A risk warning method and system for student behavior trajectory and action analysis

Abstract

The present invention relates to the field of behavioral risk warning technology, and proposes a risk warning method and system for analyzing student behavior trajectories and movements, comprising the following steps: separately collecting video data and sensor data of student behavior; preprocessing the video data and sensor data to obtain multi-source data of student behavior; performing target detection and target tracking on the multi-source data of student behavior to generate student motion trajectories; extracting student behavioral characteristics based on the student motion trajectories, identifying abnormal behavior on the behavioral characteristics, and obtaining abnormal behavior identification results; performing risk level assessment on the abnormal behavior identification results to obtain abnormal behavior risk levels; triggering warning information of corresponding levels based on the abnormal behavior risk levels, processing the warning information, generating final warning results, and pushing the final warning results to management personnel. The present invention achieves all-round monitoring, timely identification, and rapid warning of abnormal student behavior, thereby improving the efficiency of campus safety management.

Description

Technical Field

[0001] The present invention relates to the field of behavioral risk warning technology, and in particular to a risk warning method and system for analyzing student behavior trajectories and movements. Background Art

[0002] As campus safety management requirements continue to increase, real-time monitoring of student behavior and risk warnings are becoming increasingly important. Students may exhibit various abnormal behaviors during campus activities, such as climbing over fences, fainting, and staying for long periods of time. If these behaviors are not detected and addressed in a timely manner, they can lead to safety accidents.

[0003] Existing technologies generally use video surveillance to monitor campuses and adopt a fixed-route inspection mode. They lack intelligent inspection planning and path optimization, resulting in low detection efficiency, inability to timely detect and deal with abnormal behavior, and inability to fully reflect the characteristics of various dimensions of student behavior. There is a lack of risk assessment of abnormal behavior in multiple dimensions, and it is impossible to accurately assess the degree of danger and urgency of the behavior. Summary of the Invention

[0004] In view of this, the present invention proposes a risk warning method and system for student behavior trajectory and action analysis, which solves the problem that the existing technology lacks intelligent inspection planning and path optimization, resulting in low detection efficiency, inability to fully reflect the various dimensional characteristics of student behavior, and inability to accurately assess the degree of danger and urgency of the behavior.

[0005] The technical solution of the present invention is implemented as follows: In a first aspect, the present invention provides a risk warning method for analyzing student behavior trajectories and movements, comprising the following steps:

[0006] Through camera arrays and multiple types of sensors, video data and sensor data of student behavior are collected respectively;

[0007] Preprocessing the video data and the sensor data to obtain multi-source student behavior data;

[0008] Performing target detection and target tracking on the multi-source data of student behavior to generate student movement trajectories;

[0009] Extracting student behavior characteristics based on the student's movement trajectory, and performing abnormal behavior recognition on the behavior characteristics based on a preset normal behavior pattern library to obtain abnormal behavior recognition results;

[0010] Perform risk level assessment on abnormal behavior identification results to obtain abnormal behavior risk level;

[0011] According to the abnormal behavior risk level, the corresponding level of warning information is triggered, the warning information is confirmed and optimized, and the final warning result is generated, and the final warning result is pushed to the management personnel.

[0012] Based on the above technical solution, preferably, the video data and sensor data of student behavior are collected by a camera array and multiple types of sensors, specifically including:

[0013] Deploy camera arrays in key areas of the campus. Based on the spatial distribution characteristics of the key areas, the camera installation positions and angles are determined according to field of view coverage requirements and occlusion conditions. The spacing between adjacent cameras is optimized based on spatial distance and field of view overlap. The camera parameter settings are adjusted according to the lighting conditions of the camera array's collection area to collect video data of student behavior.

[0014] Multiple types of sensors are deployed in key areas of the campus, including infrared sensors, sound sensors and motion sensors. The corresponding types of sensor data are collected by the multiple types of sensors. Infrared sensors are installed at the entrances and corridors of buildings to detect the passage of people; sound sensors are installed in classrooms and activity areas to collect abnormal sound signals; motion sensors are installed around fences and dangerous areas to detect abnormal movements.

[0015] On the basis of the above technical solution, preferably, the preprocessing of the video data and the sensor data to obtain the multi-source data of student behavior specifically includes:

[0016] Performing data cleaning on the video data and the sensor data, respectively, including removing noise data, filling missing values, and eliminating outliers;

[0017] Performing image enhancement and noise reduction processing on the video data to obtain enhanced noise-reduced video data; applying a sliding window filtering algorithm to the sensor data to remove burst noise to obtain denoised sensor data; repairing missing data portions of the enhanced noise-reduced video data and the denoised sensor data using an interpolation algorithm, and identifying and eliminating outliers based on statistical methods to obtain cleaned video data and cleaned sensor data;

[0018] Based on the timestamp information, the sampling time of the cleaned video data and the cleaned sensor data is uniformly converted into a standard time format, and the cleaned video data and the cleaned sensor data are resampled based on the minimum time interval to obtain resampled video data and resampled sensor data. The linear interpolation method is used to synchronize the time of the resampled video data and the resampled sensor data to obtain multi-source data of student behavior.

[0019] Based on the above technical solution, preferably, performing target detection and target tracking on the multi-source data of student behavior to generate student movement trajectories specifically includes:

[0020] Extracting image features and signal features from the multi-source data of student behavior, calculating feature weights of different data sources, performing weighted fusion to obtain behavior fusion features, and inputting the behavior fusion features into a deep learning model to obtain target detection results;

[0021] The calculation formula of the weighted fusion is:

[0022]

[0023] Among them, W i is the fusion weight of the i-th data source, R i is the characteristic reliability index of the i-th data source, S i is the feature significance index of the i-th data source, R j is the characteristic reliability index of the j-th data source, S j is the feature significance index of the j-th data source, α i , β i , α j , β j are the feature reliability adjustment factor of the i-th data source, the feature significance adjustment factor of the i-th data source, the feature reliability adjustment factor of the j-th data source, and the feature significance adjustment factor of the j-th data source, respectively. N1 is the number of data sources;

[0024] The optimized calculation formula for target detection confidence is:

[0025]

[0026] Among them, C is the optimized confidence, C0 is the original confidence, λ is the confidence adjustment coefficient, N1 is the number of data sources, W i is the fusion weight of the i-th data source, D i is the detection score of the i-th data source;

[0027] Based on the target detection result, a target tracking algorithm with multi-feature fusion is used to track the detected target, and a target feature descriptor is constructed. The target feature descriptor includes the appearance features and motion features of the target. The target position is predicted using a Kalman filter algorithm. Data association is performed based on matching calculation to generate a continuous motion trajectory. The generated trajectory is smoothed and optimized to generate a student motion trajectory with spatiotemporal information.

[0028] The calculation formula for the smoothness evaluation is:

[0029]

[0030] Where S is the trajectory smoothness, Δθ k,k+1 is the angle change between the kth trajectory point and the k+1th trajectory point, w k is the time weight factor of the kth trajectory point, and N2 is the number of trajectory points minus 1.

[0031] On the basis of the above technical solution, preferably, the method of extracting the student's behavioral characteristics based on the student's movement trajectory, and performing abnormal behavior recognition on the behavioral characteristics based on a preset normal behavior pattern library to obtain abnormal behavior recognition results specifically includes:

[0032] Based on the student's motion trajectory, extract the student's behavioral characteristics, the behavioral characteristics including spatiotemporal characteristics and posture characteristics, the spatiotemporal characteristics including movement speed, range of activity, and dwell time, the posture characteristics including posture angle, limb movement, and movement amplitude, and construct a student behavior feature vector;

[0033] The calculation formula of the student behavior feature vector is:

[0034] F=[w t ×V t +w s ×V s +w p ×V p ];

[0035] Among them, F is the student behavior feature vector, V t 、V s 、V p are time feature vector, space feature vector and posture feature vector respectively, w t 、w s 、w p are the weight coefficients of time feature vector, space feature vector and posture feature vector respectively, w t 、w s 、w p The sum is 1;

[0036] Calculate the feature similarity between the student's behavior feature vector and each pattern in the normal behavior pattern library, comprehensively consider the feature similarity and behavior duration, calculate the abnormality degree score, determine the abnormal behavior type based on the abnormality degree score, and output the abnormal behavior recognition result, which includes the abnormal behavior type and abnormality degree score;

[0037] The calculation formula of the feature similarity is:

[0038]

[0039] Among them, Sim(·) is the similarity function, F1 is a student behavior feature vector, F2 is the feature vector to be compared in the normal behavior pattern library, exp(·) is the exponential function, γ is the distance decay coefficient, σ is the scale parameter, T1 and T2 are the behavior durations corresponding to F1 and F2 respectively, T max is the maximum time difference threshold;

[0040] The calculation formula of the abnormality degree score is:

[0041]

[0042] Among them, score(F0) is the abnormality score corresponding to another student's behavior feature vector F0, max(Sim) is the maximum feature similarity value, β0 is the time influence factor, D(F0) is the behavior duration corresponding to F0, and D0 is the benchmark duration.

[0043] Based on the above technical solution, preferably, the risk level assessment of the abnormal behavior recognition result to obtain the abnormal behavior risk level specifically includes:

[0044] Based on historical data and expert experience, we quantitatively assess the risk level of different types of abnormal behaviors, analyze the impact of behavior duration, location sensitivity, environmental complexity, and population density on risk levels, and establish risk classification standards;

[0045] The risk level classification standard is used to evaluate the risk level of abnormal behavior identification results, calculate the basic risk score of abnormal behavior, and perform risk weighting based on the duration of abnormal behavior. The regulatory effect of scenario characteristics on risk is then combined to obtain a comprehensive risk score. Based on a preset level threshold, the comprehensive risk score is mapped to the corresponding abnormal behavior risk level, which includes low risk, medium risk, and high risk.

[0046] On the basis of the above technical solution, preferably, triggering the corresponding level of warning information according to the abnormal behavior risk level, confirming and optimizing the warning information, generating a final warning result, and pushing the final warning result to the management personnel specifically includes:

[0047] Generate warning information containing behavior type, location information, risk level, and handling suggestions based on the risk level of abnormal behavior, and conduct credibility assessment and priority sorting on the warning information;

[0048] Based on the risk level and priority of the warning information, determine the push objects and push methods of the warning information, establish a hierarchical push strategy, and push the warning information to managers at the corresponding level.

[0049] In a second aspect, the present invention further provides a risk warning system for analyzing student behavior trajectories and movements, the system comprising:

[0050] A data acquisition module is used to collect video data and sensor data of student behavior through a camera array and multiple types of sensors;

[0051] A data processing module, configured to pre-process the video data and the sensor data to obtain multi-source data of student behavior;

[0052] A trajectory generation module is used to perform target detection and target tracking on the multi-source data of student behavior to generate a student movement trajectory;

[0053] An abnormality identification module is used to extract the student's behavioral characteristics based on the student's movement trajectory, and perform abnormal behavior identification on the behavioral characteristics based on a preset normal behavior pattern library to obtain an abnormal behavior identification result;

[0054] The risk assessment module is used to evaluate the risk level of abnormal behavior identification results and obtain the abnormal behavior risk level;

[0055] The early warning push module is used to trigger the corresponding level of early warning information according to the risk level of the abnormal behavior, confirm and optimize the early warning information, generate the final early warning result, and push the final early warning result to the management personnel.

[0056] In a third aspect, the present invention further provides an electronic device comprising: at least one processor, at least one memory, a communication interface, and a bus;

[0057] Among them, the processor, memory, and communication interface communicate with each other through the bus, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to implement steps such as a risk warning method for student behavior trajectory and action analysis.

[0058] In a fourth aspect, the present invention further provides a computer-readable storage medium storing computer instructions, wherein the computer instructions enable a computer to implement steps of a risk warning method for analyzing student behavior trajectories and movements.

[0059] The risk warning method and system for analyzing student behavior trajectory and movement of the present invention has the following beneficial effects compared with the prior art:

[0060] (1) By deploying camera arrays and multi-type sensors to collect multi-source data, and after pre-processing of data cleaning and timestamp alignment, the system detects and tracks student behaviors, and identifies abnormal behaviors by combining spatiotemporal and posture features. Abnormal behaviors are then graded through risk level assessment, and corresponding warning information is finally generated and pushed to management personnel, thus achieving all-round monitoring, timely identification, and rapid warning of abnormal student behaviors, and improving the efficiency and accuracy of campus safety management.

[0061] (2) By applying feature weighted fusion and deep learning models, combined with multi-feature fusion target tracking algorithm and Kalman filter prediction, the accuracy and confidence of target detection are improved, stable tracking and smooth optimization of student motion trajectories are achieved, and false detections and missed detections are reduced;

[0062] (3) Through the anomaly recognition method of multi-dimensional feature extraction and similarity calculation, combined with the weighted fusion of spatiotemporal features and posture features, and a scoring mechanism that considers time factors, accurate recognition and quantitative evaluation of students' abnormal behaviors are achieved, which reduces the false alarm rate and improves the accuracy of abnormal behavior recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0064] Figure 1 This is a flow chart of a risk warning method for analyzing student behavior trajectory and movements according to the present invention;

[0065] Figure 2 This is a structural diagram of a risk warning system for student behavior trajectory and action analysis according to the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described 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.

[0067] See also Figure 1 The present invention provides a risk warning method for analyzing student behavior trajectory and movement, comprising the following steps:

[0068] Through camera arrays and multiple types of sensors, video data and sensor data of student behavior are collected respectively;

[0069] Preprocessing the video data and the sensor data, including data cleaning and time stamp alignment, to obtain multi-source student behavior data;

[0070] Performing target detection and target tracking on the multi-source data of student behavior to generate student movement trajectories;

[0071] Based on the student's motion trajectory, extract the student's behavioral characteristics, the behavioral characteristics including spatiotemporal characteristics and posture characteristics, and perform abnormal behavior recognition on the behavioral characteristics based on a preset normal behavior pattern library to obtain an abnormal behavior recognition result;

[0072] Perform risk level assessment on abnormal behavior identification results to obtain abnormal behavior risk level;

[0073] According to the abnormal behavior risk level, the corresponding level of warning information is triggered, the warning information is confirmed and optimized, and the final warning result is generated, and the final warning result is pushed to the management personnel.

[0074] Specifically, this embodiment collects multi-source data by deploying camera arrays and multi-type sensors. After pre-processing of data cleaning and timestamp alignment, it performs target detection and tracking of student behavior, and identifies abnormal behaviors by combining spatiotemporal features and posture features. Abnormal behaviors are then graded through risk level assessment, and finally corresponding warning information is generated and pushed to management personnel, thereby achieving all-round monitoring, timely identification and rapid warning of abnormal student behaviors, effectively improving the efficiency and accuracy of campus safety management.

[0075] The camera array and multiple types of sensors are used to collect video data and sensor data of student behavior, including:

[0076] Deploy camera arrays in key areas of the campus. Based on the spatial distribution characteristics of the key areas, the camera installation positions and angles are determined according to field of view coverage requirements and occlusion conditions. The spacing between adjacent cameras is optimized based on spatial distance and field of view overlap. The camera parameter settings are adjusted according to the lighting conditions of the camera array's collection area to collect video data of student behavior.

[0077] Multiple types of sensors are deployed in key areas of the campus, including infrared sensors, sound sensors and motion sensors. The corresponding types of sensor data are collected by the multiple types of sensors. Infrared sensors are installed at the entrances and corridors of buildings to detect the passage of people; sound sensors are installed in classrooms and activity areas to collect abnormal sound signals; motion sensors are installed around fences and dangerous areas to detect abnormal movements.

[0078] In one specific embodiment, a camera array and multiple sensor types are deployed in key campus areas, such as building entrances, playgrounds, and corridors. The camera installation locations and angles are optimized based on the campus's spatial distribution to maximize field of view coverage and minimize obstructions. The spacing between adjacent cameras is adjusted based on spatial distance and field of view overlap to achieve seamless monitoring. Camera parameter settings are adjusted based on the lighting conditions in the capture area to ensure clear video data is captured under varying lighting conditions.

[0079] Specifically, this embodiment ensures comprehensive coverage of key campus areas through the rational deployment of cameras and sensors, reducing blind spots and improving monitoring efficiency. Combining video data with data from multiple sensor types provides more comprehensive behavioral information, helping to more accurately identify abnormal behavior. Dynamic adjustment of camera parameters allows adaptation to varying lighting conditions, ensuring stable data collection. The collaborative work of multiple sensor types enables more accurate detection and identification of abnormal behavior, reducing the likelihood of false positives and missed alerts.

[0080] The preprocessing of the video data and the sensor data to obtain multi-source student behavior data specifically includes:

[0081] Performing data cleaning on the video data and the sensor data, respectively, including removing noise data, filling missing values, and eliminating outliers;

[0082] Performing image enhancement and noise reduction on the video data to obtain enhanced and denoised video data, eliminating image quality issues caused by factors such as insufficient lighting and weather changes; applying a sliding window filtering algorithm to the sensor data to remove sudden noise and obtain denoised sensor data; using an interpolation algorithm to repair missing data portions of the enhanced and denoised video data and the denoised sensor data, and identifying and eliminating outliers based on statistical methods to obtain cleaned video data and cleaned sensor data;

[0083] Based on the timestamp information, the sampling time of the cleaned video data and the cleaned sensor data is uniformly converted into a standard time format, and the cleaned video data and the cleaned sensor data are resampled based on the minimum time interval to obtain resampled video data and resampled sensor data, so that the sampling frequencies of the cleaned video data and the cleaned sensor data are consistent. The linear interpolation method is used to synchronize the time of the resampled video data and the resampled sensor data to achieve precise alignment of multi-source data and obtain multi-source data of student behavior.

[0084] In a specific embodiment, preprocessing the collected video data and sensor data specifically includes:

[0085] Perform image enhancement and noise reduction on video data, use Gaussian filters to reduce noise and enhance image quality, especially in low light or weather conditions. Apply a sliding window filtering algorithm (the window size can be set to 5 data points) to the sensor data to remove sudden noise, use linear interpolation to fill missing values, and identify and eliminate outliers through statistical methods (such as the 3σ principle).

[0086] The sampling time of the cleaned video data and sensor data is uniformly converted into a standard time format (such as UTC), and the cleaned video data and sensor data are resampled based on a minimum time interval (such as 100 milliseconds) to make the sampling frequency of the two consistent. The linear interpolation method is used to synchronize the time of the resampled video data and sensor data to achieve accurate alignment of multi-source data.

[0087] Specifically, this embodiment improves the clarity of video data and the accuracy of sensor data through image enhancement and noise reduction processing, reduces the impact of noise and outliers, ensures the time synchronization of multi-source data through timestamp alignment and resampling, enables information from different data sources to be analyzed in the same time frame, improves the accuracy of data fusion, and makes the data after preprocessing more complete and consistent, providing more reliable sample data for target detection, behavior feature extraction and anomaly identification.

[0088] The target detection and target tracking of the multi-source student behavior data to generate the student movement trajectory specifically includes:

[0089] Extracting image features and signal features from the multi-source student behavior data, calculating feature weights of different data sources, performing weighted fusion to obtain behavior fusion features, inputting the behavior fusion features into a deep learning model to obtain target detection results, wherein the confidence of the target detection is optimized and calculated based on the feature fusion weights;

[0090] The calculation formula of the weighted fusion is:

[0091]

[0092] Among them, W i is the fusion weight of the i-th data source, R i is the characteristic reliability index of the i-th data source, S i is the feature significance index of the i-th data source, R j is the characteristic reliability index of the j-th data source, S j is the feature significance index of the j-th data source, α i , β i , α j , β j are the feature reliability adjustment factor of the i-th data source, the feature significance adjustment factor of the i-th data source, the feature reliability adjustment factor of the j-th data source, and the feature significance adjustment factor of the j-th data source, respectively. N1 is the number of data sources;

[0093] The optimized calculation formula for target detection confidence is:

[0094]

[0095] Among them, C is the optimized confidence, C0 is the original confidence, λ is the confidence adjustment coefficient, N1 is the number of data sources, W i is the fusion weight of the i-th data source, D i is the detection score of the i-th data source;

[0096] Based on the target detection result, a target tracking algorithm with multi-feature fusion is used to track the detected target, and a target feature descriptor is constructed. The target feature descriptor includes the appearance features and motion features of the target. The target position is predicted using a Kalman filter algorithm. Data association is performed based on matching calculation to generate a continuous motion trajectory. The generated trajectory is smoothed and optimized to generate a student motion trajectory with spatiotemporal information.

[0097] The calculation formula for the smoothness evaluation is:

[0098]

[0099] Where S is the trajectory smoothness, Δθ k,k+1 is the angle change between the kth trajectory point and the k+1th trajectory point, w k is the time weight factor of the kth trajectory point, and N2 is the number of trajectory points minus 1.

[0100] In one specific embodiment, image features and signal features are extracted from multi-source student behavior data. Image features can include color histograms and edge features, while signal features can include the frequency response of sensors. Feature weights are calculated for different data sources, for example, setting the image feature weight to 0.6 and the signal feature weight to 0.4.

[0101] The extracted features are weighted and fused to obtain the behavioral fusion features. The fusion process can use the weighted average method, in which the weight of each feature is assigned according to its importance.

[0102] The behavior fusion features are input into a pre-trained deep learning model (such as a convolutional neural network) to obtain the target detection results.

[0103] Based on the target detection results, a multi-feature fusion target tracking algorithm is used to track the detected target. A target feature descriptor is constructed, including the target's appearance features (such as color and shape) and motion features (such as speed and direction). A Kalman filter algorithm is used to predict the target's position. Data association is performed based on matching calculations to generate a continuous motion trajectory. The generated trajectory is then evaluated for smoothness and optimized to ensure accuracy and continuity.

[0104] Specifically, this embodiment improves the accuracy and confidence of target detection by applying weighted feature fusion and deep learning models, reducing false and missed detections. The target tracking method, which employs a Kalman filter algorithm and multi-feature fusion, effectively predicts target positions and performs data association, resulting in smoother and more continuous motion trajectories. This embodiment can process multi-source data in real time in complex environments, maintaining efficient target detection and tracking capabilities and adapting to varying lighting and environmental conditions.

[0105] The method of extracting the student's behavioral characteristics based on the student's movement trajectory and performing abnormal behavior recognition on the behavioral characteristics based on a preset normal behavior pattern library to obtain abnormal behavior recognition results specifically includes:

[0106] Based on the student's motion trajectory, extract the student's behavioral characteristics, the behavioral characteristics including spatiotemporal characteristics and posture characteristics, the spatiotemporal characteristics including movement speed, range of activity, and dwell time, the posture characteristics including posture angle, limb movement, and movement amplitude, and construct a student behavior feature vector;

[0107] The calculation formula of the student behavior feature vector is:

[0108] F=[w t ×V t +w s ×V s +w p ×V p ];

[0109] Among them, F is the student behavior feature vector, V t 、V s 、V p are time feature vector, space feature vector and posture feature vector respectively, w t 、w s 、w p are the weight coefficients of time feature vector, space feature vector and posture feature vector respectively, w t 、w s 、w p The sum is 1;

[0110] Calculate the instantaneous velocity and acceleration of the student's motion trajectory to obtain the motion speed; calculate the spatial distribution of the activity area to obtain the activity range; calculate the length of stay to obtain the stay time; extract posture features based on skeleton key point detection, including posture angle, limb movement and movement amplitude; normalize the extracted spatiotemporal features and posture features to construct a multidimensional feature vector, thereby obtaining the student behavior feature vector;

[0111] Calculate the feature similarity between the student's behavior feature vector and each pattern in the normal behavior pattern library, comprehensively consider the feature similarity and behavior duration, calculate the abnormality degree score, determine the abnormal behavior type based on the abnormality degree score, and output the abnormal behavior recognition result, which includes the abnormal behavior type and abnormality degree score;

[0112] The calculation formula of the feature similarity is:

[0113]

[0114] Among them, Sim(·) is the similarity function, F1 is a student behavior feature vector, F2 is the feature vector to be compared in the normal behavior pattern library, exp(·) is the exponential function, γ is the distance decay coefficient, σ is the scale parameter, T1 and T2 are the behavior durations corresponding to F1 and F2 respectively, T max is the maximum time difference threshold;

[0115] The calculation formula of the abnormality degree score is:

[0116]

[0117] Among them, score(F0) is the abnormality score corresponding to another student's behavior feature vector F0, max(Sim) is the maximum feature similarity value, β0 is the time influence factor, D(F0) is the behavior duration corresponding to F0, and D0 is the benchmark duration.

[0118] In a specific embodiment, extracting behavioral features and identifying anomalies includes:

[0119] 1) Behavioral feature extraction:

[0120] Extraction of spatiotemporal features includes: movement speed: calculating the displacement-time ratio between adjacent trajectory points; activity range: statistically analyzing the spatial distribution range of trajectory points (e.g., the range in a classroom should not exceed 20 square meters); residence time: recording the length of stay at a specific location (e.g., staying in the toilet for more than 30 minutes is considered abnormal);

[0121] The extracted posture features include: posture angle: calculating the angles of various body parts through skeletal key point detection; limb movement: identifying abnormal movements (such as falling, fighting, etc.); movement amplitude: calculating the amplitude changes of limb movement.

[0122] 2) Feature vector construction:

[0123] It is constructed by the calculation formula of the student behavior feature vector, where w t 、w s 、w p Take 0.3, 0.4, and 0.3 respectively.

[0124] 3) Abnormal behavior identification:

[0125] The feature similarity is calculated by the feature similarity calculation formula, wherein the distance decay coefficient γ is 0.5, the scale parameter σ is 1.0, and the maximum time difference threshold T is max Take 300 seconds.

[0126] The abnormality degree score is calculated using the abnormality degree score calculation formula, wherein the time impact factor β0 is set to 0.2 and the reference duration D0 is set to 180 seconds.

[0127] Specifically, this embodiment achieves a comprehensive characterization of student behavior through multi-dimensional feature extraction. The weight distribution of feature vectors ensures balanced consideration of various features. Normalization ensures comparability of different features, reducing the false positive rate. A scoring mechanism that considers time improves recognition accuracy, and multi-dimensional feature fusion enhances the robustness of abnormal behavior recognition.

[0128] The risk level assessment of the abnormal behavior identification result to obtain the abnormal behavior risk level specifically includes:

[0129] Based on historical data and expert experience, we quantitatively assess the risk level of different types of abnormal behaviors, analyze the impact of behavior duration, location sensitivity, environmental complexity, and population density on risk levels, and establish risk classification standards;

[0130] The abnormal behavior identification results are evaluated for risk level using the risk level classification criteria. A basic risk score for the abnormal behavior is calculated. This is weighted based on the duration of the abnormal behavior, and the regulatory effect of scenario characteristics on risk is comprehensively considered to obtain a comprehensive risk score. Based on a preset level threshold, the comprehensive risk score is mapped to a corresponding abnormal behavior risk level, which includes low risk, medium risk, and high risk.

[0131] The calculation formula of the comprehensive risk score is:

[0132]

[0133] Among them, R is the comprehensive risk score, B is the basic risk score, T4 is the duration of abnormal behavior, T3 is the benchmark duration of abnormal behavior, S a is the scene influence factor, α1 is the basic weight coefficient, μ is the time influence coefficient, λ1 is the scene adjustment coefficient, L, E, and M are location sensitivity, environmental complexity, and personnel density, respectively. ω1, ω2, and ω3 are the weight coefficients of location sensitivity, environmental complexity, and personnel density, respectively.

[0134] In a specific embodiment, the specific steps of performing risk level assessment include:

[0135] 1) Establishment of risk level classification standards:

[0136] Based on historical data and expert experience, a quantitative assessment of different types of abnormal behaviors was conducted, and the weights of risk influencing factors were established: the location sensitivity weight (ω1) was taken as 0.35, the environment complexity weight (ω2) was taken as 0.30, and the personnel density weight (ω3) was taken as 0.35.

[0137] 2) Calculation of comprehensive risk score:

[0138] The calculation is performed using the formula for the comprehensive risk score, wherein the basic weight coefficient α1 is 0.8, the time influence coefficient μ is 0.2, the scenario adjustment coefficient λ1 is 0.15, and the abnormal behavior duration benchmark time T3 is 300 seconds.

[0139] 3) Calculation of scene impact factors:

[0140] The calculation is performed using the scenario impact factor formula, where the location sensitivity L ranges from 0 to 1, with a value of 0.3 for ordinary areas, 0.6 for key areas, and 0.9 for dangerous areas; the environmental complexity E ranges from 0 to 1, with a value of 0.2 for simple environments, 0.5 for general environments, and 0.8 for complex environments; the population density M ranges from 0 to 1, with a value of 0.3 for sparse crowds, 0.6 for general gathered crowds, and 0.9 for dense crowds.

[0141] 4) Risk level classification:

[0142] The classification is based on the threshold of the comprehensive risk score R: when R < 0.4, it is classified as low risk; when 0.4 ≤ R < 0.7, it is classified as medium risk; when R ≥ 0.7, it is classified as high risk.

[0143] Specifically, this embodiment utilizes a quantitative risk assessment model to make risk level assessment more objective. This model considers multi-dimensional risk factors, enhancing the comprehensiveness of the assessment. Parameter optimization based on historical data ensures the reliability of the assessment results. This embodiment dynamically adjusts weight parameters based on different scenarios, introduces time-sensitive factors to enhance the timeliness of the assessment, and comprehensively considers scenario characteristics to enhance the adaptability of the assessment.

[0144] Clear level divisions facilitate quick response by managers, quantitative evaluation results facilitate statistical analysis, and the evaluation process is automated, improving work efficiency.

[0145] According to the abnormal behavior risk level, triggering the corresponding level of warning information, confirming and optimizing the warning information, generating a final warning result, and pushing the final warning result to the management personnel, specifically includes:

[0146] Generate warning information containing behavior type, location information, risk level, and handling suggestions based on the risk level of abnormal behavior, and conduct credibility assessment and priority sorting on the warning information;

[0147] Extract key characteristic information of abnormal behavior, including the time, location, duration, and risk level of the behavior; establish a warning credibility assessment model based on historical warning data; combine scenario information and historical handling experience to generate targeted handling suggestions; deduplicate and merge warning information to avoid duplicate warnings; and prioritize warning information based on risk level and urgency.

[0148] Based on the risk level and priority of the warning information, determine the push targets and push methods of the warning information, establish a hierarchical push strategy, and push the warning information to managers at the corresponding level;

[0149] Establish a three-level early warning push mechanism, push low-risk warnings to general managers, medium-risk warnings to regional supervisors, and high-risk warnings to school safety managers at the same time; select the most suitable push targets based on the on-the-job status and location information of the managers; adopt multi-channel push methods, including mobile APP push, SMS notifications and phone reminders; automatically upgrade and push warning information that has not been processed in time; record the processing process and results of warning information for subsequent optimization of warning strategies.

[0150] In a specific embodiment, the specific steps for generating and pushing warning information include:

[0151] 1) Early warning information generation and evaluation:

[0152] The warning information includes: behavior type, such as climbing over a wall, fainting, and staying for a long time; location information, accurate to specific areas, such as the basketball court on the playground, a classroom on a certain floor in a certain unit of Building A; risk level, such as low risk, medium risk, and high risk; and handling suggestions, such as if a student faints on the basketball court on the playground, immediately notify the playground management staff to provide assistance.

[0153] Calculation of warning credibility score:

[0154] A weighted calculation is performed on the risk level, timeliness, and historical accuracy, with weights of 0.4, 0.3, and 0.3 respectively, to calculate the warning credibility of the corresponding warning information.

[0155] 2) Warning priority ranking:

[0156] The priority score is calculated by weighting the risk level and urgency, with weights of 0.6 and 0.4 respectively. For priority classification, warning information with a priority score of not less than 0.8 is marked as the highest priority; warning information with a priority score between 0.5 and 0.8 is marked as high priority; warning information with a priority score of not more than 0.5 is marked as normal priority.

[0157] 3) Hierarchical push strategy:

[0158] For low-risk warnings: the push targets are ordinary management personnel, the response time limit is 30 minutes, and the push method is APP push; for medium-risk warnings: the push targets are regional supervisors, the response time limit is 15 minutes, and the push method is APP push and SMS notification at the same time; for high-risk warnings: the push targets are school safety managers, the response time limit is 5 minutes, and the push method is APP push, SMS notification, and phone reminder at the same time.

[0159] 4) Early warning upgrade mechanism:

[0160] Timeout escalation threshold: Low-risk alerts that remain unaddressed for more than 45 minutes are automatically upgraded to medium-risk alerts. Medium-risk alerts that remain unaddressed for more than 20 minutes are automatically upgraded to high-risk alerts. Update interval after upgrade: 5 minutes for the first update, 3 minutes for the second update, and 1 minute for the third update.

[0161] Specifically, this embodiment improves the accuracy of early warning information through multi-dimensional information integration, reduces the false alarm rate through a credibility assessment mechanism, and adopts hierarchical push to ensure the accuracy of information transmission.

[0162] This embodiment uses prioritized notifications to ensure timely processing of important alerts, improves information delivery through multi-channel push notifications, and employs an automatic escalation mechanism to prevent alerts from being overlooked. This tiered notification process reduces the workload of administrators, provides recommendations for handling issues, improves efficiency, and maintains alert records for subsequent optimization and analysis.

[0163] See also Figure 2 The present invention also provides a risk warning system for analyzing student behavior trajectories and movements, the system comprising:

[0164] A data acquisition module is used to collect video data and sensor data of student behavior through a camera array and multiple types of sensors;

[0165] A data processing module, configured to pre-process the video data and the sensor data to obtain multi-source data of student behavior;

[0166] A trajectory generation module is used to perform target detection and target tracking on the multi-source data of student behavior to generate a student movement trajectory;

[0167] An abnormality identification module is used to extract the student's behavioral characteristics based on the student's movement trajectory, and perform abnormal behavior identification on the behavioral characteristics based on a preset normal behavior pattern library to obtain an abnormal behavior identification result;

[0168] The risk assessment module is used to evaluate the risk level of abnormal behavior identification results and obtain the abnormal behavior risk level;

[0169] The early warning push module is used to trigger the corresponding level of early warning information according to the risk level of the abnormal behavior, confirm and optimize the early warning information, generate the final early warning result, and push the final early warning result to the management personnel.

[0170] Specifically, the risk warning system for student behavior trajectory and action analysis in this embodiment realizes the accurate collection, intelligent analysis and efficient warning of multi-source data, while ensuring the integrity of data processing and improving the efficiency of system operation, it also improves the intelligent level of campus safety management.

[0171] The present invention also discloses an electronic device, comprising: at least one processor, at least one memory communication interface and a bus: wherein the processor, memory and communication interface communicate with each other through the bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to implement a distributed photovoltaic power station drone inspection method.

[0172] The present invention also discloses a computer-readable storage medium storing computer instructions that cause the computer to implement all or part of the steps of the distributed photovoltaic power station drone inspection method described in an embodiment of the present invention. The storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0173] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A risk warning method for student behavior trajectory and action analysis, characterized in that: The following steps are involved: Through camera arrays and multiple types of sensors, video data and sensor data of student behavior are collected respectively; Preprocessing the video data and the sensor data to obtain multi-source data of student behavior; Performing target detection and target tracking on the multi-source data of student behavior to generate student movement trajectories; The target detection and target tracking of the multi-source student behavior data to generate the student movement trajectory specifically includes: Extracting image features and signal features from the multi-source data of student behavior, calculating feature weights of different data sources, performing weighted fusion to obtain behavior fusion features, and inputting the behavior fusion features into a deep learning model to obtain target detection results; Based on the target detection result, a target tracking algorithm with multi-feature fusion is used to track the detected target, and a target feature descriptor is constructed. The target feature descriptor includes the appearance features and motion features of the target. The target position is predicted using a Kalman filter algorithm. Data association is performed based on matching calculation to generate a continuous motion trajectory. The generated trajectory is smoothed and optimized to generate a student motion trajectory with spatiotemporal information. The calculation formula for the smoothness evaluation is: Where S is the trajectory smoothness, Δθ k,k+1 is the angle change between the kth trajectory point and the k+1th trajectory point, w k is the time weight factor of the kth trajectory point, and N2 is the number of trajectory points minus 1; Extracting student behavior characteristics based on the student's movement trajectory, and performing abnormal behavior recognition on the behavior characteristics based on a preset normal behavior pattern library to obtain abnormal behavior recognition results; Perform risk level assessment on abnormal behavior identification results to obtain abnormal behavior risk level; According to the abnormal behavior risk level, the corresponding level of warning information is triggered, the warning information is confirmed and optimized, and the final warning result is generated, and the final warning result is pushed to the management personnel.

2. A risk warning method for analyzing student behavior trajectory and movement as claimed in claim 1, characterized in that: The camera array and multiple types of sensors are used to collect video data and sensor data of student behavior, including: Deploy camera arrays in key areas of the campus. Based on the spatial distribution characteristics of the key areas, the camera installation positions and angles are determined according to field of view coverage requirements and occlusion conditions. The spacing between adjacent cameras is optimized based on spatial distance and field of view overlap. The camera parameter settings are adjusted according to the lighting conditions of the camera array's collection area to collect video data of student behavior. Multiple types of sensors are deployed in key areas of the campus, including infrared sensors, sound sensors and motion sensors. The corresponding types of sensor data are collected by the multiple types of sensors. Infrared sensors are installed at the entrances and corridors of buildings to detect the passage of people; sound sensors are installed in classrooms and activity areas to collect abnormal sound signals; motion sensors are installed around fences and dangerous areas to detect abnormal movements.

3. The risk warning method for analyzing student behavior trajectory and movement according to claim 1, characterized in that: The preprocessing of the video data and the sensor data to obtain multi-source student behavior data specifically includes: Performing data cleaning on the video data and the sensor data, respectively, including removing noise data, filling missing values, and eliminating outliers; Performing image enhancement and noise reduction processing on the video data to obtain enhanced noise-reduced video data; applying a sliding window filtering algorithm to the sensor data to remove burst noise to obtain denoised sensor data; repairing missing data portions of the enhanced noise-reduced video data and the denoised sensor data using an interpolation algorithm, and identifying and eliminating outliers based on statistical methods to obtain cleaned video data and cleaned sensor data; Based on the timestamp information, the sampling time of the cleaned video data and the cleaned sensor data is uniformly converted into a standard time format, and the cleaned video data and the cleaned sensor data are resampled based on the minimum time interval to obtain resampled video data and resampled sensor data. The linear interpolation method is used to synchronize the time of the resampled video data and the resampled sensor data to obtain multi-source data of student behavior.

4. A risk warning method for analyzing student behavior trajectory and movement as claimed in claim 3, characterized in that: The calculation formula of the weighted fusion is: Among them, W i is the fusion weight of the i-th data source, R i is the characteristic reliability index of the i-th data source, S i is the feature significance index of the i-th data source, R j is the characteristic reliability index of the j-th data source, S j is the feature significance index of the j-th data source, α i , β i , α j , β j are the feature reliability adjustment factor of the i-th data source, the feature significance adjustment factor of the i-th data source, the feature reliability adjustment factor of the j-th data source, and the feature significance adjustment factor of the j-th data source, respectively. N1 is the number of data sources; The optimized calculation formula for target detection confidence is: Among them, C is the optimized confidence, C0 is the original confidence, λ is the confidence adjustment coefficient, N1 is the number of data sources, W i is the fusion weight of the i-th data source, D i is the detection score of the i-th data source.

5. The risk warning method for analyzing student behavior trajectory and movement according to claim 1, characterized in that: The method of extracting the student's behavioral characteristics based on the student's movement trajectory and performing abnormal behavior recognition on the behavioral characteristics based on a preset normal behavior pattern library to obtain abnormal behavior recognition results specifically includes: Based on the student's motion trajectory, extract the student's behavioral characteristics, the behavioral characteristics including spatiotemporal characteristics and posture characteristics, the spatiotemporal characteristics including movement speed, range of activity, and dwell time, the posture characteristics including posture angle, limb movement, and movement amplitude, and construct a student behavior feature vector; The calculation formula of the student behavior feature vector is: F=[w t ×V t +w s ×V s +w p ×V p ]; Among them, F is the student behavior feature vector, V t 、V s 、V p are time feature vector, space feature vector and posture feature vector respectively, w t 、w s 、w p are the weight coefficients of time feature vector, space feature vector and posture feature vector, w t 、w s 、w p The sum is 1; Calculate the feature similarity between the student's behavior feature vector and each pattern in the normal behavior pattern library, comprehensively consider the feature similarity and behavior duration, calculate the abnormality degree score, determine the abnormal behavior type based on the abnormality degree score, and output the abnormal behavior recognition result, which includes the abnormal behavior type and abnormality degree score; The calculation formula of the feature similarity is: Among them, Sim(·) is the similarity function, F1 is a student behavior feature vector, F2 is the feature vector to be compared in the normal behavior pattern library, exp(·) is the exponential function, γ is the distance decay coefficient, σ is the scale parameter, T1 and T2 are the behavior durations corresponding to F1 and F2 respectively, T max is the maximum time difference threshold; The calculation formula of the abnormality degree score is: Among them, score(F0) is the abnormality score corresponding to another student's behavior feature vector F0, max(Sim) is the maximum feature similarity value, β0 is the time influence factor, D(F0) is the behavior duration corresponding to F0, and D0 is the benchmark duration.

6. A risk warning method for analyzing student behavior trajectory and movement according to claim 5, characterized in that: The risk level assessment of the abnormal behavior identification result to obtain the abnormal behavior risk level specifically includes: Based on historical data and expert experience, we quantitatively assess the risk level of different types of abnormal behaviors, analyze the impact of behavior duration, location sensitivity, environmental complexity, and population density on risk levels, and establish risk classification standards; The risk level classification standard is used to evaluate the risk level of abnormal behavior identification results, calculate the basic risk score of abnormal behavior, and perform risk weighting based on the duration of abnormal behavior. The regulatory effect of scenario characteristics on risk is then combined to obtain a comprehensive risk score. Based on a preset level threshold, the comprehensive risk score is mapped to the corresponding abnormal behavior risk level, which includes low risk, medium risk, and high risk.

7. The risk warning method for analyzing student behavior trajectory and movement according to claim 1, characterized in that: According to the abnormal behavior risk level, triggering the corresponding level of warning information, confirming and optimizing the warning information, generating a final warning result, and pushing the final warning result to the management personnel, specifically includes: Generate warning information containing behavior type, location information, risk level, and handling suggestions based on the risk level of abnormal behavior, and conduct credibility assessment and priority sorting on the warning information; Based on the risk level and priority of the warning information, determine the push objects and push methods of the warning information, establish a hierarchical push strategy, and push the warning information to managers at the corresponding level.

8. A risk warning system for analyzing student behavior trajectories and movements, used to implement a risk warning method for analyzing student behavior trajectories and movements as described in any one of claims 1 to 7, characterized in that: The system comprises: A data acquisition module is used to collect video data and sensor data of student behavior through a camera array and multiple types of sensors; A data processing module, configured to pre-process the video data and the sensor data to obtain multi-source data of student behavior; A trajectory generation module is used to perform target detection and target tracking on the multi-source data of student behavior to generate a student movement trajectory; An abnormality identification module is used to extract the student's behavioral characteristics based on the student's movement trajectory, and perform abnormal behavior identification on the behavioral characteristics based on a preset normal behavior pattern library to obtain an abnormal behavior identification result; The risk assessment module is used to evaluate the risk level of abnormal behavior identification results and obtain the abnormal behavior risk level; The early warning push module is used to trigger the corresponding level of early warning information according to the risk level of the abnormal behavior, confirm and optimize the early warning information, generate the final early warning result, and push the final early warning result to the management personnel.

9. An electronic device, characterized in that: include: at least one processor, at least one memory, a communication interface, and a bus; The processor, memory, and communication interface communicate with each other via the bus, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions enable a computer to implement the method according to any one of claims 1 to 7.

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