A model-based camera layout method and system

Abstract

This invention relates to the field of surveillance layout technology, and more particularly to a model-based method and system for camera layout. The method includes the following steps: performing geometric topology analysis on the venue to obtain a functional area model; performing fluid dynamics simulation on the functional area model to obtain pedestrian density distribution characteristic data; classifying the venue into risk levels based on the pedestrian density distribution characteristic data to obtain a key area assessment matrix; performing dynamic density field modeling on the venue based on the pedestrian density distribution characteristic data to obtain a crowd density field mathematical model; performing surveillance demand analysis on the venue based on the crowd density field mathematical model and the key area assessment matrix to obtain a surveillance coverage scheme; and performing density-balanced configuration of cameras based on the surveillance coverage scheme to obtain a camera distribution density scheme. This invention optimizes camera layout, avoids resource waste, and ensures continuous and blind-spot-free surveillance.

Description

Technical Field

[0001] This invention relates to the field of surveillance layout technology, and in particular to a model-based method and system for laying out cameras. Background Technology

[0002] Existing camera deployment methods often fail to adequately consider the complexity and dynamic changes of the environment, resulting in unsatisfactory monitoring performance. Take large sports stadiums as an example. These stadiums are not only vast in area but also structurally complex, including multiple entrances / exits, seating areas, and playing fields. In such scenarios, traditional camera deployment methods struggle to achieve comprehensive coverage of all critical areas. For instance, an international football match can attract tens of thousands of spectators with extremely high crowd mobility. This requires cameras to cover not only the seating area but also the surrounding area and entrances / exits. However, due to limitations in camera angles and monitoring ranges, blind spots often appear, especially in densely populated areas such as entrances and exits. In the event of an emergency in these areas, the monitoring system struggles to capture crucial information in a timely manner, thus delaying emergency response and handling. Summary of the Invention

[0003] Therefore, the present invention needs to provide a model-based camera layout method and system to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a model-based camera layout method includes the following steps:

[0005] Step S1: Perform geometric topology analysis on the venue to obtain a functional area model of the venue; perform fluid dynamics simulation on the functional area model of the venue to obtain the characteristic data of pedestrian flow density distribution; classify the venue into risk levels based on the characteristic data of pedestrian flow density distribution to obtain the evaluation matrix of key areas of the venue.

[0006] Step S2: Based on the characteristic data of crowd density distribution, perform dynamic density field modeling of the venue to obtain a mathematical model of crowd density field; based on the mathematical model of crowd density field and the evaluation matrix of key areas of the venue, perform monitoring demand analysis of the venue to obtain a monitoring coverage scheme; based on the monitoring coverage scheme, perform density equalization configuration of cameras to obtain a camera distribution density scheme.

[0007] Step S3: Divide the venue into spatial segments according to the camera distribution density scheme to obtain the venue spatial segmentation network; evaluate the field of view overlap of the cameras based on the venue spatial segmentation network to obtain the field of view coverage effect data;

[0008] Step S4: Identify blind spots in the field of view coverage data to obtain a blind spot distribution map of the venue; optimize the blind spot distribution map of the venue to obtain a target collaborative coverage scheme;

[0009] Step S5: Perform collaborative management of the camera group according to the target collaborative coverage scheme to obtain the intelligent monitoring network scheme.

[0010] This invention accurately identifies pedestrian density distribution through geometric topology analysis and fluid dynamics simulation, improving monitoring efficiency and coverage, and ensuring effective monitoring of all key areas within large sports venues. By classifying venues into risk levels, it enhances risk management capabilities, enabling targeted resource deployment and proactive prevention and response to emergencies. Through pedestrian density and key area assessment, it achieves balanced camera density configuration, optimizing camera layout, avoiding resource waste, and ensuring continuous and blind-spot-free monitoring. Dynamic density field modeling and monitoring demand analysis adapt to dynamic changes in pedestrian flow within the venue, allowing for real-time adjustments to monitoring strategies. Venue space segmentation and field-of-view overlap assessment further improve field-of-view coverage, reducing blind spots and enhancing monitoring quality. Blind spot identification and optimization help identify and optimize monitoring blind spots, enhancing the integrity and reliability of the monitoring system. Finally, through collaborative management of camera groups, an intelligent monitoring network solution is constructed, not only covering all key areas but also intelligently responding to emergencies, improving emergency response speed and processing efficiency. This invention is highly adaptable and applicable to different venue structures and pedestrian flow characteristics. It provides data-driven decision support, making the monitoring layout more scientific and reasonable, and significantly improving the safety and emergency response capabilities of the venue.

[0011] Preferably, the present invention also provides a modeled camera layout system for performing the modeled camera layout method described above, the modeled camera layout system comprising:

[0012] The regional risk assessment module is used to perform geometric topological analysis on the venue to obtain a functional area model of the venue; to perform fluid dynamics simulation on the functional area model of the venue to obtain the characteristic data of pedestrian flow density distribution; and to classify the venue into risk levels based on the characteristic data of pedestrian flow density distribution to obtain the assessment matrix of key areas of the venue.

[0013] The monitoring configuration analysis module is used to perform dynamic density field modeling of the venue based on the characteristic data of crowd density distribution, and obtain a mathematical model of crowd density field; to perform monitoring demand analysis of the venue based on the mathematical model of crowd density field and the evaluation matrix of key areas of the venue, and obtain a monitoring coverage scheme; and to perform density equalization configuration of cameras based on the monitoring coverage scheme, and obtain a camera distribution density scheme.

[0014] The field of view assessment module is used to divide the venue into spaces according to the camera distribution density scheme to obtain the venue space segmentation network; and to assess the field of view overlap of the cameras based on the venue space segmentation network to obtain the field of view coverage effect data.

[0015] The coverage optimization module is used to identify blind spots in the field of view coverage data to obtain a blind spot distribution map of the venue monitoring; the blind spot distribution map of the venue monitoring is then optimized to obtain a target collaborative coverage scheme.

[0016] The collaborative management module is used to collaboratively manage camera groups according to the target collaborative coverage scheme, thereby obtaining an intelligent monitoring network solution.

[0017] This invention achieves automated risk assessment through integrated modules, improving the efficiency and accuracy of risk assessment; ensures the rational allocation and efficient utilization of monitoring resources through precise monitoring configuration; maximizes field of view coverage and reduces blind spots through optimized spatial division; significantly improves the integrity and reliability of the monitoring system through intelligent identification and optimization of blind spots; enhances the response speed and processing efficiency of the monitoring system through intelligent collaborative management, especially in emergency situations; and improves the safety and emergency response capabilities of public places such as large stadiums through the overall system design. Attached Figure Description

[0018] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings:

[0019] Figure 1 A schematic flowchart illustrating the steps of a modeled camera layout method according to an embodiment is shown.

[0020] Figure 2 A detailed flowchart of step S2 of one embodiment is shown.

[0021] Figure 3 A detailed flowchart of step S26 of one embodiment is shown. Detailed Implementation

[0022] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0023] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0024] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a model-based camera layout method, comprising the following steps:

[0026] Step S1: Perform geometric topology analysis on the venue to obtain a functional area model of the venue; perform fluid dynamics simulation on the functional area model of the venue to obtain the characteristic data of pedestrian flow density distribution; classify the venue into risk levels based on the characteristic data of pedestrian flow density distribution to obtain the evaluation matrix of key areas of the venue.

[0027] Step S2: Based on the characteristic data of crowd density distribution, perform dynamic density field modeling of the venue to obtain a mathematical model of crowd density field; based on the mathematical model of crowd density field and the evaluation matrix of key areas of the venue, perform monitoring demand analysis of the venue to obtain a monitoring coverage scheme; based on the monitoring coverage scheme, perform density equalization configuration of cameras to obtain a camera distribution density scheme.

[0028] Step S3: Divide the venue into spatial segments according to the camera distribution density scheme to obtain the venue spatial segmentation network; evaluate the field of view overlap of the cameras based on the venue spatial segmentation network to obtain the field of view coverage effect data;

[0029] Step S4: Identify blind spots in the field of view coverage data to obtain a blind spot distribution map of the venue; optimize the blind spot distribution map of the venue to obtain a target collaborative coverage scheme;

[0030] Step S5: Perform collaborative management of the camera group according to the target collaborative coverage scheme to obtain the intelligent monitoring network scheme.

[0031] In this embodiment, firstly, 3D laser scanning technology is used to perform geometric topological analysis of the venue, generating point cloud data. Using Autodesk Revit software, a 3D model of the venue is created based on the point cloud data, and different functional areas, such as spectator seating, playing fields, and entrances / exits, are divided, forming a functional area model of the venue. Next, computational fluid dynamics software, such as ANSYS Fluent, is used to perform fluid dynamics simulation on the functional area model of the venue. By simulating crowd flow, crowd density distribution characteristic data is obtained, which shows the crowd density in different areas of the venue. Using Python and the machine learning library Scikit-learn, the venue is classified into risk levels based on the crowd density distribution characteristic data, high-risk areas are identified, and a key area assessment matrix for the venue is constructed. This matrix can determine which areas require more monitoring resources. Based on the crowd density distribution characteristic data, MATLAB is used to perform dynamic density field modeling, establishing a mathematical model of the crowd density field. Combining the key area assessment matrix of the venue, monitoring needs are analyzed, and a monitoring coverage plan is determined. Next, using surveillance layout planning software such as Milestone System Designer, the camera density is evenly configured according to the surveillance coverage plan to ensure sufficient coverage of key areas while avoiding resource waste, resulting in a camera distribution density scheme. Based on the camera distribution density scheme, Revit is used again to divide the venue space, resulting in a venue spatial segmentation network. Then, PTZ (Pan-Tilt-Zoom) camera simulation software is used to simulate the camera field of view, evaluate the field of view overlap, and obtain field of view coverage effect data. Using GIS software such as ArcGIS, blind spot areas are identified from the field of view coverage effect data, resulting in a venue surveillance blind spot distribution map. Spatial analysis tools are used to optimize and solve for surveillance blind spots, resulting in a target collaborative coverage scheme. Finally, using a network management system such as Cisco DNA Center, the camera group is collaboratively managed according to the target collaborative coverage scheme. The network parameters of the cameras are configured to realize an intelligent surveillance network scheme, ensuring the efficient operation and real-time response of the surveillance system.

[0032] Preferably, step S1 includes the following steps:

[0033] Step S11: Collect point cloud data of the venue from multiple angles to obtain the original venue point cloud data, and denoise the original venue point cloud data to obtain denoised venue point cloud data.

[0034] Specifically, five 3D laser scanners can be deployed at different locations within the venue, each covering a portion of the venue. These scanners rotate 360 ​​degrees to capture the venue's three-dimensional geometry, generating raw point cloud data. This data includes the X, Y, and Z coordinates of each point within the venue, as well as reflection intensity and color information. After acquisition, professional point cloud processing software, such as CloudCompare or PCL (Point Cloud Library), is used to denoise the raw point cloud data. The denoising process includes removing outliers, smoothing, and filtering. For example, a filter can be set to identify points whose distance from surrounding points exceeds a certain threshold as noise and remove them from the dataset. Finally, denoised venue point cloud data is obtained.

[0035] Step S12: Perform topological feature encoding on the denoised venue point cloud data to obtain venue spatial semantic encoding data, and perform 3D modeling of the venue based on the venue spatial semantic encoding data to obtain the venue point cloud model;

[0036] Specifically, topological feature encoding can be performed using GIS (Geographic Information System) software, such as ArcGIS or QGIS. Topological feature encoding is performed on each point in the denoised venue point cloud data, which involves identifying the connectivity relationships between points, such as adjacency and intersection. This encoding converts the point cloud data into spatial semantic encoded data for the venue, containing not only geometric information but also spatial relationship information. 3D modeling software, such as Autodesk Revit or SketchUp, is then used to create a 3D model of the venue based on the spatial semantic encoded data. During the modeling process, the basic structure of the venue, such as walls, columns, and roof, is first created. Based on the geometric information in the point cloud data, the size and position of these structures are adjusted to ensure they match the geometry of the actual venue. Internal facilities, such as seating, walkways, and stairs, can also be added. Through adjustments and optimization, the final point cloud model of the venue is obtained.

[0037] Step S13: Perform surface reconstruction on the venue point cloud model to obtain the venue reconstructed point cloud model, and extract surface features from the venue reconstructed point cloud model to obtain venue surface geometric feature data.

[0038] Specifically, 3D reconstruction software, such as MeshLab or Agisoft Metashape, can be used to process the venue's point cloud model. In MeshLab, its surface reconstruction tools are used to generate a reconstructed venue point cloud model by selecting dense regions in the point cloud and applying the Poisson reconstruction algorithm. Feature extraction tools are then used to identify and quantify the surface geometry of the reconstructed venue point cloud model. This includes extracting edges, corners, and regions, and calculating their geometric properties, such as curvature, normals, and distances. For example, edge detection algorithms are used to identify the edges of walls and columns in the venue model, and then the length and direction of these edges are calculated. For corners, the RANSAC (Random Sample Consensus) algorithm is used to identify the intersections of lines and planes in the point cloud and to calculate the precise location and angle of these corners.

[0039] Step S14: Calculate the spatial connectivity of the venue based on the surface geometric feature data to obtain the venue topology map;

[0040] Specifically, computational geometry software, such as CGAL (Computational Geometry Algorithms Library), can be used to analyze the venue's geometric features and calculate spatial connectivity. The venue's 3D model is imported into CGAL, and its data structures and algorithms are used to identify openings, passageways, and areas within the model. Graph theory concepts are used to represent the venue's spatial structure, where nodes represent intersections or turning points in space, and edges represent paths or corridors connecting these points. For example, all entrances and exits in the venue can be identified, and the shortest paths between them can be calculated. In this way, a venue topology map can be constructed, showing the connectivity of all spaces within the venue, including which areas are interconnected and the strength of their connections. GIS software can also be used to assist the analysis, combining the topology map with the venue's geographic information to further analyze potential paths for pedestrian and material flows. The final result is a detailed venue topology map that not only shows the venue's physical structure but also reveals the logical relationships between spaces.

[0041] Step S15: Divide the venue into structural zones according to the venue topology diagram to obtain venue functional area partitioning data, and divide the venue point cloud model according to the venue functional area partitioning data to obtain venue functional area model.

[0042] Specifically, Building Information Modeling (BIM) software, such as Autodesk Revit, can be used to import and analyze the venue's topology diagram. Revit allows different functional areas to be defined based on connectivity and spatial relationships within the topology diagram. For example, the venue can be divided into several functional areas: a seating area, a playing area, entrances / exits, lounges, and an emergency evacuation area. In Revit, a separate model group is created for each area, and the corresponding parts of the point cloud model are assigned to these groups. The boundaries of these areas are determined by identifying key nodes in the topology diagram, such as corridor intersections, staircase locations, and obvious spatial divisions. The boundaries of these areas are then refined using Revit's 3D modeling tools. For example, a virtual wall can be created between the seating area and the playing area to clearly define these two areas. Models of doors and passageways can also be added to the entrances / exits to reflect actual pedestrian flow. After completing these steps, each area is color-coded. Finally, the data for these functional areas is exported as a new point cloud model file containing a detailed breakdown of the various functional areas within the venue.

[0043] Step S16: Perform fluid dynamics simulation on the functional area model of the venue to obtain the characteristic data of pedestrian flow density distribution, and classify the risk level of the venue based on the characteristic data of pedestrian flow density distribution to obtain the evaluation matrix of key areas of the venue.

[0044] For a detailed implementation process of this embodiment, please refer to the sub-step of step S16.

[0045] This invention improves data quality through multi-angle point cloud acquisition and denoising processing. It enhances the understanding of the venue's spatial structure through topological feature encoding and spatial semantic encoding. 3D modeling and surface reconstruction capture the venue's subtle geometric features. The generation of a venue topological relationship map enables accurate calculation of spatial connectivity and understanding of pedestrian flow and aggregation patterns. Structural zoning and functional area model generation effectively divide the venue into different functional areas, providing precise zoning for obtaining pedestrian density distribution data. Fluid dynamics simulation improves the accuracy of pedestrian flow simulation, aiding in predicting pedestrian density distribution and identifying high-risk areas. Risk level classification based on pedestrian density distribution data enhances the scientific rigor of risk assessment, helping to identify and prevent potential safety issues in advance. The generation of a key area assessment matrix accurately identifies key areas, ensuring concentrated investment of monitoring resources in these areas.

[0046] Preferably, step S16 includes the following steps:

[0047] Step S161: Track the time series of people flow in the venue to obtain extreme data of people flow in the venue;

[0048] Specifically, a high-precision people counting system can be installed at the main entrance of the venue. This system includes infrared sensors, pressure sensors, and video surveillance cameras, all integrated into a people counting device such as the AxisPeople Counter. During implementation, infrared and pressure sensors are first configured to detect people passing through the entrance. When someone passes through, these sensors detect the change and trigger counting. On large events or match days, the people counting system is monitored in real time, recording the number of people entering every hour or even every minute. In this way, it is possible to track trends in people flow and identify peak periods, such as the 30 minutes before and after the event. It can also record the lowest points in people flow, such as during halftime. Extreme values ​​in people flow can be identified, including key data points such as the maximum number of people in a single hour, the maximum number of people in a single day, and the historical maximum number of people.

[0049] Step S162: Extract spatial parameters from the functional area model of the venue to obtain the venue structural feature library;

[0050] Specifically, 3D modeling software, such as SketchUp or AutoCAD, can be used to process and analyze the functional area model of the venue. In these programs, parameters such as the size, shape, and location of each functional area are measured and recorded. For example, the length and width of each row of seats in the seating area are measured, the location of each seat is recorded, and the total area of ​​each area is calculated. The width and height of entrances and exits, as well as their distances from the main activity areas, can also be extracted from the model. The width and length of passageways, and the different areas they connect, can also be measured. Spatial analysis tools, such as buffer analysis and network analysis tools in GIS software, are used to further extract and analyze these spatial parameters. For example, a buffer zone is created for each entrance / exit to determine its influence range, and accessibility between different areas is analyzed. Through these analyses, a structural feature library of the venue is ultimately obtained, containing the spatial parameters of the various functional areas and structures within the venue.

[0051] Step S163: Based on the venue structure feature library, construct a crowd flow simulation grid for the venue to obtain the venue flow domain division grid;

[0052] Specifically, simulation software such as AnyLogic or MATSim can be used, which can handle complex pedestrian flow dynamics and simulate crowd behavior. Detailed spatial parameters of the venue are extracted from a venue structural feature library, including the dimensions of each area, the width and length of passageways, and the location of entrances and exits. For example, assuming a sports stadium, based on information from the venue structural feature library, the stadium is divided into multiple grid cells. Each grid cell represents a specific area, such as seating, aisles, entrances, and exits. AutoCAD or GIS software is used to accurately draw these grids, ensuring that the size and location of each grid cell match the actual venue. In the simulation software, specific attributes are assigned to each grid cell, such as capacity, passage speed, and pedestrian flow. For example, grid cells for aisles are set to allow fast flow, while grid cells for seating are set to allow slow movement. The connection relationships between grid cells can also be set according to the venue's structural features, such as the location of emergency exits. In this way, a grid for dividing the venue's flow domain can be obtained.

[0053] Step S164: Divide the venue flow domain into a grid based on the extreme data of venue passenger flow and set boundary conditions to obtain the initial parameters of the venue flow field;

[0054] Specifically, fluid dynamics simulation software such as ANSYS Fluent or OpenFOAM can be used. These software programs can handle the simulation of people flow as a fluid and precisely set boundary conditions. Assume that extreme data on the venue's people flow at different time periods are obtained from step S161. Based on this data, boundary conditions are set for each entrance and exit of the venue's flow domain, which is then gridded. For example, based on the maximum people flow data 30 minutes before opening, the grid cells at the main entrance are set to allow 3000 people to flow in per hour. Based on the minimum people flow data at closing time, the grid cells at the exits are set to allow 1000 people to flow out per hour. Special circumstances within the venue, such as the speed and direction of people flow during emergency evacuations, can also be considered. Based on safety regulations and historical data, specific boundary conditions are set for the grid cells at emergency exits; for example, in an emergency, each emergency exit grid cell must be able to evacuate 500 people within 5 minutes. By setting these boundary conditions, the initial parameters of the venue's flow field can be obtained.

[0055] Step S165: Perform numerical simulation calculations on the initial parameters of the venue flow field to obtain crowd flow field distribution data, and extract dynamic features from the crowd flow field distribution data to obtain crowd density distribution feature data.

[0056] Specifically, computational fluid dynamics (CFD) software, such as ANSYS Fluent or CFX, can be used to perform these calculations. First, the initial parameters of the venue flow field are input into the simulation software according to the boundary conditions set in step S164. These parameters include the pedestrian velocity, direction, and density for each grid cell. During the simulation, physical models of crowd flow, such as social force models or fluid dynamic models, are applied to simulate crowd behavior. For example, a model based on the Navier-Stokes equations is used, which considers the inertia of the crowd, mutual repulsion, and environmental attraction. Using these models, the pedestrian density and velocity for each grid cell at different time points are calculated. After the simulation, the distribution data of the crowd flow field is obtained, including the pedestrian density and velocity for each grid cell at different time points. To extract the dynamic characteristics of this data, data post-processing tools, such as Tecplot or Paraview, are used to visualize and analyze the simulation results. Peak areas, flow bottlenecks, and high-flow areas of pedestrian density are identified, and this information is correlated with time to obtain the characteristics of pedestrian density changes over time. Through these analyses, we finally obtained data on the distribution characteristics of pedestrian density, which reflects the changes in pedestrian density in different areas of the venue at different times.

[0057] Step S166: Perform threshold segmentation on the crowd density distribution characteristic data to obtain the venue crowd density level zoning;

[0058] Specifically, crowd density distribution data can be imported into GIS software, and thresholds can be created based on the numerical range of crowd density. For example, crowd density can be divided into four levels: low (0-10 people / m²), medium (11-20 people / m²), high (21-30 people / m²), and very high (over 30 people / m²). Using the classification tools in the GIS software, the venue's grid cells are divided into different density level zones according to these thresholds. Each zone is assigned a specific color or symbol. In this way, the final crowd density level zoning of the venue is obtained, which displays the crowd density levels in different areas of the venue.

[0059] Step S167: Assign risk weights to the venue’s crowd density level zones to obtain the venue’s key area assessment matrix.

[0060] Specifically, risk assessment software, such as @RISK or Crystal Ball, can be used in conjunction with analytical tools in GIS software to assign risk weights. Based on the results of the crowd density zoning, the risk weight for each zone is determined. Multiple factors are considered, including crowd density, zone function (e.g., exits, entrances, emergency evacuation areas), historical safety incident records, and zone physical characteristics (e.g., narrow passages or staircases). For example, areas with very high crowd density, especially emergency evacuation areas and narrow passages, are assigned higher risk weights because these areas are more prone to congestion and safety incidents in emergencies. An assessment matrix of key areas of the venue is constructed using Excel or a professional decision support system. In this matrix, each row represents a zone, and each column represents a risk factor. A weight is assigned to each risk factor for each zone, based on expert opinion, historical data, and simulation results. For example, a higher risk weight is assigned to the "emergency evacuation capacity" factor in high-density areas, while a lower risk weight is assigned to the same factor in low-density areas. The overall risk score for each zone is calculated by weighted summation of the weights of each risk factor. The final result is a venue key area assessment matrix, which contains the overall risk score for each zone.

[0061] This invention enhances dynamic monitoring capabilities of pedestrian flow through temporal tracking, providing real-time data support for pedestrian flow management and safety monitoring. Spatial parameter extraction lays the foundation for in-depth analysis of venue structural characteristics and the establishment of a structural feature database. The construction of a pedestrian flow simulation grid makes the simulation more accurate, capable of simulating pedestrian flow within the venue. Extreme pedestrian flow data is used to scientifically set boundary conditions for the flow domain grid, ensuring the scientific validity and accuracy of the simulation calculations. Numerical simulation calculations and dynamic feature extraction provide data on the distribution of pedestrian flow fields, accurately simulating pedestrian dynamics. The application of threshold segmentation enables hierarchical management of pedestrian density, facilitating targeted monitoring. Risk weight allocation allows for refined assessment of the risk level of various areas within the venue, providing guidance for safety monitoring and emergency response.

[0062] Preferably, step S2 includes the following steps:

[0063] Step S21: Perform spatiotemporal sequence identification on the population density distribution characteristic data to obtain population density change characteristic data;

[0064] Specifically, data analysis tools, such as a Python environment combined with the Pandas and Scikit-learn libraries, can be used to process and analyze the characteristic data of crowd density distribution. First, crowd density distribution data for the past month is extracted from the database. This data was collected by multiple sensors within the venue at different times and locations. The dataset contains information such as the ID of each sensor, the number of people detected, the detection time, and the date. Using the Pandas library, this data is loaded into a DataFrame, and the timestamps are converted to Pandas' DateTime format and set as the index of the DataFrame. This yields a time-series data frame containing the crowd density data of each sensor over time. The time-series analysis tools in the Scikit-learn library are then used to analyze this data. First, Fourier transform is used to identify periodic patterns in the data, such as daily and weekly cycles, which helps to understand the daily average variation of crowd density and the weekend effect. An ARIMA model is applied to predict short-term trends in crowd density, which helps to predict peak crowd times within specific time periods. Finally, the K-means clustering algorithm is used to perform cluster analysis on the sensor data. The sensors were grouped into several clusters based on the similarity of crowd density, with each cluster representing an area within the venue with similar crowd density. By analyzing the time series data of these clusters, common characteristics of crowd density changes could be identified, such as crowd aggregation in specific areas within specific time periods. This ultimately yielded characteristic data on crowd density changes, including not only the temporal trend of crowd density but also its spatial distribution features.

[0065] Step S22: Perform Markov state transition modeling based on population density change characteristic data to obtain a dynamic evolution model of population density;

[0066] Specifically, statistical modeling software, such as the Statsmodels library in R or Python, can be used to construct Markov models. The characteristics of crowd density changes are categorized into different states, such as low density, medium density, and high density. Hidden Markov Models (HMMs) are used to model the transition probabilities between these states. For example, it was found that the probability of crowd density transitioning from a low-density state to a high-density state increases significantly before the start of a competition. Maximum likelihood estimation is used to estimate the state transition probabilities, and the Viterbi algorithm is used to determine the state sequence at each time point. Through these analyses, a dynamic evolution model of crowd density can be obtained, which can predict changes in crowd density states over different time periods.

[0067] Step S23: Parametrically describe the dynamic evolution model of population density using partial differential equations to obtain the population density field expression data, and then analytically solve the population density field expression data to obtain the mathematical model of the population density field.

[0068] Specifically, mathematical modeling software, such as MATLAB's PDE toolbox or COMSOL Multiphysics, can be used to parameterize partial differential equations. First, based on the state transition probabilities and state durations of the Markov model obtained in step S22, a continuous time-space crowd density model (crowd density dynamic evolution model) is established. It is assumed that the variation of crowd density with time and space can be described by a partial differential equation, such as a convection-diffusion equation, which considers the flow and diffusion effects of the crowd. Using the parameter estimation function in the PDE toolbox, the state transition probabilities and durations of the Markov model are input as parameters into the PDE. For example, the state transition probabilities are used as coefficients for the convection term, and the state durations as coefficients for the diffusion term. These parameters are calibrated to ensure that the solution of the PDE model matches the actual observation data. Numerical solvers, such as the finite element method (FEM), are used to solve these PDEs. In MATLAB or COMSOL, the type, boundary conditions, and initial conditions of the PDE are defined, and then the solver is run to obtain the expression data of the crowd density field. This data provides a mathematical description of the crowd density at different times and spatial locations. Finally, a mathematical model of the population density field is obtained, which can predict the distribution of population density under different conditions.

[0069] Step S24: Construct constraints on the mathematical model of the crowd density field to obtain data describing the monitoring coverage optimization problem;

[0070] Specifically, optimization software such as CPLEX or Gurobi, and mathematical modeling tools such as AMPL or Python's SciPy library, can be used to construct constraints. First, the objective function and constraints of the surveillance coverage optimization problem are defined. The objective function is to minimize the area of ​​surveillance blind spots or maximize the coverage of key areas. Constraints include camera field-of-view limitations, installation location limitations, and budget constraints. These constraints are defined using the optimization module in the SciPy library and applied to a mathematical model of the crowd density field. For example, the camera field-of-view limitations can be used as constraints to ensure that the coverage area of ​​each camera matches a high-crowd-density area. GIS software can also be used to analyze the venue's geographic information, incorporating geographic constraints, such as the location of walls and obstacles, into the optimization problem. Finally, descriptive data for the surveillance coverage optimization problem is obtained.

[0071] Step S25: Solve the monitoring coverage optimization problem description data using variational method to obtain the monitoring coverage range function, and optimize the monitoring coverage range function by gradient projection to obtain the local optimal coverage solution data;

[0072] Specifically, variational methods can be used to solve this problem using mathematical modeling and optimization software, such as MATLAB or the CVXPY library in Python. First, a surveillance coverage function is defined, representing the relationship between the camera coverage area and a mathematical model of the crowd density field. The optimal solution for this function is then found using the variational method. The variational method involves finding a function that minimizes or maximizes a functional, which is the integral of the function. In this case, the functional is the integral of the product of the surveillance coverage area and the crowd density field. Numerical optimization tools in MATLAB or Python are used to solve this variational problem. For example, in Python, the CVXPY library is used to define this functional and its built-in solver is used to find the optimal solution. This solver is based on algorithms such as gradient descent or Newton's method. Gradient projection optimization is then performed on the surveillance coverage function. Gradient projection is an optimization algorithm that finds the optimal solution by moving along the opposite direction of the gradient of the objective function and projecting back to the feasible region at each step. The gradient projection algorithm is implemented using optimization tools in MATLAB or Python. In each iteration, the gradient of the surveillance coverage function is calculated, and the solution is updated along the opposite direction of the gradient. The updated solution is projected back into the feasible region, defined by the physical constraints of the cameras and the geometric constraints of the venue. This process is repeated until convergence to a local optimum. Finally, the locally optimum covered solution data is obtained.

[0073] Step S26: Based on the evaluation matrix of key areas of the venue, the local optimal coverage solution data is reconstructed with global constraints to obtain the monitoring coverage range scheme; based on the monitoring coverage range scheme, the cameras are configured with density balance to obtain the camera distribution density scheme.

[0074] For a detailed implementation process of this embodiment, please refer to the sub-step of step S26.

[0075] This invention provides detailed dynamic data for crowd flow management and monitoring by capturing the temporal and spatial variations in crowd density. Markov state transition modeling enhances the system's ability to predict the dynamic evolution of crowd density. A mathematical model is provided through parameterized description and analytical solution of partial differential equations. The construction of constraints makes the monitoring coverage optimization problem more scientific and precise, clarifying the objectives and limitations for finding the optimal monitoring coverage area. The application of variational methods and gradient projection optimization improves the utilization efficiency and coverage effect of monitoring resources, finding locally optimal coverage solutions. Global constraint reconstruction based on the venue's key area evaluation matrix ensures that the monitoring coverage scheme considers both local optima and global security requirements. A density-balanced configuration method makes camera distribution more rational, ensuring sufficient coverage of key areas while avoiding resource waste. The optimized monitoring coverage scheme helps to promptly detect and respond to potential security risks, reduce the probability of accidents, and improve the speed and accuracy of emergency response.

[0076] Preferably, step S26 includes the following steps:

[0077] Step S261: Normalize the feature weights of the key area assessment matrix of the venue to obtain the standardized coverage weight factor;

[0078] Specifically, data processing software such as Excel or the Pandas library in Python can be used to process the venue's critical area assessment matrix. First, risk scores for each area are extracted from the matrix, including factors such as crowd density, emergency evacuation capacity, and historical security incident records. The goal is to convert these risk scores of different magnitudes and units into comparable standardized coverage weight factors. Min-max normalization, a common feature scaling technique, is used to transform the raw data to the range [0,1]. In this way, a normalized value is calculated for each risk factor in each area, and these values ​​are then aggregated to obtain the standardized coverage weight factor for each area.

[0079] Step S262: Reconstruct the local optimal coverage solution data according to the standardized coverage weight factor to obtain sensitive region mapping data;

[0080] Specifically, GIS software such as ArcGIS or QGIS, and optimization tools such as Python's SciPy library, can be used for semantic constraint reconstruction. First, the standardized coverage weight factor is taken as input, combined with the venue's geographic information and camera layout data. The goal is to adjust the locally optimal coverage solution to ensure that high-risk areas (i.e., areas with high standardized coverage weight factors) receive more monitoring resources. An optimization problem is defined using the optimization module in the SciPy library, where the objective function is to maximize the coverage of high-risk areas. Semantic constraints are added to ensure that the camera layout considers not only coverage area but also the risk weight of the area. For example, a constraint can be set such that each high-risk area is covered by at least one camera, and the weighted sum of the coverage areas of these cameras is maximized. These constraints are visualized using GIS software and applied to the locally optimal coverage solution data. The positions and orientations of the cameras are adjusted to satisfy these semantic constraints while maintaining coverage optimization as much as possible. In this way, sensitive area mapping data is finally obtained, indicating the monitoring sensitivity and priority of each area.

[0081] Step S263: Perform spatial topology constraint identification on the venue point cloud model to obtain the venue topology constraint mapping set;

[0082] Specifically, 3D modeling and analysis software, such as Autodesk Revit or Rhinoceros, can be used in conjunction with professional topology analysis tools, such as TopoShape, or the Shapely library in Python for spatial topology constraint identification. First, the venue's point cloud model is imported into Revit or Rhinoceros, and the topology analysis capabilities of these software programs are used to identify structural features in the model, such as walls, columns, stairs, and passageways. These features are marked, and their spatial locations and interrelationships are recorded. More detailed topology constraint analysis is then performed in Python using the Shapely library. For example, intersections between walls are calculated, centerlines of passageways are determined, and the precise locations of entrances and exits are identified. This information is used to construct a topology constraint map set, which contains the spatial locations and topological relationships of all key structures within the venue. Finally, these topology constraint maps are exported as a dataset. This dataset assigns a unique identifier to each structural feature and describes its spatial attributes and topological connections in detail.

[0083] Step S264: Based on the sensitive area mapping data and the venue topology constraint mapping set, perform entropy reduction optimization on the local optimal coverage solution data to obtain the monitoring coverage range scheme;

[0084] Specifically, entropy reduction optimization can be performed using optimization software, such as MATLAB or the SciPy library in Python, combined with GIS software. First, sensitive area mapping data and venue topology constraint mapping sets are merged into a unified data framework. Optimization algorithms from the SciPy library, such as linear programming or dynamic programming, are used to adjust the locally optimal coverage solution to satisfy these constraints. For example, consider a highly sensitive area, such as a VIP entrance, which needs to be completely covered by at least one camera. Information from the topology constraint mapping set is used to ensure that the camera layout does not violate physical constraints, such as cameras not being able to be installed inside walls. The camera positions are adjusted based on the sensitive area mapping data to maximize coverage of the highly sensitive area. Entropy reduction optimization is used to measure the uniformity of the camera layout and coverage efficiency. The goal of entropy reduction optimization is to reduce the uncertainty and randomness of camera layout while increasing deterministic coverage of key areas. By adjusting the position and orientation of the cameras, the entropy value is minimized, resulting in a more optimized monitoring coverage scheme. Finally, GIS software is used to visualize this optimized monitoring coverage scheme.

[0085] Step S265: Use the preset regional importance weight index to perform sensitivity clustering on the monitoring coverage scheme to obtain a hierarchical weighted monitoring priority sequence;

[0086] Specifically, data science and machine learning tools, such as the Scikit-learn library in Python, can be used to perform sensitivity clustering. First, a set of regional importance weighting indicators is defined, including pedestrian density, frequency of historical security incidents, and functional importance of the area (e.g., VIP areas, exits, entrances). A weight is assigned to each indicator, based on expert opinion and historical data analysis. The K-means clustering algorithm from the Scikit-learn library is used to cluster different areas of the venue based on sensitivity. A comprehensive sensitivity score is calculated for each area according to the preset weighting indicators, and this score is used as the basis for clustering. For example, the venue can be divided into high, medium, and low sensitivity levels. During the clustering process, parameters of the clustering algorithm, such as the number of clusters (K value) and distance metric, are adjusted to ensure the rationality and accuracy of the clustering results. After clustering, a hierarchical weighted monitoring priority sequence is obtained, indicating the monitoring importance of different areas. High-sensitivity areas will be assigned higher monitoring priority to ensure these areas receive denser monitoring coverage.

[0087] Step S266: Based on the venue topology constraint mapping set and the hierarchical weighted monitoring priority sequence, perform density equalization configuration of the venue to obtain the camera distribution density scheme.

[0088] Specifically, GIS software such as ArcGIS and optimization tools such as Python's PuLP library can be used for density balancing configuration. First, the hierarchical weighted monitoring priority sequence is merged with the venue topology constraint mapping set to form a comprehensive dataset. ArcGIS is used to visualize this data and determine the physical constraints and monitoring needs of each area. The PuLP library is used to define a linear programming problem with the goal of minimizing monitoring blind spots while considering camera cost and installation limitations. A decision variable is defined for each area, representing the number of cameras in that area, and constraints are set based on the area's sensitivity level and topological constraints. For example, high-sensitivity areas are required to have at least one camera, while low-sensitivity areas can share cameras. By solving this linear programming problem, a camera distribution density scheme can be obtained, which details the number and location of cameras in each area.

[0089] This invention improves the fairness of weight processing through feature weight normalization, ensuring the rationality of monitoring resource allocation. Semantic constraint reconstruction enhances the semantic rationality of the coverage solution, making it more aligned with actual monitoring needs. Effective utilization of spatial topology provides crucial spatial information for monitoring layout, optimizing monitoring coverage and improving monitoring efficiency and quality. Sensitivity clustering enhances the sensitivity identification of monitoring schemes, providing a basis for determining monitoring priorities. The implementation of a hierarchical weighted monitoring priority sequence enables differentiated monitoring based on regional importance, prioritizing the security of critical areas. Density-balanced configuration based on venue topology constraints and monitoring priority sequences achieves refined management of camera layout, ensuring sufficient coverage of critical areas.

[0090] Preferably, step S3 includes the following steps:

[0091] Step S31: Perform key node analysis on the venue point cloud model according to the camera distribution density scheme to obtain the venue spatial semantic seed point set;

[0092] Specifically, 3D modeling software, such as Autodesk 3ds Max, or professional point cloud processing software, such as CloudCompare, can be used to perform key node analysis. First, the camera distribution density scheme is imported into 3ds Max, which includes the location and coverage area of ​​each camera. The mesh tool in 3ds Max is used to identify key nodes in the venue's point cloud model; these nodes represent the camera installation locations or the center points of densely populated areas. CloudCompare's point cloud filtering function is used to extract these key nodes, forming a spatial semantic seed point set for the venue. For example, points located at the center of high-density areas or near venue entrances and exits are selected. It is ensured that these seed point sets are evenly distributed spatially and cover all important monitoring areas within the venue. The selection of these seed point sets considers the camera's field of view and coverage area to ensure that the final monitoring layout meets monitoring requirements.

[0093] Step S32: Perform topological connections on the semantic seed point set of the venue space to obtain the initial triangular mesh of the venue space, and perform regional segmentation on the initial triangular mesh of the venue space to obtain the venue space partition mesh;

[0094] Specifically, 3D modeling and mesh generation software, such as MeshLab or Salome, can be used to perform topological connections. First, the semantic seed point set of the venue space is imported into MeshLab. MeshLab's triangulation tool is used to topologically connect these seed points, generating an initial triangular mesh. This mesh captures the main structural features of the venue space, such as walls, passageways, and open areas. Salome's mesh segmentation tool is used to segment the initial triangular mesh into regions. The segmentation rules are defined based on the functional areas of the venue and the camera distribution density scheme. For example, the spectator area, playing area, and entrance / exit areas are segmented into different meshes, each corresponding to a specific functional area. It is ensured that each segmented mesh contains sufficient detail. These segmented meshes will be used to guide the specific installation locations and orientations of cameras to achieve optimal monitoring results. Through these operations, the final venue space segmentation mesh is obtained.

[0095] Step S33: Optimize the boundary shape of the venue space partitioning grid to obtain the venue space segmentation network;

[0096] Specifically, computer-aided design (CAD) software, such as AutoCAD, or geographic information system (GIS) software, such as ArcGIS, can be used to perform boundary morphology optimization. First, the venue's spatial partitioning grid data is imported into AutoCAD. AutoCAD's drawing and editing tools are used to adjust the grid boundaries to better fit the actual structure of the venue. For example, if the grid boundaries deviate from the actual locations of walls or passageways, these boundaries are manually adjusted. ArcGIS's geoprocessing tools are used to optimize the grid's morphology. Spatial analysis tools, such as buffer analysis and overlay analysis, are applied to identify and resolve overlaps or gaps between grids. For example, overlapping boundaries between two adjacent grids are found, or a grid boundary extends beyond the actual venue boundaries. These boundaries are adjusted to ensure that each grid is independent and fully contained within the venue. Through these operations, the final venue spatial partitioning network is obtained, which accurately reflects the venue's internal structure.

[0097] Step S34: Based on the camera distribution density scheme and the venue space segmentation network, perform geometric constraint modeling of the camera field of view to obtain the venue monitoring field of view cone model;

[0098] Specifically, 3D modeling software such as SketchUp or specialized geometric modeling software such as SolidWorks can be used to perform geometric constraint modeling. Import the data from the venue space segmentation network and camera distribution density scheme into SketchUp. Use SketchUp's modeling tools to create 3D models of the cameras and place them in the predetermined installation locations. Use SolidWorks' geometric constraint function to define the geometric constraints of the camera's field of view. Based on the camera's technical parameters, such as field of view and focal length, set the geometry of the field of view cone. For example, if a camera's field of view is 90 degrees, create a cone in SolidWorks with the camera as the center and an opening angle of 90 degrees. Ensure that the field of view cone model for each camera matches the venue space segmentation network, meaning that the boundaries of the field of view cone do not conflict with the venue's walls and other structures. Adjust the position and orientation of the cameras to ensure that the field of view cone completely covers its corresponding monitoring area and does not overlap with the field of view cones of other cameras. Through these operations, a field-of-view cone model of the venue monitoring can be obtained, which details the monitoring range and angle of each camera.

[0099] Step S35: Based on the venue monitoring field of view cone model, perform spatial cross recognition of the cameras to obtain the camera field of view overlap matrix;

[0100] Specifically, spatial intersection recognition can be performed using 3D modeling and analysis software such as Revit or 3ds Max, combined with mathematical calculation software such as MATLAB. First, the field-of-view cone models of the venue's surveillance cameras are imported into Revit. These models contain the field-of-view angle and position information for each camera. Revit's 3D analysis tools are used to simulate the field-of-view cone for each camera and compare it with the field-of-view cones of other cameras. MATLAB is used for mathematical calculations, with scripts written to calculate the intersection between each field-of-view cone. A function is defined to calculate the overlap between two field-of-view cones, based on their spatial positions and angles, to calculate their intersection area. For each pair of cameras within the venue, their field-of-view overlap is calculated and stored in a matrix—the camera field-of-view overlap matrix.

[0101] Step S36: Based on the camera field of view overlap matrix, perform occlusion suppression identification on the overlapping area to obtain the effective coverage map of the monitoring field of view;

[0102] Specifically, GIS software such as ArcGIS and image processing software such as Adobe Photoshop or GIMP can be used to perform occlusion suppression identification. First, the camera field-of-view overlap matrix is ​​imported into ArcGIS. ArcGIS's spatial analysis tools are used to identify areas with highly overlapping fields of view and mark these areas as potential occlusion areas. For example, if the overlap between the fields of view of two cameras exceeds a certain threshold, occlusion is considered to exist between the two fields of view. Photoshop or GIMP is used to process the images of these overlapping areas. The actual coverage of the camera fields of view is simulated by adjusting the transparency or using layer masks. For example, the field-of-view images of two cameras are overlaid, and their overlap and occlusion are visually displayed by adjusting the transparency. Through these operations, an effective coverage map of the monitoring field of view is finally obtained, which shows in detail the actual coverage area of ​​each camera, including those occluded areas.

[0103] Step S37: Quantitatively evaluate the coverage of the effective coverage map of the monitoring field of view to obtain the coverage effect data of the field of view.

[0104] Specifically, GIS software such as ArcGIS or QGIS, and data analysis tools such as Python's Geopandas library, can be used to perform coverage quantification assessments. First, the effective coverage map of the surveillance field of view is imported into ArcGIS. This map contains the field of view area and potential occlusion areas for each camera. ArcGIS spatial analysis tools, such as Viewshed Analysis and Overlay Analysis, are used to quantify the coverage of each area. Python's Geopandas library is then used to process and analyze this spatial data. Scripts are written to calculate the coverage area of ​​each area and compare it to the total area of ​​the venue to calculate the coverage rate. For example, the area of ​​each camera's field of view area is calculated and divided by the total area of ​​the venue to obtain the coverage percentage for each area. This data is further analyzed to identify areas with lower-than-expected coverage. For example, if an area's coverage is below a set threshold, it is marked as an area requiring additional cameras or adjustments to the positions of existing cameras. Finally, the field of view coverage effect data is obtained, including the coverage rate for each area, a list of areas with coverage below the threshold, and recommendations for camera position optimization.

[0105] This invention efficiently constructs a venue spatial grid through spatial semantic parsing and optimizes the grid to fit the actual structure of the venue. It accurately models the camera field of view, effectively identifies field-of-view overlap and obstruction areas, and achieves a quantitative assessment of monitoring coverage. This invention not only optimizes camera layout and ensures efficient utilization of monitoring resources but also improves the coverage quality of the monitoring system and reduces blind spots.

[0106] Preferably, step S4 includes the following steps:

[0107] Step S41: Perform spherical coordinate transformation on the camera's field of view based on the field of view coverage effect data to obtain the camera's panoramic spherical projection model;

[0108] Specifically, 3D graphics processing software, such as Blender, and mathematical computation software, such as MATLAB, can be used to perform spherical coordinate transformation. First, the field-of-view coverage data is imported into Blender. This data includes the position, field-of-view angle, and coverage area of ​​each camera. Blender's camera objects are used to simulate the field of view of each camera, and the corresponding field-of-view angle parameters are set. The spherical coordinate transformation is then calculated using MATLAB. A script is written to convert the field of view of each camera from the Cartesian coordinate system to the spherical coordinate system. In this process, the center of the sphere (i.e., the camera position) is defined, and the latitude and longitude coordinates of each field-of-view boundary point on the sphere are calculated. For example, for a camera with a field of view of 90 degrees, the spherical coordinates of all points on its field-of-view boundary are calculated, and a corresponding spherical model is generated in MATLAB. Through these operations, a panoramic spherical projection model of the cameras can be obtained, which accurately describes the distribution of each camera's field of view on the sphere.

[0109] Step S42: Perform panoramic mapping calculation on the camera's field of view based on the camera's panoramic spherical projection model to obtain a panoramic coverage map of the venue for monitoring;

[0110] Specifically, panoramic image processing software, such as PTGui, and GIS software, such as ArcGIS, can be used to perform panoramic mapping calculations. First, the panoramic spherical projection models of the cameras are imported into PTGui. Using PTGui's spherical panoramic stitching function, the field-of-view images of each camera are stitched together into a complete panoramic image. During this process, image stitching parameters, such as control points and projection type, are adjusted. The stitched panoramic image is then imported into ArcGIS, and its spatial analysis tools are used to calculate the panoramic coverage map. Based on the spherical coordinates of each camera's field of view, its coverage area in the panoramic image is determined, and the intersection and union of these areas are calculated. For example, the total coverage area of ​​all camera fields of view, as well as the overlapping areas between these fields of view, are calculated. Through these operations, a panoramic coverage map of the venue can be obtained, which shows in detail the total coverage of all cameras within the venue, including coverage areas and blind spots.

[0111] Step S43: Extract extreme features from the panoramic coverage map of the venue monitoring to obtain the set of extreme boundary points of the field of view, and perform field of view geometric constraint mapping on the venue space based on the set of extreme boundary points of the field of view to obtain the critical area mapping data of the field of view.

[0112] Specifically, image processing software such as Adobe Photoshop and GIS software such as ArcGIS can be used to perform extreme value feature extraction. First, the panoramic coverage map of the venue is imported into Photoshop. Photoshop's image analysis tools are used to identify extreme features in the image, such as extreme variations in brightness, color, or texture, which indicate the boundaries or special areas of the field of view. These extreme feature point sets are then imported into ArcGIS, and ArcGIS's spatial analysis tools are used to perform a field of view geometric constraint mapping on the venue space. By calculating the relative positions of these extreme boundary point sets to the venue's geometry, critical areas of the field of view are determined. For example, if certain extreme boundary point sets coincide with venue entrances / exits or emergency evacuation routes, these areas require special attention. Through these operations, the final field of view critical area mapping data is obtained, which details the key monitoring areas within the venue.

[0113] Step S44: Perform blind spot semantic recognition on the venue space based on the critical area mapping data of the field of view to obtain a table of monitoring blind spot locations, and perform spatial clustering on the monitoring blind spot location table to obtain a distribution map of monitoring blind spots in the venue.

[0114] Specifically, GIS software such as QGIS and data science tools such as Python's Scikit-learn library can be used to perform blind spot semantic recognition. First, the critical area mapping data is imported into QGIS. QGIS's spatial query tool is used to identify surveillance blind spots within these critical areas. For example, areas not covered due to camera field of view limitations are marked as surveillance blind spots. Spatial clustering analysis of the surveillance blind spot location table is performed using Python's Scikit-learn library. The K-means clustering algorithm is applied to group these surveillance blind spots, with each group representing a specific surveillance blind spot. Clustering parameters, such as the number of clusters and distance metrics, are set based on the location, size, and importance of the blind spots. Through these operations, a distribution map of the venue's surveillance blind spots is obtained, showing in detail the location and distribution of all surveillance blind spots within the venue.

[0115] Step S45: Optimize the distribution map of blind spots in the venue monitoring to obtain the target collaborative coverage scheme.

[0116] For a detailed implementation process of this embodiment, please refer to the sub-step of step S45.

[0117] This invention achieves a panoramic monitoring field of view through spherical coordinate transformation and panoramic mapping calculation, providing a wider monitoring view and enhancing the monitoring system's field of view and functionality. It optimizes the identification and optimization of blind spots by extracting extreme value features to identify boundary point sets of the field of view. By acquiring mapping data of critical regions of the field of view, it effectively identifies blind spots in the venue space, improving monitoring coverage and reducing blind spots. Spatial clustering manages the locations of blind spots, generating a distribution map of blind spots. By optimizing the target collaborative coverage scheme obtained in the solution process, it achieves the rational allocation and efficient utilization of monitoring resources, improving the overall performance of the monitoring system.

[0118] Preferably, step S45 includes the following steps:

[0119] Step S451: Construct an objective function for the distribution map of blind spots in the venue monitoring to obtain a set of objective collaborative coverage constraints;

[0120] Specifically, mathematical modeling and optimization software, such as MATLAB or the PuLP library in Python, can be used to construct the objective function. First, the data of the venue's blind spot distribution map is imported into MATLAB. MATLAB's optimization toolbox is used to define an objective function that aims to minimize the total area of ​​the blind spots while maximizing the coverage efficiency of the cameras. The installation cost, maintenance cost, and coverage effect of the cameras are considered as parameters of the objective function. A series of constraints are defined, including the camera's field of view, installation location limitations, and the area that must be covered. For example, it is stipulated that each camera must cover at least a certain area of ​​high-risk areas, and the total installation cost of all cameras must not exceed the budget. Through these operations, the final set of constraints for the objective collaborative coverage is obtained, which contains the mathematical expression of all camera layout optimization problems.

[0121] Step S452: Perform topological feature mapping on the target cooperative coverage constraint set to obtain the constraint space connectivity graph, and perform random walk dimensionality reduction on the feasible solution search space in the constraint space connectivity graph to obtain the candidate solution search subspace set;

[0122] Specifically, topology analysis software, such as TopoSimplify, and data science tools, such as Python's NetworkX library, can be used to perform topology feature mapping. First, the set of target collaborative coverage constraints is imported into TopoSimplify. TopoSimplify's topology analysis tools are used to identify connected regions within the venue and map these regions onto a constraint space connectivity graph. This graph shows the connectivity between different regions within the venue and their relationship to camera coverage. Python's NetworkX library is used to process this connectivity graph, and a random walk algorithm is applied for dimensionality reduction. Nodes (representing potential camera locations) are randomly selected in this graph, and a random walk process is simulated to explore feasible camera layout schemes. This method reduces the search space from the entire venue space to a smaller subset of candidate solution search spaces, containing all possible camera layout schemes. Through these operations, the final candidate solution search subset is obtained, which significantly reduces the number of camera layout schemes to consider while preserving the possibility of an optimal solution.

[0123] Step S453: Perform a global optimal solution search on the candidate solution search subspace set to obtain the target cooperative coverage candidate solution set;

[0124] Specifically, mathematical optimization software, such as CPLEX or Gurobi, can be used to perform a global optimum search. First, all camera layout schemes from the candidate solution search subspace are imported into CPLEX. CPLEX's optimization algorithms are used to evaluate the coverage and cost-effectiveness of each scheme. An objective function is defined to maximize surveillance coverage and reduce costs, while considering camera field-of-view overlap and blind spots. A series of constraints are set, including camera installation locations, field of view angles, and key areas that must be covered. CPLEX uses its built-in algorithms, such as branch and bound or cutting plane methods, to search for a global optimum. During the search, CPLEX evaluates each candidate solution and compares it with the objective function and constraints to determine its merits. Finally, CPLEX returns a set of candidate solutions that provide optimal surveillance coverage while satisfying all constraints. Through these operations, a candidate solution set for the objective cooperative coverage is obtained, which contains all potentially optimal camera layout schemes.

[0125] Step S454: Perform Pareto optimal evaluation and screening on the candidate solution set of the target cooperative coverage to obtain the selected solution set of the target cooperative coverage;

[0126] Specifically, multi-objective optimization tools, such as MATLAB's Pareto Frontier toolbox or Python's Pyomo library, can be used to perform Pareto optimality evaluation and screening. First, the candidate solution set for cooperative coverage is imported into MATLAB, and its Pareto Frontier toolbox is used to evaluate the multi-objective performance of each solution. Two main objectives are defined: maximizing surveillance coverage and minimizing cost. For each candidate solution, its performance on these two objectives is calculated and compared on the Pareto front. MATLAB's graphical tools are used to visualize these solutions and identify Pareto optimal solutions. These solutions offer the best trade-off between the two objectives; no other solution can improve the other objective without sacrificing one. The Pyomo library is used to further screen these Pareto optimal solutions. An optimization model is defined, including objective functions for surveillance coverage and cost, along with relevant constraints. Pyomo's solver is used to evaluate each Pareto optimal solution, and those most feasible and efficient in practical applications are selected. Through these operations, a final set of selected solutions for cooperative coverage is obtained, containing the optimal camera layout schemes with the greatest potential to achieve the surveillance objectives.

[0127] Step S455: Perform final configuration on the selected solution set for target collaborative coverage to obtain the target collaborative coverage scheme.

[0128] Specifically, project management and decision support software, such as Microsoft Project, or specialized decision analysis software, such as Decision Lens, can be used to perform the final configuration. First, the selected solution set for collaborative coverage is imported into Microsoft Project. This solution set contains camera layout schemes filtered through Pareto optimality evaluation, each with different monitoring coverage and cost-effectiveness. Microsoft Project's resource planning and scheduling functions are used to evaluate the feasibility of each scheme. Factors such as camera installation time, maintenance cycles, and coordination with other venue facilities are considered. For example, if a scheme requires camera installation on match days, which would affect the venue's normal operation, the scheme needs to be adjusted to avoid this conflict. Decision Lens's decision analysis tools are used to comprehensively evaluate each scheme. A decision-making team composed of venue managers, security experts, and financial personnel is invited to score and discuss the advantages and disadvantages of each scheme based on their respective technical expertise and experience. Decision Lens's weighted scorecard and decision tree tools are used to integrate the team's opinions and identify the final optimal scheme. This scheme meets the monitoring coverage requirements while also considering cost-effectiveness and implementation feasibility. Through these operations, the final target collaborative coverage solution is obtained, which details the installation location, field of view, and expected monitoring effect of each camera.

[0129] This invention enhances the ability to identify blind spots in monitoring by constructing an objective function and performing topological feature mapping. Dimensionality reduction using random walks improves solution search efficiency and reduces problem dimensionality. Combining global optimal solution search and Pareto optimal evaluation filtering optimizes the quality of candidate solutions and selects superior solutions. Pareto optimal evaluation filtering assesses solution quality from multiple dimensions, improving the rationality of monitoring resource allocation and optimizing resource distribution. The final configured collaborative coverage scheme integrates multiple factors, achieving optimal monitoring coverage and improving the overall performance and efficiency of the monitoring system.

[0130] Preferably, step S5 includes the following steps:

[0131] Step S51: Deploy the camera monitoring system in the venue according to the target collaborative coverage scheme, and construct a distributed network for the camera monitoring system to obtain the monitoring data transmission architecture;

[0132] Specifically, network design and deployment tools, such as Cisco Packet Tracer or Huawei's eSight network management system, can be used to deploy a camera surveillance system. First, the camera locations and numbers determined in the target collaborative coverage scheme are imported into Cisco Packet Tracer as basic data. The network layout of the venue is simulated, including camera locations, network switch placement, and fiber optic or cable cabling paths. Each camera is ensured to be connected to the network, and the network architecture is capable of supporting high-definition video streaming. The actual network devices, such as cameras, switches, and routers, are configured using the eSight network management system. A unique IP address is assigned to each camera, and network parameters, such as subnet mask and default gateway, are set. Sufficient network bandwidth is ensured to support all cameras online simultaneously, and future expansion is considered. Through these operations, the final monitoring data transmission architecture is obtained, which details how cameras connect to the network and how data flows within it.

[0133] Step S52: Design the communication protocol for the cameras based on the monitoring data transmission architecture to obtain the venue monitoring network control scheme;

[0134] Specifically, communication protocol analysis tools, such as Wireshark, and network configuration tools, such as Cisco's IOS command-line interface (CLI), can be used to perform communication protocol design. First, analyze the monitoring data transmission architecture to determine the required communication protocols. Select protocols suitable for video streaming, such as H.264 or H.265 encoding protocols, and protocols suitable for network communication, such as TCP / IP or UDP. Use Wireshark to capture and analyze data packets in the network to ensure that the selected protocols can stably transmit data over the network. Adjust protocol parameters, such as transmission rate and buffer size, to optimize video stream quality and latency. Use Cisco's IOS CLI to configure network devices, such as cameras and switches, to support the selected communication protocols. Set QoS (Quality of Service) rules to ensure that video stream data is prioritized and to reduce the impact of network congestion. Through these operations, the final venue monitoring network control scheme is obtained, which details how cameras transmit data over the network and how to manage and control these data streams.

[0135] Step S53: Allocate bandwidth for the venue monitoring network control scheme to obtain the monitoring network resource scheduling strategy;

[0136] Specifically, network management software, such as SolarWinds Network Performance Monitor or PRTG Network Monitor, can be used to perform bandwidth allocation. First, analyze the data transmission requirements of all cameras within the venue, including video resolution, frame rate, and encoding format. Determine the amount of data generated by each camera and predict the total data traffic during peak hours. Use SolarWinds Network Performance Monitor to monitor the bandwidth usage of the existing network, identifying bottlenecks and idle resources. Based on the monitoring data and predicted traffic demand, allocate bandwidth to each camera or camera group. For example, allocate more bandwidth to cameras in high-risk areas to ensure video stream quality in critical areas. Set QoS rules to prioritize video stream transmission from critical cameras while allocating appropriate bandwidth to cameras in non-critical areas. Through these operations, a monitoring network resource scheduling strategy is ultimately derived, which details how to rationally allocate network resources.

[0137] Step S54: Divide the camera group into regional management areas according to the monitoring network resource scheduling strategy to obtain the camera group control unit;

[0138] Specifically, network configuration and management tools, such as Cisco's DNA Center or Aruba Central, can be used to perform zoned management. First, the venue is divided into multiple management zones based on its physical layout and camera coverage areas. For example, the venue can be divided into an entrance area, a spectator seating area, a competition area, and a support area. Cisco's DNA Center is used to configure network parameters and security policies for each zone. Independent SSIDs and VLANs are set for each zone's camera group to isolate traffic and improve security. Different bandwidth limits and QoS policies are configured for each zone to meet the monitoring needs of different areas. Aruba Central is used to monitor and manage the camera group in each zone. Automated fault detection and recovery policies are set up to ensure uninterrupted monitoring of the affected area in the event of equipment failure or network problems. Through these operations, the final result is the camera group control unit, which details how the cameras in each zone are managed and controlled.

[0139] Step S55: Based on the camera group control unit, the camera monitoring system makes collaborative decisions to obtain a set of venue monitoring and management strategies;

[0140] Specifically, intelligent surveillance management systems, such as Genetec Security Center or Verint Video Intelligence, can be used to perform collaborative decision-making. First, data from the camera group control unit is imported into Genetec Security Center. This unit contains the camera configuration and network parameters for each area. Genetec Security Center's collaborative decision-making tools are used to analyze the data flow and surveillance coverage of each camera group within the venue. A series of collaborative decision-making rules are set; for example, when the crowd density in a certain area exceeds a preset threshold, the system automatically adjusts the monitoring parameters of the cameras in that area, such as increasing the frame rate or adjusting the focus, to obtain a clearer monitoring image. Simultaneously, the system will increase the monitoring priority of that area and allocate camera resources from other areas to provide additional monitoring support. These collaborative decisions are implemented using video analytics tools from Verint Video Intelligence. For example, behavioral analysis functions are used to identify abnormal behaviors, such as crowd gathering or rapid movement, and then the monitoring strategies of the relevant cameras are automatically adjusted to respond to these situations. Through these operations, a venue surveillance management strategy set is ultimately obtained, which details how to adjust the camera monitoring strategies based on real-time monitoring data and preset rules.

[0141] Step S56: Perform consistency maintenance on the venue monitoring and management strategy set to obtain the intelligent monitoring network solution.

[0142] Specifically, network management and configuration tools such as Ansible or Puppet, and monitoring and analysis tools such as Nagios or Zabbix, can be used to perform consistency maintenance. First, import the venue monitoring management policy set into Ansible. Use Ansible's automation scripting capabilities to ensure that all cameras and network devices are configured and updated according to the policy set requirements. For example, write Ansible scripts to automatically update camera firmware, configure network parameters, and assign IP addresses. Use Nagios to monitor the performance and health of the entire monitoring network. Set a series of monitoring metrics, such as CPU utilization, memory utilization, network latency, and packet loss rate, and set thresholds for each metric. When any metric exceeds the threshold, Nagios will automatically issue an alert and trigger preset response measures, such as restarting devices or reallocating network resources. Through these operations, an intelligent monitoring network solution is ultimately obtained.

[0143] This invention provides a stable architecture for monitoring data transmission through distributed network construction, ensuring efficient data transmission and processing. Optimized communication protocol design improves the reliability and efficiency of communication between cameras. The implementation of bandwidth allocation and resource scheduling strategies intelligently optimizes network resource usage, enhancing network performance and user experience. Regional management enables the management of camera groups, enhancing the flexibility and response speed of the monitoring system. Collaborative decision-making based on camera group control units improves monitoring management efficiency and effectiveness, achieving optimal allocation of monitoring resources. Consistent maintenance of the monitoring management strategy set enhances system stability and reliability, improving resilience.

[0144] Preferably, the present invention also provides a modeled camera layout system for performing the modeled camera layout method described above, the modeled camera layout system comprising:

[0145] The regional risk assessment module is used to perform geometric topological analysis on the venue to obtain a functional area model of the venue; to perform fluid dynamics simulation on the functional area model of the venue to obtain the characteristic data of pedestrian flow density distribution; and to classify the venue into risk levels based on the characteristic data of pedestrian flow density distribution to obtain the assessment matrix of key areas of the venue.

[0146] The monitoring configuration analysis module is used to perform dynamic density field modeling of the venue based on the characteristic data of crowd density distribution, and obtain a mathematical model of crowd density field; to perform monitoring demand analysis of the venue based on the mathematical model of crowd density field and the evaluation matrix of key areas of the venue, and obtain a monitoring coverage scheme; and to perform density equalization configuration of cameras based on the monitoring coverage scheme, and obtain a camera distribution density scheme.

[0147] The field of view assessment module is used to divide the venue into spaces according to the camera distribution density scheme to obtain the venue space segmentation network; and to assess the field of view overlap of the cameras based on the venue space segmentation network to obtain the field of view coverage effect data.

[0148] The coverage optimization module is used to identify blind spots in the field of view coverage data to obtain a blind spot distribution map of the venue monitoring; the blind spot distribution map of the venue monitoring is then optimized to obtain a target collaborative coverage scheme.

[0149] The collaborative management module is used to collaboratively manage camera groups according to the target collaborative coverage scheme, thereby obtaining an intelligent monitoring network solution.

[0150] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0151] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A model-based camera layout method, characterized in that, Includes the following steps: Step S1: Perform geometric topology analysis on the venue to obtain a functional area model of the venue; perform fluid dynamics simulation on the functional area model of the venue to obtain the characteristic data of pedestrian flow density distribution; classify the venue into risk levels based on the characteristic data of pedestrian flow density distribution to obtain the evaluation matrix of key areas of the venue. Step S2: Based on the characteristic data of crowd density distribution, perform dynamic density field modeling of the venue to obtain a mathematical model of crowd density field; based on the mathematical model of crowd density field and the evaluation matrix of key areas of the venue, perform monitoring demand analysis of the venue to obtain a monitoring coverage scheme; based on the monitoring coverage scheme, perform density equalization configuration of cameras to obtain a camera distribution density scheme. Step S3: Divide the venue into spatial segments according to the camera distribution density scheme to obtain the venue spatial segmentation network; evaluate the field of view overlap of the cameras based on the venue spatial segmentation network to obtain the field of view coverage effect data; Step S4: Identify blind spots in the field of view coverage data to obtain a blind spot distribution map of the venue; optimize the blind spot distribution map of the venue to obtain a target collaborative coverage scheme; Step S5: Perform collaborative management of the camera group according to the target collaborative coverage scheme to obtain the intelligent monitoring network scheme.

2. The modeled camera layout method according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Collect point cloud data of the venue from multiple angles to obtain the original venue point cloud data, and denoise the original venue point cloud data to obtain denoised venue point cloud data. Step S12: Perform topological feature encoding on the denoised venue point cloud data to obtain venue spatial semantic encoding data, and perform 3D modeling of the venue based on the venue spatial semantic encoding data to obtain the venue point cloud model; Step S13: Perform surface reconstruction on the venue point cloud model to obtain the venue reconstructed point cloud model, and extract surface features from the venue reconstructed point cloud model to obtain venue surface geometric feature data. Step S14: Calculate the spatial connectivity of the venue based on the surface geometric feature data to obtain the venue topology diagram; Step S15: Divide the venue into structural zones according to the venue topology diagram to obtain venue functional area partitioning data, and divide the venue point cloud model according to the venue functional area partitioning data to obtain venue functional area model. Step S16: Perform fluid dynamics simulation on the functional area model of the venue to obtain the characteristic data of pedestrian flow density distribution, and classify the risk level of the venue based on the characteristic data of pedestrian flow density distribution to obtain the evaluation matrix of key areas of the venue.

3. The modeled camera layout method according to claim 2, characterized in that, Step S16 includes the following steps: Step S161: Track the time series of people flow in the venue to obtain extreme data of people flow in the venue; Step S162: Extract spatial parameters from the functional area model of the venue to obtain the venue structural feature library; Step S163: Based on the venue structure feature library, construct a crowd flow simulation grid for the venue to obtain the venue flow domain division grid; Step S164: Divide the venue flow domain into a grid based on the extreme data of venue passenger flow and set boundary conditions to obtain the initial parameters of the venue flow field; Step S165: Perform numerical simulation calculations on the initial parameters of the venue flow field to obtain crowd flow field distribution data, and extract dynamic features from the crowd flow field distribution data to obtain crowd density distribution feature data. Step S166: Perform threshold segmentation on the crowd density distribution characteristic data to obtain the venue crowd density level zoning; Step S167: Assign risk weights to the venue’s crowd density level zones to obtain the venue’s key area assessment matrix.

4. The modeled camera layout method according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Perform spatiotemporal sequence identification on the population density distribution characteristic data to obtain population density change characteristic data; Step S22: Perform Markov state transition modeling based on population density change characteristic data to obtain a dynamic evolution model of population density; Step S23: Parametrically describe the dynamic evolution model of population density using partial differential equations to obtain the population density field expression data, and then analytically solve the population density field expression data to obtain the mathematical model of the population density field. Step S24: Construct constraints on the mathematical model of the crowd density field to obtain data describing the monitoring coverage optimization problem; Step S25: Solve the monitoring coverage optimization problem description data using variational method to obtain the monitoring coverage range function, and optimize the monitoring coverage range function by gradient projection to obtain the local optimal coverage solution data; Step S26: Based on the evaluation matrix of key areas of the venue, the local optimal coverage solution data is reconstructed with global constraints to obtain the monitoring coverage range scheme; based on the monitoring coverage range scheme, the cameras are configured with density balance to obtain the camera distribution density scheme.

5. The modeled camera layout method according to claim 4, characterized in that, Step S26 includes the following steps: Step S261: Normalize the feature weights of the key area assessment matrix of the venue to obtain the standardized coverage weight factor; Step S262: Reconstruct the local optimal coverage solution data according to the standardized coverage weight factor to obtain sensitive region mapping data; Step S263: Perform spatial topology constraint identification on the venue point cloud model to obtain the venue topology constraint mapping set; Step S264: Based on the sensitive area mapping data and the venue topology constraint mapping set, perform entropy reduction optimization on the local optimal coverage solution data to obtain the monitoring coverage range scheme; Step S265: Use the preset regional importance weight index to perform sensitivity clustering on the monitoring coverage scheme to obtain a hierarchical weighted monitoring priority sequence; Step S266: Based on the venue topology constraint mapping set and the hierarchical weighted monitoring priority sequence, perform density equalization configuration of the venue to obtain the camera distribution density scheme.

6. The modeled camera layout method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Perform key node analysis on the venue point cloud model according to the camera distribution density scheme to obtain the venue spatial semantic seed point set; Step S32: Perform topological connections on the semantic seed point set of the venue space to obtain the initial triangular mesh of the venue space, and perform regional segmentation on the initial triangular mesh of the venue space to obtain the venue space partition mesh; Step S33: Optimize the boundary shape of the venue space partitioning grid to obtain the venue space segmentation network; Step S34: Based on the camera distribution density scheme and the venue space segmentation network, perform geometric constraint modeling of the camera field of view to obtain the venue monitoring field of view cone model; Step S35: Based on the venue monitoring field of view cone model, perform spatial cross recognition of the cameras to obtain the camera field of view overlap matrix; Step S36: Based on the camera field of view overlap matrix, perform occlusion suppression identification on the overlapping area to obtain the effective coverage map of the monitoring field of view; Step S37: Quantitatively evaluate the coverage of the effective coverage map of the monitoring field of view to obtain the coverage effect data of the field of view.

7. The modeled camera layout method according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Perform spherical coordinate transformation on the camera's field of view based on the field of view coverage effect data to obtain the camera's panoramic spherical projection model; Step S42: Perform panoramic mapping calculation on the camera's field of view based on the camera's panoramic spherical projection model to obtain a panoramic coverage map of the venue for monitoring; Step S43: Extract extreme features from the panoramic coverage map of the venue monitoring to obtain the set of extreme boundary points of the field of view, and perform field of view geometric constraint mapping on the venue space based on the set of extreme boundary points of the field of view to obtain the critical area mapping data of the field of view. Step S44: Perform blind spot semantic recognition on the venue space based on the critical area mapping data of the field of view to obtain a table of monitoring blind spot locations, and perform spatial clustering on the monitoring blind spot location table to obtain a distribution map of monitoring blind spots in the venue. Step S45: Optimize the distribution map of blind spots in the venue monitoring to obtain the target collaborative coverage scheme.

8. The modeled camera layout method according to claim 7, characterized in that, Step S45 includes the following steps: Step S451: Construct an objective function for the distribution map of blind spots in the venue monitoring to obtain a set of objective collaborative coverage constraints; Step S452: Perform topological feature mapping on the target cooperative coverage constraint set to obtain the constraint space connectivity graph, and perform random walk dimensionality reduction on the feasible solution search space in the constraint space connectivity graph to obtain the candidate solution search subspace set; Step S453: Perform a global optimal solution search on the candidate solution search subspace set to obtain the target cooperative coverage candidate solution set; Step S454: Perform Pareto optimal evaluation and screening on the candidate solution set of the target cooperative coverage to obtain the selected solution set of the target cooperative coverage; Step S455: Perform final configuration on the selected solution set for target collaborative coverage to obtain the target collaborative coverage scheme.

9. The modeled camera layout method according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Deploy the camera monitoring system in the venue according to the target collaborative coverage scheme, and construct a distributed network for the camera monitoring system to obtain the monitoring data transmission architecture; Step S52: Design the communication protocol for the cameras based on the monitoring data transmission architecture to obtain the venue monitoring network control scheme; Step S53: Allocate bandwidth for the venue monitoring network control scheme to obtain the monitoring network resource scheduling strategy; Step S54: Divide the camera group into regional management areas according to the monitoring network resource scheduling strategy to obtain the camera group control unit; Step S55: Based on the camera group control unit, the camera monitoring system makes collaborative decisions to obtain a set of venue monitoring and management strategies; Step S56: Perform consistency maintenance on the venue monitoring and management strategy set to obtain the intelligent monitoring network solution.

10. A model-based camera layout system, characterized in that, For performing the modeled camera layout method as described in claim 1, the modeled camera layout system includes: The regional risk assessment module is used to perform geometric topological analysis on the venue to obtain a functional area model of the venue; to perform fluid dynamics simulation on the functional area model of the venue to obtain the characteristic data of pedestrian flow density distribution; and to classify the venue into risk levels based on the characteristic data of pedestrian flow density distribution to obtain the assessment matrix of key areas of the venue. The monitoring configuration analysis module is used to perform dynamic density field modeling of the venue based on the characteristic data of crowd density distribution, and obtain a mathematical model of crowd density field; to perform monitoring demand analysis of the venue based on the mathematical model of crowd density field and the evaluation matrix of key areas of the venue, and obtain a monitoring coverage scheme; and to perform density equalization configuration of cameras based on the monitoring coverage scheme, and obtain a camera distribution density scheme. The field of view assessment module is used to divide the venue into spaces according to the camera distribution density scheme to obtain the venue space segmentation network; and to assess the field of view overlap of the cameras based on the venue space segmentation network to obtain the field of view coverage effect data. The coverage optimization module is used to identify blind spots in the field of view coverage data to obtain a blind spot distribution map of the venue monitoring; the blind spot distribution map of the venue monitoring is then optimized to obtain a target collaborative coverage scheme. The collaborative management module is used to collaboratively manage camera groups according to the target collaborative coverage scheme, thereby obtaining an intelligent monitoring network solution.

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