Intelligent security and protection dynamic risk assessment and prevention and control system based on multi-source data fusion

Abstract

The invention particularly relates to an intelligent security and protection dynamic risk assessment and prevention and control system based on multi-source data fusion, and the system comprises a data collection module which collects corresponding data and carries out the preprocessing of the data; the data processing module is used for analyzing the data to obtain a risk assessment coefficient; the risk assessment module is used for obtaining a risk level based on the risk assessment coefficient and making a corresponding prevention and control strategy; and a prevention and control decision optimization module. According to the invention, the data acquisition module integrates sensor data, service system data and external data, and realizes omnibearing data coverage of a security scene; real-time data collected by a visual sensor, an environment sensor and the like are combined with business system data of access control, vehicle management and the like, then external information of meteorology, public security, public opinions and the like is fused, feature extraction and weight distribution are carried out by using technologies of deep learning, an entropy weight method and the like through a data processing module, and finally an accurate risk assessment coefficient is obtained. More risk factors are comprehensively considered, and potential safety hazards are effectively identified.

Description

Technical Field

[0001] The present invention relates to the field of intelligent security technology, and particularly to an intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion. Background Art

[0002] With the development of society and the improvement of security requirements, the limitations of traditional security systems have become increasingly prominent. Early security systems mostly relied on a single data source, such as only relying on surveillance cameras or access control systems, making it difficult to comprehensively capture potential risks. For example, in industrial sites, relying solely on video surveillance cannot monitor key environmental factors such as temperature, humidity, and gas concentration, resulting in hidden dangers being difficult to detect in a timely manner.

[0003] At the same time, each security subsystem is independent, with large differences in data formats, standards, and communication protocols, and lacks an effective integration and coordination mechanism. Data from access control, vehicle management, monitoring, and other systems cannot be correlated and analyzed. When dealing with security incidents, it is difficult to quickly obtain complete information, seriously affecting the efficiency of emergency response.

[0004] In addition, the development of network technology has brought new security threats, such as cyberattacks and data leaks, and traditional security systems lack effective countermeasures.

[0005] To solve these problems and meet modern security requirements, an intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion is needed. By integrating multiple types of data and applying advanced technologies, it can achieve dynamic risk assessment and precise prevention and control, improve the intelligence and comprehensive prevention and control capabilities of the security system, and provide strong guarantee for social security. Summary of the Invention

[0006] The purpose of the present invention is to propose an intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion to solve the above problems.

[0007] To achieve the above purpose, the present invention adopts the following technical solutions: An intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion, comprising: A data acquisition module: collecting corresponding data and preprocessing the data; A data processing module: obtaining a risk assessment coefficient after analyzing the data; A risk assessment module: obtaining a risk level based on the risk assessment coefficient and making corresponding prevention and control strategies; A prevention and control decision optimization module: optimizing the prevention and control resources.

[0008] Preferably, the data acquisition module specifically includes: sensor data acquisition, business system data acquisition, and external data acquisition; And cleaning and standardizing the data.

[0009] Preferably, the data processing module specifically includes: Video feature extraction: Using a deep learning model to extract target features in the video, including the postures, actions, and facial expressions of pedestrians; Temporal data feature extraction: Extracting frequency domain features from the temporal data collected by sensors; After assigning corresponding weight factors to the video features and the temporal data features respectively, multiplying the video features and the temporal data features by their corresponding weight factors and then summing them up to obtain a risk assessment coefficient.

[0010] Preferably, the process of determining the weight factors of the video features and the temporal data features includes: Obtaining the minimum and maximum values of each feature index through the analytic hierarchy process, and obtaining the difference between the entropy value of each index and 1. Presetting a gap threshold, comparing the difference between the entropy value of each index and 1 with the gap threshold, and using the comparison result of the difference between the entropy value of each index and 1 and the gap threshold as the basis for converting the entropy value difference of each index into a weight.

[0011] Preferably, the risk assessment module specifically includes: Presetting the value ranges of three groups of thresholds, each value range of the thresholds corresponding to a risk level, comparing the risk assessment coefficient with the value ranges of the three groups of thresholds to obtain the risk level corresponding to the risk assessment coefficient; where the risk levels include low risk, medium risk, and high risk.

[0012] Preferably, when the risk level is low risk, the security area is patrolled at a set patrol interval time and with a certain number of security personnel; When the risk level is medium risk, on the basis of the low risk level, reducing the patrol interval time and increasing the number of security personnel; and increasing the verification level of the access control system; When the risk level is high risk, activating an emergency plan, including immediately evacuating the personnel in a specific area, and blocking the relevant area to restrict the entry and exit of personnel and vehicles.

[0013] Preferably, the prevention and control decision optimization module specifically includes: Classifying various resources in the security system in detail; including human, material, financial, and information resources; Establishing a resource information database to record the detailed information of each resource, including the model, performance, location, and usage status of equipment, and the skills, working hours, and shift arrangements of personnel; Representing the resource scheduling plan as a chromosome; The fitness function is used to evaluate the quality of the chromosome and is usually related to the objective function; The fitness function is the reciprocal of the objective function ; Calculating the risk level of each area according to the current resource scheduling scheme and the degree of loss , substituting them into the objective function for calculation and then taking the reciprocal to obtain the fitness value; According to the fitness value, use the roulette wheel selection method to select excellent chromosomes from the current population to enter the next generation; After calculating the selection probability of each chromosome, simulate the roulette wheel through a random number generator for selection; Select two parent chromosomes, exchange genes at random positions according to a certain crossover probability to generate two offspring chromosomes; Mutate some gene positions of the chromosome with a certain mutation probability, generate a random number between 0 and 1 for each gene position, and if the random number is less than 0.1, mutate the gene position; By continuously performing selection, crossover, and mutation operations, the chromosomes in the population evolve continuously to approach the optimal resource scheduling scheme.

[0014] Preferably, the system further includes a simulation and emulation module: Using 3D modeling technology, construct an accurate virtual environment model based on the building layout, terrain, and road planning of the actual security area; Model the security-related objects such as personnel, vehicles, and equipment; the personnel model needs to consider the impact of age, gender, and behavior habits on their action ability in emergency situations; the vehicle model should cover the dimensions, speeds, and handling performance parameters of different vehicle types; the equipment model needs to accurately simulate the working principles, performance indicators, and response mechanisms of various sensors, access control systems, and monitoring devices, including the shooting angles, resolutions, and detection ranges of cameras; Establish behavior models of personnel and vehicles in different scenarios by analyzing historical data and expert experience; According to the results of the risk assessment module, set the risk parameters in the simulation scenario; Determine the configuration parameters of the prevention and control resources in the simulation scenario, including the number, distribution locations, patrol routes, and response times of security personnel; the coverage areas, resolutions, and frame rates of surveillance cameras; the opening and closing rules and verification methods of the access control system to simulate the prevention and control effects under different resource configurations.

[0015] Preferably, the simulation and emulation module further includes: Adopt the method of discrete event simulation or continuous time simulation, and advance the simulation process according to the set time step; within each time step, update the state and position information of each object according to the behavior model of the object and environmental changes; During the simulation process, trigger corresponding events when specific conditions are met; During the simulation process, various types of data are collected in real time, including the occurrence time, location, and impact scope of events; the response time and disposal effect of prevention and control resources; the evacuation situation, casualties, and losses of personnel and vehicles; Compare and analyze the simulation results under different prevention and control strategies to identify the advantages, disadvantages, and applicable scenarios of each plan; Adjust the relevant parameters of the prevention and control strategy based on the simulation results and evaluation and analysis.

[0016] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows: 1. The present invention integrates sensor data, business system data, and external data through a data acquisition module to achieve full - coverage data of the security prevention scenario; the real - time data collected by visual sensors, environmental sensors, etc., combined with business system data such as access control and vehicle management, and then integrated with external information such as meteorology, public security, and public opinion. The data processing module uses technologies such as deep learning and entropy weight method for feature extraction and weight assignment, and finally obtains an accurate risk assessment coefficient; by comprehensively considering more risk factors, potential security hazards can be effectively identified.

[0017] 2. Based on accurate risk assessment, the risk assessment module of the system can divide low, medium, and high risk levels and formulate corresponding prevention and control strategies; the prevention and control decision - making optimization module uses technologies such as genetic algorithms to evaluate resource scheduling plans with a fitness function and continuously optimize through selection, crossover, and mutation operations to achieve intelligent allocation of prevention and control resources; at the same time, the simulation and emulation module pre - rehearses and iteratively optimizes prevention and control strategies through constructing a virtual environment, object modeling, and behavior simulation. This intelligent dynamic prevention and control mechanism enables the system to adjust resources and strategies in real time according to risk changes. For example, in case of high risks, the emergency plan can be quickly activated to efficiently evacuate personnel and block areas, significantly improving the response speed and prevention and control efficiency of the security prevention system and minimizing the losses caused by security incidents. Brief Description of the Drawings

[0018] In the following description of exemplary embodiments with reference to the drawings, more details, features, and advantages of the present application are disclosed. In the drawings: Figure 1 is a flowchart of the present invention; Detailed Description of the Embodiments

[0019] The following will describe several embodiments of the present application in more detail with reference to the drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0020] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0021] Please refer to Figure 1 as shown, the present invention provides a technical solution: An intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion, comprising: Data acquisition module: Collect corresponding data and preprocess the data; Specifically including: sensor data acquisition, business system data acquisition, external data acquisition; And perform data cleaning and standardization processing.

[0022] Sensor data acquisition includes: Visual sensor: The high-definition camera is the main visual acquisition device, and the frame rate of the video stream data it acquires can reach 25 - 30 frames per second, and the resolution is commonly 1920×1080 or higher; in the intelligent security scenario, the camera is not only used for real-time monitoring, but also can perform target detection, recognition and tracking through computer vision technology.

[0023] For example, by using the YOLO (You Only Look Once) algorithm, it can quickly detect targets such as pedestrians, vehicles, and suspicious items in the video, and the detection accuracy can reach more than 90%; Environmental sensor: Environmental sensors such as temperature and humidity sensors, smoke alarms, and gas concentration sensors are used to monitor the environmental state. Taking an industrial warehouse as an example, the temperature and humidity sensor collects data every 10 seconds, and when the temperature exceeds 35°C or the humidity is lower than 20%RH, the system will issue an early warning. The smoke alarm adopts the principle of photoelectric smoke sensing or ion smoke sensing, and when the detected smoke concentration reaches the preset threshold (such as 0.015mg / m³), it immediately triggers an alarm signal; Position sensor: Infrared sensors, RFID (Radio Frequency Identification) sensors, etc. are used to determine the position information of personnel or objects. In a large shopping mall, an infrared sensor array is deployed, and by detecting the human body's thermal radiation, the preliminary positioning of the personnel's activity trajectory can be realized; the RFID sensor can be combined with the employee's work card and product label to track the positions of personnel and products in real time; Business system data acquisition includes: Access control system: The access control system records information such as the time a person swipes their card, their ID, and the access point number. For example, an office building's access control system generates thousands of card swipe records every day. By analyzing these records, it is possible to analyze people's get off work schedules and unusual entry and exit times. If the same person frequently swipes their card to enter and exit during non-working hours (such as 2 a.m.), the system will mark this as abnormal behavior. Vehicle Management System: The vehicle management system collects data such as vehicle entry and exit times, license plate numbers, checkpoint numbers, and vehicle types. At highway toll booths, the license plate recognition system has an accuracy rate of up to 99%. Combined with ETC data, it can analyze vehicle travel paths, dwell times, and other information to track suspicious vehicles. Visitor Management System: The visitor management system records visitor name, ID number, visit time, visitor, items carried, etc. By connecting with the public security system database, the visitor's identity can be verified to prevent illegal entry; External data collection includes: Meteorological data: By connecting to the Meteorological Bureau's API interface, real-time meteorological data is obtained, including temperature, humidity, wind speed, precipitation, lightning warnings, etc. Severe weather conditions (such as heavy rain and strong winds) may increase the risk of building collapse and circuit failure, and the system needs to incorporate these factors into risk assessments; Public security data: Obtain public security case data in the surrounding area from the public security department, such as case type, time of occurrence, location, etc. If a certain area has recently experienced multiple theft cases, the system will increase the risk level of the area and strengthen patrol and control; Public opinion data: Web crawlers are used to capture public opinion information from social media, news websites, and other platforms, and analyze security-related topics and events. For example, if public opinion about large-scale protests occurs in a certain area, the system can provide early warning and formulate corresponding prevention and control measures. Data processing module: Analyze the data to obtain the risk assessment coefficient; Specifically include: Video feature extraction: Use deep learning models (such as convolutional neural networks (CNNs)) to extract target features from videos, including pedestrian postures, movements, and facial expressions. Taking abnormal behavior detection as an example, by extracting pedestrian action features (such as running, falling), combined with preset rules, it is determined whether it is abnormal behavior; Time series data feature extraction: For time series data collected by sensors (such as changes in temperature and humidity over time), Fourier transform, wavelet transform and other methods are used to extract frequency domain features and analyze the changing patterns and trends of the data; For example, temperature data in the time domain can be converted to the frequency domain through Fourier transform to analyze the frequency components of temperature fluctuations; After assigning corresponding weight factors to the video features and the time-series data features respectively, calculate the product of the video features and the time-series data features and their corresponding weight factors and then sum them up to obtain the risk assessment coefficient; The process of determining the weight factors of the video features and the time-series data features includes: Principle of the entropy weight method: Information entropy is an index to measure the uncertainty of information. In risk assessment, the smaller the information entropy of a certain index, the greater the degree of variation of its data, the more information it provides, and the more important its role in risk assessment. A higher weight should be assigned; Obtain the minimum value and the maximum value of each feature index through the analytic hierarchy process, and obtain the difference between the entropy value of each index and 1. Preset a gap threshold, compare the difference between the entropy value of each index and 1 with the gap threshold, and based on the comparison result of the difference between the entropy value of each index and 1 and the gap threshold, use it as the basis for converting the entropy value difference of each index into a weight; The process includes: Let the original data matrix be ; where n is the number of risk indicators and m is the number of risk indicators; Calculate the probability matrix: Calculate the proportion of the j-th indicator in the i-th sample ; Obtain the probability matrix ; Calculate the entropy value: The entropy value of the j-th indicator ; where The value range is within ; The closer it is to 1, the more stable the data of this indicator and the less information it provides; Calculate the weight: The weight of the j-th indicator ; Risk assessment module: Obtain the risk level based on the risk assessment coefficient and make corresponding prevention and control strategies; Specifically include: Preset the value ranges of three groups of thresholds. The value range of each group of thresholds corresponds to a risk level. Compare the risk assessment coefficient with the value ranges of the three groups of thresholds to obtain the risk level corresponding to the risk assessment coefficient; among them, the risk levels include low risk, medium risk, and high risk; When the risk level is low risk, patrol the security area at the set patrol interval time and with the set number of security personnel; When the risk level is medium risk, on the basis of the low risk level, reduce the patrol interval time and increase the number of security personnel; and improve the verification level of the access control system; When the risk level is high risk, activate the emergency plan, including immediately evacuating the personnel in a specific area, blocking the relevant area, and restricting the entry and exit of personnel and vehicles; Prevention and control decision optimization module: Optimize prevention and control resources; Specifically include: Classify various resources in the security system in detail; including human resources (such as security personnel, patrol teams, etc.), material resources (such as surveillance cameras, access control devices, alarm devices, etc.), financial resources (such as security budgets, equipment maintenance funds, etc.), and information resources (such as various data, intelligence, etc.); Establish a resource information database to record the detailed information of each resource, including the model, performance, location, and usage status of the equipment, and the skills, working hours, and shift arrangements of the personnel; Provide accurate data support for resource scheduling through real-time update and management of resource information; Represent the resource scheduling plan as a chromosome; For example, for the security personnel allocation problem, the number of security personnel assigned to each area can be encoded as a gene position of the chromosome; The fitness function is used to evaluate the quality of the chromosome and is usually related to the objective function; The fitness function is the reciprocal of the objective function ; Calculate the risk level of each area according to the current resource scheduling plan and the degree of loss , substitute it into the objective function for calculation and then take its reciprocal to obtain the fitness value; According to the fitness value, use the roulette wheel selection method to select excellent chromosomes from the current population to enter the next generation; After calculating the selection probability of each chromosome, use a random number generator to simulate the roulette wheel for selection; For example, if the fitness value of a certain chromosome is 0.8 and the sum of the fitness values of all chromosomes in the population is 5, then the probability of its being selected is 0.8 divided by 5, which is equal to 0.16; Select two parent chromosomes, exchange genes at a random position according to a certain crossover probability to generate two offspring chromosomes; Mutate some gene positions of the chromosome with a certain mutation probability, generate a random number between 0 and 1 for each gene position, and if the random number is less than 0.1, mutate the gene position; By continuously performing selection, crossover, and mutation operations, the chromosomes in the population evolve continuously to approach the optimal resource scheduling plan; Simulation and emulation module: Use 3D modeling technology to construct an accurate virtual environment model based on the building layout, terrain, and road planning of the actual security area; For example, in the scenario of a large commercial complex, details such as each floor, store, fire escape, elevator, etc. of the shopping mall are presented completely, including information such as the size, material, and spatial relationship of each area, providing a basic framework for subsequent simulations. Model relevant objects for personnel, vehicles, and equipment security; for the personnel model, the impact of factors such as age, gender, and behavior habits on their mobility in emergency situations needs to be considered; the vehicle model should cover the dimensions, speeds, and handling performance parameters of different vehicle models; the equipment model needs to accurately simulate the working principles, performance indicators, and response mechanisms of various sensors, access control, and monitoring devices, including the shooting angles, resolutions, and detection ranges of cameras. By analyzing historical data and expert experience, establish behavior models of personnel and vehicles in different scenarios. For example, under normal circumstances, the walking speeds and residence time distributions of people in the shopping mall; during emergencies such as fires, the evacuation behavior patterns of people, including the impact of panic levels on movement speeds and the tendency to choose evacuation routes; the driving path planning and avoidance behaviors of vehicles under traffic control or emergencies. Based on the results of the risk assessment module, set the risk parameters in the simulation scenario. For example, the probability of a fire occurring, the fire spread speed, the smoke diffusion range and concentration; the modus operandi and target selection preferences of theft incidents; the potential threat types and attack methods of terrorist attacks. These parameters will affect the development and evolution of events during the simulation process. Determine the configuration parameters of prevention and control resources in the simulation scenario, including the number, distribution locations, patrol routes, and response times of security personnel; the coverage areas, resolutions, and frame rates of surveillance cameras; the opening and closing rules and verification methods of the access control system to simulate the prevention and control effects under different resource configurations. The simulation and emulation module also includes: Adopt the method of discrete event simulation or continuous time simulation, and advance the simulation process according to the set time step; within each time step, update the state and position information of each object according to the object's behavior model and environmental changes, such as the movement of personnel and the change of monitoring data of equipment. During the simulation process, trigger corresponding events when specific conditions are met; for example, when a person enters a restricted area, an alarm event is triggered, and when the temperature exceeds the threshold, a fire warning event is triggered; the system simulates the response and handling process of prevention and control resources to events according to the preset event handling rules and prevention and control strategies, such as security personnel going to the scene to check, starting fire extinguishing equipment, and adjusting the viewing angle of surveillance cameras for key monitoring, etc. During the simulation process, collect various data in real time, including the occurrence time, location, and impact range of events; the response time and disposal effects of prevention and control resources; the evacuation situation, casualties, and losses of personnel and vehicles. Compare and analyze the simulation results under different prevention and control strategies to identify the advantages, disadvantages, and applicable scenarios of each plan; for example, compare the impact of different evacuation route plans on the evacuation efficiency of personnel, and the reduction effect of different security patrol plans on the incidence of theft incidents, etc. According to the simulation results and evaluation analysis, adjust the relevant parameters of the prevention and control strategy; such as optimizing the patrol routes and time intervals of security personnel, adjusting the layout and parameter settings of surveillance cameras, improving the location and design of evacuation indication signs, etc., and then conduct simulations again to verify the effectiveness of the optimized strategy. Through continuous iterative optimization, find the best prevention and control strategy.

[0024] The above formulas are all obtained through software simulation by collecting a large amount of data, and a formula close to the true value is selected. The influence weight factors and specific coefficient values in the formula are set by those skilled in the art according to the actual situation and can be adjusted and modified subsequently.

[0025] The above description of the embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart security dynamic risk assessment and prevention and control system based on multi-source data fusion, characterized in that Including: Data acquisition module: Collect corresponding data and preprocess the data. Data processing module: Obtain a risk assessment coefficient after analyzing the data. Risk assessment module: Obtain a risk level based on the risk assessment coefficient and make corresponding prevention and control strategies. Prevention and control decision optimization module: Optimize the prevention and control resources.

2. The intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion according to claim 1, wherein, The data acquisition module specifically includes: sensor data acquisition, business system data acquisition, and external data acquisition. And clean and standardize the data.

3. The intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion according to claim 1, characterized in that, The data processing module specifically includes: Video feature extraction: Use a deep learning model to extract target features in the video, including the postures, actions, and facial expressions of pedestrians. Temporal data feature extraction: Extract frequency domain features for the temporal data collected by sensors. After assigning corresponding weight factors to the video features and the temporal data features respectively, calculate the product of the video features and the temporal data features and their corresponding weight factors and then sum them to obtain a risk assessment coefficient.

4. The intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion according to claim 3, characterized in that, The process of determining the weight factors of the video features and the temporal data features includes: Obtain the minimum and maximum values of each feature index through the analytic hierarchy process, and obtain the difference between the entropy value of each index and 1. Preset a gap threshold, compare the difference between the entropy value of each index and 1 with the gap threshold, and based on the comparison result of the difference between the entropy value of each index and 1 and the gap threshold, use it as the basis for converting the entropy value difference of each index into a weight.

5. The intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion according to claim 4, wherein, The risk assessment module specifically includes: Preset the value ranges of three groups of thresholds. Each value range of the thresholds corresponds to a risk level. Compare the risk assessment coefficient with the value ranges of the three groups of thresholds to obtain the risk level corresponding to the risk assessment coefficient; among them, the risk levels include low risk, medium risk, and high risk.

6. The intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion according to claim 5, characterized in that, When the risk level is low risk, patrol the security area at a set patrol interval time and with a certain number of security personnel. When the risk level is medium risk, on the basis of the low risk level, reduce the patrol interval time and increase the number of security personnel; and improve the verification level of the access control system. When the risk level is high risk, start an emergency plan, including immediately evacuating the personnel in a specific area, blocking the relevant area, and restricting the entry and exit of personnel and vehicles.

7. The intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion according to claim 6, wherein The prevention and control decision optimization module specifically includes: Classify various resources in the security system in detail; including human, material, financial, and information resources. Establish a resource information database to record the detailed information of each resource, including the model, performance, location, and usage status of equipment, and the skills, working hours, and shift schedules of personnel. Represent the resource scheduling plan as a chromosome. The fitness function is used to evaluate the quality of the chromosome and is usually related to the objective function. The fitness function is the reciprocal of the objective function ; Calculating the risk level of each region according to the current resource scheduling scheme and the degree of loss , substituting them into the objective function for calculation, and then taking the reciprocal to obtain the fitness value; According to the fitness value, use the roulette wheel selection method to select excellent chromosomes from the current population to enter the next generation. After calculating the selection probability of each chromosome, use a random number generator to simulate the roulette wheel for selection. Select two parent chromosomes and exchange genes at a random position according to a certain crossover probability to generate two offspring chromosomes. Mutate certain gene positions of the chromosome with a certain mutation probability. Generate a random number between 0 and 1 for each gene position. If the random number is less than 0.1, mutate the gene position. By continuously performing selection, crossover, and mutation operations, the chromosomes in the population evolve continuously to approach the optimal resource scheduling scheme.

8. The intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion according to claim 7, characterized in that, It also includes a simulation and emulation module: Using 3D modeling technology, construct an accurate virtual environment model based on the building layout, terrain, and road planning of the actual security area. Model the objects related to personnel, vehicles, and equipment security; the personnel model needs to consider the impact of factors such as age, gender, and behavior habits on their action ability in case of emergency; the vehicle model should cover the size, speed, and handling performance parameters of different vehicle types; the equipment model needs to accurately simulate the working principles, performance indicators, and response mechanisms of various sensors, access controls, and monitoring devices, including the shooting angle, resolution, and detection range of cameras. Establish behavior models of personnel and vehicles in different scenarios by analyzing historical data and expert experience. Set the risk parameters in the simulation scenario according to the results of the risk assessment module. Determine the configuration parameters of the prevention and control resources in the simulation scenario, including the number, distribution locations, patrol routes, and response times of security personnel. The coverage area, resolution, and frame rate of surveillance cameras; the opening and closing rules and verification methods of the access control system to simulate the prevention and control effects under different resource configurations.

9. The intelligent security dynamic risk assessment and prevention and control system based on multi-source data fusion according to claim 8, characterized in that, The simulation and emulation module also includes: Adopt the method of discrete event simulation or continuous time simulation, and advance the simulation process according to the set time step; within each time step, update the state and position information of each object according to the behavior model of the object and environmental changes. During the simulation process, trigger corresponding events when specific conditions are met. During the simulation process, collect various types of data in real time, including the occurrence time, location, and impact range of events; the response time and disposal effect of prevention and control resources; the evacuation situation, casualties, and losses of personnel and vehicles. Conduct a comparative analysis of the simulation results under different prevention and control strategies to find out the advantages, disadvantages, and applicable scenarios of each scheme. Adjust the relevant parameters of the prevention and control strategy according to the simulation results and evaluation analysis.

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