Intelligent fire-fighting visual platform

Abstract

The invention belongs to the technical field of visual monitoring, and discloses an intelligent fire-fighting visual platform, which comprises a facility monitoring module, a fire-fighting resource adjustment module, an emergency response module, a combustion rate identification module, a dynamic early warning module and a fire-fighting guidance module. By monitoring the distribution and operation states of the fire-fighting facilities in real time and analyzing the signal intensity and transmission delay, the response speed and efficiency of the intelligent fire-fighting visual platform are effectively improved, the state data of the fire-fighting facilities are updated in time, accurate basis can be provided for optimization of a resource network, a water supply distribution route is adjusted, and the water supply efficiency is improved. By collecting combustion data in real time, analyzing the heat release rate and evaluating the fire behavior risk, the understanding of fire behavior development is enhanced, a scientific basis is provided for fire behavior early warning, in the aspect of personnel evacuation, an evacuation route is adjusted according to a real-time building structure and people flow data, and the safety of personnel evacuation is improved. And a more efficient and safer solution strategy is provided for fire safety.

Description

Technical Field

[0001] The present invention belongs to the technical field of visual monitoring, and in particular, relates to a smart firefighting visualization platform. Background Art

[0002] Visual monitoring technology involves the monitoring, control and analysis of real-time data through a graphical interface, integrating advanced data processing algorithms, graphic design and interactive interface design, so that operators can intuitively understand complex information and dynamic processes; visual monitoring systems include data acquisition, data processing and analysis, and graphical display of data; they are widely used in various industrial and safety-critical fields, such as traffic management, environmental monitoring, production process control and emergency response systems; key technologies include real-time data stream processing, graphical user interface (GUI) design, and event-driven alarm systems, providing efficient monitoring capabilities that can promptly identify and respond to potential risks and abnormal conditions.

[0003] Among them, the smart fire visualization platform aims to improve the efficiency and response speed of fire safety management. By using advanced visualization technology, it can display key information such as fire alarm status, smoke development and personnel distribution in the building in real time; its main uses include monitoring the status of fire protection facilities in the building, updating the visual map of fire development in real time, and assisting the fire command center to make quick decisions; the platform can also integrate video surveillance, smoke detection and automatic sprinkler fire extinguishing systems to form a comprehensive fire prevention, alarm and emergency response mechanism; through intelligent and automated methods, the smart fire visualization platform greatly improves fire safety and the timeliness of emergency response.

[0004] Although existing visual monitoring technology has made some achievements in real-time data monitoring and graphical user interface design, it is insufficient in the rapidity of emergency response systems and resource allocation optimization. Existing systems experience delays when processing large-scale data, especially in the case of rapid changes in the fire scene. Delays will affect the timeliness and accuracy of emergency response; existing technologies lack flexibility in resource allocation and optimization, and cannot adjust resource allocation in real time, resulting in the inability to maximize the use of existing firefighting resources at critical moments; for example, when the fire situation suddenly changes, the resource reconfiguration speed of the existing system cannot keep up with the development of the fire, affecting the overall rescue efficiency. Under high-pressure conditions, it leads to an inability to respond in time or misjudge the risk of fire, increasing safety hazards and potential losses. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an intelligent fire visualization platform, which can monitor the distribution and operation status of fire-fighting facilities in real time and analyze signal strength and transmission delay, effectively improve the response speed and efficiency of the intelligent fire visualization platform, evaluate fire risks, provide a scientific basis for fire warning, and adjust evacuation routes according to real-time building structure and crowd flow data, providing a more efficient and safe solution strategy for fire safety.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: A smart fire visualization platform, comprising: The facility monitoring module identifies the distribution of firefighting facilities and monitors the operating status of firefighting facilities, analyzes the operating efficiency through signal strength and transmission delay indicators, and generates facility operating status data; The fire resource adjustment module uses the smart fire visualization platform to analyze the connectivity of the fire resource network and the distance between fire resources based on the facility operation status data, and generates a resource network optimization plan by adjusting the connection weights; The emergency response module implements the resource network optimization plan, adjusts the water supply distribution route according to the real-time resource configuration and fire situation, optimizes resource utilization efficiency, and generates emergency resource dispatch instructions; The combustion rate identification module collects combustion data using the intelligent fire visualization platform according to the emergency resource dispatch instruction, analyzes the heat release rate, evaluates the risk of the fire, and generates combustion dynamic analysis results; The dynamic warning module calculates and predicts the fire spread path based on the combustion dynamic analysis results, analyzes the risk area, and generates a fire warning signal through the intelligent fire visualization platform; The fire guidance module receives the fire warning signal, formulates an evacuation route according to the building structure and human flow data, adjusts the exit opening sequence and the safety area identification, optimizes the evacuation of personnel, and generates a fire evacuation guide.

[0007] The following is a further optimization of the above technical solution by the present invention: The facility operation status data includes pump station current load, fire hydrant switch status, smoke concentration level and sprinkler flow data; the resource network optimization plan includes resource connection degree, resource response time and priority setting; the emergency resource dispatch instruction includes adjusted water supply pressure parameters, new resource allocation route and fire truck dispatch schedule; the combustion dynamic analysis results include real-time fire spread map, hot spot area distribution and predicted fire development trend; the fire warning signal includes emergency evacuation instructions, risk level update and key area monitoring enhancement; the fire evacuation guide includes building emergency exit guidance, crowd guidance strategy and safe area reception capacity.

[0008] Further optimization: The facility monitoring module includes: The facility distribution identification submodule extracts the distribution data of fire protection facilities from the building information management, uses the convolutional neural network algorithm to identify the location and type of water pumps, fire hydrants, smoke detectors and automatic sprinkler equipment, and generates a facility location list; The status monitoring submodule monitors the working status of the fire protection facilities in real time based on the facility location list, collects key performance parameters such as operating current, pressure readings and alarm status, and generates real-time status identification results; The efficiency analysis submodule uses the real-time status recognition results to analyze the operating efficiency of the fire-fighting facilities, calculates the signal strength and transmission delay indicators of the fire-fighting facilities, analyzes the performance bottlenecks and optimization points in operation, adjusts the configuration and settings of the fire-fighting facilities, and generates facility operation status data.

[0009] Further optimization: The formula of the convolutional neural network algorithm is as follows: in, For the Convolution output feature map, is the activation function, is the bias term, For input data, is the convolution kernel parameter, * indicates the convolution operation, is the learning rate adjustment coefficient, is the processing offset of the input data, is the adjustment coefficient of the convolution kernel parameters, is the output offset.

[0010] Further optimization: The fire resource adjustment module includes: The resource connectivity identification submodule analyzes the structure of water pumps, fire hydrants and sprinklers based on the facility operation status data, evaluates the connectivity of multiple nodes in the fire protection facility network, extracts key connection points and potential breakpoints in the fire protection facility network, and generates connectivity analysis results; The distance calculation submodule uses the connectivity analysis results to calculate the real-time physical distance and path between firefighting resources, and uses the smart firefighting visualization platform to refer to the speed of firefighting facilities to the fire, evaluate the efficiency and response speed of resource deployment, and generate distance evaluation results; The weight adjustment submodule adjusts the resource connection weights in the fire resource network according to the distance evaluation results, optimizes the response speed and resource utilization, makes real-time adjustments to the resource allocation strategy, and generates a resource network optimization plan.

[0011] Further optimization: The emergency response module includes: The resource adjustment submodule analyzes the real-time fire resource configuration based on the resource network optimization scheme, captures the distribution and effectiveness of fire resources, identifies the strengths and weaknesses in resource configuration, and generates resource configuration efficiency analysis results; The situation analysis submodule uses the resource allocation efficiency analysis results to evaluate the location, scale and expansion trend of the fire scene, analyzes the priority and resource requirements of the fire scene response by evaluating the available resources around the fire scene, and generates a fire scene demand analysis result; The water supply route adjustment submodule uses a genetic algorithm to optimize the water supply route and resource allocation according to the fire demand analysis results, analyzes the optimal path from the water source to the fire scene, and generates emergency resource dispatch instructions.

[0012] Further optimization: The formula of the genetic algorithm is as follows: in, is the matching degree of the route, For water supply route plan, is the basic cost of the route, is the population coverage of the route, is the terrain difficulty coefficient of the route, is the seasonal influence factor, is the weight coefficient of the factor.

[0013] Further optimization: the burning rate identification module includes: Based on the emergency resource dispatch instruction, the data collection submodule collects combustion-related data from multiple parts of the fire scene through the smart fire visualization platform, identifies temperature, smoke density and flame conditions, and generates a real-time combustion data set; The heat calculation submodule uses the real-time combustion data set to analyze and calculate the heat release rate in the combustion, evaluate the development speed and potential spread range of the fire, and generate a heat release rate analysis result; The fire risk assessment submodule assesses the potential risk of fire spread and the threat to surrounding structures based on the heat release rate analysis results, predicts the fire spread path, performs quantitative risk assessment, and generates combustion dynamic analysis results.

[0014] Further optimization: The dynamic warning module includes: The path calculation submodule calculates the fire diffusion path based on the combustion dynamic analysis results, using the geographic information and environmental model of the smart fire visualization platform, and generates a predicted fire path analysis result with reference to wind direction, building layout and combustible distribution; The risk identification submodule uses the predicted fire path analysis results to analyze potential risk areas for fire spread, identify key protection targets and emergency evacuation needs, and perform regional safety ratings to generate risk area analysis results; The safety warning submodule compiles and sends warning notifications, including evacuation instructions, safety warnings and response plans, through the smart fire visualization platform based on the risk area analysis results, and generates a fire warning signal.

[0015] Further optimization: The fire guidance module includes: The route design submodule calculates the optimal evacuation route based on the fire warning signal, combined with the building structure data and the real-time crowd distribution, checks the route to avoid the risk area, and generates an evacuation route map; The scheme planning submodule evaluates the real-time status and passenger flow load of differentiated exits according to the evacuation route map, reconfigures the exit opening sequence balance and evacuation flow, avoids potential safety hazards, and generates a dynamic exit adjustment scheme; The safety area identification submodule uses the dynamic exit adjustment scheme to identify the safety area in the building, update the safety signs and evacuation guidance signs, and generate a fire evacuation guide.

[0016] The present invention adopts the above technical solution with ingenious conception. By real-time monitoring of the distribution and operation status of fire-fighting facilities and analyzing the signal strength and transmission delay, the response speed and efficiency of the smart fire-fighting visualization platform are effectively improved. In an emergency, timely updating of the fire-fighting facility status data can provide an accurate basis for the optimization of the resource network, adjust the water supply distribution route, and achieve maximum resource utilization. By collecting combustion data in real time and analyzing the heat release rate, the fire risk is evaluated, which not only enhances the understanding of the development of the fire, but also provides a scientific basis for fire warning. The data-driven dynamic warning mechanism can predict the fire spread path and significantly improve the accuracy and timeliness of the warning. In terms of personnel evacuation, the evacuation route is adjusted according to the real-time building structure and crowd flow data, providing a more efficient and safe solution strategy for fire safety.

[0017] The present invention is further described below in conjunction with the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a platform flow chart of an embodiment of the present invention; Figure 2 Schematic diagram of a platform framework in an embodiment of the present invention; Figure 3 is a flow chart of a facility monitoring module in an embodiment of the present invention; Figure 4 This is a flow chart of a fire resource adjustment module in an embodiment of the present invention; Figure 5 is a flow chart of an emergency response module in an embodiment of the present invention; Figure 6 is a flow chart of a burning rate identification module in an embodiment of the present invention; Figure 7 This is a flow chart of a dynamic early warning module in an embodiment of the present invention; Figure 8 Flow chart of the fire guidance module in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] like Figure 1 to Figure 2 As shown, a smart fire visualization platform includes: The facility monitoring module identifies the distribution of fire-fighting facilities, monitors the operating status of water pumps, fire hydrants, and smoke detectors, analyzes operating efficiency through signal strength and transmission delay indicators, and updates and processes data in real time to generate facility operating status data; The fire resource adjustment module uses the smart fire visualization platform to analyze the connectivity of the fire resource network and the distance between fire resources based on the facility operation status data, optimizes the resource flow path by adjusting the connection weight, and generates a resource network optimization plan; The emergency response module implements the resource network optimization plan, adjusts the water supply pressure and distribution route according to the real-time resource allocation and fire situation, optimizes resource utilization efficiency, and generates emergency resource dispatch instructions; The combustion rate identification module collects combustion data based on the emergency resource dispatch instructions and uses the smart fire visualization platform to quantitatively analyze the heat release rate, assess the risk of the fire based on the ambient temperature and chemical reaction rate, and generate combustion dynamic analysis results; The dynamic warning module calculates and predicts the fire spread path based on the combustion dynamic analysis results, analyzes the risk area, and generates a fire warning signal through the smart fire visualization platform; The fire guidance module receives fire warning signals, formulates evacuation routes according to building structure and crowd flow data, adjusts the order of exit opening and safety area identification, optimizes personnel evacuation, and generates fire evacuation guidelines.

[0020] Facility operation status data include pump station current load, fire hydrant switch status, smoke concentration level and sprinkler flow data; resource network optimization plan includes resource connection degree, resource response time and priority setting; emergency resource dispatch instructions include adjusted water supply pressure parameters, new resource allocation routes and fire truck dispatch schedules; combustion dynamic analysis results include real-time fire spread maps, hot spot area distribution and predicted fire development trends; fire warning signals include emergency evacuation instructions, risk level updates and enhanced monitoring of key areas; fire evacuation guidelines include building emergency exit guidance, crowd guidance strategies and safe area reception capacity.

[0021] See also Figure 2 , 3 , the facility monitoring module includes: The facility distribution identification submodule extracts the distribution data of fire protection facilities from the building information management, and uses the convolutional neural network algorithm to identify the location and type of water pumps, fire hydrants, smoke detectors, and automatic sprinkler equipment. The execution process of generating the facility location list is as follows; The facility distribution identification submodule uses data provided by building information management, including detailed building blueprints and previous installation records, which contain information about the specific location and type of water pumps, fire hydrants, smoke detectors, and automatic sprinkler equipment; it preliminarily organizes the data and converts it into a unified data format for subsequent processing; it conducts a detailed verification of the facility data, including verifying the coordinates and type of the equipment to ensure that all information is correct and complete; the verified data is processed through a series of logic and algorithms to remove any duplicate or erroneous entries, optimize the data structure, and generate a facility location list.

[0022] The formula of the convolutional neural network algorithm is as follows: in, For the Convolution output feature map, is the activation function, is the bias term, For input data, is the convolution kernel parameter, * indicates the convolution operation, is the learning rate adjustment coefficient, is the processing offset of the input data, is the adjustment coefficient of the convolution kernel parameters, is the output offset.

[0023] The execution flow is as follows: Input Data Plus processing offset , through the convolution kernel parameters , plus the adjustment factor Perform convolution operation and then multiply by the learning rate adjustment coefficient , plus the bias , through the activation function Perform nonlinear conversion and add output offset , and get the output feature map , learning rate adjustment coefficient Adjusted during the back propagation process of the gradient descent method to ensure optimal network training efficiency, output offset Set based on statistical bias in the training data.

[0024] The status monitoring submodule monitors the working status of fire protection facilities in real time based on the facility location list, collects key performance parameters such as operating current, pressure readings and alarm status, and generates real-time status identification results. The execution process is as follows; The status monitoring submodule collects the operating current, pressure and alarm status of each device regularly based on the facility location list, collects data in real time, and transmits it to the central monitoring through the network; standardizes the collected data and builds a real-time monitoring database; for each monitored data point, calculates the degree of deviation from the normal range, and generates an alarm in real time when an abnormal indicator is found; monitoring not only improves the efficiency of fire safety management, but also enhances the ability to respond quickly to emergencies; through continuous data analysis, the module can predict the maintenance needs of the facility, optimize the maintenance plan, and generate real-time status recognition results. The formula used is: in, is the comprehensive monitoring status of the facility, It is a facility The number of is the current coefficient, is the pressure coefficient, is the alarm status coefficient.

[0025] The efficiency analysis submodule uses the real-time status recognition results to analyze the operating efficiency of fire-fighting facilities. By calculating the signal strength and transmission delay indicators of fire-fighting facilities, it analyzes the performance bottlenecks and optimization points in operation, adjusts the configuration and settings of fire-fighting facilities, and generates the execution process of facility operation status data as follows; The efficiency analysis submodule uses the real-time status recognition results to evaluate the operating efficiency of firefighting facilities and calculate the signal strength and transmission delay of each facility. The indicators are directly related to the response speed and reliability of the facilities. By comparing the data with industry standards or historical performance data, it can identify equipment with low operating efficiency. Based on the analysis results, it recommends reconfiguration or parameter adjustment of the facilities to solve performance bottlenecks. Through optimization measures, the efficiency and responsiveness of the entire firefighting platform are improved to ensure the best performance in emergency situations. The facility operation status data is generated using the formula: in, It is the comprehensive operating efficiency of fire protection facilities. and is the weight coefficient, It's a signal The number of and They are to adjust the signal strength and transmission delay respectively.

[0026] See also Figure 2 , 4, the fire resource adjustment module includes: The resource connectivity identification submodule analyzes the structure of water pumps, fire hydrants and sprinklers based on the facility operation status data, evaluates the connectivity of multiple nodes in the fire protection facility network, extracts key connection points and potential breakpoints in the fire protection facility network, and generates the execution process of the connectivity analysis results as follows; The resource connectivity identification submodule is based on the facility operation status data. The data covers the current working status of water pumps, fire hydrants and automatic sprinkler equipment. It can depict the structural diagram of the fire protection facility network and identify each node and connection in the network. It involves evaluating the function and importance of each node to determine whether the node is a key connection point or a potential breakpoint. It uses graph theory principles to analyze the connectivity between multiple nodes, and uses algorithms to identify and mark weak links and critical paths in the network. It simulates network responses in different emergency situations, evaluates the scope of fault impact and response strategies, and generates connectivity analysis results.

[0027] The distance calculation submodule uses the connectivity analysis results to calculate the real-time physical distance and path between firefighting resources. It uses the smart firefighting visualization platform to refer to the speed of firefighting facilities to the fire, evaluates the efficiency and response speed of resource deployment, and generates the distance evaluation results. The execution process is as follows; The distance calculation submodule uses the connectivity analysis results to calculate the real-time physical distance and optimal path between firefighting resources based on the network structure data, displays each path through the smart fire visualization platform, and calculates the travel time of each path with reference to the average speed from the firefighting facilities to the fire point. Through calculation, the module can evaluate the efficiency and response speed of resource deployment, identify the paths that affect the response efficiency due to long distances or obstacles, and generate distance evaluation results using the formula: in, is the overall efficiency score of resource deployment, Is a node The number of Indicates to The speed of the node, is the estimated time to reach the node.

[0028] The weight adjustment submodule adjusts the resource connection weights in the fire resource network according to the distance evaluation results, optimizes the response speed and resource utilization, and makes real-time adjustments to the resource allocation strategy to generate the execution process of the resource network optimization plan as follows; The weight adjustment submodule adjusts the resource connection weights in the fire resource network according to the distance assessment results, analyzes the relative importance and usage frequency of each resource, adjusts the weights between resources according to the data, and optimizes the overall response speed and resource utilization rate; through real-time monitoring and data analysis, it continuously adjusts the resource allocation strategy to cope with changing needs and emergencies, and generates a resource network optimization plan using the formula: in, is the optimized resource network efficiency, Representation Resources The adjusted weight of It is a resource The number of is the utilization score of the resource.

[0029] See also Figure 2 , 5 , the emergency response module includes: The resource adjustment submodule analyzes the real-time fire resource configuration based on the resource network optimization scheme, captures the distribution and effectiveness of fire resources, identifies the strengths and weaknesses in resource configuration, and generates the resource configuration efficiency analysis results. The execution process is as follows; The resource adjustment submodule is based on the resource network optimization scheme, deeply analyzes the existing fire resource configuration, collects and organizes real-time data of various fire resources, including equipment location, status and historical response records; can draw a distribution map of current resources, identify the effectiveness and limitations of each resource in actual operation; pays special attention to the redundancy and insufficient coverage areas in resource configuration, and evaluates the strengths and weaknesses of each resource configuration; uses analytical tools to deeply mine the data, determine the priority and specific direction of resource adjustment, and generate resource allocation efficiency analysis results, using the formula: in, represents the overall resource allocation efficiency, It is a resource The number of Representative The performance rating of the class resources, Indicates the priority of the resource in the configuration.

[0030] The situation analysis submodule uses the resource allocation efficiency analysis results to evaluate the location, scale and expansion trend of the fire. By evaluating the available resources around the fire, it analyzes the priority and resource requirements of the fire response. The execution process of generating the fire demand analysis results is as follows; The situation analysis submodule uses the results of resource allocation efficiency analysis to conduct a comprehensive assessment of the specific situation of the fire scene, analyze the location, scale and expansion trend of the fire scene, and evaluate the response priority and resource requirements; determine the most suitable response strategy by comparing the distance and type between the fire scene location and the surrounding available resources; consider the potential path of fire expansion and prioritize the deployment of key resources that can control the spread of the fire; the analysis results will help decision makers understand the current needs of the fire scene and provide a scientific basis for resource scheduling in emergency situations, generating fire scene demand analysis results, using the formula: in, Represents the overall score of fire scene demand, Representative resources Availability rating, It is a resource The number of Represents the suitability score of the resource.

[0031] The water supply route adjustment submodule uses genetic algorithms to optimize water supply routes and resource allocation based on the fire demand analysis results, analyzes the optimal path from the water source to the fire scene, and generates the execution process of emergency resource dispatch instructions as follows; The water supply route adjustment submodule optimizes the water supply route and resource allocation according to the fire demand analysis results, collects relevant geographical and resource data, and analyzes the path from the water source to the fire site; by simulating the effects of different water supply schemes, the water supply efficiency and reliability of each path are evaluated, and various variables such as road conditions, traffic flow and environmental factors are considered; the optimal water supply route is proposed, and the relevant resource allocation is adjusted to maximize the response speed and efficiency, and emergency resource dispatch instructions are generated.

[0032] The formula of genetic algorithm is as follows: in, is the matching degree of the route, For water supply route plan, is the basic cost of the route, is the population coverage of the route, is the terrain difficulty coefficient of the route, is the seasonal influence factor, is the weight coefficient of the factor.

[0033] The execution flow is as follows: Calculate water supply route options Basic Cost , including distance, time and water resource utilization, calculate population coverage , reflects the population density covered by the route and calculates the terrain difficulty coefficient , taking into account the additional cost of terrain in the water supply process and calculating the seasonal impact factor , considering the impact of different seasons on water supply efficiency, each factor is weighted by the corresponding weight coefficient Adjustments can be made, and the weight coefficient can be determined through historical data analysis, using statistical or machine learning methods to optimize, to ensure that each coefficient reflects the actual degree of impact, and the value is substituted into the formula to calculate the comprehensive matching degree of the route.

[0034] See also Figure 2 , 6 , the burning rate identification module includes: Based on the emergency resource dispatch instructions, the data collection submodule collects combustion-related data from multiple parts of the fire scene through the smart fire visualization platform, identifies temperature, smoke density and flame conditions, and generates a real-time combustion data set. The execution process is as follows; Based on the emergency resource dispatch instructions and with the support of the smart fire visualization platform, the data collection submodule conducts comprehensive data collection on the fire scene. It is equipped with multiple high-precision sensors distributed in key parts of the fire scene to monitor and collect data on temperature, smoke density and flame conditions in real time. The sensors can resist the erosion of high temperature and smoke to ensure the accuracy and reliability of the data. The collected data is transmitted back to the data center through an encrypted network and formatted to provide continuous fire monitoring information, which not only supports subsequent analysis, but also provides decision support for the emergency response team to generate a real-time combustion data set using the formula: in, Represents the comprehensive quality index of the combustion real-time data set, Representative The temperature index of each monitoring point, It is a monitoring point The number of represents the smoke density index, Stands for flame activity index.

[0035] The heat calculation submodule uses the real-time combustion data set to analyze and calculate the heat release rate in combustion, evaluate the development speed and potential spread of the fire, and generate the heat release rate analysis results. The execution process is as follows; The heat calculation submodule uses the real-time combustion data set to analyze the heat release during the combustion process. By analyzing the temperature readings and flame conditions of each monitoring point, the heat release rate of the entire fire scene is calculated; the rate is a key parameter for evaluating the speed and spread of the fire; using advanced thermodynamic models, the temperature and flame data are converted into quantitative indicators of heat release, the potential development trend of the fire is predicted, and the heat release rate analysis results are formed. The formula used is: in, Represents the total heat release rate of the fire, Representative The temperature readings of the monitoring points, It is a monitoring point The number of Activity index representing heat release.

[0036] The fire risk assessment submodule evaluates the potential risk of fire spread and the threat to surrounding structures based on the heat release rate analysis results, predicts the fire spread path, and conducts quantitative risk assessment to generate the combustion dynamic analysis results. The execution process is as follows; The fire risk assessment submodule conducts risk assessment of fire spread based on the results of heat release rate analysis, comprehensively considers factors such as temperature, heat release rate and wind direction, and predicts the potential spread path of the fire; the module analyzes the potential threat of the fire to the surrounding structures and quantitatively assesses the risk level brought by the fire; by simulating different fire spread scenarios, it can provide specific risk control suggestions for the emergency management team and generate combustion dynamic analysis results. The formula used is: in, Represents the overall risk score of the fire, Representative The heat release index of each monitoring point, It is a monitoring point The number of Represents the potential for fire expansion. Represents the impact index of environmental factors.

[0037] See also Figure 2 , 7 , the dynamic warning module includes: The path calculation submodule calculates the fire spread path based on the combustion dynamic analysis results, using the geographic information and environmental model of the smart fire visualization platform, and refers to the wind direction, building layout and combustible distribution to generate the predicted fire path analysis results. The execution process is as follows; Based on the results of combustion dynamic analysis, the path calculation submodule combines geographic information systems and environmental models to calculate the potential spread path of the fire, obtain the current state and spread direction of the fire, consider key factors such as wind direction, building layout and combustible distribution, use advanced geographic analysis tools to simulate the dynamics of fire spread, comprehensively evaluate the impact of various factors on the fire path, and give priority to high-risk areas and densely populated buildings; through iterative simulation and algorithm optimization, the module can predict the potential expansion path of the fire and generate the fire path analysis results, using the formula: in, Represents the potential danger of the fire spread path, Represents the weight of the impact of wind direction on fire spread, It is a region The number of The coefficient representing the effect of building density on fire intensity, Represents the hazard factor of combustible material distribution.

[0038] The risk identification submodule uses the predicted fire path analysis results to analyze the potential risk areas of fire spread, identify key protection targets and emergency evacuation needs, and perform regional safety ratings. The execution process of generating risk area analysis results is as follows; The risk identification submodule uses the predicted fire path analysis results to deeply analyze the potential risk areas of fire spread and identify key protection targets on the predicted fire path, such as hospitals, schools and important public facilities; analyze the emergency evacuation needs of the region, evaluate the safety and feasibility of the evacuation routes, and conduct regional safety ratings. According to the potential speed of fire spread and the scope of impact, the risk level of different regions is given; through comprehensive evaluation, the risk area analysis results are generated, and the formula used is: in, Represents the total risk rating of each risk area, Representative The potential for fire spread in an area, It is a region The number of Represents the difficulty of emergency evacuation. Represents the security priority of critical facilities.

[0039] The safety warning submodule compiles and sends warning notifications based on the risk area analysis results through the smart fire visualization platform, including evacuation instructions, safety warnings and response plans. The execution process of generating fire warning signals is as follows; The safety warning submodule compiles and sends warning notifications based on the risk area analysis results through the smart fire visualization platform, identifies high-risk areas that require immediate action, and formulates specific evacuation instructions and safety warnings for the areas; through the fire platform, the information is quickly conveyed to relevant personnel and residents to ensure that they can be warned in time and take corresponding measures; through emergency service dispatch, resource allocation and the establishment of temporary shelters, a fire warning signal is generated, using the formula: in, Represents the effectiveness of the fire warning signal, Represents the risk level, It is a region The number of represents the evacuation time efficiency, Represents the urgency of the area covered by the warning.

[0040] See also Figure 2 , 8 , the fire guidance module includes: The route design submodule calculates the optimal evacuation route based on the fire warning signal, combined with the building structure data and real-time crowd distribution, checks the route to avoid the risk area, and generates the evacuation route map. The execution process is as follows; The route design submodule is based on the fire warning signal and connected to the smart fire visualization platform to obtain building structure data and real-time crowd distribution; use the information to calculate and design the optimal evacuation route; through advanced simulation algorithms, consider multiple factors, such as the length, width, obstacles and current crowd density of the evacuation route to ensure that each evacuation route can efficiently and safely guide people to quickly leave the dangerous area; simulate different fire expansion situations to ensure that the evacuation route is still effective in a changing environment, update the route status in real time to avoid newly emerging risk areas or crowd congestion points, and generate an evacuation route map using the formula: in, represents the overall optimization degree of the evacuation route, Representative The distance index of the evacuation paths, It is an evacuation route The number of The pedestrian density index representing the path, Represents the suitability index for avoiding high-risk areas.

[0041] The program planning submodule evaluates the real-time status and passenger flow load of differentiated exits according to the evacuation route map, reconfigures the exit opening sequence balance and evacuation flow, avoids potential safety hazards, and generates the execution process of the dynamic adjustment plan of the exit as follows; The program planning submodule analyzes the real-time status of each exit and the current passenger flow load according to the evacuation route map, and uses data analysis tools to evaluate the capacity and efficiency of each exit; through real-time monitoring and simulation of passenger flow, the order and time of exit opening are reconfigured to balance the evacuation flow and avoid overcrowding. Taking into account safety hazards such as structural weaknesses and flammable areas, the use of exits that can minimize risks is prioritized, and a dynamic exit adjustment plan is generated. The formula used is: in, represents the total efficiency of the export dynamic adjustment scheme, Representative The safety score of each exit, It is export The number of Represents the real-time traffic load, Represents the management efficiency of export opening.

[0042] The safety area identification submodule uses the dynamic exit adjustment scheme to identify the safety areas in the building, update the safety signs and evacuation guidance signs, and generate the execution process of the fire evacuation guide as follows; The safety area identification submodule uses the dynamic adjustment scheme of the exit to identify the safe areas in the building, update the safety signs and evacuation guidance signs in the building, ensure that they are clearly visible and can correctly guide the flow of people in an emergency; it also evaluates the safety of each area in the building and determines the safest area based on the fire spread prediction and the density of people; the safe area will be clearly marked and displayed in real time on the smart fire visualization platform so that the emergency response team and the people in the building can quickly understand the safe evacuation destination and generate a fire evacuation guide. The formula used is: in, stands for Comprehensive Safety of Fire Evacuation Guide, Representative Visibility of safe areas, It is a safe area The number of Represents geographic location security, Represents the practicality of evacuation instructions.

[0043] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A smart fire visualization platform, characterized by: include: The facility monitoring module identifies the distribution of firefighting facilities and monitors the operating status of firefighting facilities, analyzes the operating efficiency through signal strength and transmission delay indicators, and generates facility operating status data; The fire resource adjustment module uses the smart fire visualization platform to analyze the connectivity of the fire resource network and the distance between fire resources based on the facility operation status data, and generates a resource network optimization plan by adjusting the connection weights; The emergency response module implements the resource network optimization plan, adjusts the water supply distribution route according to the real-time resource configuration and fire situation, optimizes resource utilization efficiency, and generates emergency resource dispatch instructions; The combustion rate identification module collects combustion data using the intelligent fire visualization platform according to the emergency resource dispatch instruction, analyzes the heat release rate, evaluates the risk of the fire, and generates combustion dynamic analysis results; The dynamic warning module calculates and predicts the fire spread path based on the combustion dynamic analysis results, analyzes the risk area, and generates a fire warning signal through the intelligent fire visualization platform; The fire guidance module receives the fire warning signal, formulates an evacuation route according to the building structure and human flow data, adjusts the exit opening sequence and the safety area identification, optimizes the evacuation of personnel, and generates a fire evacuation guide.

2. According to claim 1, the intelligent fire visualization platform is characterized by: The facility operation status data includes pump station current load, fire hydrant switch status, smoke concentration level and sprinkler flow data; the resource network optimization plan includes resource connection degree, resource response time and priority setting; the emergency resource dispatch instruction includes adjusted water supply pressure parameters, new resource allocation route and fire truck dispatch schedule; the combustion dynamic analysis results include real-time fire spread map, hot spot area distribution and predicted fire development trend; the fire warning signal includes emergency evacuation instructions, risk level update and key area monitoring enhancement; the fire evacuation guide includes building emergency exit guidance, crowd guidance strategy and safe area reception capacity.

3. According to claim 2, a smart fire visualization platform is characterized by: The facility monitoring module includes: The facility distribution identification submodule extracts the distribution data of fire protection facilities from the building information management, uses the convolutional neural network algorithm to identify the location and type of water pumps, fire hydrants, smoke detectors and automatic sprinkler equipment, and generates a facility location list; The status monitoring submodule monitors the working status of the fire protection facilities in real time based on the facility location list, collects key performance parameters such as operating current, pressure readings and alarm status, and generates real-time status identification results; The efficiency analysis submodule uses the real-time status recognition results to analyze the operating efficiency of the fire-fighting facilities, calculates the signal strength and transmission delay indicators of the fire-fighting facilities, analyzes the performance bottlenecks and optimization points in operation, adjusts the configuration and settings of the fire-fighting facilities, and generates facility operation status data.

4. The intelligent fire visualization platform according to claim 3 is characterized by: The formula of the convolutional neural network algorithm is as follows: in, For the Convolution output feature map, is the activation function, is the bias term, For input data, is the convolution kernel parameter, * indicates the convolution operation, is the learning rate adjustment coefficient, is the processing offset of the input data, is the adjustment coefficient of the convolution kernel parameters, is the output offset.

5. The intelligent fire visualization platform according to claim 4 is characterized in that: The fire resource adjustment module includes: The resource connectivity identification submodule analyzes the structure of water pumps, fire hydrants and sprinklers based on the facility operation status data, evaluates the connectivity of multiple nodes in the fire protection facility network, extracts key connection points and potential breakpoints in the fire protection facility network, and generates connectivity analysis results; The distance calculation submodule uses the connectivity analysis results to calculate the real-time physical distance and path between firefighting resources, and uses the smart firefighting visualization platform to refer to the speed of firefighting facilities to the fire, evaluate the efficiency and response speed of resource deployment, and generate distance evaluation results; The weight adjustment submodule adjusts the resource connection weights in the fire resource network according to the distance evaluation results, optimizes the response speed and resource utilization, makes real-time adjustments to the resource allocation strategy, and generates a resource network optimization plan.

6. The intelligent fire visualization platform according to claim 5 is characterized by: The emergency response module comprises: The resource adjustment submodule analyzes the real-time fire resource configuration based on the resource network optimization scheme, captures the distribution and effectiveness of fire resources, identifies the strengths and weaknesses in resource configuration, and generates resource configuration efficiency analysis results; The situation analysis submodule uses the resource allocation efficiency analysis results to evaluate the location, scale and expansion trend of the fire scene, analyzes the priority and resource requirements of the fire scene response by evaluating the available resources around the fire scene, and generates a fire scene demand analysis result; The water supply route adjustment submodule uses a genetic algorithm to optimize the water supply route and resource allocation according to the fire demand analysis results, analyzes the optimal path from the water source to the fire scene, and generates emergency resource dispatch instructions.

7. The intelligent fire visualization platform according to claim 6 is characterized by: The formula of the genetic algorithm is as follows: in, is the matching degree of the route, For water supply route plan, is the basic cost of the route, is the population coverage of the route, is the terrain difficulty coefficient of the route, is the seasonal influence factor, is the weight coefficient of the factor.

8. The intelligent fire visualization platform according to claim 7 is characterized by: The burning rate identification module comprises: Based on the emergency resource dispatch instruction, the data collection submodule collects combustion-related data from multiple parts of the fire scene through the smart fire visualization platform, identifies temperature, smoke density and flame conditions, and generates a real-time combustion data set; The heat calculation submodule uses the real-time combustion data set to analyze and calculate the heat release rate in the combustion, evaluate the development speed and potential spread range of the fire, and generate a heat release rate analysis result; The fire risk assessment submodule assesses the potential risk of fire spread and the threat to surrounding structures based on the heat release rate analysis results, predicts the fire spread path, performs quantitative risk assessment, and generates combustion dynamic analysis results.

9. The intelligent fire visualization platform according to claim 8 is characterized by: The dynamic early warning module comprises: The path calculation submodule calculates the fire diffusion path based on the combustion dynamic analysis results, using the geographic information and environmental model of the smart fire visualization platform, and generates a predicted fire path analysis result with reference to wind direction, building layout and combustible distribution; The risk identification submodule uses the predicted fire path analysis results to analyze potential risk areas for fire spread, identify key protection targets and emergency evacuation needs, and perform regional safety ratings to generate risk area analysis results; The safety warning submodule compiles and sends warning notifications, including evacuation instructions, safety warnings and response plans, through the smart fire visualization platform based on the risk area analysis results, and generates a fire warning signal.

10. The intelligent fire visualization platform according to claim 9 is characterized in that: The fire guidance module includes: The route design submodule calculates the optimal evacuation route based on the fire warning signal, combined with the building structure data and the real-time crowd distribution, checks the route to avoid the risk area, and generates an evacuation route map; The scheme planning submodule evaluates the real-time status and passenger flow load of differentiated exits according to the evacuation route map, reconfigures the exit opening sequence balance and evacuation flow, avoids potential safety hazards, and generates a dynamic exit adjustment scheme; The safety area identification submodule uses the dynamic exit adjustment scheme to identify the safety area in the building, update the safety signs and evacuation guidance signs, and generate a fire evacuation guide.

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