Real-time rapid analysis method and system for risk level of bioterrorist attack in public place

Abstract

The invention discloses a public place bioterrorist attack risk level real-time rapid analysis method and system, and belongs to the technical field of public safety. Comprising a data acquisition layer, a data preprocessing module, a multi-modal data fusion module, a dynamic weighted scoring module, a data processing layer, a risk assessment model, a multi-level threshold alarm module, a risk assessment layer and a quick response module. The data preprocessing module divides the biosensors into high-risk equipment and low-risk equipment according to different types of the biosensors; dividing the biological factor data into a plurality of risk grades according to the concentration value of each type of biological factors; according to the number of people in public places, the public places are divided into high-risk places and low-risk places. According to the invention, through the biosensor, the multi-parameter data fusion and weighted scoring system, the multi-level threshold alarm mechanism and the quick response module, the bioterrorism risk is monitored in real time, evaluated accurately and responded quickly, and the public safety guarantee level is improved.

Description

Technical Field

[0001] The present invention belongs to the field of public safety technology, and in particular relates to a real-time rapid analysis method and system for the risk level of biological terrorist attacks in public places. Background Art

[0002] In today's globalized era, biological terrorist attacks have become a deeply worrying security threat and pose a serious threat to human society. This type of attack involves the use of potent biological factors that are highly lethal or pathogenic to humans, which are generally referred to as biological warfare agents. These biological warfare agents include but are not limited to anthrax bacillus, plague bacillus, botulinum toxin, smallpox virus, and Brucella. The use of these pathogens or toxins is intended to cause harm to humans through various means, thereby achieving the terrorists' goals.

[0003] There are many ways to carry out biological terrorist attacks, including but not limited to contaminating food and water sources, carrying pathogens through mail, releasing pathogens through air conditioning systems, and releasing them in large public places such as stations, subways, and shopping malls where there are large crowds of people. In some cases, terrorists may even use airplanes to spread pathogens to achieve a wider spread effect. These attack methods are extremely concealed, making detection and prevention extremely difficult.

[0004] The characteristics of biological terrorist attacks are their concealment and delay, which makes them more difficult to be discovered and dealt with in a timely manner compared with other types of terrorist attacks. Such attacks not only lead to high morbidity and mortality, but also cause social chaos and panic among the people. Obviously, public bioterrorism incidents are an illegal act of spreading bioterrorism incidents in public places, which is extremely harmful to human society.

[0005] With the acceleration of globalization and the improvement of urbanization, the density and complexity of public places are increasing, which has significantly increased the risk of biological terrorist attacks. Biological terrorist attacks use pathogens or biological toxins to attack, which are characterized by strong concealment, rapid spread, and great destructive power. Once they occur, they often lead to serious public health crises and social panic. In recent years, governments have attached great importance to biosecurity issues and have introduced a series of policies and regulations to strengthen prevention. For example, the United States has issued the National Biodefense Strategy, emphasizing the improvement of the detection and response capabilities of biological threats; the European Union has also passed the Biosecurity Law, requiring member states to establish and improve biosecurity management systems.

[0006] However, the current bioterrorism risk assessment methods still have problems such as slow detection speed, insufficient accuracy, incomplete risk assessment and lack of rapid response mechanism, which cannot meet the needs of real-time rapid response in public places. Therefore, how to achieve real-time rapid response to bioterrorism risks in public places has become a key issue that needs to be solved urgently. In order to meet this challenge, countries need to further strengthen cooperation and jointly develop more advanced bioterrorism detection technologies and rapid response systems to ensure the safety and stability of public places. At the same time, the public's awareness of prevention and emergency response capabilities also need to be improved to deal with possible bioterrorism attacks. Summary of the invention

[0007] The purpose of the present invention is to meet actual needs and provide a real-time and rapid analysis method and system for the risk level of bioterrorism attacks in public places. By integrating high-sensitivity biosensors, multi-parameter data fusion and weighted scoring systems, time window trend analysis technology, multi-level threshold alarm mechanism and rapid response system, real-time monitoring, accurate assessment and rapid response to bioterrorism risks can be achieved, thereby improving the level of public security protection.

[0008] To achieve the above-mentioned invention objectives, the first object of the present invention is to provide a real-time rapid analysis system for the risk level of biological terrorist attacks in public places, comprising:

[0009] The data collection layer obtains data on various biological factors at different locations in public places by deploying different types of biosensors at different locations in public places;

[0010] The data preprocessing module divides the biosensors into high-risk devices and low-risk devices according to the types of biosensors; divides the biofactor data into several risk levels according to the concentration value of each type of biofactor; and divides public places into high-risk places and low-risk places according to the number of people in the public places;

[0011] The multimodal data fusion module constructs a multidimensional data set based on the location, type and concentration of the biological factor data output by the data acquisition layer; sets a concentration threshold and issues an early warning when the concentration of biological particles at a certain location exceeds the threshold;

[0012] The dynamic weighted scoring module assigns weights to the output of each biosensor at different times t, and then performs weighted scoring to obtain high and low risk values;

[0013] The data processing layer inputs high and low risk values ​​into the Bayesian network model for parameter learning and updates the Bayesian dynamic network probability;

[0014] Risk assessment model, using the scoring results of the expert questionnaire on bioterrorism risk in public places to train the Bayesian network model and obtain the risk assessment model;

[0015] Multi-level threshold alarm module: first, calculate the risk of bioterrorism incidents in public places according to the risk assessment model, and obtain three probability values ​​of low, medium and high risk; then set multi-level thresholds, calculate the risk value, and trigger corresponding alarms according to different risk levels; finally, the alarm system triggers the corresponding alarm mechanism;

[0016] The risk assessment layer evaluates the three probability values, risk values ​​and multi-level thresholds;

[0017] The rapid response module activates the rapid response mechanism when a high-risk alarm is triggered.

[0018] Preferably, the plurality of biological factors include bacteria, viruses and biotoxins.

[0019] Preferably, the biological factor data is divided into several risk levels according to the concentration value of each type of biological factor, including:

[0020] When the monitoring data of the biosensor is the biological particle concentration value, it is high risk if it is greater than 500ppm, otherwise it is low risk;

[0021] When the monitoring data of the biosensor is the type of biological particles, it is divided into BSL1-4 categories according to the reference pathogen biosafety level.

[0022] Preferably, the formula for weighted scoring is:

[0023]

[0024] Among them, w i represents the weight of the i-th parameter, D i (t) represents the biological factor data of the i-th biosensor at time t;

[0025] Preferably, the setting of multi-level thresholds to trigger corresponding alarms according to different risk levels includes: regular monitoring at levels I and II, early warning measures at level III, and immediate emergency response mechanism at levels IV and V;

[0026] Risk value RV = 0.2P low +0.3P mid +0.5P high , then the calculation formula of risk level L is:

[0027]

[0028] Preferably, the rapid response mechanism includes sealing off the scene, activating air filtration and disinfection equipment, notifying relevant emergency departments and issuing public safety alerts to ensure that risks are controlled in the shortest possible time and protect public safety.

[0029] Preferably, it also includes:

[0030] An information release module that sends information about the risk level of bioterrorism attacks in public places;

[0031] The emergency response layer produces corresponding response strategies based on the risk level of bioterrorism attacks in public places.

[0032] The second object of the present invention is to provide a real-time rapid analysis method for the risk level of bioterrorism attacks in public places, comprising:

[0033] Data collection: by deploying different types of biosensors at different locations in public places, we can obtain data on various biological factors at different locations in public places;

[0034] Data preprocessing: according to the different types of biosensors, the biosensors are divided into high-risk devices and low-risk devices; according to the concentration value of each type of biological factor, the biological factor data is divided into several risk levels; according to the number of people in public places, public places are divided into high-risk places and low-risk places;

[0035] Multimodal data fusion: construct a multidimensional data set based on the location, type and concentration of the biological factor data output by the data collection layer; set a concentration threshold and issue an early warning when the concentration of biological particles at a certain location exceeds the threshold;

[0036] Dynamic weighted scoring, at different times t, assigns weights to the output of each biosensor, and then performs weighted scoring to obtain high and low risk values;

[0037] Data processing: input high and low risk values ​​into the Bayesian network model for parameter learning and update the Bayesian dynamic network probability;

[0038] Risk assessment: using the scoring results of the expert questionnaire on bioterrorism risk in public places to train the Bayesian network model and obtain a risk assessment model;

[0039] Multi-level threshold alarm: First, calculate the risk of bioterrorism incidents in public places according to the risk assessment model, and obtain three probability values ​​of low, medium and high risk; then set multi-level thresholds, calculate the risk value, and trigger corresponding alarms according to different risk levels; finally, the alarm system triggers the corresponding alarm mechanism;

[0040] Risk assessment, evaluating the three probability values, risk values ​​and multi-level thresholds;

[0041] Quick response: when a high-risk alarm is triggered, the quick response mechanism is activated.

[0042] Preferably, the biological factor data is divided into several risk levels according to the concentration value of each type of biological factor, including:

[0043] When the monitoring data of the biosensor is the biological particle concentration value, it is high risk if it is greater than 500ppm, otherwise it is low risk;

[0044] When the monitoring data of the biosensor is the type of biological particles, it is divided into BSL1-4 categories according to the reference pathogen biosafety level.

[0045] Preferably, the formula for weighted scoring is:

[0046]

[0047] Among them, w i represents the weight of the i-th parameter, D i (t) represents the biological factor data of the i-th biosensor at time t;

[0048] Preferably, the setting of multi-level thresholds to trigger corresponding alarms according to different risk levels includes: regular monitoring at levels I and II, early warning measures at level III, and immediate emergency response mechanism at levels IV and V;

[0049] Risk value RV = 0.2P low +0.3P mid +0.5P high , then the calculation formula of risk level L is:

[0050]

[0051] A third object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned real-time and rapid analysis method of the risk level of biological terrorist attacks in public places.

[0052] A fourth object of the present invention is to provide a computer program product, comprising a computer program, which, when executed by a processor, implements the above-mentioned real-time and rapid analysis method of the risk level of biological terrorist attacks in public places.

[0053] The advantages and positive effects of this application are:

[0054] The present invention realizes real-time monitoring, accurate assessment and rapid response to bioterrorism risks by integrating multiple advanced technologies. First, by accessing highly sensitive biosensors, multiple biological factors in the air, including bacteria, viruses and biotoxins, are monitored in real time. The sensor network covers the entire public place to ensure monitoring without blind spots. Secondly, the data of multiple monitoring parameters are fused, and the comprehensive risk score is calculated through the risk assessment model. Each parameter in this model is given different weights according to its importance and risk level to ensure the accuracy and comprehensiveness of the assessment results. In addition, multi-level thresholds are set to trigger corresponding alarms according to different risk levels. Routine monitoring is carried out when the risk is low at level I and II, early warning measures are initiated when the risk is medium at level III, and emergency response mechanisms are immediately initiated when the risk is high at level IV and level V. Finally, once a high-risk alarm is triggered, the system automatically starts a rapid response mechanism, including blocking the site, starting air filtration and disinfection equipment, notifying relevant emergency departments and issuing public safety alerts, etc., to ensure that risks are controlled in the shortest time and protect public safety. Through the above-mentioned technical means, the patented method can realize real-time, accurate assessment and rapid response to bioterrorism risks in complex public places, and effectively improve the level of public safety protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 A system block diagram provided by a preferred embodiment of the present invention is shown;

[0057] Figure 2 A data flow diagram in a preferred embodiment of the present invention is shown;

[0058] Figure 3 A schematic diagram of a bioterrorism risk assessment model for public places based on a Bayesian network in a preferred embodiment of the present invention is shown. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] See also Figures 1 to 3 ;

[0061] The first embodiment is a real-time rapid analysis system for the risk level of bioterrorism attacks in public places, which mainly includes:

[0062] The data collection layer obtains data on various biological factors at different locations in public places by deploying different types of biosensors at different locations in public places;

[0063] This embodiment preferentially uses highly sensitive biosensors to monitor multiple biological factors in air samples in real time, including bacteria, viruses, and biotoxins, etc. The sensor network covers the entire public place to ensure no blind spot monitoring.

[0064] Sensor detection data D i (t) represents the reading of the i-th sensor at time t. If there are n sensors, the monitoring data matrix is:

[0065]

[0066] The sensor network transmits data to the central processing unit (CPU) through the wireless communication module, and the CPU pre-processes and stores the data.

[0067] The data preprocessing module divides the biosensors into high-risk devices and low-risk devices according to the types of biosensors; divides the biofactor data into several risk levels according to the concentration value of each type of biofactor; and divides public places into high-risk places and low-risk places according to the number of people in the public places; wherein:

[0068] The biological factor data is divided into several risk levels according to the concentration value of each type of biological factor, including:

[0069] When the monitoring data of the biosensor is the biological particle concentration value, it is high risk if it is greater than 500ppm, otherwise it is low risk;

[0070] When the monitoring data of the biosensor is the type of biological particles, it is divided into BSL1-4 categories according to the reference pathogen biosafety level.

[0071] The multimodal data fusion module constructs a multidimensional data set based on the location, type and concentration of the biological factor data output by the data acquisition layer; sets a concentration threshold and issues an early warning when the concentration of biological particles at a certain location exceeds the threshold;

[0072] The dynamic weighted scoring module assigns weights to the output of each biosensor at different times t, and then performs weighted scoring to obtain high and low risk values;

[0073] Data fusion and weighted scoring. The data of multiple monitoring parameters are integrated, and the comprehensive risk score of the internal safety facilities of the site is obtained through expert questionnaire scoring. Each parameter is dynamically updated according to the real-time equipment operation status, and different weights are assigned according to its importance and risk level to ensure the accuracy and comprehensiveness of the evaluation results. Specific formula:

[0074] The calculation formula of comprehensive risk score R(t) is:

[0075]

[0076] Among them, w i Represents the weight of the i-th parameter, satisfying

[0077] The weighted scoring system optimizes weight distribution through machine learning algorithms and uses expert questionnaire data and historical data for training to improve the accuracy of the scoring system;

[0078] The data processing layer inputs high and low risk values ​​into the Bayesian network model for parameter learning and updates the Bayesian dynamic network probability;

[0079] Risk assessment model, using the scoring results of the expert questionnaire on bioterrorism risk in public places to train the Bayesian network model and obtain the risk assessment model;

[0080] Multi-level threshold alarm module: firstly, the risk of bioterrorism incidents in public places is calculated according to the risk assessment model, and three probability values ​​of low, medium and high risk are obtained; then, multi-level thresholds are set, risk values ​​are calculated, and corresponding alarms are triggered according to different risk levels; finally, the alarm system triggers the corresponding alarm mechanism; specifically, it includes:

[0081] The first step is to calculate the risk assessment model based on the Bayesian network model. Figure 2 Calculate the risk of bioterrorism incidents in the venue and obtain three probability values ​​P of high, medium and low risk low , P mid , P high (The sum of the three is 1).

[0082] The second step is to set multi-level thresholds and trigger corresponding alarms according to different risk levels. Routine monitoring is carried out at levels I and II, early warning measures are initiated at level III, and emergency response mechanisms are immediately activated at levels IV and V.

[0083] Risk value RV = 0.2P low +0.3P mid +0.5P high , then the calculation formula of risk level L is:

[0084]

[0085] The alarm system automatically triggers the corresponding alarm mechanism based on the above calculation results to achieve multi-level risk warning and response.

[0086] The risk assessment layer evaluates the three probability values, risk values ​​and multi-level thresholds;

[0087] The rapid response module activates the rapid response mechanism when a high-risk alarm is triggered.

[0088] This embodiment utilizes a rapid response module. Once a high-risk alarm is triggered, the system automatically activates a rapid response mechanism, including blocking the scene, starting air filtration and disinfection equipment, notifying relevant emergency departments, and issuing public safety alerts, etc., to ensure that risks are controlled in the shortest time possible and public safety is protected.

[0089] Table 1 shows the relationship between risk alerts and response measures

[0090]

[0091] The rapid response module includes the emergency plan management module, the equipment control module and the information release module. Each module works together to achieve rapid response.

[0092] Through the integration of the above-mentioned multiple modules, the present invention provides an efficient and reliable public place bioterrorism risk level assessment system, which can achieve real-time and accurate risk assessment and rapid response in complex environments, and effectively improve the level of public security protection.

[0093] The rapid response mechanism includes sealing off the scene, activating air filtration and disinfection equipment, notifying relevant emergency departments and issuing public safety alerts to ensure that risks are controlled in the shortest possible time and public safety is protected.

[0094] An information release module that sends information about the risk level of bioterrorism attacks in public places;

[0095] The emergency response layer produces corresponding response strategies based on the risk level of bioterrorism attacks in public places.

[0096] The second embodiment is a real-time rapid analysis method for the risk level of bioterrorism attacks in public places, comprising:

[0097] Data collection: by deploying different types of biosensors at different locations in public places, we can obtain data on various biological factors at different locations in public places;

[0098] This embodiment preferentially uses highly sensitive biosensors to monitor multiple biological factors in air samples in real time, including bacteria, viruses, and biotoxins, etc. The sensor network covers the entire public place to ensure no blind spot monitoring.

[0099] Sensor detection data D i (t) represents the reading of the i-th sensor at time t. If there are n sensors, the monitoring data matrix is:

[0100]

[0101] The sensor network transmits data to the central processing unit (CPU) through the wireless communication module, and the CPU pre-processes and stores the data.

[0102] Data preprocessing: according to different types of biosensors, the biosensors are divided into high-risk devices and low-risk devices; according to the concentration value of each type of biological factor, the biological factor data is divided into several risk levels; according to the number of people in public places, public places are divided into high-risk places and low-risk places; wherein:

[0103] The biological factor data is divided into several risk levels according to the concentration value of each type of biological factor, including:

[0104] When the monitoring data of the biosensor is the biological particle concentration value, it is high risk if it is greater than 500ppm, otherwise it is low risk;

[0105] When the monitoring data of the biosensor is the type of biological particles, it is divided into BSL1-4 categories according to the reference pathogen biosafety level.

[0106] Multimodal data fusion: construct a multidimensional data set based on the location, type and concentration of the biological factor data output by the data collection layer; set a concentration threshold and issue an early warning when the concentration of biological particles at a certain location exceeds the threshold;

[0107] Dynamic weighted scoring, at different times t, assigns weights to the output of each biosensor, and then performs weighted scoring to obtain high and low risk values;

[0108] Data fusion and weighted scoring. The data of multiple monitoring parameters are integrated, and the comprehensive risk score of the internal safety facilities of the site is obtained through expert questionnaire scoring. Each parameter is dynamically updated according to the real-time equipment operation status, and different weights are assigned according to its importance and risk level to ensure the accuracy and comprehensiveness of the evaluation results. Specific formula:

[0109] The calculation formula of comprehensive risk score R(t) is:

[0110]

[0111] Among them, w i Represents the weight of the i-th parameter, satisfying

[0112] The weighted scoring system optimizes weight distribution through machine learning algorithms and uses expert questionnaire data and historical data for training to improve the accuracy of the scoring system;

[0113] Data processing: input high and low risk values ​​into the Bayesian network model for parameter learning and update the Bayesian dynamic network probability;

[0114] Risk assessment: using the scoring results of the expert questionnaire on bioterrorism risk in public places to train the Bayesian network model and obtain a risk assessment model;

[0115] Multi-level threshold alarm: First, calculate the risk of bioterrorism incidents in public places according to the risk assessment model, and obtain three probability values ​​of low, medium and high risk; then set multi-level thresholds, calculate the risk value, and trigger corresponding alarms according to different risk levels; finally, the alarm system triggers the corresponding alarm mechanism;

[0116] Risk assessment, evaluating the three probability values, risk values ​​and multi-level thresholds;

[0117] Quick response: when a high-risk alarm is triggered, the quick response mechanism is activated.

[0118] The multi-level threshold is set to trigger corresponding alarms according to different risk levels, including: regular monitoring at level I and level II, early warning measures at level III, and emergency response mechanism immediately at level IV and level V;

[0119] Risk value RV = 0.2P low +0.3P mid +0.5P high , then the calculation formula of risk level L is:

[0120]

[0121] A third embodiment is a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for real-time and rapid analysis of the risk level of bioterrorism attacks in public places.

[0122] A fourth embodiment is a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for real-time and rapid analysis of the risk level of bioterrorism attacks in public places.

[0123] Uncertainty analysis: This invention significantly improves the speed and real-time performance of bioterrorism risk assessment. Traditional methods rely on subjective judgment and manual scoring by experts, which usually takes a lot of time and is difficult to respond quickly to emergencies. However, this invention integrates highly sensitive biosensors and multimodal data fusion and dynamic weighted scoring systems to automatically complete data collection, processing and risk assessment in a short time, greatly shortening the response time.

[0124] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When the use is implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (e.g., infrared, wireless, microwave, etc.) mode) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidState Disk (SSD)), etc.

[0125] The above is only a preferred embodiment of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A real-time rapid analysis system for the risk level of biological terrorist attacks in public places, characterized in that: include: The data collection layer obtains data on various biological factors at different locations in public places by deploying different types of biosensors at different locations in public places; A data preprocessing module, which classifies the biosensors into high-risk devices and low-risk devices according to different types of biosensors; According to the concentration value of each type of biological factor, the biological factor data is divided into several risk levels; according to the number of people in the public place, the public place is divided into high-risk places and low-risk places; The multimodal data fusion module constructs a multidimensional data set based on the location, type and concentration of the biological factor data output by the data acquisition layer; sets a concentration threshold and issues an early warning when the concentration of biological particles at a certain location exceeds the threshold; The dynamic weighted scoring module assigns weights to the output of each biosensor at different times t, and then performs weighted scoring to obtain high and low risk values; The data processing layer inputs high and low risk values ​​into the Bayesian network model for parameter learning and updates the Bayesian dynamic network probability; Risk assessment model, using the scoring results of the expert questionnaire on bioterrorism risk in public places to train the Bayesian network model and obtain the risk assessment model; Multi-level threshold alarm module: firstly, the risk of bioterrorism incidents in public places is calculated according to the risk assessment model, and three probability values ​​of low, medium and high risk are obtained; Then set multi-level thresholds, calculate risk values, and trigger corresponding alarms according to different risk levels; finally, the alarm system triggers the corresponding alarm mechanism; The risk assessment layer evaluates the three probability values, risk values ​​and multi-level thresholds; The rapid response module activates the rapid response mechanism when a high-risk alarm is triggered.

2. The real-time rapid analysis system for the risk level of biological terrorist attacks in public places according to claim 1 is characterized in that: The various biological factors include bacteria, viruses and biotoxins.

3. The real-time rapid analysis system for the risk level of biological terrorist attacks in public places according to claim 1 is characterized in that: The biological factor data is divided into several risk levels according to the concentration value of each type of biological factor, including: When the monitoring data of the biosensor is the biological particle concentration value, it is high risk if it is greater than 500ppm, otherwise it is low risk; When the monitoring data of the biosensor is the type of biological particles, it is divided into BSL1-4 categories according to the reference pathogen biosafety level.

4. The real-time rapid analysis system for the risk level of bioterrorism attacks in public places according to claim 1 is characterized in that: The formula for weighted scoring is: Among them, w i represents the weight of the i-th parameter, D i (t) represents the biological factor data of the i-th biosensor at time t; 5. The real-time rapid analysis system for the risk level of biological terrorism in public places according to claim 4 is characterized in that: The multi-level threshold is set to trigger corresponding alarms according to different risk levels, including: regular monitoring at level I and level II, early warning measures at level III, and emergency response mechanism immediately at level IV and level V; Risk value RV = 0.2P low +0.3P mid +0.5P high , then the calculation formula of risk level L is:

6. The real-time rapid analysis system for the risk level of biological terrorism in public places according to claim 1 is characterized in that: The rapid response mechanism includes sealing off the scene, activating air filtration and disinfection equipment, notifying relevant emergency departments and issuing public safety alerts to ensure that risks are controlled in the shortest possible time and public safety is protected.

7. The real-time rapid analysis system for the risk level of bioterrorism attacks in public places according to claim 1 is characterized in that: Also includes: An information release module that sends information about the risk level of bioterrorism attacks in public places; The emergency response layer produces corresponding response strategies based on the risk level of bioterrorism attacks in public places.

8. A real-time rapid analysis method for the risk level of biological terrorist attacks in public places, characterized in that: include: Data collection: by deploying different types of biosensors at different locations in public places, we can obtain data on various biological factors at different locations in public places; Data preprocessing, classifying the biosensors into high-risk devices and low-risk devices according to different types of biosensors; According to the concentration value of each type of biological factor, the biological factor data is divided into several risk levels; Divide public places into high-risk places and low-risk places according to the number of people in them; Multimodal data fusion: construct a multidimensional data set based on the location, type and concentration of the biological factor data output by the data collection layer; set a concentration threshold and issue an early warning when the concentration of biological particles at a certain location exceeds the threshold; Dynamic weighted scoring, at different times t, assigns weights to the output of each biosensor, and then performs weighted scoring to obtain high and low risk values; Data processing: input high and low risk values ​​into the Bayesian network model for parameter learning and update the Bayesian dynamic network probability; Risk assessment: using the scoring results of the expert questionnaire on bioterrorism risk in public places to train the Bayesian network model and obtain a risk assessment model; Multi-level threshold alarm: First, the risk of bioterrorism incidents in public places is calculated according to the risk assessment model, and three probability values ​​of low, medium and high risk are obtained; Then set multi-level thresholds, calculate risk values, and trigger corresponding alarms according to different risk levels; finally, the alarm system triggers the corresponding alarm mechanism; Risk assessment, evaluating the three probability values, risk values ​​and multi-level thresholds; Quick response: when a high-risk alarm is triggered, the quick response mechanism is activated.

9. The real-time rapid analysis method for the risk level of bioterrorism attacks in public places according to claim 7 is characterized by: The biological factor data is divided into several risk levels according to the concentration value of each type of biological factor, including: When the monitoring data of the biosensor is the biological particle concentration value, it is high risk if it is greater than 500ppm, otherwise it is low risk; When the monitoring data of the biosensor is the type of biological particles, it is divided into BSL1-4 categories according to the reference pathogen biosafety level.

10. The real-time rapid analysis method for the risk level of bioterrorism attacks in public places according to claim 7 is characterized by: The formula for weighted scoring is: Among them, w i represents the weight of the i-th parameter, D i (t) represents the biological factor data of the i-th biosensor at time t; 11. The real-time rapid analysis method for the risk level of bioterrorism attacks in public places according to claim 7 is characterized by: The multi-level threshold is set to trigger corresponding alarms according to different risk levels, including: regular monitoring at level I and level II, early warning measures at level III, and emergency response mechanism immediately at level IV and level V; Risk value RV = 0.2P low +0.3P mid +0.5P high , then the calculation formula of risk level L is:

Images