Security situation analysis system for drug rehabilitation place

Abstract

The invention discloses a security situation analysis system for a drug rehabilitation place, which relates to the field of security situation analysis, comprises an integrated monitoring module, a security risk analysis module, a psychological state evaluation module and an emergency response module, efficiently processes large-scale monitoring data, updates security situations in real time, and improves the safety of the drug rehabilitation place. Rapid data exchange and unified processing among the modules are ensured; the management efficiency and safety of a drug rehabilitation place are improved through comprehensive assessment of safety risks and psychological states, a safety situation map is generated according to an assessment result, an emergency response program is automatically triggered, potential safety risks are quickly responded, the problem that in the prior art, large-data-size and various-variety data cannot be effectively integrated and analyzed is solved, and the risk of drug rehabilitation is reduced. According to the system, multi-directional monitoring and analysis are carried out on the safety of the drug rehabilitation place, and the safety, the real-time performance and the coping capacity of place management are enhanced by combining data fusion analysis, psychological state evaluation and an intelligent emergency response mechanism.

Description

Technical Field

[0001] The present invention relates to the field of security situation analysis, and in particular to a security situation analysis system for a drug rehabilitation facility. Background Art

[0002] Drug rehabilitation is of great significance to maintaining social stability and promoting individual health. As an important place to help individuals quit drug addiction and restore health, the safety management of drug rehabilitation facilities is directly related to the success or failure of drug rehabilitation work. Therefore, ensuring the safety and stability of drug rehabilitation facilities is the primary task of drug rehabilitation work. The management of drug rehabilitation facilities involves many aspects, including daily management of drug addicts, psychological intervention, medical treatment, safety precautions, etc. These tasks are intertwined to form a complex management system. At the same time, drug addicts themselves have their own particularities. They may face both physical and mental problems, which increases the difficulty of management.

[0003] With the rapid development of modern science and technology, more and more advanced technologies and equipment are being applied to the field of security management. For example, technologies such as video surveillance, electronic access control, and face recognition have been widely used in drug rehabilitation facilities, effectively improving the security prevention capabilities of the facilities. However, these technologies generate a large amount of data with a wide variety of types. How to effectively integrate and analyze this data has become a new challenge facing the security management of drug rehabilitation facilities.

[0004] Therefore, a safety situation analysis system for drug rehabilitation facilities is needed to solve the above problems. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention discloses a safety situation analysis system for drug rehabilitation facilities. By integrating multiple monitoring means, advanced data analysis technology and timely emergency response mechanism, comprehensive safety protection is provided for drug rehabilitation facilities. It not only focuses on the behavior monitoring of personnel, but also comprehensively considers environmental factors and psychological state, thereby improving the accuracy and real-time nature of the safety situation judgment of drug rehabilitation facilities, and has a high degree of automation and intelligence.

[0006] The present invention adopts the following technical solutions:

[0007] A safety situation analysis system for a drug rehabilitation facility, comprising:

[0008] An integrated monitoring module, which includes a behavior monitoring unit, an environment monitoring unit, a personnel positioning unit, and an access control monitoring unit. The behavior monitoring unit, the environment monitoring unit, the personnel positioning unit, and the access control monitoring unit use a message queue middleware for asynchronous communication and data exchange, and integrate various monitoring data into a cloud platform for collaborative processing through a PCIE data bus;

[0009] A security risk analysis module, which receives a variety of monitoring data integrated on the cloud platform, and uses a multi-PCA data fusion analysis model to perform fusion analysis on the received different types of monitoring data. The multi-PCA data fusion analysis model uses a density clustering algorithm to select local density feature quantities of different types of monitoring data center points, and performs primary and secondary fusion of different types of monitoring data through a BP neural network;

[0010] A psychological state assessment module, which performs real-time dynamic assessment of the psychological state of drug addicts through a psychological data analysis model. The psychological data analysis model constructs a multi-dimensional quantitative assessment framework of the psychological state of drug addicts based on psychological theory, and uses a time series analysis method to capture the real-time change trend of the psychological state of drug addicts;

[0011] The emergency response module generates a security situation map based on the security risk analysis results and the psychological state assessment results, and automatically triggers the emergency response program according to the security risks existing in the security situation map.

[0012] Furthermore, the behavior monitoring unit captures and records the behavior monitoring data of drug addicts in real time through surveillance cameras and infrared sensors installed in the drug rehabilitation facility. The environment monitoring unit monitors and records the environment monitoring data in the drug rehabilitation facility in real time by deploying an environmental sensor network. The environment monitoring data at least includes the air component content, temperature, humidity and noise in the drug rehabilitation facility. The personnel positioning unit obtains the location monitoring data of drug addicts in real time through an RTLS positioning bracelet. The access control monitoring unit obtains the entry and exit monitoring data of personnel at the entrances and exits of the drug rehabilitation facility through biometric access control equipment and infrared cameras.

[0013] Furthermore, the message queue middleware realizes decoupling and data flow between the behavior monitoring unit, the environment monitoring unit, the personnel positioning unit and the access control monitoring unit through asynchronous communication and distributed data exchange. The PCIE data bus connects the data aggregation node with the external interfaces of the behavior monitoring unit, the environment monitoring unit, the personnel positioning unit and the access control monitoring unit, and is integrated into the cloud platform in real time through the real-time stream processing engine.

[0014] Furthermore, the cloud platform adopts a three-layer security firewall and permission authentication control mechanism to perform security protection and access control on various monitoring data.

[0015] Furthermore, the working method of the multi-PCA data fusion analysis model comprises the following steps:

[0016] S1. Receive the behavior monitoring data, environment monitoring data, location monitoring data and personnel entry and exit monitoring data integrated on the cloud platform, and use the data rule engine to perform data cleaning, denoising and standardization operations on the received various monitoring data;

[0017] S2, using the principal component analysis method to perform dimensionality reduction processing on different types of monitoring data, the principal component analysis method calculates the covariance matrix between different types of monitoring data according to the preprocessed data, and calculates the eigenvalues ​​and corresponding eigenvectors of the covariance matrix by a linear algebra method, and selects the principal component according to the size of the eigenvalue;

[0018] S3, using a density clustering algorithm to perform cluster analysis on the data features of the dimensionality reduction process, and selecting the center point of each cluster in the clustering results, wherein the density clustering algorithm identifies different clusters according to the local density of the data feature points, and determines the number of clusters and cluster centers by calculating the local density feature value of the data points and the minimum distance threshold;

[0019] S4, taking the selected center point as input, using the forward propagation algorithm of the BP neural network to perform preliminary fusion processing on different types of monitoring data, realizing preliminary classification and identification of different types of monitoring data, and performing secondary data fusion processing based on the back propagation algorithm of the BP neural network and the adjustment of network weights and biases to identify the potential relationship between different types of monitoring data;

[0020] S5. Conduct risk assessment and prediction based on the results of the secondary fusion to identify safety risks in drug rehabilitation facilities.

[0021] Further, in step S3 and step S4, the different types of monitoring data sets are M = {x i}, x i represents the i-th type of monitoring data, i represents the ordinal number of the monitoring data type, the fused monitoring data set is H = {1,,2,...,N}, N represents the total number of fused monitoring data, and different types of monitoring data x i and x j The difference in information distance is D ij , the distance between different types of monitoring data is defined as D c , the local density characteristic quantity ρ of different types of monitoring data center points is expressed by the following function:

[0022]

[0023] In formula (1), δ represents the relationship factor between different types of monitoring data, which is used to indicate the degree of correlation between different types of monitoring data. In the initial fusion process, the δ function is started as the hidden layer excitation function, and the iterative equation of the neuron weight coefficient in the BP neural network is:

[0024]

[0025] In formula (2), ΔW u k v represents the weight coefficient between the uth neuron in the kth layer and the vth neuron in the k-1th layer in the BP neural network, W represents the weight coefficient, γ represents the learning rate, which is used to control the speed of weight update, and E represents the error function of the BP neural network, which is used to calculate the distance between the actual output of the output layer neuron and the expected output. Represents the error function to the weight coefficient The partial derivative of ; the data information after preliminary fusion processing is:

[0026]

[0027] In formula (3), H1 represents the monitoring data information after preliminary fusion processing, f(·) represents the activation function of the output layer of the BP neural network, A is the number of input nodes of the BP neural network, and W g represents the weight coefficient from the gth input layer to the hidden layer, α g is the g-th data input node in the BP neural network, and b represents the threshold of the output layer. In the process of secondary data fusion, the DS evidence algorithm is used to globally fuse different types of monitoring data. The data information after secondary fusion is:

[0028]

[0029] In formula (4), H2 represents the monitoring data information after secondary fusion processing, W ir represents the weight coefficient of the i-th monitoring data in the r-th fusion dimension, ρ ro It represents the fusion degree of the r-th monitoring data on the o-th fusion dimension, o and r represent the ordinal numbers of the fusion dimensions of different types of monitoring data, and n represents the total number of fusion dimensions of different types of monitoring data.

[0030] Furthermore, the psychological data analysis model conducts a quantitative assessment of the psychological state of drug addicts based on a psychological scale, and uses a combination of autoregressive and moving average methods to capture the autocorrelation of the psychological state at different time points. When the quantitative value of the psychological state exceeds or equals the warning value, a warning prompt is triggered.

[0031] Furthermore, the security situation map sets a security risk level threshold according to different security risks, and when the security risk exceeds the preset security risk level threshold, an emergency response corresponding to the security risk level is automatically initiated.

[0032] The beneficial effects of the present invention are:

[0033] 1. The present invention uses multiple monitoring units to capture and record the behavioral data of drug addicts, the environmental data in drug rehabilitation facilities, the location data of drug addicts, and the entry and exit data of people at the entrances and exits in real time. The high degree of integration and real-time monitoring capabilities ensure the comprehensiveness and accuracy of the data, which helps to timely discover and respond to potential security risks. Message queue middleware is used for asynchronous communication and data exchange, and multiple monitoring data are integrated into the cloud platform for collaborative processing through the PCIE data bus. This efficient data processing method can quickly integrate and analyze large amounts of data, improving the response speed and accuracy of the system.

[0034] 2. The present invention adopts a multi-PCA data fusion analysis model to deeply explore and identify potential safety risks, improve the accuracy of risk identification, select the local density characteristics of the center points of different types of monitoring data through a density clustering algorithm, and combine it with the BP neural network for primary and secondary fusion. Through fusion analysis, the system can more accurately identify and warn of abnormal behaviors, environmental changes and potential safety hazards of drug addicts, thereby helping managers to take countermeasures in advance and reduce the occurrence of safety accidents.

[0035] 3. The present invention uses a multi-dimensional quantitative evaluation framework based on psychological theory and combines it with time series analysis methods to monitor and evaluate the psychological state of drug addicts in real time. It can capture the changing trend of the psychological state of drug addicts in real time, help staff to understand the psychological condition of drug addicts in a timely manner, facilitate the adoption of appropriate psychological intervention measures, and enhance the effect of drug rehabilitation and the safety of site management.

[0036] 4. The present invention automatically generates a security situation map based on the results of the security risk analysis module and the psychological state assessment module, and triggers the corresponding emergency response procedure when potential risks are identified. This automated emergency handling method can effectively shorten the response time and ensure that measures can be taken quickly and accurately when a security incident occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the overall system architecture of the present invention;

[0038] Figure 2 This is a schematic diagram of the architecture of the digital integrated monitoring module in the present invention;

[0039] Figure 3 Schematic diagram of the workflow of the multi-PCA data fusion analysis model in the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the attached embodiment of the present invention Figure 1 To Attachment Figure 3 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] The embodiment of the present invention discloses a security situation analysis system for drug rehabilitation facilities, as shown in the attached Figure 1 As shown, it includes an integrated monitoring module, a safety risk analysis module, a psychological state assessment module and an emergency response module. Through efficient data exchange and collaborative processing mechanisms, each module can achieve comprehensive monitoring and analysis of the safety situation of drug rehabilitation facilities.

[0042] Integrated monitoring module, used to collect, monitor and process the behavior, environment, personnel and access control data in the drug rehabilitation center in real time. Figure 2 As shown, it includes a behavior monitoring unit, an environment monitoring unit, a personnel positioning unit and a door access monitoring unit.

[0043] The behavior monitoring unit monitors the behavior of drug addicts in real time by installing high-definition cameras and infrared sensors, and uses video analysis and motion recognition technology (such as convolutional neural network CNN and YOLO target detection algorithm) to analyze the behavior captured by the camera. The monitoring data is transmitted asynchronously through the message queue middleware to ensure real-time and efficiency.

[0044] The environmental monitoring unit monitors air composition, temperature, humidity, noise, etc. by deploying an environmental sensor network. It uses the environmental sensor network (including MQ series gas sensors, temperature and humidity sensors, noise sensors) to conduct real-time detection of environmental changes in drug rehabilitation facilities, and transmits environmental data to the data platform through message queues to facilitate multi-dimensional analysis.

[0045] The personnel positioning unit uses RTLS positioning bracelets, combined with Wi-Fi, Bluetooth or UWB and other technologies to achieve real-time positioning of personnel, using the real-time positioning system (RTLS) to track the location of drug addicts, and cooperates with sensors to identify the behavior and movement trajectory of drug addicts. The positioning data sent by the bracelet is integrated through the cloud platform to update the personnel location information in real time.

[0046] The access control monitoring unit uses biometric access control systems and infrared cameras. Biometric access control systems (such as fingerprint recognition and iris scanning) ensure the safety of drug addicts and staff entering and leaving. Infrared cameras can automatically identify people entering and leaving, and access control data is transmitted to the cloud platform for monitoring through message queues.

[0047] The safety risk analysis module is used to fuse and analyze data from different monitoring units to determine the safety situation of drug rehabilitation facilities. PCA (principal component analysis) technology is used to reduce the dimension and integrate different types of monitoring data to improve the efficiency of data analysis. Density clustering (such as DBSCAN) is used to identify the density distribution of different types of monitoring data to extract abnormal data points. Different types of monitoring data are fused once and twice through BP neural network to generate more accurate safety risk assessment results. Multi-PCA data fusion analysis models are trained and optimized using historical data and real-time data. The model is updated regularly to ensure that it can handle different types of emergencies (such as emotional fluctuations and abnormal behaviors of drug addicts).

[0048] The psychological state assessment module conducts real-time assessment of the psychological state of drug addicts through a psychological data analysis model to ensure that potential mental health problems can be identified in a timely manner. A multi-dimensional psychological state assessment framework is constructed based on psychological theory (such as emotions, stress, anxiety and other psychological factors). The time series analysis method is used to model the changing trend of the psychological state of drug addicts and capture the dynamic changes in their psychological state. A psychological state assessment model is established by combining behavioral monitoring data, environmental data, personnel location data, etc. Real-time data related to psychological state is processed, including emotional fluctuations and personnel activity patterns captured by behavioral monitoring.

[0049] The emergency response module automatically triggers a safety emergency response based on the analysis results to ensure that potential safety hazards are dealt with in a timely manner. A comprehensive safety situation map is generated based on the results of safety risk analysis and psychological state assessment. Graphical presentation is used to display the safety status of drug rehabilitation facilities, including information such as high-risk areas, people with abnormal behavior, and abnormal psychological states. Emergency response processes (such as alarms, automatic control of access control systems, and activation of high-voltage modes of surveillance cameras) are automatically triggered based on the safety situation map. Multiple emergency plans are set based on different types of security incidents (such as personnel conflicts, psychological crises, etc.), and the system automatically selects the optimal response strategy based on the situation. The emergency response module combines manual intervention to ensure the safety of on-site personnel through alarm notifications, implementation of safety measures, and other means.

[0050] The system hardware layer includes surveillance cameras, sensors (temperature, humidity, gas, noise, etc.), RTLS positioning systems, access control devices and other hardware that form the basis of data collection. The cloud platform server is used to process big data and provide computing and storage resources.

[0051] Deploy various hardware facilities in drug rehabilitation facilities, such as cameras, sensors, access control systems, etc. Build a cloud platform for data collection, storage, and processing. Set up a remote monitoring and alarm system to ensure that remote staff can view data in real time and take emergency measures. Regularly check the working status of sensors, monitoring equipment, and access control systems to ensure the normal operation of the system. Adjust the security risk analysis model and psychological state assessment model based on feedback and actual application conditions to improve the accuracy and response speed of the system.

[0052] The system can provide comprehensive security for drug rehabilitation facilities by integrating multiple monitoring methods, advanced data analysis technology and timely emergency response mechanism. The system not only focuses on the behavior monitoring of personnel, but also comprehensively considers environmental factors and psychological state, improving the accuracy and real-time nature of the safety situation judgment of drug rehabilitation facilities.

[0053] The message queue middleware realizes the decoupling and data flow between the behavior monitoring unit, the environment monitoring unit, the personnel positioning unit and the access control monitoring unit through asynchronous communication and distributed data exchange. The PCIE data bus connects the data aggregation node with the external interface of the behavior monitoring unit, the environment monitoring unit, the personnel positioning unit and the access control monitoring unit, and integrates it into the cloud platform in real time through the real-time stream processing engine.

[0054] The cloud platform uses a three-layer security firewall and permission authentication control mechanism to provide security protection and access control for various monitoring data. The goal of the three-layer firewall design is to filter and manage network traffic from different security protection perspectives. They are:

[0055] Application layer firewalls are responsible for filtering, analyzing, and managing traffic based on application layer protocols (such as HTTP, FTP, SMTP, etc.). This layer not only checks the header information of network traffic, but also parses the content of data packets to ensure that they do not carry malicious code, viruses, or improper application behavior. Application layer firewalls usually perform in-depth inspections of data streams, data packets, and their contents through protocol decoders (such as HTTP decoders) and regular expressions (used to match specific data patterns). By identifying and intercepting potential attacks or illegal requests (such as SQL injections, cross-site scripting attacks, etc.), it can provide effective protection at the application layer in the cloud.

[0056] Software firewalls, deployed at the operating system layer of the cloud platform, are responsible for monitoring and controlling network connections in and out of the cloud host. They can filter and manage all inbound and outbound traffic within the operating system, especially access to applications and services on a specific host. By implementing a rule-based filtering mechanism, software firewalls can dynamically decide whether to allow or block a certain traffic based on information such as protocol type, source IP address, and destination port. In addition, software firewalls can be combined with intrusion detection / prevention systems (IDS / IPS) to further identify potential malicious behavior.

[0057] Hardware firewalls, located at the edge of the network, are responsible for screening and filtering all inbound and outbound data packets. Its work is mainly concentrated on the network layer (such as IP layer, transport layer), performing preliminary screening and control of traffic. Hardware firewalls filter network traffic entering and leaving the cloud platform according to preset policies (such as IP address, port, protocol, etc.). If the data packet does not pass the filtering rules, it will be discarded or marked as unsafe. Hardware firewalls are usually deployed at the edge of the network to protect the cloud platform from threats from external networks.

[0058] Due to the high computational overhead of the SSL / TLS encryption layer, firewalls need to have strong computing power to handle large amounts of encrypted traffic. For this reason, SSL Secure Sockets Layer accelerator cards are introduced. SSL accelerator cards process encryption and decryption operations in the SSL / TLS protocol through hardware acceleration, reducing the computing burden on the firewall's internal server. Especially when facing large-scale encrypted traffic, firewalls can use hardware accelerator cards to significantly improve processing efficiency. By handing over the encryption and decryption operations of SSL / TLS connections to dedicated hardware accelerator cards, firewalls can process data packets more quickly and ensure the real-time and efficiency of traffic. Accelerator cards can be deeply integrated with firewalls to ensure that data is not leaked during the encryption and decryption process. It usually supports the latest encryption algorithms (such as AES, RSA) and protocol versions to ensure the integrity and confidentiality of secure communications.

[0059] In addition to firewall protection, the permission authentication control mechanism also plays a key role; by requiring users to provide multiple authentication methods (such as passwords, SMS verification codes, biometrics, etc.), the security of access control is enhanced. Only users who have passed strict authentication can access sensitive data and applications in the cloud platform. The role-based access control mechanism ensures that users can only access data and resources within their functional scope by setting different user roles and permissions. Administrators can flexibly define the access rights of different users according to different business needs. By recording all user access behaviors and system operation logs, potential security threats can be tracked and monitored. For example, administrators can view logs to understand whether any users attempt to unauthorized access or tamper with data, so as to make timely responses.

[0060] Specific implementation steps

[0061] Design firewall strategy: Design appropriate firewall strategy based on the cloud platform architecture and business requirements, including defining access control rules and traffic filtering standards for each layer of firewall, and how to coordinate the three layers of firewall to work together.

[0062] Deploy hardware firewalls: Deploy hardware firewalls at the network edge of the cloud platform and configure rules based on security requirements, such as limiting unnecessary port access and prohibiting connections from suspicious IP addresses.

[0063] Install software firewall: Install and configure software firewall at the cloud host operating system level to monitor all traffic in and out of the host. Combined with intrusion detection / prevention system, enhance the ability to prevent intranet attacks.

[0064] Integrated application layer firewall: Deploy an application layer firewall, especially in an environment that needs to support large-scale Web applications. Configure the application layer firewall to filter HTTP / HTTPS traffic to prevent malicious Web attacks.

[0065] Configure an SSL accelerator card: Integrate an SSL accelerator card on the firewall device to optimize the processing performance of SSL / TLS encrypted traffic and ensure that the firewall can efficiently handle a large number of encrypted requests.

[0066] Enable permission authentication: Implement multi-factor authentication and role-based access control to ensure that only authenticated users can access sensitive information and that each user’s permissions are strictly limited.

[0067] Regular audits and updates: Conduct regular security audits to check the effectiveness of firewall configuration and permission control, and update firewall rules and access control policies in a timely manner to respond to new security threats.

[0068] Through the design and implementation of a three-layer security firewall architecture, combined with an SSL accelerator card and a strict permission authentication mechanism, a strong security guarantee can be provided for the cloud platform. In this way, the cloud platform can not only effectively prevent external attacks, but also ensure that internal data access is strictly controlled to prevent data leakage and abuse. This multi-level, multi-dimensional security protection system can significantly improve the cloud platform's defense capabilities when facing complex threats.

[0069] In the security risk analysis module, the working method of the multi-PCA data fusion analysis model is as follows: Figure 3 As shown, the following steps are included:

[0070] S1: Data reception and preprocessing

[0071] Receive integrated behavior monitoring data, environmental monitoring data, location monitoring data, and personnel entry and exit monitoring data from the cloud platform. These data come from different data sources and are usually structured or semi-structured. Eliminate incomplete, duplicate, or erroneous data. Remove outliers or irrelevant signals through different noise removal techniques, such as mean filtering or median filtering. Since different types of monitoring data may have different units and ranges, standardization can make the data scales of different data sources consistent (for example, through Z-score standardization).

[0072] S2: Use principal component analysis to reduce the dimension of the data

[0073] The principal component analysis (PCA) method is used to reduce the dimensionality of the processed data, reducing the dimension of the data while retaining the main characteristic information. PCA calculates the covariance matrix of the data, finds the principal components (directions) in the data, and extracts the eigenvectors through eigenvalue decomposition of linear algebra. The first step of PCA is to calculate the covariance matrix of the data before dimensionality reduction to measure the linear correlation between different variables. By calculating the eigenvalues ​​and eigenvectors of the covariance matrix, the larger the eigenvalue, the greater the variation occupied by the principal component in the data. By selecting the principal component corresponding to the larger eigenvalue, the data dimension can be reduced while retaining as much information as possible.

[0074] S3: Use density clustering algorithm to perform cluster analysis on the reduced-dimensional data

[0075] A density clustering algorithm (such as DBSCAN) is used to perform cluster analysis on the data after PCA dimensionality reduction. This algorithm is based on the local density characteristics of data points and regards areas with higher density as the same cluster. The density clustering algorithm determines whether it belongs to a cluster with higher density by calculating the local density of each data point (that is, the number of points in a specified neighborhood). In the cluster analysis results, the center point of each cluster is selected as the representative. The center point is the point with the smallest distance to all data points in the cluster.

[0076] S4: Secondary data fusion based on BP neural network

[0077] The cluster center points in the previous clustering results are used as input data, and the forward propagation algorithm of the BP neural network is used for preliminary fusion processing. The input data is calculated through each layer of the neural network (input layer, hidden layer, output layer), and the output result is obtained after being processed by the activation function. This stage is the preliminary classification and identification of different types of monitoring data.

[0078] Through the back propagation algorithm, the weights and biases of the neural network are adjusted according to the error (loss function) between the predicted results and the actual labels to optimize the network's learning process. The weights and biases adjusted by back propagation are used to perform secondary fusion processing on the data to further optimize the classification results and identify the potential relationships between different types of monitoring data. This process helps to discover the complex connections and influencing factors between data.

[0079] S5: Risk Assessment and Prediction

[0080] Based on the data results after the secondary fusion, safety risk assessments are conducted on specific places such as drug rehabilitation centers. By analyzing the fused monitoring data, potential safety hazards can be identified, such as crowds, abnormal behaviors, environmental changes, etc. Based on the fusion results of historical data and real-time data, safety risk predictions can also be conducted, and possible risk events can be warned, providing decision-making basis for managers.

[0081] The process of the multi-PCA data fusion analysis model combines technologies such as multi-source data integration, dimensionality reduction, cluster analysis, neural network fusion and risk assessment, which can help to comprehensively analyze data in complex monitoring scenarios (such as drug rehabilitation facilities), discover potential safety risks and make effective predictions. Each step has its unique function and role, and combined together, it can greatly improve the accuracy and efficiency of data analysis.

[0082] In step S3 and step S4, the different types of monitoring data sets are M = {x i}, x i represents the i-th type of monitoring data, i represents the ordinal number of the monitoring data type, and the fused output monitoring data set is H = {1,,2,...,N}, N represents the total number of fused monitoring data, and different types of monitoring data x i and x j The information difference distance is D ij , the distance between different types of monitoring data is defined as D c , the local density characteristic quantity ρ of different types of monitoring data center points is expressed by the following function:

[0083]

[0084] In formula (1), δ represents the relationship factor between different types of monitoring data, which is used to indicate the degree of correlation between different types of monitoring data. In the initial fusion process, the δ function is started as the hidden layer excitation function, and the iterative equation of the neuron weight coefficient in the BP neural network is:

[0085]

[0086] In formula (2), represents the weight coefficient between the uth neuron in the kth layer and the vth neuron in the k-1th layer in the BP neural network, W represents the weight coefficient, γ represents the learning rate, which is used to control the speed of weight update, E represents the error function of the BP neural network, which is used to measure the distance between the actual output and the expected output of the neurons in the output layer, Represents the error function to the weight coefficient The partial derivative of ; the data information after preliminary fusion processing is:

[0087]

[0088] In formula (3), H1 represents the monitoring data information after preliminary fusion processing, f(·) represents the activation function of the output layer of the BP neural network, A is the number of input nodes of the BP neural network, and W g represents the weight coefficient from the gth input layer to the hidden layer, α g is the g-th data input node in the BP neural network, and b represents the threshold of the output layer. In the process of secondary data fusion, the DS evidence algorithm is used to globally fuse different types of monitoring data. The data information after secondary fusion is:

[0089]

[0090] In formula (4), H2 represents the monitoring data information after secondary fusion processing, W ir represents the weight coefficient of the i-th monitoring data in the r-th fusion dimension, ρ ro It represents the fusion degree of the r-th monitoring data on the o-th fusion dimension, o and r represent the ordinal numbers of the fusion dimensions of different types of monitoring data, and n represents the total number of fusion dimensions of different types of monitoring data.

[0091] In the psychological state assessment module, the psychological data analysis model quantitatively evaluates the psychological state of drug addicts based on the psychological scale, and combines the autoregression (AR) and moving average (MA) methods to analyze the autocorrelation of the psychological state, thereby achieving continuous monitoring and early warning of the psychological health status of drug addicts. The following are the specific implementation steps of the model:

[0092] 1. Data collection and quantitative evaluation

[0093] Select scales suitable for psychological assessment of drug addicts, such as depression scales (such as BDI), anxiety scales (such as SAS), suicide risk assessment scales, etc. These scales can reflect the mental health of drug addicts. Regularly assess the psychological state of drug addicts, collect relevant data, and form time series data. For example, you can collect quantitative scores of psychological states through weekly or monthly scale assessment results.

[0094] The autoregressive moving average (ARMA) model combines the advantages of autoregression and moving average to construct an ARMA model for more accurate modeling of psychological states. Use historical data to train AR and MA models. Estimate model parameters by least squares or maximum likelihood estimation. Usually, it is necessary to use AIC (Akaike Information Criterion) or BIC (Bayesian Information Criterion) to select the optimal order (p and q values). Use residual analysis (such as ACF and PACF graphs) to test the fitting effect of the model to ensure that the model can better capture the time series characteristics of the psychological state. By analyzing the autocorrelation of the quantitative score of the psychological state, observe its changing trend between different time points and capture possible abnormal fluctuations. Especially for drug addicts, the fluctuation of psychological state may be affected by many factors, such as emotional fluctuations and changes in social support during the drug rehabilitation process. Use the ARMA model to predict the trend of changes in psychological state in the future, and compare historical data to observe whether there are abnormal fluctuations.

[0095] According to the normal range of the psychological state and the preset critical value, set a "warning value" for the psychological state. For example, it can be set that if the score of a certain psychological state scale exceeds a certain threshold (such as an anxiety score greater than 40 points), it means that the psychological state is abnormal. When the ARMA model prediction value or actual score exceeds the warning threshold, the early warning prompt system is triggered. The early warning system can be an automatically generated report, or it can remind relevant managers or psychologists to intervene in real time through text messages, APP notifications, etc. Different warning levels can be set according to the severity of the scale score. For example, mild, moderate and severe early warning prompts correspond to different treatment measures (such as strengthening monitoring, increasing psychological counseling, emergency intervention, etc.).

[0096] Combined with the monitoring system, the psychological state data of drug addicts is collected in real time to ensure that psychological changes can be reflected and processed in a timely manner. After the warning is triggered, relevant personnel should take corresponding measures in a timely manner (such as psychological counseling, drug adjustment, etc.), and continuously adjust the parameters of the model through the feedback mechanism to improve the accuracy of the early warning system. According to the operating effect of the early warning system, the ARMA model is continuously optimized. The prediction ability of the model can be enhanced by adding more features (such as social support, living habits, etc.). With the increase of data and the continuous discovery of the laws of changes in psychological state, the model should be updated regularly to ensure its adaptability and accuracy.

[0097] In the emergency response module, a comprehensive safety assessment of drug rehabilitation facilities is conducted to identify potential safety risk points. For example: environmental risks: such as equipment failure, fire hazards, building structure problems, etc.; personnel risks: such as unstable behavior, violent tendencies, self-harming behaviors of drug addicts; psychological risks: such as fluctuations in the psychological state of drug addicts, which may lead to emotional loss of control or emergencies. Psychological state assessment is particularly important in drug rehabilitation facilities, and the psychological state of drug addicts is directly related to their behavioral performance and safety risks. Based on the analysis and assessment results, a security situation map is drawn using visualization tools. The security situation map intuitively displays the distribution, changing trends and possible safety hazards of security risks in drug rehabilitation facilities. The security situation map uses different colors, shapes or symbols to represent different levels of security risks. The security situation map is a visual presentation of the current security situation, including all identified security risks and their levels. According to the results of security risk analysis and psychological state assessment, different types of security risks (environmental, personnel, psychological, etc.) are marked on the map to show their geographical location or importance. Risk points are classified according to severity through risk level thresholds, which are divided into three levels: low, medium and high. High-risk areas are marked with special alerts. The security situation map should be updated in real time to reflect new security issues or psychological state fluctuations at any time.

[0098] After generating the security situation map, the next step is to set thresholds based on risk levels. The thresholds are set based on different types of risks, for example: Environmental risk: The risk of equipment failure may be lower than the risk of fire, so its threshold is lower; once the fire risk reaches a certain dangerous level, the emergency response procedure should be triggered immediately. Psychological risk: For example, drug addicts are emotionally unstable. When the emotional fluctuation exceeds a certain threshold, the psychological intervention procedure is automatically started. The threshold can be set based on historical data, expert experience, legal requirements and other considerations.

[0099] Once the security risk exceeds the preset threshold, the system will automatically trigger the corresponding emergency response procedure. This process includes the following steps: The system will first issue an alarm to notify security personnel, management personnel or relevant departments to remind them of the security risk.

[0100] According to the different types and levels of risks, the system will initiate corresponding emergency response measures. For example: if the mood of drug addicts fluctuates greatly, psychological intervention measures will be triggered, and professional psychologists will be arranged to intervene. When major environmental risks such as fire and explosion occur in the venue, the evacuation procedure will be automatically initiated to guide personnel to evacuate safely. In the event of violent behavior, the system will automatically notify security personnel or initiate automatic control measures. Depending on the type and level of risk, the system may need to dispatch different resources, such as emergency medical services, police support, fire brigade, etc. After the emergency response is initiated, the system continues to monitor the situation in real time to ensure that the emergency measures are effective and adjust the response plan in a timely manner.

[0101] The effectiveness of the emergency response module requires continuous monitoring and feedback to ensure that it can achieve the best results in actual operation. Conduct post-event analysis of each emergency response event, summarize lessons learned, and optimize risk assessment models, threshold settings, and response procedures. Conduct emergency response drills regularly to ensure that all staff are familiar with emergency procedures and improve their ability to respond to emergencies.

[0102] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A safety situation analysis system for drug rehabilitation facilities, characterized in that: include: An integrated monitoring module, which includes a behavior monitoring unit, an environment monitoring unit, a personnel positioning unit, and an access control monitoring unit. The behavior monitoring unit, the environment monitoring unit, the personnel positioning unit, and the access control monitoring unit use a message queue middleware for asynchronous communication and data exchange, and integrate various monitoring data into a cloud platform for collaborative processing through a PCIE data bus; A security risk analysis module, which receives a variety of monitoring data integrated on the cloud platform, and uses a multi-PCA data fusion analysis model to perform fusion analysis on the received different types of monitoring data. The multi-PCA data fusion analysis model uses a density clustering algorithm to select local density feature quantities of different types of monitoring data center points, and performs primary and secondary fusion of different types of monitoring data through a BP neural network; A psychological state assessment module, which performs real-time dynamic assessment of the psychological state of drug addicts through a psychological data analysis model. The psychological data analysis model constructs a multi-dimensional quantitative assessment framework of the psychological state of drug addicts based on psychological theory, and uses a time series analysis method to capture the real-time change trend of the psychological state of drug addicts; The emergency response module generates a security situation map based on the security risk analysis results and the psychological state assessment results, and automatically triggers the emergency response program according to the security risks existing in the security situation map.

2. The safety situation analysis system for drug rehabilitation facilities according to claim 1 is characterized in that: The behavior monitoring unit captures and records the behavior monitoring data of drug addicts in real time through the surveillance cameras and infrared sensors installed in the drug rehabilitation facility. The environment monitoring unit monitors and records the environment monitoring data in the drug rehabilitation facility in real time by deploying an environmental sensor network. The environment monitoring data at least includes the air component content, temperature, humidity and noise in the drug rehabilitation facility. The personnel positioning unit obtains the location monitoring data of drug addicts in real time through the RTLS positioning bracelet. The access control monitoring unit obtains the entry and exit monitoring data of personnel at the entrances and exits of the drug rehabilitation facility through biometric access control equipment and infrared cameras.

3. The safety situation analysis system for drug rehabilitation facilities according to claim 2 is characterized in that: The message queue middleware realizes decoupling and data flow between the behavior monitoring unit, the environment monitoring unit, the personnel positioning unit and the access control monitoring unit through asynchronous communication and distributed data exchange. The PCIE data bus connects the data aggregation node with the external interfaces of the behavior monitoring unit, the environment monitoring unit, the personnel positioning unit and the access control monitoring unit, and is integrated into the cloud platform in real time through the real-time stream processing engine.

4. The safety situation analysis system for drug rehabilitation facilities according to claim 3 is characterized in that: The cloud platform uses a three-layer security firewall and authority authentication control mechanism to provide security protection and access control for various monitoring data.

5. The safety situation analysis system for drug rehabilitation facilities according to claim 1, characterized in that: The working method of the multi-PCA data fusion analysis model comprises the following steps: S1. Receive the behavior monitoring data, environment monitoring data, location monitoring data and personnel entry and exit monitoring data integrated on the cloud platform, and use the data rule engine to perform data cleaning, denoising and standardization operations on the received various monitoring data; S2, using the principal component analysis method to perform dimensionality reduction processing on different types of monitoring data, the principal component analysis method calculates the covariance matrix between different types of monitoring data according to the preprocessed data, and calculates the eigenvalues ​​and corresponding eigenvectors of the covariance matrix by a linear algebra method, and selects the principal component according to the size of the eigenvalue; S3, using a density clustering algorithm to perform cluster analysis on the data features of the dimensionality reduction process, and selecting the center point of each cluster in the clustering results, wherein the density clustering algorithm identifies different clusters according to the local density of the data feature points, and determines the number of clusters and cluster centers by calculating the local density feature value of the data points and the minimum distance threshold; S4, taking the selected center point as input, using the forward propagation algorithm of the BP neural network to perform preliminary fusion processing on different types of monitoring data, realizing preliminary classification and identification of different types of monitoring data, and performing secondary data fusion processing based on the back propagation algorithm of the BP neural network and the adjustment of network weights and biases to identify the potential relationship between different types of monitoring data; S5. Conduct risk assessment and prediction based on the results of the secondary fusion to identify safety risks in drug rehabilitation facilities.

6. The safety situation analysis system for drug rehabilitation facilities according to claim 5, characterized in that: In step S3 and step S4, the different types of monitoring data sets are M = {x i }, x i represents the i-th type of monitoring data, i represents the ordinal number of the monitoring data type, the fused monitoring data set is H = {1,,2,...,N}, N represents the total number of fused monitoring data, and different types of monitoring data x i and x j The difference in information distance is D ij , the distance between different types of monitoring data is defined as D c , the local density characteristic quantity ρ of different types of monitoring data center points is expressed by the following function: In formula (1), δ represents the relationship factor between different types of monitoring data, which is used to indicate the degree of correlation between different types of monitoring data. In the initial fusion process, the δ function is started as the hidden layer excitation function, and the iterative equation of the neuron weight coefficient in the BP neural network is: In formula (2), represents the weight coefficient between the uth neuron in the kth layer and the vth neuron in the k-1th layer in the BP neural network, W represents the weight coefficient, γ represents the learning rate, which is used to control the speed of weight update, and E represents the error function of the BP neural network, which is used to calculate the distance between the actual output of the output layer neuron and the expected output. Represents the error function to the weight coefficient The partial derivative of ; the data information after preliminary fusion processing is: In formula (3), H1 represents the monitoring data information after preliminary fusion processing, f(·) represents the activation function of the output layer of the BP neural network, A is the number of input nodes of the BP neural network, and W g represents the weight coefficient from the gth input layer to the hidden layer, α g is the g-th data input node in the BP neural network, and b represents the threshold of the output layer. In the process of secondary data fusion, the DS evidence algorithm is used to globally fuse different types of monitoring data. The data information after secondary fusion is: In formula (4), H2 represents the monitoring data information after secondary fusion processing, W ir represents the weight coefficient of the i-th monitoring data in the r-th fusion dimension, ρ ro It represents the fusion degree of the r-th monitoring data on the o-th fusion dimension, o and r represent the ordinal numbers of the fusion dimensions of different types of monitoring data, and n represents the total number of fusion dimensions of different types of monitoring data.

7. The safety situation analysis system for drug rehabilitation facilities according to claim 1, characterized in that: The psychological data analysis model quantitatively evaluates the psychological state of drug addicts based on a psychological scale, and uses a combination of autoregression and moving average methods to capture the autocorrelation of psychological states at different time points. When the quantitative value of the psychological state exceeds or equals the warning value, a warning prompt is triggered.

8. The safety situation analysis system for drug rehabilitation facilities according to claim 1, characterized in that: The security situation map sets security risk level thresholds according to different security risks. When the security risk exceeds the preset security risk level threshold, an emergency response corresponding to the security risk level is automatically initiated.

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