A campus safety management method and related equipment based on smart campus

Abstract

The present invention provides a campus safety management method based on a smart campus and related equipment, which are applied to the field of data processing technology. The present application obtains the teaching information of the target campus and the target user information matching the teaching information of the target campus; processes the target user information to generate the physiological state information of the target user; processes the teaching information of the target campus to generate the initial teaching information; obtains the server address and the target server of the teaching warning model matching the teaching influence factor; processes the initial teaching information and the physiological state information of the target user based on the teaching warning model stored in the target server to generate the teaching adjustment information; processes the teaching information of the target campus based on the teaching adjustment information to generate the target teaching information; processes the target teaching information and the target user information based on the teaching warning model to generate the learning feedback information of the target user.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a campus safety management method based on a smart campus and related equipment. Background Art

[0002] In the traditional teaching model, teachers usually teach according to a set teaching plan. The fixed teaching plan is often accompanied by a single evaluation system, which may not be able to fully evaluate the comprehensive ability of students. Under the fixed teaching model, it may be difficult for teachers to allocate resources and provide support in a personalized manner according to the needs of different students. Although this model helps maintain teaching order and progress, it also has some potential problems:

[0003] Fixed teaching plans may ignore individual differences among students, causing some students to be unable to keep up with the progress, while others may find the content too simple. Unified teaching content and progress may result in some complex concepts not being fully explained and students not understanding them deeply enough. If the course content does not stimulate students' interest, students may be easily distracted, appearing dazed or communicating with each other. If the communication between students is not effectively managed, it may turn into a classroom discipline problem and affect the learning of other students. If students frequently ask questions or show impatience because they do not understand, it may cause conflicts and misunderstandings between teachers and students. If classroom discipline problems and conflicts between teachers and students are not effectively prevented and resolved, it may affect the overall safety and stability of the campus.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of this application is to provide a campus safety management method based on smart campus and related equipment and systems, which at least to some extent overcome the problems existing in the prior art, by collecting teaching information and user data, analyzing students' physiological status, using the teaching warning model on the server, combining physiological status information, adjusting teaching strategies, and forming personalized teaching content. The model processes teaching information and user feedback, generates learning feedback information, optimizes the teaching process and improves campus safety.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present invention.

[0007] According to one aspect of the present application, a campus safety management method based on a smart campus is provided, including: obtaining teaching information of a target campus and target user information matching the teaching information of the target campus, wherein the target user information includes at least one user; processing the target user information to generate physiological state information of the target user; processing the teaching information of the target campus to generate initial teaching information; obtaining a server address of a teaching warning model matching a teaching influence factor; processing the server address based on preset rules to obtain a target server; processing the initial teaching information and the physiological state information of the target user based on the teaching warning model stored in the target server to generate teaching adjustment information; processing the teaching information of the target campus based on the teaching adjustment information to generate target teaching information; processing the target teaching information and the target user information based on the teaching warning model to generate learning feedback information of the target user.

[0008] In one embodiment of the present application, the teaching information of the target campus is processed to generate initial teaching information, including: processing the teaching information of the target campus to generate preset teaching information; obtaining feedback information generated by the target user for the preset teaching information, wherein the feedback information includes feedback data and the corresponding feedback time point generated by the target user after viewing the preset teaching information, and the feedback data includes action feedback data and voice feedback data; processing the feedback information to generate initial teaching information, wherein the initial teaching information is the teaching content of the target user within a preset time period in the future.

[0009] In one embodiment of the present application, the feedback information is processed to generate initial teaching information, including: processing the action feedback data and the voice feedback data to generate an initial understanding level value of the target user for the preset teaching information; if the initial understanding level value is lower than the initial standard value, calculating the interval duration between the feedback time point and the viewing time point, wherein the viewing time point is the time point when the target user views the preset teaching information; if it is determined that the interval duration is lower than the preset duration, it is determined that the target user's understanding level value of the preset teaching information is lower than the target standard value; or, if it is determined that the initial understanding level value is not lower than the initial standard value, it is determined that the target user's understanding level value of the preset teaching information is not lower than the target standard value; based on the understanding level value, the initial teaching information is generated.

[0010] In one embodiment of the present application, initial teaching information is generated based on the understanding level value, including: if it is determined that the understanding level value is lower than the target standard value, determining the planned teaching content for the target user based on the historical teaching content, wherein the historical teaching content is generated by the teaching content included in the preset teaching information; or, if it is determined that the understanding level value is not lower than the target standard value, determining the planned teaching content for the target user based on the next stage teaching content.

[0011] In one embodiment of the present application, the server address is processed based on preset rules to obtain the target server, including: obtaining the number of servers storing the teaching warning model based on the server address; if there are multiple servers storing the teaching warning model, obtaining the business load status of multiple servers; and taking the server with the lowest business load status as the target server.

[0012] In one embodiment of the present application, the initial teaching content and the physiological state information of the target user are processed based on the teaching warning model stored in the target server to generate teaching adjustment information, including: processing the initial teaching content and the physiological state information of the target user based on the teaching warning model to generate a teaching influence factor; processing the teaching influence factor and the physiological state information of the target user to generate a score for each teaching influence factor; processing the score of each teaching influence factor to generate a target score for the initial teaching content; acquiring several other teaching contents with the same target score; processing the initial teaching content based on the other teaching contents to generate a target teaching influence factor; processing the target teaching influence factor to generate teaching adjustment information.

[0013] In one embodiment of the present application, after the target teaching information and the target user information are processed based on the teaching warning model to generate the learning feedback information of the target user, it also includes: processing the target user information based on the target user's learning feedback information to generate the target user's attribute information, wherein the target user's attribute information includes the real-time intimacy information between the target user and the teacher and the target user's entry and exit information; processing the real-time intimacy information between the target user and the teacher and the target user's entry and exit information to generate a classroom risk value for the target user; if the classroom risk value of the target user is higher than a preset threshold, generating warning information, wherein the warning information is used to characterize that the target user is in an abnormal state.

[0014] Another aspect of the present application is a campus security management device based on a smart campus, characterized in that it includes: an acquisition module, used to acquire teaching information of a target campus and target user information matching the teaching information of the target campus, wherein the target user information includes at least one user; acquiring a server address of a teaching warning model matching a teaching influence factor; a processing module, used to process the target user information and generate physiological state information of the target user; processing the teaching information of the target campus to generate initial teaching information; processing the server address based on preset rules to acquire a target server; processing the initial teaching information and the physiological state information of the target user based on the teaching warning model stored in the target server to generate teaching adjustment information; processing the teaching information of the target campus based on the teaching adjustment information to generate target teaching information; processing the target teaching information and the target user information based on the teaching warning model to generate learning feedback information of the target user.

[0015] According to another aspect of the present application, an electronic device is characterized in that it includes: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the above-mentioned campus safety management method based on the smart campus by executing the executable instructions.

[0016] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a second processor, the campus safety management method based on the smart campus is implemented.

[0017] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a third processor, the campus safety management method based on the smart campus is implemented.

[0018] The campus safety management method and related equipment provided in this application adopt a more flexible and personalized teaching strategy, use technical means to collect and analyze student data, and adjust teaching content and methods in a timely manner to improve teaching quality and student satisfaction, while maintaining campus safety and order.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart showing a campus safety management method based on a smart campus provided by an embodiment of the present application;

[0021] Figure 2 A schematic diagram of the structure of a campus safety management device based on a smart campus provided in an embodiment of the present application is shown;

[0022] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;

[0023] Figure 4 A schematic diagram of a storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0025] Combine the following Figure 1 To describe the campus safety management method based on smart campus according to the exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown for the sake of understanding the spirit and principle of the present application, and the implementation of the present application is not limited in this regard. On the contrary, the implementation of the present application can be applied to any applicable scenario.

[0026] In one implementation, the present application also proposes a campus safety management method and related equipment based on a smart campus. Figure 1 The following schematically shows a flow chart of a campus safety management method based on a smart campus according to an embodiment of the present application. Figure 1 As shown, the method is applied to a server, comprising:

[0027] S101, obtaining teaching information of a target campus and target user information matching the teaching information of the target campus.

[0028] In one implementation, the teaching information of the target campus includes, but is not limited to, course information of different grades, different subjects, and different classes (such as elite classes and ordinary classes). Different classes, such as information classes and ordinary classes, have obvious differences in course settings. The information class has added information technology courses, programming, big data, artificial intelligence and other content, while the ordinary class is mainly based on basic subjects. Compared with ordinary classes, the information class may have more characteristics in teaching methods, such as adopting a project-based teaching method to allow users to improve their skills by solving practical problems. The information class emphasizes the cultivation of users' information literacy, including the ability to judge the authenticity of information, evaluate the value of information, and use information reasonably, while the ordinary class focuses more on the teaching of basic knowledge and the improvement of test scores. In addition, different guidance is given according to the different academic levels of users, and tiered teaching within the class and grade tiered teaching are implemented to adapt to users with different learning levels. The target user information includes at least one user, which can be a specific user, or a specific group, or a user of a class, etc. The applicant can set it according to actual needs.

[0029] S102: Process the target user information to generate physiological state information of the target user.

[0030] In one implementation, the target user information is processed to generate physiological status information of the target user, such as whether the user is sleepy, not paying attention in class, daydreaming, whispering with the user, or not in his seat. The relevant data can be obtained in the following ways: teachers can evaluate the physiological and psychological status of users by directly observing their performance in class. For example, the user's body language, eye contact, and reaction speed can provide clues. Users are encouraged to self-evaluate their classroom performance, including concentration, fatigue, etc., which can be done in the form of questionnaires or oral reports. Users can observe each other and evaluate each other's classroom participation and mental state.

[0031] Smart bracelets or wearable devices can be used to monitor users' physiological indicators, such as heart rate and activity level, which indirectly reflect the user's mental state. User participation can be assessed by the frequency and quality of classroom interaction. Lack of participation or low participation may mean that the user is not paying attention or is not in his seat. Video recording of the class, and then analyzing the user's behavior patterns through video analysis software or manual observation. Using advanced physiological signal monitoring systems, such as electroencephalogram (EEG) monitoring, users' concentration can be more accurately assessed. In addition, a learning management system (LMS) can be used to track users' online learning activities, analyze their participation and learning behavior, understand users' mental state and classroom performance through regular feedback and communication mechanisms, and use artificial intelligence technologies, such as computer vision analysis, to monitor users' behavior and expressions in class to determine whether they are sleepy or distracted.

[0032] S103, processing the teaching information of the target campus to generate initial teaching information.

[0033] In one implementation, the teaching information of the target campus is processed to generate preset teaching information, and feedback information generated by the target user for the preset teaching information is obtained, wherein the feedback information includes feedback data and the corresponding feedback time point generated by the target user after viewing the preset teaching information, and the feedback data includes action feedback data and voice feedback data. The feedback information is processed to generate initial teaching information, wherein the initial teaching information is the teaching content for the target user within a preset time period in the future.

[0034] Specifically, in order to improve the teaching quality and teaching efficiency, it is inseparable from the close cooperation between users and teachers, which includes the teacher's observation of whether the user understands what he is saying. It can be understood that the user's behavior in the classroom is an important part of the teacher's observation of whether the user understands what he is saying. However, when the number of users is large, it may cause the teacher to be unable to take into account the status of all users, which in turn leads to a decline in teaching efficiency and teaching quality. Therefore, it is necessary to design teaching content and teaching activities according to teaching objectives and user characteristics, produce teaching materials such as teaching videos, presentations, and teaching texts, and use digital teaching platforms or classrooms to directly display preset teaching information. Through classroom observation or video analysis, record the user's action reactions, such as nodding, raising hands, taking notes, etc., use recording equipment to collect the user's oral answers, discussion content and other voice data, and record the time points corresponding to the feedback data to facilitate the analysis of the timeliness of the feedback.

[0035] Use speech recognition technology to convert speech feedback data into text data, use behavioral analysis software to process action feedback data, extract key actions and reaction patterns, classify and quantify the collected feedback data, and identify the user's mastery of the teaching content, points of interest and doubts. According to the feedback information, adjust and optimize the teaching content, generate initial teaching information that meets user needs, and design personalized learning paths and supplementary teaching materials. Formulate a teaching plan for the future preset time period, including course arrangements, learning activities and evaluation methods. According to the feedback information collected continuously, continuously update the teaching content and methods, use educational software, artificial intelligence, big data analysis and other technical tools to assist in the generation and adjustment of teaching information, and combine the teacher's professional experience and understanding of users to conduct a final review and adjustment of the generated initial teaching information. In addition, interactive links and feedback mechanisms can be designed to encourage users to actively participate in teaching activities and provide real-time feedback. Through the above process, teachers can understand the user's learning status and needs more accurately, thereby providing more personalized and effective teaching support. At the same time, this process also supports the continuous improvement and innovation of teaching content and methods.

[0036] In another embodiment, specifically, processing the feedback information to generate the initial teaching information also includes: processing the action feedback data and the voice feedback data to generate the target user's initial understanding value of the preset teaching information; if the initial understanding value is lower than the initial standard value, calculating the interval between the feedback time point and the viewing time point, wherein the viewing time point is the time point when the target user views the preset teaching information; if it is determined that the interval is lower than the preset time, it is determined that the target user's understanding value of the preset teaching information is lower than the target standard value. Or, if it is determined that the initial understanding value is not lower than the initial standard value, it is determined that the target user's understanding value of the preset teaching information is not lower than the target standard value, and based on the understanding value, the initial teaching information is generated.

[0037] When the target user watches the preset teaching information, the action feedback data and voice feedback data are collected simultaneously. The collected voice feedback data is converted into text data using speech recognition technology, and the action feedback data is analyzed for video to extract key actions and expressions. The voice feedback text is analyzed using natural language processing technology to evaluate the user's language comprehension, emotional tendencies, etc., and combined with action feedback analysis, the user's participation, concentration, etc. are evaluated.

[0038] According to the teaching objectives and expected learning outcomes, set a standard value for the initial level of understanding. For example, determine the specific teaching objectives of the course or teaching unit. These objectives should be specific, measurable, achievable, relevant and time-bound (SMART). Refer to the curriculum standards set by the education department or school to ensure that the teaching objectives are consistent with these standards. Analyze the expected learning outcomes, including knowledge, skills, attitudes and values, consider the user's previous knowledge, skill level and learning style to predict the level of understanding they may achieve, and design tools and methods to evaluate the user's understanding, such as tests, questionnaires, observation records, etc. According to the teaching objectives and learning outcomes, set a qualified standard, that is, the minimum level of understanding that the user must achieve. In addition to the minimum standard, you can also set different levels of understanding standards, such as basic, intermediate and advanced. Recognize the individual differences of users and set different standard values ​​for users of different ability levels. Compare the generated initial level of understanding with the initial standard value. If the initial level of understanding is lower than the initial standard value, calculate the interval between the feedback time point and the viewing time point. If the interval is lower than the preset time, further analyze the reason why the user's understanding value is lower than the target standard value.

[0039] If the initial understanding value is not lower than the initial standard value, it is determined that the user's understanding value of the preset teaching information meets the target standard. According to the understanding value, the teaching content and methods are adjusted to generate initial teaching information. If the understanding level is insufficient, design supplementary teaching activities or additional tutoring materials. If the understanding level is good, higher-level learning challenges or expansion materials can be designed. Customize personalized teaching plans for each user or user group to meet their specific needs. Use the generated initial teaching information as the starting point for a new round of teaching, continue to collect feedback information, form a closed-loop continuous improvement process, and combine the teacher's professional judgment and in-depth understanding of the user to make final adjustments to the generated initial teaching information. Through the above process, teachers can more accurately evaluate the user's learning status and adjust teaching strategies based on real-time feedback from users to improve teaching effectiveness and user learning outcomes.

[0040] In one method, the viewing time point is the time point when the user views the preset teaching information. When the smart classroom teaching platform preliminarily determines that the user's initial understanding of the preset teaching information is low (that is, lower than the initial standard value), it is also necessary to calculate the time point when the user sends feedback data such as shaking his head, frowning, or "hmm?". It can be understood that only when the time point when the user generates feedback data is close to the time interval between viewing the preset teaching information (that is, lower than the preset duration, the preset duration can be, for example, 2 seconds or 3 seconds, etc.), can it represent that the user is sending feedback data for the preset teaching information. And then, only at this time can it be determined that the user's understanding of the preset teaching information is low (that is, lower than the target standard value).

[0041] For example, the preset duration is 3 seconds. For example, the time point when the user finishes watching the preset teaching information (i.e., the viewing time point) is 1:00:00, and the time point when the user sends feedback data such as shaking his head, frowning, or sending "Hmm?" (i.e., the feedback time point) is 1:00:01. Since the interval between the two is shorter than the preset duration, it is considered that the user sent feedback data for the preset teaching information. Based on this, it is determined that the user's understanding of the preset teaching information is low.

[0042] In another embodiment, based on the understanding level value, initial teaching information is generated, specifically including: if it is determined that the understanding level value is lower than the target standard value, the planned teaching content is determined for the target user based on the historical teaching content, wherein the historical teaching content is generated by the teaching content included in the preset teaching information. The process of generating initial teaching information based on the understanding level value requires careful planning and adjustment of teaching strategies to ensure that users can get appropriate teaching support based on their understanding level. Specifically, by collecting and analyzing the action and voice feedback data, the user's understanding level value is determined, and according to the teaching objectives and curriculum standards, a target standard value of the expected user's understanding level is set, and the user's actual understanding level value is compared with the target standard value. If the understanding level value is lower than the target standard value, the historical teaching content is reviewed and analyzed, including previous teaching videos, presentations, teaching texts, etc., and based on the historical teaching content, the concepts, skills or knowledge points that need to be reviewed or strengthened are determined, and personalized teaching activities are designed for users with insufficient understanding, such as additional exercises, group discussions or one-on-one tutoring, and supplementary learning materials are provided, such as additional reading materials, online resources or video tutorials. Based on user feedback, adjust teaching methods, which may include slower teaching speed, more examples, or different explanation styles. Develop a remedial teaching plan with clear goals, timelines, and expected outcomes.

[0043] When it is determined that the user's understanding of the preset teaching information is low, it is necessary to instruct the teacher to re-explain part or all of the content of the preset teaching information (i.e., the historical teaching content). In other words, the teacher's planned teaching content for the user in the future preset time period is to re-explain part or all of the teaching content of the preset teaching information.

[0044] The length of time for planning teaching content can be determined by the numerical difference between the user's understanding level value and the target standard value. In other words, when the user's understanding of the content of the preset teaching information is lower (that is, the numerical difference between the understanding level value and the target standard value is greater), the value of the preset time period needs to be higher.

[0045] For example, if the target standard value is 100% (complete understanding), when the user's understanding value is 70% (only 70% of the content is understood), the time for planning teaching content can be 2 minutes. When the user's understanding value is 50% (only 50% of the content is understood), the time for planning teaching content can be 5 minutes, and so on.

[0046] If it is determined that the understanding level value is not lower than the target standard value, the teaching content is planned for the target user based on the next stage of teaching content. Specifically, if the understanding level value is not lower than the target standard value, the teaching content of the next stage is planned to ensure that the user can continue to improve at the current level, set new learning goals for the next stage of teaching, ensure that they match the user's current level of understanding, design more advanced teaching activities, and encourage users to deepen their understanding and apply what they have learned. Ensure that each user can get appropriate teaching support according to their level of understanding, thereby improving learning outcomes and promoting the personalized development of each user.

[0047] S104, obtaining the server address of the teaching early warning model that matches the teaching impact factor.

[0048] In one implementation manner, this embodiment does not limit the method for obtaining the server address, as long as this solution can be implemented.

[0049] S105: Process the server address based on a preset rule to obtain a target server.

[0050] In one implementation, the number of servers storing the teaching warning model is obtained based on the server address. If there are multiple servers storing the teaching warning model, the business load status of the multiple servers is obtained; the server with the lowest business load status is used as the target server. Specifically, by obtaining the business load status of the server with the teaching warning model, the problem of slow business progress of the related servers due to the large current load of a server (for example, including at least one of the business load and the machine operation load) is reduced. This embodiment will adjust the client to send the teaching information of the target campus and the target user information matching the teaching information of the target campus to the server with lower load in real time according to the business load status of multiple servers, thereby reducing the business processing pressure of a single or multiple servers and greatly achieving the purpose of load balancing.

[0051] S106, processing the initial teaching information and the physiological state information of the target user based on the teaching warning model stored in the target server to generate teaching adjustment information.

[0052] In one implementation, the initial teaching content and the physiological status information of the target user are processed based on the teaching warning model to generate a teaching influence factor, the teaching influence factor and the physiological status information of the target user are processed to generate a score for each teaching influence factor, and the score for each teaching influence factor is processed to generate a target score for the initial teaching content. Several other teaching contents with the same target score are obtained, and the initial teaching content is processed based on the other teaching contents to generate a target teaching influence factor, and the target teaching influence factor is processed to generate teaching adjustment information.

[0053] Based on the teaching warning model, the teaching content and students' physiological status information are processed, and teaching adjustment information is generated. Specifically, it can be implemented based on the following contents: using wearable devices or other monitoring tools to collect students' physiological status information, such as heart rate, skin conductance, eye tracking and other indicators, and conducting a detailed analysis of the initial teaching content, including teaching objectives, content difficulty, expected learning outcomes, etc. Using the teaching warning model, combined with students' physiological status information and initial teaching content, factors that may affect learning effects are identified.

[0054] Quantify each teaching influencing factor and assign a corresponding score based on its potential impact on learning outcomes. Specifically, identify all factors that may affect teaching outcomes, such as teaching methods, classroom environment, student engagement, course difficulty, etc., and define quantifiable indicators for each influencing factor. For example, student engagement can be quantified by the number of questions asked, discussion contributions, etc. Collect data related to each quantitative indicator, which may include classroom observation records, student feedback, test scores, etc., establish scoring criteria for each quantitative indicator, clarify the score range corresponding to different levels of performance, and assign weights based on the importance of the influencing factor to the learning outcome. Use expert opinions, historical data or statistical analysis to determine the weights, standardize the collected data to ensure the comparability of data for different indicators, and calculate the score of each influencing factor based on the scoring criteria and weights. In addition, statistical models or machine learning algorithms can be applied to analyze the data and predict the impact of each influencing factor on the learning outcome. The accuracy and reliability of the model can be verified through historical data or pre-experiments. The scores and weights of the influencing factors can be dynamically adjusted based on the model results and actual teaching feedback. Combined with the professional judgment of teachers, the quantitative results can be interpreted and adjusted. Consider the characteristics of different student groups, ensure the fairness and applicability of quantitative methods, use quantitative results as part of teaching feedback, and continuously optimize teaching methods and strategies.

[0055] According to the teaching objectives and expected learning outcomes, a target score is set for each teaching impact factor, and several other teaching contents matching the target score are obtained as a reference for comparison and adjustment. Based on other teaching contents, comparative analysis is performed to determine the target teaching impact factor that has the greatest impact on learning outcomes. For example, suppose there are two different schools (school A and school B) offering the same mathematics course. If you want to analyze which teaching impact factors have the greatest impact on students' mathematics scores, first collect the following data from the two schools: student mathematics scores (as a measure of learning outcomes), teaching methods (such as lectures, discussions, cooperative learning, etc.), teaching resources (such as textbooks, online resources, experimental tools, etc.), teacher qualifications (such as educational background, teaching experience, etc.), student participation (such as classroom interaction, homework submission rate, etc.), and classroom environment (such as class size, teaching facilities, etc.). Use statistical methods (such as principal component analysis or factor analysis) to quantify and simplify these impact factors, convert them into several main factors, such as "teaching interaction" and "resource availability", and compare the performance of the two schools on each quantitative impact factor: for example, school A has higher student participation and interaction in the classroom, and school B provides more diverse teaching resources.

[0056] Using multiple regression analysis to determine which factors have the greatest impact on students' math scores, the analysis results may show that an increase in teaching interaction can significantly improve student scores, and resource availability, although important, has a less significant impact than teaching interaction. Suppose the analysis results are as follows: School A's average math score is 80 points, and the student engagement score is 4.5 / 5; School B's average math score is 75 points, and the student engagement score is 3.5 / 5. The regression analysis results show that for every 1 point increase in the student engagement score, the average math score increases by 5 points. Based on the analysis results, it can be concluded that teaching interaction is the biggest factor affecting learning outcomes. Therefore, School B may need to improve its teaching methods and increase classroom interaction and student engagement to improve student math scores. After implementing new teaching strategies, continue to monitor student performance and feedback to ensure that teaching adjustments are effective and make further optimizations as needed.

[0057] According to the target teaching impact factor, suggestions for adjustments in teaching content, teaching methods, teaching progress, etc. are proposed. According to the teaching adjustment information, teachers make necessary adjustments to the teaching plan to improve the teaching effect. After the adjustment is implemented, the students' physiological state and learning feedback are continuously monitored to evaluate the effect of the adjustment. The feedback information after the teaching adjustment is input into the teaching early warning model again, and a new round of analysis and adjustment is carried out to form a continuously optimized teaching cycle. Data analysis tools such as statistical analysis and machine learning are applied to improve the accuracy of the teaching impact factor score and the relevance of the adjustment suggestions. In the process of analysis and adjustment, individual differences of students are taken into consideration to ensure that the teaching adjustment meets the learning needs of different students, and the key factors affecting the teaching effect can be more accurately identified and adjusted, so as to achieve personalized teaching and improve the teaching quality and student learning outcomes.

[0058] S107, processing the teaching information of the target campus based on the teaching adjustment information to generate target teaching information.

[0059] In one implementation, based on the previous analysis of teaching influencing factors, collect adjustment suggestions on teaching methods, resources, teacher qualifications, student engagement, and classroom environment, evaluate the existing teaching plans, course arrangements, teaching strategies, and student feedback of the target campus, and determine the teaching areas that need to be improved or enhanced based on the teaching adjustment information. Develop a new teaching plan that meets the teaching objectives and learning outcomes, including course content, teaching methods, and assessment strategies, ensure that the newly designed teaching information integrates necessary teaching resources, such as teaching materials, technical tools, and auxiliary materials, and develop a detailed implementation plan, including a timetable, responsibility allocation, and expected milestones.

[0060] Train teachers on new teaching methods and tools to ensure they can effectively implement new teaching plans, and clearly communicate the purpose and expected results of teaching adjustments to students and parents to ensure their understanding and support. Implement new teaching information in small-scale pilot courses, collect data and evaluate its effectiveness, and make necessary adjustments and optimizations to the teaching information based on feedback from the pilot phase. After implementation, continuously monitor the teaching effect and make continuous improvements based on students' learning progress and feedback, record the process and results of teaching adjustments, share experiences and best practices with colleagues in the education sector, ensure that their teaching information matches students' learning needs and expected results, and continuously optimize teaching practices to improve the quality of education.

[0061] S108: Process the target teaching information and the target user information based on the teaching warning model to generate learning feedback information for the target user.

[0062] In one implementation, determine which indicators will be used to evaluate students' learning feedback, such as understanding, participation, satisfaction, etc., collect detailed information about students, including previous learning history, grades, participation, interests and learning styles, evaluate teaching content, teaching methods, course structure and teaching resources to determine how they meet students' learning needs. Use teaching early warning models to predict students' possible reactions and learning outcomes to specific teaching content, monitor students' participation in teaching activities in real time, collect data on their learning behaviors, generate preliminary learning feedback information based on the collected data and the predictions of the early warning model, and measure students' mastery of teaching content through tests, assignments and other evaluation tools.

[0063] Conduct in-depth analysis of the collected learning feedback, identify the strengths and areas for improvement in the learning process, adjust teaching methods and content based on learning feedback information to better meet students' learning needs, generate personalized learning feedback for each student, including their strengths, weaknesses and improvement suggestions, provide feedback to students in a constructive and supportive manner to help them understand their learning status. Share learning feedback information with teachers and parents, seek their support and cooperation, and jointly promote students' learning progress, record feedback information and subsequent learning performance, track the actual impact of feedback on learning outcomes, incorporate learning feedback information into the teaching early warning model, and continuously optimize the accuracy and effectiveness of the model. Teachers can understand and respond to students' learning needs in a timely manner, continuously improve teaching methods and content, thereby improving teaching quality and students' learning outcomes. At the same time, students can obtain timely feedback, better understand their own learning status, and take corresponding actions to improve learning outcomes.

[0064] This application collects teaching information and related user information of the target campus, covering physiological status and feedback data, processes user information, and generates physiological status information, such as sleepiness or attention status. Based on the teaching information and user feedback, the initial teaching content is generated and further adjusted to meet user needs. The teaching warning model on the optimal server is selected, and the teaching adjustment information is generated based on the teaching content and the user's physiological state. According to the warning model and user feedback, the target teaching information is generated to achieve personalized teaching. The teaching information and user information are processed by the teaching warning model to generate learning feedback information.

[0065] Based on learning feedback, user attribute information and classroom risk values ​​are generated, and early warnings are issued in a timely manner. In a multi-server environment, the target server is selected according to the business load to ensure service efficiency. If the user's understanding level is lower than the standard, the teaching is adjusted based on the historical teaching content; if it is not lower than the standard, the next stage of teaching content is planned. Through intelligent management, the teaching quality is improved, the learning experience is optimized, and problems in teaching are identified and handled in a timely manner to ensure campus safety and the effectiveness of education.

[0066] Optionally, in another embodiment of the method of the present application, after the target teaching information and the target user information are processed based on the teaching warning model to generate learning feedback information of the target user, the method further includes:

[0067] Processing the target user information based on the target user's learning feedback information to generate attribute information of the target user, wherein the attribute information of the target user includes real-time intimacy information between the target user and the teacher and the target user's entry and exit information;

[0068] Process the real-time intimacy information between the target user and the teacher and the target user's entry and exit information to generate the target user's classroom risk value;

[0069] If the classroom risk value of the target user is higher than a preset threshold, a warning message is generated, wherein the warning message is used to indicate that the target user is in an abnormal state.

[0070] In another implementation, the target user's attribute information is generated by combining the student's class participation, homework submission, test scores and other data with the real-time intimacy information and access information, including the evaluation of the intimacy between the student and the teacher and the student's attendance. The real-time intimacy information between the user and the teacher and the user's access information are analyzed and processed to evaluate the student's class participation and attendance frequency, thereby generating the target user's class risk value. Based on the analysis results of intimacy and access information, the class risk value of each student is calculated, which reflects the risk level of the student that may affect the class order and learning effect.

[0071] If the target user's classroom risk value is higher than the preset threshold, it indicates that the student may have behavioral problems or learning disabilities and needs the attention and intervention of teachers or school administrators. When it is determined that the student's classroom risk value exceeds the normal range, the system will automatically generate a warning message, indicating that the target user may be in an abnormal state and needs timely attention and intervention. According to the warning information, teachers can take corresponding measures, such as one-on-one communication with students to understand their problems and needs, or adjust teaching methods to improve students' participation and learning interest. Teachers and school administrators should continuously monitor students' classroom performance and learning progress, and constantly adjust teaching strategies based on feedback to reduce students' classroom risk values ​​and improve teaching quality. It can not only provide teachers with feedback on students' learning status, but also help teachers to promptly discover and solve problems that may affect students' learning, thereby creating a more positive and effective learning environment for students.

[0072] In one embodiment, if Figure 2 As shown, the present application also provides a campus security management device based on a smart campus, including:

[0073] The acquisition module 201 is used to acquire the teaching information of the target campus and the target user information matching the teaching information of the target campus, wherein the target user information includes at least one user; acquire the server address of the teaching early warning model matching the teaching impact factor;

[0074] The processing module 202 is used to process the target user information to generate the physiological state information of the target user; process the teaching information of the target campus to generate initial teaching information; process the server address based on preset rules to obtain the target server; process the initial teaching information and the physiological state information of the target user based on the teaching warning model stored in the target server to generate teaching adjustment information; process the teaching information of the target campus based on the teaching adjustment information to generate target teaching information; process the target teaching information and the target user information based on the teaching warning model to generate learning feedback information of the target user.

[0075] In another embodiment of the present application, the processing module 202 is configured to process the teaching information of the target campus to generate initial teaching information, including:

[0076] Processing the teaching information of the target campus to generate preset teaching information;

[0077] Acquire feedback information generated by the target user for the preset teaching information, wherein the feedback information includes feedback data generated by the target user after viewing the preset teaching information and the corresponding feedback time point, and the feedback data includes action feedback data and voice feedback data;

[0078] The feedback information is processed to generate initial teaching information, wherein the initial teaching information is the teaching content for the target user within a preset time period in the future.

[0079] In another embodiment of the present application, the processing module 202 is configured to process the feedback information to generate initial teaching information, including:

[0080] Processing the action feedback data and the voice feedback data to generate an initial understanding degree value of the target user for the preset teaching information;

[0081] If the initial understanding level value is lower than the initial standard value, calculating the interval between the feedback time point and the viewing time point, wherein the viewing time point is the time point when the target user views the preset teaching information;

[0082] If it is determined that the interval duration is less than the preset duration, it is determined that the target user's understanding level of the preset teaching information is less than the target standard value; or,

[0083] If it is determined that the initial understanding degree value is not lower than the initial standard value, it is determined that the target user's understanding degree value of the preset teaching information is not lower than the target standard value;

[0084] Based on the understanding level value, initial teaching information is generated.

[0085] In another embodiment of the present application, the processing module 202 is configured to generate initial teaching information based on the understanding level value, including:

[0086] If it is determined that the understanding level value is lower than the target standard value, the planned teaching content is determined for the target user based on the historical teaching content, wherein the historical teaching content is generated by the teaching content included in the preset teaching information; or,

[0087] If it is determined that the understanding level value is not lower than the target standard value, the planned teaching content is determined for the target user based on the next stage teaching content.

[0088] In another embodiment of the present application, the processing module 202 is configured to process the server address based on a preset rule to obtain a target server, including:

[0089] Acquire the number of servers storing the teaching early warning model based on the server address;

[0090] If there are multiple servers storing the teaching warning model, then obtaining the business load status of the multiple servers;

[0091] The server with the lowest business load status is used as the target server.

[0092] In another embodiment of the present application, the processing module 202 is configured to process the initial teaching content and the physiological state information of the target user based on the teaching warning model stored in the target server to generate teaching adjustment information, including:

[0093] The initial teaching content and the physiological state information of the target user are processed based on the teaching warning model to generate a teaching influence factor; the teaching influence factor and the physiological state information of the target user are processed to generate a score for each teaching influence factor; the score for each teaching influence factor is processed to generate a target score for the initial teaching content; a number of other teaching contents with the same target score are obtained; the initial teaching content is processed based on the other teaching contents to generate a target teaching influence factor; the target teaching influence factor is processed to generate teaching adjustment information.

[0094] In another embodiment of the present application, the processing module 202 is configured to process the target teaching information and the target user information based on the teaching warning model, and after generating the learning feedback information of the target user, further includes:

[0095] The target user information is processed based on the target user's learning feedback information to generate attribute information of the target user, wherein the attribute information of the target user includes real-time intimacy information between the target user and the teacher and entry and exit information of the target user; the real-time intimacy information between the target user and the teacher and the entry and exit information of the target user are processed to generate a classroom risk value of the target user; if the classroom risk value of the target user is higher than a preset threshold, a warning information is generated, wherein the warning information is used to characterize that the target user is in an abnormal state.

[0096] The present application embodiment provides an electronic device, such as Figure 3 As shown, the electronic device 3 includes a first processor 300, a memory 301, a bus 302 and a communication interface 303, and the first processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the first processor 300, and when the first processor 300 runs the computer program, it executes the campus safety management method based on the smart campus provided in any of the aforementioned embodiments of the present application.

[0097] The memory 301 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0098] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. Among them, the memory 301 is used to store programs, and the first processor 300 executes the program after receiving the execution instruction. The campus safety management method based on the smart campus disclosed in any implementation method of the aforementioned embodiment of the present application can be applied to the first processor 300, or implemented by the first processor 300.

[0099] The first processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 300. The above-mentioned first processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in a decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0100] The electronic device provided by the above-mentioned embodiments of the present application and the campus safety management method based on the smart campus provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0101] The present application embodiment provides a computer-readable storage medium, such as Figure 4 As shown, the computer-readable storage medium stores 401 a computer program, and when the computer program is read and executed by the second processor 402, the campus safety management method based on the smart campus as described above is implemented.

[0102] The technical solution of the embodiment of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be an air conditioner, a refrigeration device, a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0103] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the campus safety management method based on the smart campus provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0104] An embodiment of the present application provides a computer program product, including a computer program, wherein the computer program is executed by a third processor to implement the method described above.

[0105] The computer program product provided by the above-mentioned embodiments of the present application and the campus safety management method based on the smart campus provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0106] It should be noted that, in this application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0107] Each embodiment in this application is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for evaluating the campus safety management method based on smart campus, electronic device, electronic device, and readable storage medium embodiment, since it is basically similar to the above-mentioned campus safety management method based on smart campus, the description is relatively simple, and the relevant parts can be referred to the partial description of the above-mentioned campus safety management method based on smart campus embodiment.

[0108] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope defined by the claims.

Claims

1. A campus safety management method based on a smart campus, characterized in that: include: Acquire teaching information of a target campus and target user information matching the teaching information of the target campus, wherein the target user information includes at least one user; Processing the target user information to generate physiological state information of the target user; Processing the teaching information of the target campus to generate initial teaching information; Obtain the server address of the teaching early warning model that matches the teaching impact factor; Processing the server address based on preset rules to obtain a target server; Processing the initial teaching information and the physiological state information of the target user based on the teaching early warning model stored in the target server to generate teaching adjustment information; Processing the teaching information of the target campus based on the teaching adjustment information to generate target teaching information; Processing the target teaching information and the target user information based on the teaching early warning model to generate learning feedback information of the target user; Processing the teaching information of the target campus to generate initial teaching information includes: processing the teaching information of the target campus to generate preset teaching information; obtaining feedback information generated by the target user for the preset teaching information, wherein the feedback information includes feedback data generated by the target user after watching the preset teaching information and the corresponding feedback time point, and the feedback data includes action feedback data and voice feedback data; processing the feedback information to generate initial teaching information, wherein the initial teaching information is the teaching content of the target user in a preset time period in the future; Processing the feedback information to generate initial teaching information includes: processing the action feedback data and the voice feedback data to generate an initial understanding degree value of the target user for the preset teaching information; if the initial understanding degree value is lower than the initial standard value, calculating the interval time between the feedback time point and the viewing time point, wherein the viewing time point is the time point when the target user views the preset teaching information; if it is determined that the interval time is lower than the preset time, determining that the understanding degree value of the preset teaching information by the target user is lower than the target standard value; or, if it is determined that the initial understanding degree value is not lower than the initial standard value, determining that the understanding degree value of the preset teaching information by the target user is not lower than the target standard value; generating initial teaching information based on the understanding degree value; Based on the comprehension level value, generating initial teaching information includes: if it is determined that the comprehension level value is lower than the target standard value, determining the planned teaching content for the target user based on the historical teaching content, wherein the historical teaching content is generated by the teaching content included in the preset teaching information; or, if it is determined that the comprehension level value is not lower than the target standard value, determining the planned teaching content for the target user based on the teaching content of the next stage; The initial teaching content and the physiological state information of the target user are processed based on the teaching warning model stored in the target server to generate teaching adjustment information, including: processing the initial teaching content and the physiological state information of the target user based on the teaching warning model to generate a teaching influence factor; processing the teaching influence factor and the physiological state information of the target user to generate a score for each teaching influence factor; processing the score of each teaching influence factor to generate a target score for the initial teaching content; acquiring a number of other teaching contents with the same target score; processing the initial teaching content based on the other teaching contents to generate a target teaching influence factor; processing the target teaching influence factor to generate teaching adjustment information.

2. The method according to claim 1, characterized in that The processing of the server address based on a preset rule to obtain a target server includes: Acquire the number of servers storing the teaching early warning model based on the server address; If there are multiple servers storing the teaching warning model, then obtaining the business load status of the multiple servers; The server with the lowest business load status is used as the target server.

3. The method according to claim 1, characterized in that After the target teaching information and the target user information are processed based on the teaching early warning model to generate learning feedback information of the target user, the method further includes: Processing the target user information based on the target user's learning feedback information to generate attribute information of the target user, wherein the attribute information of the target user includes real-time intimacy information between the target user and the teacher and the target user's entry and exit information; Process the real-time intimacy information between the target user and the teacher and the target user's entry and exit information to generate the target user's classroom risk value; If the classroom risk value of the target user is higher than a preset threshold, a warning message is generated, wherein the warning message is used to indicate that the target user is in an abnormal state.

4. A campus safety management device based on a smart campus, characterized in that: The device comprises: An acquisition module is used to acquire teaching information of a target campus and target user information matching the teaching information of the target campus, wherein the target user information includes at least one user; and acquire a server address of a teaching early warning model matching the teaching impact factor; A processing module is used to process the target user information to generate physiological state information of the target user; process the teaching information of the target campus to generate initial teaching information; process the server address based on a preset rule to obtain a target server; process the initial teaching information and the physiological state information of the target user based on a teaching warning model stored in the target server to generate teaching adjustment information; process the teaching information of the target campus based on the teaching adjustment information to generate target teaching information; process the target teaching information and the target user information based on the teaching warning model to generate learning feedback information of the target user; Processing the teaching information of the target campus to generate initial teaching information includes: processing the teaching information of the target campus to generate preset teaching information; obtaining feedback information generated by the target user for the preset teaching information, wherein the feedback information includes feedback data generated by the target user after watching the preset teaching information and the corresponding feedback time point, and the feedback data includes action feedback data and voice feedback data; processing the feedback information to generate initial teaching information, wherein the initial teaching information is the teaching content of the target user in a preset time period in the future; Processing the feedback information to generate initial teaching information includes: processing the action feedback data and the voice feedback data to generate an initial understanding degree value of the target user for the preset teaching information; if the initial understanding degree value is lower than the initial standard value, calculating the interval time between the feedback time point and the viewing time point, wherein the viewing time point is the time point when the target user views the preset teaching information; if it is determined that the interval time is lower than the preset time, determining that the understanding degree value of the preset teaching information by the target user is lower than the target standard value; or, if it is determined that the initial understanding degree value is not lower than the initial standard value, determining that the understanding degree value of the preset teaching information by the target user is not lower than the target standard value; generating initial teaching information based on the understanding degree value; Based on the comprehension level value, generating initial teaching information includes: if it is determined that the comprehension level value is lower than the target standard value, determining the planned teaching content for the target user based on the historical teaching content, wherein the historical teaching content is generated by the teaching content included in the preset teaching information; or, if it is determined that the comprehension level value is not lower than the target standard value, determining the planned teaching content for the target user based on the teaching content of the next stage; The initial teaching content and the physiological state information of the target user are processed based on the teaching warning model stored in the target server to generate teaching adjustment information, including: processing the initial teaching content and the physiological state information of the target user based on the teaching warning model to generate a teaching influence factor; processing the teaching influence factor and the physiological state information of the target user to generate a score for each teaching influence factor; processing the score of each teaching influence factor to generate a target score for the initial teaching content; acquiring a number of other teaching contents with the same target score; processing the initial teaching content based on the other teaching contents to generate a target teaching influence factor; processing the target teaching influence factor to generate teaching adjustment information.

5. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; Wherein, the first processor is configured to execute the campus safety management method based on smart campus described in any one of claims 1 to 3 by executing the executable instructions.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the campus safety management method based on the smart campus described in any one of claims 1 to 3 is implemented.

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