Drug rehabilitation place management and control method based on security and protection internet-of-things sensing platform

Abstract

The invention discloses a drug rehabilitation place management and control method based on a security and protection Internet of Things sensing platform, and relates to the technical field of intelligent management and control. Deducing the behavior trajectory of the drug addict by using a neural differential prediction model based on the video stream to obtain a behavior trajectory evolution path coordinate sequence, establishing a high-risk behavior pattern library based on historical relapse action feature rules, and marking feature fragments in combination with the behavior trajectory evolution path coordinate sequence; establishing a symbol rule base based on the historical behavior characteristic data, and generating a risk report according to the characteristic fragment, the behavior track evolution path coordinate sequence and the symbol rule base; matching an emergency disposal scheme according to the risk report, positioning the position of a high-risk person, and scheduling the nearest security resource to execute a hierarchical intervention action; according to the method, the spatial position rule, the motion mode rule and the physiological response rule are integrated through the symbol rule base, so that collaborative analysis of multi-source heterogeneous data and accurate matching of risk characteristics are realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent management and control technology, and in particular to a management and control method for drug rehabilitation centers based on a security IoT sensing platform. Background Technology

[0002] With the deep integration of IoT and AI technologies, security management in drug rehabilitation centers has gradually shifted from manual control to intelligent and data-driven approaches. Early management in these centers relied primarily on manual patrols, basic video surveillance, and simple physiological indicator monitoring (such as heart rate and body temperature), with the core focus on identifying high-risk behaviors through fixed rules. In recent years, with the widespread adoption of security IoT sensing platforms, drug rehabilitation centers have begun to introduce multimodal data fusion technologies. These include using high-definition cameras to collect video streams, wearable devices to monitor physiological data, and deploying environmental sensors to collect environmental parameters such as temperature and humidity, enabling preliminary analysis of inmate behavior. Simultaneously, some centers are experimenting with combining big data analytics to build behavioral feature databases and using rule engines to match risk patterns.

[0003] Despite the progress made by existing methods in data collection and preliminary risk identification, two shortcomings remain. First, existing behavior prediction methods rely on pre-set rule bases and historical feature databases, making it difficult to address the dynamic evolution of drug addicts' behavioral trajectories. Second, existing methods have weak collaborative analysis capabilities for multi-source heterogeneous data and lack dynamic fusion mechanisms for cross-modal data. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for managing drug rehabilitation centers based on a security IoT sensing platform to address the problems of lag in dynamic behavior evolution prediction and insufficient collaborative analysis of multi-source heterogeneous data.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for managing drug rehabilitation facilities based on a security IoT sensing platform, comprising,

[0008] Collect video streams and physiological data;

[0009] Based on video streams, a neural differential prediction model is used to extrapolate the behavioral trajectories of drug addicts, resulting in a sequence of coordinates for the evolutionary paths of the behavioral trajectories. A high-risk behavioral pattern database is established based on historical relapse action feature rules, and feature segments are marked by combining the sequence of coordinates for the evolutionary paths of the behavioral trajectories.

[0010] A symbol rule base is established based on historical behavioral feature data, and a risk report is generated based on feature fragments, behavioral trajectory evolution path coordinate sequences, and the symbol rule base.

[0011] Based on the risk report, match the emergency response plan, locate the high-risk personnel, and dispatch the nearest security resources to carry out tiered intervention actions;

[0012] The parameters of the neural differential prediction model are updated based on physiological data from the execution of graded intervention actions, and the parameters in the symbol rule base are adjusted.

[0013] As a preferred embodiment of the drug rehabilitation facility management method based on a security IoT sensing platform described in this invention, the step of using a neural differential prediction model based on video streams to deduce the behavioral trajectory of drug addicts and obtain the coordinate sequence of the behavioral trajectory evolution path is as follows:

[0014] Based on video stream recognition, the location of key points on the body of drug addicts is identified, and a three-dimensional coordinate sequence of key points is generated.

[0015] The three-dimensional coordinate sequence of the joints is input into the neural differential prediction model, and the ordinary differential equation solver is used to calculate the displacement difference of the joints to obtain the velocity change trend vector of the joints.

[0016] Perform a difference operation on the velocity change trend vector of the joints to obtain the acceleration change trend vector of the joints;

[0017] Based on the trend vectors of joint velocity and acceleration, a sequence of coordinates for the evolution path of the behavioral trajectory is generated.

[0018] As a preferred embodiment of the drug rehabilitation facility management method based on a security IoT sensing platform described in this invention, the step of establishing a high-risk behavior pattern database based on historical relapse behavior feature rules, and marking feature segments by combining the behavior trajectory evolution path coordinate sequence, includes the following specific steps.

[0019] The behavior trajectory evolution path coordinate sequence is divided into coordinate segments of continuous time frames. In each coordinate segment, the Euclidean distance between joints is calculated frame by frame. The displacement difference of joints in adjacent frames is calculated synchronously to obtain the instantaneous velocity of the joints.

[0020] The Euclidean distance between key points and the instantaneous velocity of key points are compared with a high-risk behavior pattern database to obtain the comparison results and label the feature segments.

[0021] As a preferred embodiment of the drug rehabilitation facility management method based on a security IoT sensing platform described in this invention, the specific steps for generating a risk report based on feature fragments, behavioral trajectory evolution path coordinate sequences, and symbol rule bases are as follows:

[0022] The behavior trajectory evolution path coordinate sequence is segmented into behavior trajectory evolution path coordinate sequence segments based on feature fragments;

[0023] Calculate the instantaneous velocity vector and acceleration vector of the joints based on the coordinate sequence fragments of the behavior trajectory evolution path;

[0024] The matching result record is generated based on the rules of the symbol rule base, the instantaneous velocity vector of the joint, and the acceleration vector of the joint;

[0025] Based on the matching results, any one of the high-risk conclusions and medium-to-low-risk conclusions is generated. The high-risk conclusion, medium-to-low-risk conclusion, feature fragments, and behavioral trajectory evolution path coordinate sequences are integrated into a risk report.

[0026] As a preferred embodiment of the drug rehabilitation site management method based on a security IoT sensing platform described in this invention, the calculation of the instantaneous velocity vector and the acceleration vector of the joint point based on the coordinate sequence segment of the behavior trajectory evolution path refers to performing time difference operation on the three-dimensional coordinates of the joint point of the coordinate sequence segment of the behavior trajectory evolution path to obtain the instantaneous velocity vector of the joint point, and performing time difference operation on the instantaneous velocity vector of the joint point to obtain the acceleration vector of the joint point.

[0027] As a preferred embodiment of the drug rehabilitation facility management method based on a security IoT sensing platform described in this invention, the specific steps for generating matching result records based on a symbol rule library, instantaneous velocity vectors of key points, and acceleration vectors of key points are as follows:

[0028] The rules in the symbol rule base refer to spatial location rules, motion pattern rules, and physiological response rules;

[0029] Calculate the source-target Euclidean distance corresponding to the spatial location rule, compare the source-target Euclidean distance with the spatial location rule, and obtain the Boolean matching result of the spatial location rule;

[0030] The magnitude of the instantaneous velocity vector of the joint is used as the instantaneous velocity value corresponding to the motion pattern rule. The instantaneous velocity value and the motion pattern rule are compared to obtain the Boolean matching result of the motion pattern rule.

[0031] Calculate the percentage difference between physiological data in adjacent time frames, compare the percentage difference with the physiological response rule, and obtain the Boolean matching result of the physiological response rule;

[0032] The source-target Euclidean distance, instantaneous velocity value, difference percentage, spatial location rules and corresponding Boolean matching results, motion pattern rules and corresponding Boolean matching results, and physiological response rules and corresponding Boolean matching results are recorded as matching result records.

[0033] As a preferred embodiment of the drug rehabilitation facility management method based on a security IoT sensing platform described in this invention, the specific steps for locating high-risk individuals and dispatching the nearest security resources to execute tiered intervention actions are as follows:

[0034] Generate grid locations of high-risk individuals based on the coordinate sequence of behavioral trajectory evolution paths in the risk report;

[0035] Obtain the location of security resources and generate the optimal scheduling path by combining the grid location of high-risk personnel;

[0036] Based on the emergency response plan, tiered intervention actions are generated and executed according to the optimal scheduling path.

[0037] As a preferred embodiment of the drug rehabilitation facility management method based on a security IoT sensing platform described in this invention, the steps of updating the parameters of the neural differential prediction model based on physiological data during the execution of graded intervention actions and adjusting the parameters in the symbolic rule base are as follows:

[0038] Collect three-dimensional coordinate sequences and physiological data of joint points during the execution of graded intervention actions;

[0039] The intervention effect was labeled based on physiological data during the tiered intervention process;

[0040] Based on the three-dimensional coordinate sequence of joints during the execution of graded intervention actions, the prediction error of the neural differential prediction model is calculated, and the parameters of the neural differential prediction model are updated using the Adam optimizer.

[0041] The parameters in the symbol rule base are adjusted based on the intervention effect and matching results.

[0042] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the method for managing drug rehabilitation sites based on a security IoT sensing platform as described in the first aspect of the present invention.

[0043] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for managing and controlling drug rehabilitation sites based on a security IoT sensing platform as described in the first aspect of the present invention.

[0044] The beneficial effects of this invention are as follows: By using a neural differential prediction model to dynamically model the three-dimensional coordinate sequence of key points in a video stream, real-time dynamic extrapolation of the behavioral trajectory of drug addicts is achieved. This enables the capture of instantaneous changes in the behavioral trajectory, thereby overcoming the limitations of static rule bases, improving the accuracy and response speed of identifying sudden high-risk behaviors, and reducing the misjudgment rate caused by neglecting dynamic characteristics in existing methods. By integrating spatial location rules, motion pattern rules, and physiological response rules through a symbol rule base, collaborative analysis of multi-source heterogeneous data and accurate matching of risk characteristics are achieved, improving the comprehensiveness and real-time nature of risk warnings. This allows the invention to adapt to long-term changes in the behavioral patterns of drug addicts and avoids misjudgment or missed detection problems caused by fixed rules. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of a management and control method for drug rehabilitation centers based on a security IoT sensing platform.

[0047] Figure 2 This is a schematic diagram of neural differential prediction and trajectory extrapolation.

[0048] Figure 3 This diagram illustrates the symbol rule base matching and risk report generation.

[0049] Figure 4 This is a schematic diagram of security resource scheduling and model updates. Detailed Implementation

[0050] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0051] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0053] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for managing drug rehabilitation centers based on a security IoT sensing platform, including the following steps:

[0054] S1: Acquire video streams and physiological data.

[0055] The specific steps are as follows:

[0056] High-definition smart cameras are deployed in the cells, activity areas, and corridors of the drug rehabilitation center. These cameras are configured to capture real-time video streams, which are compressed using H.265 encoding to reduce bandwidth consumption. The cameras operate continuously at a fixed frame rate, capturing images of drug addicts' behavior. Each camera has a built-in timestamp generator, adding a millisecond-level timestamp to each frame of the video stream. All of the above information has been obtained with the users' consent and is used for legitimate purposes.

[0057] Wearable devices are distributed to drug addicts. These devices are worn on the wrist to monitor physiological data in real time. The wearable devices integrate a heart rate sensor and a UWB positioning chip. The physiological data includes heart rate values ​​and three-dimensional location coordinates. The wearable devices transmit physiological data to a local gateway via Bluetooth 5.0 protocol. The local gateway adds millisecond-level timestamps to the physiological data, which are synchronized with the video stream.

[0058] The local gateway transmits timestamp-aligned video streams and physiological data to edge computing nodes via a dedicated 5G network. During transmission, data packets are encapsulated using the MQTT protocol, and the data packets contain video streams, physiological data, and corresponding timestamps.

[0059] Edge computing nodes preprocess video streams and physiological data. Preprocessing includes data cleaning and format standardization. Data cleaning refers to removing invalid data points. Format standardization refers to converting video streams into tensor format and physiological data into numerical arrays. The preprocessed video streams and physiological data are output as a unified structured multi-source dataset.

[0060] It should also be noted that: the deployment of high-definition smart cameras and wearable devices enabled accurate capture and timestamp alignment of multi-source data, ensuring the synchronization and integrity of video streams and physiological data; the H.265 encoding of high-definition smart cameras reduced bandwidth requirements, while the Bluetooth 5.0 protocol of wearable devices ensured real-time transmission of physiological data; the preprocessing operations of edge computing nodes eliminated invalid data points, converted video streams into tensor format, and physiological data into numerical arrays, forming a unified and structured multi-source dataset; standardized processing avoided parsing delays caused by data heterogeneity, providing high-quality input for subsequent neural differential prediction models and high-risk behavior pattern libraries.

[0061] S2: Based on the video stream, a neural differential prediction model is used to deduce the behavioral trajectory of drug addicts, and the coordinate sequence of the behavioral trajectory evolution path is obtained. A high-risk behavior pattern library is established based on the historical relapse action feature rules. Combined with the behavioral trajectory evolution path coordinate sequence, feature segments are marked.

[0062] The specific steps are as follows:

[0063] Video stream tensor data is separated from a unified structured multi-source dataset. The human skeleton key point detection algorithm is used to parse the video stream frame by frame to identify the body joint positions of the drug addicts in each time frame, and obtain a set of two-dimensional joint coordinates for each time frame.

[0064] The vertical distance values ​​of the joint points in three-dimensional space are obtained by using a high-definition smart camera to obtain depth information, which is used to map two-dimensional pixel coordinates to three-dimensional spatial coordinates. The two-dimensional joint point coordinate set of each frame is fused with the depth information, and the depth information is used as the third-dimensional position to map the two-dimensional coordinates to three-dimensional spatial coordinates, generating the three-dimensional coordinates of the joint points in each time frame.

[0065] The 3D coordinates of key points in consecutive frames are cached in chronological order to form a temporal sequence structure. Each element in the temporal sequence structure corresponds to the 3D coordinates of key points in one frame. The temporal sequence structure is then smoothed using a moving average method to obtain a sequence of 3D coordinates of key points for drug addicts.

[0066] The three-dimensional coordinate sequence of key points of historical drug addicts and the corresponding high-risk behavior trajectory are integrated into a training dataset, and then divided into a training set and a validation set according to the proportion. The training set is used for parameter optimization, and the validation set is used to prevent overfitting.

[0067] The weight matrix of the neural differential prediction model is initialized using a Xavier uniform distribution, and the bias vector is initialized to zero; the step size of the ordinary differential equation solver is set to adaptive learning mode, and the initial learning rate and momentum parameters are set.

[0068] The three-dimensional coordinate sequence of the joints of the drug addicts in the training set is input into the neural differential prediction model. The ordinary differential equation solver is used to calculate the motion trajectory of the joints. The solution process includes the calculation of position derivative and velocity derivative, and the evolution path of the predicted behavior trajectory is obtained.

[0069] The mean squared error loss function, combined with L2 regularization, is used to quantify the difference between the predicted behavioral trajectory evolution path and the high-risk behavioral trajectory. The expression is as follows:

[0070]

[0071] in, Here, y represents the total loss function value of the neural differential prediction model, T represents the total number of time steps in the predicted behavioral trajectory evolution path, t represents the index of the time step, and y represents the total loss function value of the model.t The three-dimensional coordinates of key points in the high-risk behavior trajectory of drug addicts at time step t. To predict the three-dimensional coordinates of the key points of the drug addict at time step t in predicting the behavioral trajectory evolution path, λ is a hyperparameter controlling the strength of L2 regularization, usually taken as 0.01, and W is the weight matrix of the neural differential prediction model. 2 The square of the Euclidean norm;

[0072] Calculate the partial derivatives of the total loss function of the neural differential prediction model with respect to the weight matrix and bias vector, and update the neural differential prediction model parameters using the Adam optimizer. After each fixed training batch, evaluate the loss value on the validation set. If the validation loss value does not decrease for a fixed number of consecutive training batches, reduce the learning rate. After reaching the maximum number of training rounds, update the neural differential prediction model parameters using the neural differential prediction model with the smallest loss value on the validation set to obtain the trained neural differential prediction model.

[0073] The three-dimensional coordinate sequence of the joints of the drug addicts is input into the trained neural differential prediction model. The ordinary differential equation solver is used to calculate the displacement difference of the joints in adjacent time frames to obtain the vector of joint velocity change.

[0074] The neural differential prediction model performs a difference operation on the joint velocity change trend vector to generate the joint acceleration change trend vector; the joint velocity change trend vector and the joint acceleration change trend vector are input into the dynamic motion equation to solve the position evolution in the future time period and generate the coordinate sequence of the behavioral trajectory evolution path.

[0075] It should be noted that the dynamic motion equations are constructed based on Newton's second law, with the joint velocity change trend vector as the first-order state variable and the joint acceleration change trend vector as the second-order state variable; the initial conditions of the dynamic motion equations are the joint position coordinates, velocity change trend vector, and acceleration change trend vector of the current time frame.

[0076] The ordinary differential equation solver uses a numerical iterative method to process dynamic motion equations. It advances the solution process within a fixed time step, iterates and updates the joint position coordinates at each step, and caches the updated joint position coordinates to the behavior trajectory evolution path coordinate sequence.

[0077] Specifically, the ordinary differential equation solver discretizes the time axis into an equally spaced sequence with a fixed step size. Starting from the iteration start time, the ordinary differential equation solver processes each discrete time point in sequence and performs state update calculations at each time point.

[0078] State update calculation: The dynamic motion equation uses Newton's mechanics principle to calculate the rate of change of the joint position coordinates, and the product of the rate of change of the joint position coordinates and the fixed step size is used as the position variable increment;

[0079] The updated joint position coordinates are obtained by adding the position variable increment to the current joint position coordinates.

[0080] Historical relapse behavior feature rules are stored as a high-risk behavior pattern library. The historical relapse behavior feature rules include spatial relationship constraints and motion pattern constraints. Spatial relationship constraints are defined as distance thresholds between specific key points, and motion pattern constraints are defined as the speed change range of key points.

[0081] Arrange the coordinate sequence of the behavior trajectory evolution path in chronological order, and extract the three-dimensional coordinates of the key points in the current time frame; calculate the Euclidean distance between the key points, compare the Euclidean distance between the key points with the spatial relationship constraints of the high-risk behavior pattern library, and obtain the comparison results of whether the current time frame satisfies or not the spatial relationship constraints; calculate the key point displacement between the current time frame and the previous time frame, obtain the instantaneous velocity of the key points, compare the instantaneous velocity of the key points with the motion pattern constraints of the high-risk behavior pattern library, and obtain the comparison results of whether the current time frame satisfies or not the motion pattern constraints;

[0082] When the spatial relationship constraint comparison results of a fixed number of consecutive time frames and the motion pattern constraint comparison results are both satisfied, the time interval corresponding to the fixed number of consecutive time frames is marked as a feature segment.

[0083] It should also be noted that: the neural differential prediction model uses an ordinary differential equation solver to calculate the joint displacement difference and acceleration change trend vector, generating a sequence of coordinates for the behavioral trajectory evolution path; the high-risk behavior pattern library, combined with historical relapse action feature rules, segments and compares the coordinates for the behavioral trajectory evolution path, marking feature segments. The Xavier initialization weights and adaptive step size design of the neural differential prediction model ensure the stability of trajectory prediction, while the mean squared error loss function combined with L2 regularization optimizes the accuracy of the neural differential prediction model; through AI vision-driven joint angular velocity and Euclidean distance analysis, high sensitivity detection of sudden behaviors is achieved, reducing the lag of the static rule base; the feature segment marking mechanism relies on the comparison of spatial relationship constraints and motion pattern constraints in continuous time frames, enhancing the accuracy of high-risk behavior identification and avoiding misjudgments caused by neglecting dynamic characteristics in existing methods.

[0084] S3: Establish a symbol rule base based on historical behavioral feature data, and generate a risk report based on feature fragments, behavioral trajectory evolution path coordinate sequences, and the symbol rule base.

[0085] The specific steps are as follows:

[0086] Logical judgment rules are extracted from historical behavioral feature data using association rule mining algorithms and decision tree classifiers. Specifically, the association rule mining algorithm mines frequent behavioral patterns from historical behavioral feature data to obtain spatial location rules and physiological response rules. The decision tree classifier extracts motion pattern rules using the Gini coefficient minimization principle. The spatial location rules, motion pattern rules, and physiological response rules are then combined to form logical judgment rules. Spatial location rules are defined as safe distances at joint points, motion pattern rules are set as safe values ​​for joint angular velocities, and physiological response rules are defined as critical values ​​for sudden heart rate changes. Logical judgment rules are stored as "IF-THEN" logical expressions.

[0087] The behavior trajectory evolution path coordinate sequence is divided into behavior trajectory evolution path coordinate sequence segments according to feature segments; the start and end time frames of the feature segments are consistent with the start and end time frames of the behavior trajectory evolution path coordinate sequence segments.

[0088] The three-dimensional coordinates of the joints are read frame by frame in the behavior trajectory evolution path coordinate sequence segment; the three-dimensional coordinates of the joints in the behavior trajectory evolution path coordinate sequence segment are subjected to time difference operation to obtain the instantaneous velocity vector of the joint; the instantaneous velocity vector of the joint is subjected to time difference operation to obtain the acceleration vector of the joint.

[0089] The neural symbolic hybrid reasoning engine iterates through the symbolic rule base rule by rule. For spatial location rules, it extracts the source and target keypoints. It reads the 3D coordinates of the source and target keypoints from the keypoint 3D coordinates of the current time frame, calculates the Euclidean distance between the source and target keypoints, and obtains the source-target Euclidean distance. If the source-target Euclidean distance does not exceed the safe distance of the keypoints corresponding to the spatial location rule, it outputs True; otherwise, it outputs False.

[0090] For motion pattern rules, extract the target joints of the motion pattern rules, read the instantaneous velocity vector of the target joint from the instantaneous velocity vector of the joint, and take the magnitude of the instantaneous velocity vector of the target joint as the instantaneous velocity value. If the instantaneous velocity value is not less than the joint angular velocity safety value corresponding to the motion pattern rule, output True; if the instantaneous velocity value is less than the joint angular velocity safety value corresponding to the motion pattern rule, output False.

[0091] For the physiological response rule, calculate the percentage difference between the physiological data of the current time frame and the previous time frame. If the percentage difference is not less than the heart rate mutation threshold corresponding to the physiological response rule, output True; if the percentage difference is less than the heart rate mutation threshold corresponding to the physiological response rule, output False.

[0092] The source-target Euclidean distance, instantaneous velocity value, and difference percentage are called actual values. The joint safety distance, joint angular velocity safety value, and heart rate mutation threshold are called rule thresholds. A matching result record is created for each rule. The matching result record contains rule ID, True / False, actual value, and rule threshold.

[0093] A high-risk conclusion is generated when the matching results of the spatial location rule, movement pattern rule, and physiological response rule are all True; a medium- or low-risk conclusion is generated when the matching results of the spatial location rule, movement pattern rule, and physiological response rule are not all True. Both high-risk and medium- or low-risk conclusions contain corresponding matching result records. The high / medium- or low-risk conclusions, feature segments, and behavioral trajectory evolution path coordinate sequences are integrated into a risk report.

[0094] It should also be noted that: the symbol rule base integrates spatial location rules, motion pattern rules, and physiological response rules to form logical judgment expressions; the neural symbolic hybrid inference engine segments the coordinate sequence of the behavioral trajectory evolution path based on feature fragments, calculates the instantaneous velocity vector and acceleration vector of the joint point, and performs Boolean matching operations to generate matching result records; the source-target Euclidean distance corresponding to the spatial location rule, the instantaneous velocity value magnitude corresponding to the motion pattern rule, and the percentage difference corresponding to the physiological response rule are calculated to ensure the collaborative analysis of multi-source heterogeneous data; the comprehensiveness of rule matching is enhanced through AI vision technology, generating a high-risk conclusion when all rules match True, and otherwise generating a medium- or low-risk conclusion; the risk report integration mechanism outputs the conclusion, feature fragments, and behavioral trajectory evolution path coordinate sequence in a unified manner, improving the real-time performance and coverage of risk warnings, solving the problem of insufficient cross-modal data fusion in existing methods, and avoiding misjudgment or missed detection.

[0095] S4: Match emergency response plans based on risk reports, locate high-risk personnel, and dispatch the nearest security resources to carry out tiered intervention actions.

[0096] The specific steps are as follows:

[0097] Match corresponding emergency response plans based on the high / medium-low risk conclusions in the risk report; use the three-dimensional coordinates of the key points of the end time frame of the behavioral trajectory evolution path coordinate sequence in the risk report as the location coordinates of high-risk personnel.

[0098] The location coordinates of high-risk individuals are mapped to the grid coordinates of the electronic map of the drug rehabilitation center to obtain the grid location of high-risk individuals; the location of security resources is collected in real time through UWB positioning chips deployed on duty management personnel, GPS components of drones, and RFID tags of emergency vehicles.

[0099] The grid coordinates of the electronic map of the drug rehabilitation center are converted into a graph structure. Each grid cell is used as a node in the graph structure, and edges are established between adjacent grid cells. The weight of the edges is set to a fixed distance value.

[0100] The nodes corresponding to security resource locations are called security resource location nodes, and the nodes corresponding to high-risk personnel grid locations are called high-risk personnel grid location nodes. A distance dictionary is created for security resource location nodes to store the estimated shortest distance from all nodes to security resource location nodes. The estimated shortest distance of security resource location nodes is set to zero, and the estimated shortest distance of nodes other than security resource location nodes is set to the maximum value.

[0101] Create a priority queue, sorted by estimated shortest distance, and add security resource location nodes to the priority queue; perform the following operations in a loop: take the node with the smallest estimated shortest distance from the priority queue as the current node, traverse all adjacent nodes of the current node, and record the distance value from the current node to the adjacent node; compare the distance value with the estimated shortest distance of the adjacent node in the distance dictionary, and if the distance value is smaller, update the estimated shortest distance of the adjacent node in the distance dictionary to the distance value.

[0102] The loop terminates when the priority queue is reached or when a high-risk personnel grid location node is processed. Starting from the high-risk personnel grid location node, the previous node is traced back, and the nodes are connected in reverse order of the previous node to form a path node sequence. The path node sequence starts from the security resource location node and ends at the high-risk personnel grid location node.

[0103] Each node in the path node sequence is converted into its corresponding grid coordinates, and all grid coordinates are connected to form the optimal scheduling path.

[0104] The emergency response plan generates tiered intervention actions, including high-risk intervention actions and medium- and low-risk intervention actions. The high-risk intervention actions include: on-duty management personnel going to the location of high-risk personnel to carry out physical intervention, drones patrolling and monitoring and dropping warning signals, and vehicles transporting blocking agents along the optimal dispatch route to the location of high-risk personnel.

[0105] The intervention measures for low- and medium-risk areas include: broadcasting voice warnings and temporarily locking the areas where high-risk individuals are located using access control devices.

[0106] It should also be noted that: by matching high-risk or low-to-medium-risk conclusions in risk reports with emergency response plans, the coordinate sequence of behavioral trajectory evolution paths is converted into the grid location of high-risk personnel; the security resource location database is combined with the electronic map of the drug rehabilitation center to generate an optimal scheduling path algorithm for graph structure, calculating a distance dictionary and priority queue, forming a sequence of path nodes from security resource location nodes to high-risk personnel grid location nodes; graded intervention actions, such as physical intervention by on-duty management personnel, drone deployment of warning signals, or broadcast equipment playing voice messages, are all executed according to the optimal scheduling path; through grid coordinate conversion, the precise positioning and shortest-time deployment of intervention responses are ensured, avoiding delays in security resource scheduling and improving the timeliness and coordination of overall control.

[0107] S5: Update the parameters of the neural differential prediction model based on the physiological data during the execution of graded intervention actions, and adjust the parameters in the symbol rule base.

[0108] The specific steps are as follows:

[0109] Physiological data are collected in real time during the implementation of graded intervention actions; the three-dimensional coordinate sequence of joint points, physiological data and temperature and humidity data during the implementation of graded intervention actions are packaged into an intervention dataset;

[0110] The percentage decrease in heart rate over a fixed time period before and after the implementation of the graded intervention is calculated as the heart rate decrease. The intervention effect threshold is set using the percentile method based on historical heart rate decreases. The intervention effect is marked according to the physiological data during the graded intervention: graded interventions with a heart rate decrease of not less than the intervention effect threshold are marked as effective interventions, and graded interventions with a heart rate decrease of less than the intervention effect threshold are marked as ineffective interventions.

[0111] Input the three-dimensional coordinate sequence of the joints in the intervention dataset into the neural differential prediction model, calculate the total loss function value of the neural differential prediction model as the prediction error, and use the Adam optimizer to perform backpropagation update operation: calculate the partial derivative of the prediction error with respect to the weight matrix, reduce the learning rate by a fixed ratio, iterate and update the weight matrix three times and then stop to obtain the parameters of the updated neural differential prediction model.

[0112] Extract the symbol rule IDs of the rule base where the matching result is True during the execution of the graded intervention action, and analyze the difference between the actual value and the rule threshold in the matching result record: if the rule is triggered three times consecutively and the intervention is effective, the corresponding rule threshold is reduced proportionally; if the rule is triggered but the intervention is ineffective, the corresponding rule threshold is increased proportionally.

[0113] Deploy the updated parameters of the neural differential prediction model and the rule thresholds of the symbol rule base to edge computing nodes.

[0114] It should also be noted that: the three-dimensional coordinate sequence of key points and physiological data during the implementation of graded intervention actions are collected to form an intervention dataset, and the intervention effect is labeled based on the magnitude of heart rate decline; the prediction error is calculated and the weight matrix is ​​updated by backpropagation using the Adam optimizer; the symbolic rule base records and adjusts the rule thresholds according to the intervention effect and matching results, such as reducing the threshold when the intervention is continuous and effective; the dynamic optimization of the model and rules is enhanced through an AI visual feedback mechanism, and the updated neural differential prediction model parameters and symbolic rule base rule thresholds are deployed to edge computing nodes to ensure the adaptability to long-term behavioral pattern changes, avoid the outdated problem caused by fixed rules, and improve the continuous learning ability and risk response accuracy of the control method.

[0115] This embodiment also provides a computer device applicable to a drug rehabilitation facility management method based on a security IoT sensing platform, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the drug rehabilitation facility management method based on a security IoT sensing platform as proposed in the above embodiment.

[0116] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0117] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements a method for managing a drug rehabilitation center based on a security IoT sensing platform, as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0118] In summary, this invention achieves real-time dynamic extrapolation of the behavioral trajectories of drug addicts by using a neural differential prediction model to dynamically model the three-dimensional coordinate sequence of key points in a video stream. This captures the instantaneous changing trends of behavioral trajectories, thus overcoming the limitations of static rule bases, improving the accuracy and response speed for identifying sudden high-risk behaviors, and reducing the misjudgment rate caused by neglecting dynamic characteristics in existing methods. By integrating spatial location rules, motion pattern rules, and physiological response rules through a symbolic rule base, it achieves collaborative analysis of multi-source heterogeneous data and accurate matching of risk characteristics, improving the comprehensiveness and real-time nature of risk warnings. This allows it to adapt to long-term changes in the behavioral patterns of drug addicts and avoids misjudgments or missed detections caused by fixed rules.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for managing drug rehabilitation centers based on a security IoT sensing platform, characterized in that: The application relates to a drug rehabilitation personnel behavior risk prediction method and device. Collecting a video stream and physiological data; Deriving a behavior trajectory of a drug rehabilitation personnel based on the video stream using a neural differential prediction model to obtain a behavior trajectory evolution path coordinate sequence, establishing a high-risk behavior mode library based on historical relapse action feature rules, marking a feature segment in combination with the behavior trajectory evolution path coordinate sequence; Generating a risk report based on a symbol rule library according to the feature segment, the behavior trajectory evolution path coordinate sequence and the symbol rule library; Matching an emergency disposal scheme according to the risk report, positioning a high-risk personnel position and dispatching the nearest security resource to execute a hierarchical intervention action; Updating parameters of the neural differential prediction model based on physiological data when the hierarchical intervention action is executed, and adjusting parameters in the symbol rule library. 2.The drug rehabilitation place management method based on the security and protection Internet of Things sensing platform of claim 1, wherein: The specific steps are as follows, Recognizing body joint position of the drug rehabilitation personnel based on the video stream to generate a joint three-dimensional coordinate sequence; Inputting the joint three-dimensional coordinate sequence into the neural differential prediction model, using a common differential equation solver to calculate a joint displacement difference value to obtain a joint speed change trend vector; Performing a difference operation on the joint speed change trend vector to obtain a joint acceleration change trend vector; Generating the behavior trajectory evolution path coordinate sequence according to the joint speed change trend vector and the joint acceleration change trend vector. 3.The drug rehabilitation place management method based on the security Internet of Things sensing platform of claim 1, wherein: The specific steps are as follows, Segmenting the behavior trajectory evolution path coordinate sequence into coordinate segments of continuous time frames, calculating the Euclidean distance between the joints in each coordinate segment frame by frame, synchronously calculating the joint displacement difference value of adjacent frames to obtain the joint instantaneous speed; Comparing the Euclidean distance between the joints and the joint instantaneous speed with the high-risk behavior mode library respectively to obtain comparison results and mark the feature segment.

4. The drug rehabilitation place management method based on the security Internet of Things sensing platform according to claim 1, characterized in that: The specific steps are as follows, Segmenting the behavior trajectory evolution path coordinate sequence into behavior trajectory evolution path coordinate sequence segments according to the feature segment; Calculating a joint instantaneous speed vector and a joint acceleration vector based on the behavior trajectory evolution path coordinate sequence segment; Generating a matching result record according to the rules of the symbol rule library, the joint instantaneous speed vector and the joint acceleration vector; Generating any one of a high-risk conclusion and a medium-low-risk conclusion based on the matching result record, integrating any one of the high-risk conclusion and the medium-low-risk conclusion, the feature segment and the behavior trajectory evolution path coordinate sequence into a risk report.

5. The drug rehabilitation place management method based on the security Internet of Things sensing platform according to claim 4, characterized in that: The calculation of the joint instantaneous speed vector and the joint acceleration vector based on the behavior trajectory evolution path coordinate sequence segment refers to time difference operation on the joint three-dimensional coordinates of the behavior trajectory evolution path coordinate sequence segment to obtain the joint instantaneous speed vector, and time difference operation on the joint instantaneous speed vector to obtain the joint acceleration vector.

6. The drug rehabilitation place management method based on the security Internet of Things sensing platform according to claim 4, characterized in that: The matching result record is generated according to the symbol rule base, the joint instantaneous velocity vector and the joint acceleration vector, and the specific steps are as follows, The rules of the symbol rule base refer to spatial position rules, motion mode rules and physiological response rules; The source-target Euclidean distance corresponding to the spatial position rule is calculated, and the source-target Euclidean distance is compared with the spatial position rule to obtain the Boolean matching result of the spatial position rule; The length of the joint instantaneous velocity vector is taken as the instantaneous velocity value corresponding to the motion mode rule, and the instantaneous velocity value is compared with the motion mode rule to obtain the Boolean matching result of the motion mode rule; The difference percentage of physiological data of adjacent time frames is calculated, and the difference percentage is compared with the physiological response rule to obtain the Boolean matching result of the physiological response rule; The source-target Euclidean distance, the instantaneous velocity value, the difference percentage, the spatial position rule and the corresponding Boolean matching result, the motion mode rule and the corresponding Boolean matching result, and the physiological response rule and the corresponding Boolean matching result are recorded as the matching result record.

7. The drug rehabilitation place management method based on the security Internet of Things sensing platform according to claim 1, characterized in that: The specific steps of positioning the position of the high-risk personnel and scheduling the nearest security resource to perform the hierarchical intervention action are as follows, The high-risk personnel grid position is generated according to the behavior trajectory evolution path coordinate sequence in the risk report; The security resource position is obtained, and the optimal scheduling path is generated in combination with the high-risk personnel grid position; The hierarchical intervention action is generated based on the emergency disposal scheme, and the hierarchical intervention action is performed according to the optimal scheduling path. 8.The drug rehabilitation place management method based on the security and protection Internet of Things sensing platform of claim 1, wherein: The parameters of the neural differential prediction model are updated based on the physiological data during the execution of the hierarchical intervention action, and the parameters in the symbol rule base are adjusted, and the specific steps are as follows, The joint three-dimensional coordinate sequence and the physiological data during the execution of the hierarchical intervention action are collected; The intervention effect is labeled according to the physiological data during the hierarchical intervention action; The prediction error of the neural differential prediction model is calculated based on the joint three-dimensional coordinate sequence during the execution of the hierarchical intervention action, and the parameters of the neural differential prediction model are updated using the Adam optimizer; The parameters in the symbol rule base are adjusted according to the intervention effect and the matching result record. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the drug rehabilitation place control method based on the security Internet of Things sensing platform in any one of claims 1-8.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the drug rehabilitation place control method based on the security Internet of Things sensing platform in any one of claims 1-8.

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