area is a pressing issue. Summary of the Invention

[0005] The purpose of this invention is to solve the problems existing in traditional stadium teaching management, such as unreasonable allocation of teaching resources, insufficient ability to cope with severe weather, lack of safety assessment of teaching areas, and unscientific allocation of teaching space resources, and propose a new teaching intelligent management method and system based on big data.

[0006] To achieve the above objectives, the present invention adopts the following technology to develop a teaching intelligent management method based on big data, which includes the following steps: Allocate different classes at the same time to multiple use areas of the stadium according to teaching needs; Determine whether the area where the stadium is located has rain characteristics. If so: The indoor space of the stadium is divided into multiple teaching areas according to the exit routes. The total obstacle coefficient in each teaching area is obtained. A comprehensive evaluation is conducted on each teaching area based on the total obstacle coefficient, thereby dividing the teaching area into a safe area and a dangerous area. The steps for obtaining the obstacle coefficient are as follows: obtaining the static influence factor and dynamic weight of each obstacle in each teaching area, thereby obtaining the obstacle coefficient of each obstacle in the teaching area; summing the obstacle coefficients of all obstacles in the teaching area to obtain the total obstacle coefficient of the teaching area; Calculate the activity areas of different classes, calculate the actual area of the safety zone, and match the activity areas of different classes with the actual area of the safety zone; If the class activity area is larger than the actual area of a single safety zone, the difference is determined and recorded as the safety gap area; If the obstacle in a single safety zone is a cleaning vehicle, obtain the area occupied by the obstacle and the total obstacle coefficient, thereby defining the number of cleaning vehicles that need to be removed from the single safety zone; Collect the start time of each class activity to determine the deadline for the cleanup vehicle to leave; The total time required to complete the task of the cleaning vehicle is calculated based on the task, and whether it can be evacuated from a single safe area is determined based on the total time and the remaining available time of the cleaning vehicle, so as to achieve a match between the activity area and the safe area.

[0007] The teaching demand principle includes the following steps: Obtain the course types, teaching equipment requirements, and student age and physical fitness of different classes; The gymnasium is allocated according to the course types, teaching equipment requirements, and the age and physical fitness of students in different classes.

[0008] The obstacle coefficient is determined by the following steps: Determine the length, width, height parameters and projection correction coefficient of obstacles in each teaching area, and thus obtain the static impact factor of each obstacle; Determine the speed of each obstacle in each teaching area, thereby determining the dynamic level corresponding to each obstacle. According to the dynamic level of each obstacle, determine the dynamic weight of each obstacle. Based on the static influence factor of each obstacle, obtain the obstacle coefficient of each obstacle in the teaching area. The obstacle coefficients of each obstacle in each teaching area are summed to obtain the total obstacle coefficient of each teaching area.

[0009] If the obstacle causes the path to be changed due to demand, the teaching area through which the obstacle passes is obtained, and the total obstacle coefficient of the teaching area through which the obstacle passes is recalculated.

[0010] The activity areas of different classes are obtained by the following steps: Obtain course activity coefficients and number of participants in different classes; The activity areas of different classes are obtained by weighted multiplication of the course activity coefficients and the number of participants in the activities of different classes.

[0011] The actual area of the safety zone is obtained by the following steps: Obtain the total area of the safety zone, the total obstacle coefficient of each obstacle, and the area of each obstacle; Extract the total area of the safety zone, the total obstacle coefficient of each obstacle, and the area of each obstacle; Multiply the area of each obstacle by the total obstacle coefficient of each obstacle and add them up to get the total blocking area; The actual area of the safety zone is obtained by subtracting the total area of the safety zone from the total blocking area.

[0012] The steps of determining whether the area where the stadium is located has rain characteristics are as follows: Get the ground humidity change value at the stadium; If the ground humidity change value at the stadium location exceeds the safety standard, it is determined that rain characteristics exist.

[0013] The method for matching the activity areas of different classes with the actual areas of the safety zone includes: Get the priority of each class; Classes from high priority to low priority are matched with the safe area closest to the actual area based on the activity area.

[0014] Based on the start time of each class activity, the earliest start time of all classes is used as the benchmark, and the safety buffer time is set in advance as the deadline for the cleaning vehicle to complete the cleaning and withdraw from the safe area; The cleaning points in the stadium are grouped based on the calculated number of cleaning vehicles. A density-based clustering algorithm is used to perform cluster analysis based on the geographical coordinates of the cleaning points. The neighborhood radius ϵ and the minimum number of points parameter MinPts are set to group geographically close cleaning points into a group, with each group corresponding to a cleaning vehicle. The total time required for each vehicle to complete the task is calculated based on the cleaning vehicle speed, path length, and cleaning point operation time. The cleaning point operation time is preset to different times according to the cleaning task type. Compare the total time taken by each cleaning vehicle to complete the task with the remaining available time of the cleaning vehicle. If the total time taken by a cleaning vehicle to complete the task is greater than the remaining available time of the cleaning vehicle, readjust its cleaning point allocation and route planning, or increase the number of cleaning vehicles.

[0015] The teaching intelligent management system based on big data includes: Data collection unit: responsible for collecting course information of different classes, number of students, stadium weather data and water level data around the stadium; Data analysis and processing unit: processes and analyzes the collected data, performs operations such as determining rain characteristics, calculating the actual area of class activity areas and safe areas, evaluating the safety of teaching areas, and allocating and matching class areas according to teaching needs; Area allocation and scheduling unit: According to the results of the data analysis and processing unit, different classes are allocated to the stadium use area or safety area, and the classes are split and reallocated; Communication and feedback unit: responsible for data communication with the monitoring equipment and meteorological department in the stadium, and receiving feedback information from teachers and students.

[0016] In summary, due to the use of the above-mentioned technology based on big data intelligent teaching management method and system, the beneficial effects of the present invention are: 1. Accurately allocate teaching resources: Accurately collect class course types, teaching equipment requirements, student age and physical fitness data, and use them as the basis for allocating teaching areas. This enables intelligent matching of teaching resources with course types, effectively avoiding equipment conflicts and venue mismatches, significantly improving teaching efficiency, and fully tapping the potential of professional stadium resources, laying a solid foundation for the safe and efficient conduct of teaching activities. 2. Improve the ability to respond to environmental changes: Establish an intelligent rain feature judgment mechanism based on ground humidity changes, enabling real-time and accurate perception of the impact of weather changes on stadium use. When rain features are detected, the stadium's indoor space is scientifically divided according to building specifications and three-dimensional models to ensure the safe evacuation of teachers and students in inclement weather, while also achieving efficient use of indoor space and ensuring that teaching activities can proceed in an orderly manner even in complex environments. 3. Ensure the safety of teaching areas: Build a complete and scientific obstacle impact assessment system, comprehensively consider the static size and dynamic motion characteristics of obstacles, and accurately assess the safety of each teaching area through the total obstacle coefficient of each teaching area. Exclude dangerous areas, effectively reduce safety risks caused by obstacles, create a safe and reliable space environment for teaching activities, and improve the safety and rationality of the use of indoor space in the stadium; 4. Optimize the allocation of teaching space resources: By calculating the actual area of the class activity area and the safety zone, and combining it with a priority mechanism for intelligent matching, the refined allocation of teaching space resources is achieved. This can fully meet the teaching space needs of different classes, while also improving the utilization rate of the safety zone and avoiding resource waste. In cases where the class activity area is larger than a single safety zone, the system calculates the area of the safety gap and intelligently plans the evacuation plan for the cleaning vehicle. It combines the DBSCAN clustering algorithm to group cleaning points, optimize the cleaning vehicle path and task allocation, and allocate nearby cleaning points to other vehicles through the clustering algorithm, while planning the shortest path. By setting safety buffer time and task time constraints, it ensures that the cleaning work is completed before students arrive, ensuring the smooth progress of activities and improving the flexibility of teaching management and emergency response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shown is a flow chart of the method of the present invention; Figure 2 Shown is a system block diagram of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technology in the embodiments of the present invention, namely, the teaching intelligent management method and system based on big data. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] In order to more clearly and intuitively demonstrate the practical application effects and advantages of the intelligent teaching management method based on big data of the present invention and verify its feasibility and effectiveness, the present invention is further described below with reference to the following examples. Through specific scenario simulation and data calculation, how this method plays a role in actual teaching management is explained in detail to help readers better understand the technical details and practical value of the invention. The present invention is further described below with reference to the following examples; In daily teaching scenarios, school stadiums often need to meet the teaching needs of multiple classes at the same time; Example 1 (see Figure 1 ): The teaching intelligent management method based on big data provided by the present invention comprises the following steps: In daily teaching scenarios, school stadiums often need to meet the teaching needs of multiple classes at the same time. The present invention first allocates different classes at the same time to multiple use areas in the stadium according to the teaching needs principle; The teaching needs principle includes the following steps: Through the school's academic management system, physical education curriculum scheduling system, and student physical health record system, key data such as course types, teaching equipment requirements, and student age and physical fitness are obtained for different classes. For example, for physical education courses, the system will record different course types such as basketball, football, and yoga. At the same time, it will clearly define the teaching equipment required for each course, such as basketballs and basketball stands for basketball classes, and footballs and goals for football classes. Student age and physical fitness data are obtained from the school's student health records, which include information such as age, height, weight, and physical fitness test scores. Based on the collected data on different classes' course types, teaching equipment requirements, and students' age and physical fitness, intelligent algorithms are used to allocate stadium usage areas to different classes. For example, for basketball classes, since they require a larger venue and specific basketball hoop equipment, the system will prioritize allocating basketball courts to basketball classes to ensure that the teaching equipment is accurately matched to the course type. At the same time, taking into account the differences in students' age and physical fitness, classes with younger and less physically fit students will be assigned to relatively flat areas with fewer obstacles to ensure the safety and effectiveness of teaching activities. By accurately collecting class teaching demand data, we can achieve intelligent matching of teaching resources and course types, avoid the problem of low teaching efficiency caused by equipment conflicts or venue mismatches in different classes, maximize the use of the stadium's professional venue resources, and make regional allocations based on students' age and physical characteristics, laying the foundation for the safety and effectiveness of subsequent teaching activities.

[0020] Determine whether there is rain in the area where the stadium is located. This is achieved by the following steps: Using humidity sensors installed on the stadium floor, the system captures real-time humidity changes at the stadium. The system has a preset safety threshold. If the humidity changes at the stadium exceed this threshold, the system determines that rain is present. For example, if continuous rainfall causes the ground humidity to rise rapidly and exceed the preset safety threshold (e.g., 80%), the system immediately triggers a rain feature detection. When rain features are present: The indoor space of the stadium is divided into multiple teaching areas according to the exit routes, as follows: Obtain a 3D model of the indoor space through architectural drawings or laser scanning technology. Laser scanning technology can quickly and accurately obtain detailed 3D data of the stadium's indoor space, including spatial shape, dimensions, height, and other information. At the same time, combined with architectural drawings, the distribution of main exits, backup exits, and evacuation routes can be clearly identified, thereby accurately locating the location of each exit in the stadium's indoor space. Refer to the requirements of "JGJ31-2003" and divide the teaching area according to the location of each exit. If multiple exits are interconnected, the areas between the multiple exits will be divided equally; ensure that each teaching area has a reasonable evacuation path and spatial layout; for example, if there are three interconnected exits in the stadium, the system will divide the area between the three exits into three teaching areas. Each area is guaranteed to have an independent and unobstructed evacuation channel to ensure that teachers and students can evacuate safely in an emergency.

[0021] By establishing an intelligent rainwater characterization mechanism based on ground humidity fluctuations, this invention can dynamically detect the impact of weather changes on stadium usage in real time, ensuring that teaching activities can continue in an orderly manner even in inclement weather. Furthermore, by zoning indoor areas based on building codes and three-dimensional models, it not only ensures the safe evacuation of teachers and students in emergency situations, but also achieves efficient use of indoor space, providing a reliable solution for teaching management in complex environments.

[0022] In order to accurately assess the safety of the teaching area, the present invention systematically analyzes the obstacles in each teaching area and determines its obstacle coefficient. The specific steps are as follows: Determine the length, width, height parameters and projection correction coefficient of obstacles in each teaching area, and thus obtain the static impact factor of each obstacle; The speed of each obstacle in each teaching area is determined, and the dynamic level corresponding to each obstacle is determined. According to the dynamic level of each obstacle, the dynamic weight of each obstacle is determined. Based on the static influence factor of each obstacle, the obstacle coefficient of each obstacle in the teaching area is obtained, as shown below:

[0023]

[0024] Where: is the static impact factor of the obstacle; the length of the obstacle ,Width ,high , characterizes the actual spatial size of the object; is the projection correction factor (1 for vertical projection and 1.2-1.5 for oblique projection); The reference area of the teaching area (unit: m2) is used for normalization processing, usually the total area of the teaching area.

[0025]

[0026]

[0027]

[0028] Where: is the dynamic weight; Dynamic level is the obstacle coefficient of the obstacle; is the weight coefficient; The obstacle coefficients of each obstacle in each teaching area are summed to obtain the total obstacle coefficient of each teaching area, as shown below:

[0029] Where: is the total obstacle coefficient for each teaching area; is the obstacle coefficient of the i-th obstacle in the teaching area, Indicates the total number of obstacles in the teaching area; If the obstacle causes the path to change due to demand (for example, the automatic cleaning robot in the stadium, the material transport AGV car due to task requirements), then obtain the teaching area through which the obstacle passes, and recalculate the total obstacle coefficient of the teaching area through which the obstacle passes , ensuring the real-time and accuracy of the data; if the obstacle changes its path due to task requirements, such as the automatic cleaning vehicle in the stadium, the positioning sensors and motion monitoring equipment installed in the stadium are used to obtain the teaching area passed by the obstacle, and the total obstacle coefficient of the teaching area passed by the obstacle is recalculated to ensure the real-time and accuracy of the data.

[0030] Conduct a comprehensive assessment of each teaching area based on the total obstacle coefficient, and divide the teaching area into safe areas and dangerous areas; Safe Area: ≤0.3 (empirical threshold, adjustable); Dangerous areas: >0.3, exclude the dangerous area; A comprehensive assessment of each teaching area is conducted based on the total obstacle coefficient. Areas with a total obstacle coefficient ≤ 0.3 (this empirical threshold can be adjusted according to actual conditions) are classified as safe areas, while dangerous areas with a total obstacle coefficient > 0.3 are excluded and not used for teaching activities. For example, if the total obstacle coefficient calculated for a teaching area is 0.25, then the area is classified as a safe area; if the total obstacle coefficient of another teaching area is 0.35, then it is excluded and teaching activities are avoided in that area. A comprehensive obstacle impact assessment system has been established, comprehensively considering both the static dimensions and dynamic motion characteristics of obstacles. Through scientific calculations and real-time data updates, it accurately assesses the safety of each teaching area. This eliminates dangerous areas, provides a safe and reliable environment for teaching activities, effectively reduces safety risks caused by obstacles, and improves the safety and rationality of indoor space use in the stadium.

[0031] Calculate the activity areas of different classes. The activity areas of different classes are obtained by the following steps: Obtain course activity coefficients and number of participants in different classes; The activity areas of different classes are obtained by weighted multiplication of the course activity coefficients and the number of participants, as shown below:

[0032] Where: is the class activity area (unit: m2); The activity coefficient of the course (dynamically adjusted according to the course type, such as 1.5㎡ / person for basketball class and 0.8㎡ / person for yoga class); is the number of people participating in the activity; Calculate the actual area of the safe zone. The actual area of the safe zone is obtained by the following steps: Obtain the total area of the safety zone, the total obstacle coefficient of each obstacle, and the area of each obstacle; these data are obtained through the previous area division and obstacle coefficient calculation; Extract the total area of the safety zone, the total obstacle coefficient of each obstacle, and the area of each obstacle, and convert the area of each obstacle and the total obstacle coefficient of each obstacle into Multiply and weighted sum to get the total blocking area; The area of an obstacle directly increases the absolute value of the total blocking area, significantly expanding the total blocking area and causing the detour path to grow. Spherical obstacles only affect a local area due to their symmetry, while complex-shaped obstacles (such as cylinders and cuboids) quantify the global flow field deformation through the perturbation matrix, indirectly expanding the effective blocking range. Mobile obstacles (such as automatic cleaning robots and material transport AGVs) use the dynamic window method to adjust the total blocking area in real time. For example, the faster the obstacle, the larger the area covered by its future trajectory, requiring a larger safety space to be reserved in advance. The obstacle coefficient quantifies the ratio of the area covered by the obstacle to the potential area covered when the obstacle moves. The obstacle coefficient multiplied by the obstacle area is the blocking area that personnel need to reserve to deal with the obstacle. The total area of the safe zone The actual area of the safe area is obtained by subtracting the total blocking area ; Match the activity areas of different classes with the actual areas of the safety zone. The specific methods are as follows: Get the priority of each class;

[0033] Where: Priority for each class; The course type coefficient for each class (the course type coefficient is a numerical value used to adjust the weight or priority of different courses in teaching management, reflecting the differences in course complexity, resource requirements, and teaching objectives); The urgency coefficient for each class (the higher the urgency coefficient, the more immediate the task needs to be handled); The number of students in each class; 、 、 is the weight coefficient; Classes are assigned the safe zone closest to their actual area based on their activity area, from high to low priority. For example, if there are three classes, Class A has the highest priority and an activity area of 50 square meters. In this case, the system will search the safe zone closest to 50 square meters and assign it to Class A. After matching the activity areas of different classes with the actual area of the safety zone, if the activity area of a class is larger than the actual area of a single safety zone; If the obstacle in a single safety zone is a cleaning vehicle, obtain the difference between the actual area of the single safety zone and the class activity area; record this as the safety gap area. Obtain the area occupied by a single cleaning vehicle in the single safety zone and the total obstacle coefficient of the cleaning vehicles. Calculate the number of cleaning vehicles that need to be removed from the single safety zone as follows:

[0034]

[0035] Where: is the activity area of the i-th class (unit: m2); is the actual area of the jth single safety zone (unit: m2); is the safety gap area of the i-th class (needed to be vacated by removing the cleaning vehicle); The floor space occupied by a single cleaning vehicle (unit: m2); is the total obstacle coefficient of the cleaning vehicle; is the number of cleaning vehicles that need to be removed from the j-th single safety zone (rounded up); Get the start time of each class activity, and from this, get the evacuation time of the cleaning vehicle. Based on the earliest activity start time of all classes, calculate the deadline for the cleaning vehicle to complete the cleaning and evacuate the single safe area, as shown below:

[0036] Where: The earliest activity start time among all classes (unit: minutes); The safety buffer time preset for the system (e.g. ΔT = 10 minutes); The deadline for cleanup vehicles to complete cleanup and evacuate; The cleaning points in the stadium are grouped based on the calculated number of cleaning vehicles. Cluster analysis is performed using the density-based clustering algorithm (DBSCAN) combined with the geographic coordinates of the cleaning points (obtained through sensor positioning). The neighborhood radius ϵ and the minimum number of points parameter MinPts are set to group geographically close cleaning points into a group, with each group corresponding to a cleaning vehicle. The specific settings are as follows: Neighborhood radius ϵ: defines the maximum distance threshold between cleaning points (unit: meter); Minimum number of points MinPts: The minimum number of cleanup points to form a valid cluster; if the distance between the cleanup points ≤ϵ and the number of points in the group ≥MinPts, then they are divided into the same group, corresponding to one cleaning vehicle; The total time required for each vehicle to complete the task is calculated based on the cleaning vehicle speed, path length, and cleaning point operation time. The cleaning vehicle speed is fed back in real time by the vehicle control system. The path length is calculated by accumulating the distances between each node on the path. The cleaning point operation time has different preset time parameters based on the cleaning task type (such as garbage cleaning, equipment handling, etc.), as shown below:

[0037] Where: is the total length of the path of the mth cleaning vehicle (unit: meter); is the real-time speed of the mth cleaning vehicle (unit: m / s); The number of cleaning points allocated to the mth cleaning vehicle; The preset operation time for the nth cleaning point (unit: seconds, set according to the task type); The total time taken to complete the task for the mth cleaning vehicle (in seconds); Compare the total time taken by each cleaning vehicle to complete the task with the deadline. If a cleaning vehicle is expected to be unable to complete the task on time, readjust its cleaning point allocation and route planning, or increase the number of cleaning vehicles to ensure that all cleaning vehicles complete the cleaning and withdraw from the single safe area before the students reach the single safe area to ensure the smooth progress of the activity; Task duration constraints: ( is the remaining available time for the cleaning vehicle), where ( The real time when the system triggers the scheduling calculation (unit: seconds or minutes); Deadline for cleanup vehicles to complete cleanup and evacuate); like , through the shortest path algorithm to re-plan the shortest path for the cleaning vehicle to complete the task in a single safe area, reducing ( is the total length of the cleaning vehicle path, in meters); transfer some cleaning points to other vehicles; if optimization is not possible, increase the number of cleaning vehicles , regroup the cleaning points; The present invention realizes the optimal allocation of teaching space resources; it can not only meet the teaching space needs of different classes, but also make efficient use of safety areas and avoid waste of resources; for large class sizes, by rationally cleaning a single safety area, the normal development of teaching activities is guaranteed, the flexibility and adaptability of teaching management are significantly improved, and the efficiency and accuracy of intelligent teaching management driven by big data are fully demonstrated. In the case where the class activity area is larger than a single safety area, the system calculates the area of the safety gap and intelligently plans the evacuation plan of the cleaning vehicle; combines the DBSCAN clustering algorithm to group the cleaning points, optimizes the cleaning vehicle path and task allocation, and allocates nearby cleaning points to other vehicles through the clustering algorithm, while planning the shortest path. By setting the safety buffer time and task time constraints, it ensures that the cleaning work is completed before the students arrive, ensuring the smooth development of activities, significantly improving the flexibility of teaching management and emergency handling capabilities, and fully demonstrating the efficiency and accuracy of intelligent management driven by big data.

[0038] Teaching intelligent management system based on big data (see Figure 2 ),include: Data collection unit: responsible for collecting course information of different classes, number of students, stadium meteorological data and water level data around the stadium.

[0039] Data analysis and processing unit: processes and analyzes the collected data, performs operations such as determining rain characteristics, calculating the class activity area and the actual area of the safe area, evaluating the safety of the teaching area, and allocating and matching class areas according to teaching needs.

[0040] Area allocation and scheduling unit: According to the results of the data analysis and processing unit, different classes are allocated to the stadium usage area or safety area, and the classes are split and reallocated.

[0041] Communication and feedback unit: responsible for data communication with monitoring equipment, meteorological departments, etc. in the stadium, and receiving feedback information from teachers and students.

[0042] The above is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited to this. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes to the teaching intelligent management method and system based on big data and its inventive concept according to the technology of the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. The teaching intelligent management method based on big data is characterized by: The following steps are involved: Allocate different classes at the same time to multiple use areas of the stadium according to teaching needs; Determine whether the area where the stadium is located has rain characteristics. If so: The indoor space of the stadium is divided into multiple teaching areas according to the exit routes. The total obstacle coefficient in each teaching area is obtained. A comprehensive evaluation is conducted on each teaching area based on the total obstacle coefficient, thereby dividing the teaching area into a safe area and a dangerous area. The steps for obtaining the obstacle coefficient are as follows: obtaining the static influence factor and dynamic weight of each obstacle in each teaching area, thereby obtaining the obstacle coefficient of each obstacle in the teaching area; summing the obstacle coefficients of all obstacles in the teaching area to obtain the total obstacle coefficient of the teaching area; Calculate the activity areas of different classes, calculate the actual area of the safety zone, and match the activity areas of different classes with the actual area of the safety zone; If the class activity area is larger than the actual area of a single safety zone, the difference is determined and recorded as the safety gap area; If the obstacle in a single safety zone is a cleaning vehicle, obtain the area occupied by the obstacle and the total obstacle coefficient, thereby defining the number of cleaning vehicles that need to be removed from the single safety zone; Collect the start time of each class activity to determine the deadline for the cleanup vehicle to leave; The total time required to complete the task of the cleaning vehicle is calculated based on the task, and whether it can be evacuated from a single safe area is determined based on the total time and the remaining available time of the cleaning vehicle, so as to achieve a match between the activity area and the safe area.

2. The teaching intelligent management method based on big data according to claim 1 is characterized in that: The teaching demand principle includes the following steps: Obtain the course types, teaching equipment requirements, and student age and physical fitness of different classes; The gymnasium is allocated according to the course types, teaching equipment requirements, and the age and physical fitness of students in different classes.

3. The teaching intelligent management method based on big data according to claim 1 is characterized in that: The obstacle coefficient is determined by the following steps: Determine the length, width, height parameters and projection correction coefficient of obstacles in each teaching area, and thus obtain the static impact factor of each obstacle; Determine the speed of each obstacle in each teaching area, thereby determining the dynamic level corresponding to each obstacle. According to the dynamic level of each obstacle, determine the dynamic weight of each obstacle. Based on the static influence factor of each obstacle, obtain the obstacle coefficient of each obstacle in the teaching area. The obstacle coefficients of each obstacle in each teaching area are summed to obtain the total obstacle coefficient of each teaching area.

4. The teaching intelligent management method based on big data according to claim 1 is characterized in that: If the obstacle causes the path to be changed due to demand, the teaching area through which the obstacle passes is obtained, and the total obstacle coefficient of the teaching area through which the obstacle passes is recalculated.

5. The teaching intelligent management method based on big data according to claim 1 is characterized in that: The activity areas of different classes are obtained by the following steps: Obtain course activity coefficients and number of participants in different classes; The activity areas of different classes are obtained by weighted multiplication of the course activity coefficients and the number of participants in the activities of different classes.

6. The teaching intelligent management method based on big data according to claim 1 is characterized in that: The actual area of the safety zone is obtained by the following steps: Obtain the total area of the safety zone, the total obstacle coefficient of each obstacle, and the area of each obstacle; Extract the total area of the safety zone, the total obstacle coefficient of each obstacle, and the area of each obstacle; Multiply the area of each obstacle by the total obstacle coefficient of each obstacle and add them up to get the total blocking area; The actual area of the safety zone is obtained by subtracting the total area of the safety zone from the total blocking area.

7. The teaching intelligent management method based on big data according to claim 1 is characterized in that: The steps of determining whether the area where the stadium is located has rain characteristics are as follows: Get the ground humidity change value at the stadium; If the ground humidity change value at the stadium location exceeds the safety standard, it is determined that rain characteristics exist.

8. The teaching intelligent management method based on big data according to claim 1 is characterized in that: The method for matching the activity areas of different classes with the actual areas of the safety zone includes: Get the priority of each class; Classes from high priority to low priority are matched with the safe area closest to the actual area based on the activity area.

9. The teaching intelligent management method based on big data according to claim 1 is characterized in that: Based on the start time of each class activity, the earliest start time of all classes is used as the benchmark, and the safety buffer time is set in advance as the deadline for the cleaning vehicle to complete the cleaning and withdraw from the safe area; The cleaning points in the stadium are grouped based on the calculated number of cleaning vehicles. A density-based clustering algorithm is used to perform cluster analysis based on the geographical coordinates of the cleaning points. The neighborhood radius ϵ and the minimum number of points parameter MinPts are set to group geographically close cleaning points into a group, with each group corresponding to a cleaning vehicle. The total time required for each vehicle to complete the task is calculated based on the cleaning vehicle speed, path length, and cleaning point operation time. The cleaning point operation time is preset to different times according to the cleaning task type. Compare the total time taken by each cleaning vehicle to complete the task with the remaining available time of the cleaning vehicle. If the total time taken by a cleaning vehicle to complete the task is greater than the remaining available time of the cleaning vehicle, readjust its cleaning point allocation and route planning, or increase the number of cleaning vehicles.

10. A teaching intelligent management system based on big data, applying the teaching intelligent management method based on big data according to any one of claims 1 to 9, characterized in that: include: Data collection unit: responsible for collecting course information of different classes, number of students, stadium weather data and water level data around the stadium; Data analysis and processing unit: processes and analyzes the collected data, performs operations such as determining rain characteristics, calculating the actual area of class activity areas and safe areas, evaluating the safety of teaching areas, and allocating and matching class areas according to teaching needs; Area allocation and scheduling unit: According to the results of the data analysis and processing unit, different classes are allocated to the stadium use area or safety area, and the classes are split and reallocated; Communication and feedback unit: responsible for data communication with the monitoring equipment and meteorological department in the stadium, and receiving feedback information from teachers and students.

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