Intelligent fire-fighting linkage control method and system for avoiding fire-fighting risk

Abstract

The invention discloses an intelligent fire-fighting linkage control method and system for avoiding fire-fighting risks, and relates to the related technical field of fire-fighting linkage control, and the method comprises the steps: carrying out the simulation construction of a fire dynamic simulator; acquiring real-time monitoring parameters, and acquiring coverage combustible distribution; harmful gas diffusion prediction is carried out in a predetermined future time zone, and harmful gas prediction distribution is generated; the smoke exhaust linkage control scheme is optimized by combining the layout of the smoke exhaust device, and the optimal smoke exhaust linkage control scheme is output; and performing smoke discharge control on the target building in the preset future time zone. The technical problems that in the prior art, fire alarm is difficult to adjust according to different building structures and fire scenes, diffusion paths of fire and harmful gas cannot be predicted in real time, an emergency linkage control scheme lacks pertinence, and fire emergency response is not timely and accurate enough are solved. The technical effects of improving the timeliness and accuracy of fire emergency response and reducing the risk of harmful gas in an evacuation channel and the overall fire safety risk are achieved.

Description

Technical Field

[0001] This application relates to the technical field of fire linkage control, and specifically relates to an intelligent fire linkage control method and system for avoiding fire risks. Background Art

[0002] With the continuous increase in the number of complex building structures such as high-rise buildings and large commercial complexes, the functional layout inside buildings has become more diverse, resulting in an increase in the risk and complexity of fire occurrence. During the fire emergency response process, traditional fire linkage control systems mainly rely on preset fixed linkage logics and simple sensor alarm mechanisms. Usually, based on basic sensing devices such as smoke detectors and temperature sensors, combined with simple logical judgments, they conduct linkage control of fire-fighting equipment such as smoke exhaust and sprinkler systems. It is difficult to dynamically adjust according to different building structures and fire scenarios, unable to predict the development trend of fires and the diffusion paths of harmful gases in real time, and lacking comprehensive consideration of key factors such as the specific structure of the building, the layout of evacuation passages, and the distribution of combustibles. This leads to the lack of optimization of the emergency linkage control plan, unable to achieve precise response to different fire scenarios, resulting in untimely and inaccurate emergency responses, increasing the risk of harmful gases in evacuation passages and the overall fire safety risk, and unable to effectively reduce the risk of casualties and property losses.

[0003] Therefore, in the current related technologies, there are technical problems such as the difficulty of fire alarms to be adjusted according to different building structures and fire scenarios, the inability to predict the diffusion paths of fires and harmful gases in real time, resulting in the lack of pertinence of the emergency linkage control plan and the lack of timeliness and accuracy of fire emergency responses. Summary of the Invention

[0004] By providing an intelligent fire linkage control method and system for avoiding fire risks, this application solves the technical problems in the prior art that fire alarms are difficult to be adjusted according to different building structures and fire scenarios, unable to predict the diffusion paths of fires and harmful gases in real time, resulting in the lack of pertinence of the emergency linkage control plan and the lack of timeliness and accuracy of fire emergency responses, and achieves the technical effects of improving the timeliness and accuracy of fire emergency responses, reducing the risk of harmful gases in evacuation passages and the overall fire safety risk.

[0005] This application provides an intelligent fire linkage control method for avoiding fire risks. The method includes: based on the building structure characteristics, evacuation passage layout, and combustible distribution information of the target building, simulating and constructing a fire dynamic simulator of the target building; when a fire occurs, using a multi-sensor alarm device to obtain real-time monitoring parameters at fixed points, combining with the fire dynamic simulator to conduct fire simulation in a predetermined future time zone, and obtaining the coverage of combustible distribution; predicting the diffusion of harmful gases in a predetermined future time zone based on the real-time monitoring parameters and the coverage of combustible distribution, and generating a harmful gas prediction distribution; based on the harmful gas prediction distribution, with the predetermined limit concentration in the evacuation passage as a constraint, aiming to minimize the overall harmful gas risk and the harmful gas risk in the evacuation passage, combining with the layout of smoke exhaust devices to optimize the smoke exhaust linkage control scheme, and outputting the optimal smoke exhaust linkage control scheme; performing smoke exhaust control of the target building in the predetermined future time zone according to the optimal smoke exhaust linkage control scheme.

[0006] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks further performs the following processing: The combustible distribution information includes the type, location, scale of combustibles, and the characteristics of harmful gases generated during combustion, where the characteristics of harmful gases include the type of harmful gases and the concentration of harmful gases generated per unit time.

[0007] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks further performs the following processing: Using the building structure characteristics and evacuation passage layout as conditional constraints, retrieving fire cases through big data, collecting a sample monitoring parameter set and a sample combustible distribution set, and annotating the harmful gas distribution in a predetermined historical time zone to obtain a sample harmful gas distribution set, where the time interval between the predetermined historical time zone and the predetermined future time zone is the same; using the sample monitoring parameter set and the sample combustible distribution set as inputs, and the sample harmful gas distribution set as supervision, training the BP neural network until convergence to generate a harmful gas diffusion prediction model; using the harmful gas diffusion prediction model, predicting the diffusion of harmful gases in a predetermined future time zone based on the real-time monitoring parameters and the coverage of combustible distribution, and outputting the harmful gas prediction distribution.

[0008] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks further performs the following processing: The layout of the smoke exhaust devices includes several natural ventilation windows, several mechanical smoke exhaust devices, and several differential pressure smoke exhaust devices, where each smoke exhaust device is marked with position coordinates.

[0009] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks further performs the following processes: randomly generating a plurality of simulated smoke exhaust linkage control schemes based on the regulation parameter thresholds of the plurality of natural ventilation windows, a plurality of mechanical smoke exhaust devices, and a plurality of differential pressure smoke exhaust devices; simulating and constructing a smoke exhaust dynamic simulation plug-in based on the building structure characteristics and evacuation passage layout of the target building, and rendering the predicted distribution of harmful gases to the smoke exhaust dynamic simulation plug-in to generate a real-time smoke exhaust dynamic simulation plug-in; in the real-time smoke exhaust dynamic simulation plug-in, respectively performing simulated smoke exhaust according to the plurality of simulated smoke exhaust linkage control schemes to obtain a plurality of simulated smoke exhaust results; screening the plurality of simulated smoke exhaust results with a predetermined limit concentration in the evacuation passage as a constraint to obtain a plurality of qualified simulated smoke exhaust results, and performing an overall harmful gas risk and evacuation passage harmful gas risk analysis on the plurality of qualified simulated smoke exhaust results to evaluate and determine a plurality of smoke exhaust fitness levels; performing an optimization of the smoke exhaust linkage control scheme according to the regulation parameter thresholds and the plurality of smoke exhaust fitness levels, and outputting the optimal smoke exhaust linkage control scheme.

[0010] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks further performs the following processes: randomly selecting a first qualified simulated smoke exhaust result, where the first qualified simulated smoke exhaust result includes a first non-evacuation passage simulated smoke exhaust result and a first evacuation passage simulated smoke exhaust result, where the first non-evacuation passage simulated smoke exhaust result includes the type of harmful gas and the concentration of harmful gas, and the first evacuation passage simulated smoke exhaust result includes the gas diffusion area, the type of gas, and the gas concentration; performing weight configuration according to the type of harmful gas, and performing weighted calculation on the concentration of harmful gas according to the weight configuration result to obtain an overall harmful gas risk coefficient; performing weighted calculation on the gas concentration according to the weight configuration result to obtain an initial evacuation passage harmful gas risk coefficient, and compensating the initial evacuation passage harmful gas risk coefficient according to the gas diffusion area to output an evacuation passage harmful gas risk coefficient; evaluating and obtaining a first smoke exhaust fitness level according to the overall harmful gas risk coefficient and the evacuation passage harmful gas risk coefficient, and adding it to the plurality of smoke exhaust fitness levels, where the smoke exhaust fitness level is negatively correlated with the overall harmful gas risk coefficient and the evacuation passage harmful gas risk coefficient.

[0011] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks further performs the following processes: using the regulation parameter thresholds as the optimization space and the predetermined limit concentration in the evacuation passage as a constraint, and using a genetic algorithm to perform an optimization of the smoke exhaust linkage control scheme according to the plurality of smoke exhaust fitness levels until a predetermined convergence condition is reached, and outputting the scheme with the maximum smoke exhaust fitness level as the optimal smoke exhaust linkage control scheme.

[0012] The present application also provides an intelligent fire linkage control system for avoiding fire risks, including: a fire dynamic simulator construction module, configured to simulate and construct a fire dynamic simulator of a target building based on the building structure characteristics, evacuation passage layout, and combustible distribution information of the target building; a covered combustible distribution acquisition module, configured to, when a fire occurs, use a multi-sensor alarm device to obtain real-time monitoring parameters at fixed points, and combine with the fire dynamic simulator to perform fire simulation in a predetermined future time zone to obtain the covered combustible distribution; a harmful gas prediction distribution generation module, configured to perform prediction of harmful gas diffusion in a predetermined future time zone based on the real-time monitoring parameters and the covered combustible distribution to generate a harmful gas prediction distribution; a smoke exhaust linkage control scheme optimization module, configured to, based on the harmful gas prediction distribution, with a predetermined limit concentration in the evacuation passage as a constraint, and with the goal of minimizing the overall harmful gas risk and the harmful gas risk in the evacuation passage, combine with the smoke exhaust device layout to optimize the smoke exhaust linkage control scheme and output an optimal smoke exhaust linkage control scheme; and a smoke exhaust control module, configured to perform smoke exhaust control of the target building in the predetermined future time zone according to the optimal smoke exhaust linkage control scheme.

[0013] It is intended to simulate and construct a fire dynamic simulator through the intelligent fire linkage control method and system for avoiding fire risks proposed in the present application; obtain real-time monitoring parameters and the covered combustible distribution; perform prediction of harmful gas diffusion in a predetermined future time zone to generate a harmful gas prediction distribution; combine with the smoke exhaust device layout to optimize the smoke exhaust linkage control scheme and output an optimal smoke exhaust linkage control scheme; and perform smoke exhaust control of the target building in the predetermined future time zone. This solves the technical problems existing in the prior art, such as that fire alarms are difficult to adjust according to different building structures and fire scenarios, and it is impossible to predict the diffusion paths of fires and harmful gases in real time, resulting in the lack of pertinence of emergency linkage control schemes and the lack of timeliness and accuracy of fire emergency responses. It achieves the technical effects of improving the timeliness and accuracy of fire emergency responses, reducing the harmful gas risk in the evacuation passage, and the overall fire safety risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in the present application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the operations before or below do not necessarily need to be executed precisely in sequence. On the contrary, according to needs, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0015] Figure 1 It is a schematic flowchart of the intelligent fire linkage control method for avoiding fire risks provided by the embodiments of the present application.

[0016] Figure 2Schematic diagram of the intelligent fire linkage control system for avoiding fire risks provided by the embodiments of the present application.

[0017] Explanation of reference numerals in the drawings: Fire dynamic simulator construction module 10, combustible distribution acquisition module 20 covering the target building, harmful gas prediction distribution generation module 30, smoke exhaust linkage control scheme optimization module 40, smoke exhaust control module 50. Detailed implementation manners

[0018] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0019] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0020] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0021] The embodiments of the present application provide an intelligent fire linkage control method for avoiding fire risks, as Figure 1 shown, the method includes:

[0022] Step S100, based on the building structure characteristics, evacuation passage layout and combustible distribution information of the target building, simulate and construct a fire dynamic simulator of the target building.

[0023] Step S100 further includes that the combustible distribution information includes the type, location, scale of combustibles, and the characteristics of harmful gases generated during combustion, where the characteristics of harmful gases include the type of harmful gases and the concentration of harmful gases generated per unit time.

[0024] Preferably, based on the actual physical characteristics and fire behavior characteristics of the building, computer modeling and simulation technologies are used to create a virtual model that can dynamically simulate the fire development and harmful gas diffusion process, that is, the fire dynamic simulator of the target building. This fire dynamic simulator can predict the fire spread path, the change of harmful gas concentration, and its potential impact on personnel evacuation and safety under different fire scenarios. Specifically, the building structure characteristics of the target building include information such as the number of floors of the building, room layout, materials of walls and partitions, positions of doors and windows, ventilation system, and opening sizes, which determine the propagation path, speed, and temperature distribution of flames and smoke, and affect the development dynamics of the fire; the evacuation passage layout includes information such as the positions of safety exits, widths, lengths of evacuation passages, and stair and elevator layouts, which determine the feasibility and efficiency of personnel evacuation and are also important parameters for optimizing the smoke exhaust linkage control strategy; the combustible distribution information includes the type of combustibles (such as wood, plastic, textiles, paper, etc., with different ignition points and combustion characteristics for different materials), location and scale (accurately calibrating the distribution of combustibles in the building to determine the potential combustion sources and fire spread directions of the fire), and the characteristics of harmful gases generated during combustion, where the characteristics of harmful gases include the type of harmful gases (such as carbon monoxide, hydrogen cyanide, etc.) and the concentration of harmful gases generated per unit time; by combining this information and using simulation tools such as computational fluid dynamics (CFD) and fire dynamics models (such as FDS), the temperature field, smoke diffusion, and harmful gas concentration distribution of the fire at different time nodes are simulated, and the fire dynamic simulator of the target building is constructed, which can reflect the dynamic changes of fire development and harmful gas diffusion in real time and continuously correct the model based on the data obtained by the fire scene sensors to improve the prediction accuracy.

[0025] Step S200, when a fire occurs, use a multi-sensor alarm device to obtain real-time monitoring parameters at fixed points, and combine with the fire dynamic simulator to perform fire simulation in a predetermined future time zone to obtain coverage of the combustible distribution.

[0026] Preferably, if a fire occurs, real-time monitoring parameters are obtained at fixed points through a multi-sensor alarm device (i.e., multiple types of sensors), that is, the environmental data at the fire scene is monitored in real time, and the future fire scenarios are predicted and analyzed in combination with a fire dynamic simulator to obtain the distribution of combustibles covered. Specifically, the types of sensors may include temperature sensors to monitor the temperature changes in the fire area to help identify the location of the fire and the development of the fire; smoke sensors to detect the smoke concentration in the air to judge the spread of the fire; harmful gas sensors (such as CO, CO2, HCN, SO2, etc.) to monitor the concentration of harmful gases in the air and capture the changes in toxic gases generated by the fire in real time; PM sensors to measure the concentration of particulate matter in the air to evaluate the pollution degree caused by the fire; and wind speed sensors to monitor the wind speed to judge the impact of air flow changes during the fire on the spread of the fire. Then, the obtained real-time monitoring data is input into the fire dynamic simulator to simulate the fire in a predetermined future time zone, that is, to simulate and predict the fire in a predetermined future time zone, specifically including simulating the spread speed, temperature distribution, etc. of the fire at different time nodes, predicting the concentration distribution of harmful gases (such as CO, HCN, etc.) during the fire process, and then calculating the impact of the harmful gases on the evacuation path and safe area of personnel. Based on the distribution of combustibles in the building and in combination with the fire simulator, the spread speed and direction of the fire are predicted, and then the scope of the fire spread, the diffusion of harmful gases, and the impact on personnel evacuation in the future time zone are determined. Finally, the distribution of combustibles covered is generated to more accurately judge the dangerous area during the fire and predict the fire behavior, so as to provide a more intelligent and accurate fire control strategy and optimize the smoke exhaust linkage control and evacuation path planning.

[0027] Step S300, predict the diffusion of harmful gases in a predetermined future time zone according to the real-time monitoring parameters and the distribution of combustibles covered, and generate a predicted distribution of harmful gases.

[0028] Preferably, based on the real-time monitored data (such as temperature, smoke, concentration of harmful gases, etc.) and the distribution information of combustibles inside the building, combined with the prediction ability of the fire dynamic simulator, the diffusion trend of harmful gases generated by the fire, such as carbon monoxide (CO), hydrogen cyanide (HCN), carbon dioxide (CO2), etc., is predicted for the future time zone, and the concentration distribution maps of these harmful gases at different time points (i.e., the predicted distribution of harmful gases) are generated. Specifically, the environmental parameters at the fire scene, temperature, smoke concentration, concentration of combustible gases (such as CO, CO2, HCN, etc.), as well as the building layout and the distribution information of combustibles are input into the fire dynamic simulator to simulate the generation and diffusion process of harmful gases during the fire. Among them, different types of combustibles release different types and concentrations of harmful gases when burning, and the generated harmful gases will diffuse around, and the diffusion of harmful gases is affected by various factors such as the fire intensity, building structure, ventilation conditions, etc. Then, a model is constructed to predict the concentration changes of the gas at different time nodes and spatial positions and the diffusion of harmful gases, and further determine the predicted distribution of harmful gases, which is used to show the concentration changes of harmful gases at the fire scene at different time nodes after the fire occurs, and also includes whether the concentration of harmful gases in each area, such as evacuation passages, important exit areas, etc., is affected by high-concentration gases, so as to improve the intelligence and response ability of the entire fire-fighting linkage control system.

[0029] Further, step S300 further includes step S310. With the building structure characteristics and the layout of evacuation passages as conditional constraints, big data is used to retrieve fire cases, collect a sample monitoring parameter set and a sample combustible distribution set, and label the harmful gas distribution in a predetermined historical time zone to obtain a sample harmful gas distribution set, where the time interval between the predetermined historical time zone and the predetermined future time zone is the same; step S320, with the sample monitoring parameter set and the sample combustible distribution set as inputs and the sample harmful gas distribution set as supervision, train the BP neural network until convergence to generate a harmful gas diffusion prediction model; step S330, use the harmful gas diffusion prediction model to predict the diffusion of harmful gases in a predetermined future time zone according to the real-time monitoring parameters and the covered combustible distribution, and output the predicted distribution of harmful gases.

[0030] Preferably, taking the structural characteristics of the target building (such as the number of building floors, room layout, evacuation passage settings, etc.) and the evacuation passage layout (including exits, stairs, corridors, etc.) as conditions, combined with big data technology to retrieve existing fire cases, these cases provide monitoring data and fire development models in real fire scenarios, collect a sample monitoring parameter set and a sample combustible distribution set. Among them, the sample monitoring parameter set includes real-time monitoring data under different fire conditions, such as temperature, smoke concentration, concentrations of various harmful gases (such as CO, HCN, etc.), and the sample combustible distribution set records the combustible distribution under different fire scenarios, including the types, locations, scales, etc. of combustibles. Then, label the harmful gas distribution in a predetermined historical time zone, that is, trace back the historical fire data and label the harmful gas distribution at that time to obtain a sample harmful gas distribution set, and the time intervals of the predetermined historical time zone and the predetermined future time zone are the same.

[0031] Preferably, use the collected sample monitoring parameter set and sample combustible distribution set as input data, and the historical time zone harmful gas distribution set as supervision data, and use a BP (backpropagation) neural network for training to capture the complex relationship between the input data and harmful gas diffusion. Among them, the BP neural network is a common deep learning method. During the training process, the neural network will gradually adjust its parameters according to the supervision signals of historical data until the network converges to generate a harmful gas diffusion prediction model, that is, the harmful gas diffusion prediction model can accurately map the input data (such as monitoring parameters and combustible distribution) to the harmful gas distribution; finally, use the harmful gas diffusion prediction model to predict harmful gas diffusion, that is, input the current real-time monitoring data and combustible distribution information in the building, and predict the harmful gas diffusion situation in the predetermined future time zone, including gas types, concentration distribution, diffusion paths, etc., to form a harmful gas prediction distribution, which helps to judge which areas need to be evacuated first or smoke exhaust control is required, thus significantly improving the accuracy and safety of fire emergency response.

[0032] Step S400, based on the harmful gas prediction distribution, with the predetermined limit concentration in the evacuation passage as a constraint, aiming to minimize the overall harmful gas risk and the harmful gas risk in the evacuation passage, combined with the layout of smoke exhaust devices to optimize the smoke exhaust linkage control scheme, and output the optimal smoke exhaust linkage control scheme.

[0033] Step S400 further includes that the layout of the smoke exhaust devices includes several natural ventilation windows, several mechanical smoke exhaust devices and several differential pressure smoke exhaust devices, where each smoke exhaust device is marked with position coordinates.

[0034] Preferably, using the existing predicted distribution results of harmful gases and combining with the layout information of the smoke exhaust devices inside the building, optimize the design of the smoke exhaust system, that is, search for the optimal smoke exhaust linkage control plan, and output the optimal smoke exhaust linkage control plan to ensure that the concentration of harmful gases can be effectively reduced during a fire, guarantee the safety of the evacuation passage for personnel, and minimize the overall fire risk. Among them, the predicted distribution of harmful gases shows the concentration of harmful gases in different areas during a fire, especially in the evacuation passage. Specifically, the predetermined limit concentration refers to the concentration of harmful gases in some key areas (such as stairs, corridors, exits, etc.) in the evacuation passage must be lower than a certain limit value to ensure the safe evacuation of personnel. For example, during a fire, the concentration of CO must be maintained at a level harmless to the human body to avoid poisoning. This is used as a constraint condition for optimizing the smoke exhaust linkage control plan to ensure that the concentration of harmful gases in the evacuation passage does not exceed the standard during a fire and guarantee that personnel can pass safely.

[0035] Preferably, with the goal of minimizing the overall harmful gas risk and the harmful gas risk in the evacuation passage, the overall harmful gas risk refers to the impact of the concentration of harmful gases in all areas inside the building on the safety of personnel, while the harmful gas risk in the evacuation passage focuses on the harmful gas risk in the evacuation passage to ensure that personnel can avoid contact with high-concentration harmful gases during the evacuation process, especially the risks of oxygen deficiency and poisoning, that is, simultaneously reduce the concentration of harmful gases in the entire building and the concentration of harmful gases in the evacuation passage, thereby reducing the harm of the fire to human health and life safety; then combine with the layout of the smoke exhaust devices to search for the optimal smoke exhaust linkage control plan. Among them, the layout of the smoke exhaust devices includes several different types of smoke exhaust equipment, namely several natural ventilation windows (discharging smoke through opening windows or natural ventilation openings), several mechanical smoke exhaust equipment (such as smoke exhaust fans, forcibly discharging smoke through mechanical means), and several differential pressure smoke exhaust equipment (creating a differential pressure in the evacuation passage to maintain a negative pressure in this area and prevent smoke from entering the evacuation passage). Each smoke exhaust device is marked with position coordinates to help determine the contribution of different equipment to the smoke exhaust effect.

[0036] Preferably, the optimization of the smoke exhaust linkage control scheme refers to automatically adjusting the start / stop and power output of the smoke exhaust equipment according to the concentration of harmful gases during a fire and the layout of the smoke exhaust devices inside the building to achieve the best smoke exhaust effect. Specifically, the optimization goal is to achieve optimization in two aspects by controlling the linkage of the smoke exhaust equipment, namely, minimizing the concentration of harmful gases. By reasonably scheduling the smoke exhaust devices, the concentration of harmful gases in the building is minimized, especially in the evacuation routes; ensuring the safety of the evacuation routes for personnel, ensuring that the smoke exhaust system can effectively remove the harmful gases in the evacuation routes and ensuring the safe evacuation of personnel; finally, outputting the optimal smoke exhaust linkage control scheme, including the start / stop timing sequence of the smoke exhaust equipment, the operating status of the fans, and the opening and closing conditions of the smoke exhaust channels, etc., and can be adjusted in real time according to the fire situation to ensure that during the fire, the concentration of harmful gases in the building is effectively controlled, especially when people are evacuating, keeping the air in the evacuation routes fresh and safe and effectively reducing the fire fighting risks in the fire.

[0037] Further, step S400 further includes step S410 of randomly generating a plurality of simulated smoke exhaust linkage control schemes based on the regulation parameter thresholds of the plurality of natural ventilation windows, a plurality of mechanical smoke exhaust devices, and a plurality of differential pressure smoke exhaust devices; step S420 of constructing a smoke exhaust dynamic simulation plug-in by simulating the building structure characteristics and evacuation route layout of the target building and rendering the predicted distribution of harmful gases to the smoke exhaust dynamic simulation plug-in to generate a real-time smoke exhaust dynamic simulation plug-in; step S430 of performing simulated smoke exhaust according to the plurality of simulated smoke exhaust linkage control schemes respectively in the real-time smoke exhaust dynamic simulation plug-in to obtain a plurality of simulated smoke exhaust results; step S440 of screening the plurality of simulated smoke exhaust results with the predetermined limit concentration in the evacuation route as a constraint to obtain a plurality of qualified simulated smoke exhaust results, and performing an overall harmful gas risk and evacuation route harmful gas risk analysis on the plurality of qualified simulated smoke exhaust results to evaluate and determine a plurality of smoke exhaust fitness levels; step S450 of optimizing the smoke exhaust linkage control scheme according to the regulation parameter thresholds and the plurality of smoke exhaust fitness levels and outputting the optimal smoke exhaust linkage control scheme.

[0038] Preferably, during a fire, according to the different types of multiple smoke exhaust devices (such as natural ventilation windows, mechanical smoke exhaust devices, differential pressure smoke exhaust devices), set their control parameter thresholds (such as opening degree, wind speed, pressure, etc.) to randomly generate multiple simulated smoke exhaust linkage control schemes, covering different possible operating conditions and fire response requirements. Then, simulate and construct a smoke exhaust dynamic simulation plug-in to simulate how the smoke exhaust system operates during a fire, especially how to guide and remove smoke according to the building structure and the layout of evacuation routes during the fire development process. Specifically, the smoke exhaust dynamic simulation plug-in models according to the structure of the target building (such as the number of floors, the position of doors and windows, partition settings, etc.) and the design of evacuation routes (such as corridors, stairs, exits, etc.) to provide accurate smoke exhaust simulation.

[0039] Preferably, according to different simulated smoke exhaust linkage control schemes, the smoke exhaust dynamic simulation plug-in performs multiple simulated smoke exhausts, that is, simulates the smoke exhaust effect and the change of harmful gases under each scheme, and then obtains multiple simulated smoke exhaust results, including the change of harmful gas concentration in the evacuation route, the clearance of smoke, etc., demonstrating the effects of different smoke exhaust schemes. Then, according to the predetermined limit concentration of the evacuation route, screen out the smoke exhaust results that can ensure that the harmful gas concentration is lower than the safety limit as multiple qualified simulated smoke exhaust results, that is, the schemes that can ensure the safe evacuation of personnel; then conduct an overall harmful gas risk analysis (analyze the harmful gas concentration in each area of the whole building to ensure that the air quality in all areas meets the safety standards during a fire and reduce the harm of poisonous gas to human health) and an evacuation route harmful gas risk analysis (pay attention to the harmful gas concentration in the evacuation route to ensure that personnel will not be affected by high-concentration harmful gases during evacuation, such as CO concentration, smoke concentration, etc.) for the multiple qualified simulated smoke exhaust results, that is, evaluate the fitness of each scheme according to multiple simulated smoke exhaust results, including the degree to which the scheme can reduce the harmful gas concentration, especially the safety during personnel evacuation; finally, output the optimal smoke exhaust linkage control scheme, which can not only maximize the reduction of harmful gas concentration inside the building but also ensure the safety of personnel in the evacuation route, thereby significantly improving the efficiency and accuracy of fire emergency response.

[0040] Further, step S440 further includes step S441, randomly selecting a first qualified simulated smoke exhaust result, where the first qualified simulated smoke exhaust result includes a first non-evacuation passage simulated smoke exhaust result and a first evacuation passage simulated smoke exhaust result. The first non-evacuation passage simulated smoke exhaust result includes the type of harmful gas and the concentration of harmful gas, and the first evacuation passage simulated smoke exhaust result includes the gas diffusion area, the type of gas, and the gas concentration; step S442, performing weight configuration according to the type of harmful gas, and performing weighted calculation on the harmful gas concentration according to the weight configuration result to obtain the overall harmful gas risk coefficient; step S443, performing weighted calculation on the gas concentration according to the weight configuration result to obtain the initial harmful gas risk coefficient of the evacuation passage, compensating the initial harmful gas risk coefficient of the evacuation passage according to the gas diffusion area, and outputting the harmful gas risk coefficient of the evacuation passage; step S444, evaluating the first smoke exhaust fitness according to the overall harmful gas risk coefficient and the harmful gas risk coefficient of the evacuation passage, and adding it to the multiple smoke exhaust fitness values, where the smoke exhaust fitness is negatively correlated with the overall harmful gas risk coefficient and the harmful gas risk coefficient of the evacuation passage.

[0041] Preferably, randomly select one from multiple qualified simulated smoke exhaust results as the first qualified simulated smoke exhaust result, including the first non-evacuation passage simulated smoke exhaust result and the first evacuation passage simulated smoke exhaust result. The first non-evacuation passage simulated smoke exhaust result includes the type of harmful gas (such as CO, CO2, HCN, etc.) and the concentration of harmful gas (the concentration of harmful gas at different time and space points), and the first evacuation passage simulated smoke exhaust result includes the gas diffusion area (the spatial range of harmful gas diffusion), the type of gas (specific types of harmful gases), and the gas concentration (the gas concentration in these areas); different types of harmful gases have different degrees of harm to personnel. For example, CO (carbon monoxide) is a relatively dangerous toxic gas, while CO2 (carbon dioxide) is relatively less likely to cause immediate harm to the human body. According to the type of harmful gas, a weight is configured for each harmful gas to reflect the severity of its harm, and then the harmful gas concentration is weighted according to the weight configuration result to obtain the overall harmful gas risk coefficient reflecting the overall fire hazard.

[0042] Preferably, based on the gas concentrations at various points in the evacuation passage (according to the simulation results), the initial harmful gas risk coefficient of the evacuation passage is calculated by weighting these concentration values. Then, the initial harmful gas risk coefficient of the evacuation passage is compensated according to the gas diffusion area. Specifically, the distribution range of harmful gases in the evacuation passage (gas diffusion area) affects the risk coefficient. If the diffusion area of harmful gases is large, it means that more areas will be affected, the evacuation difficulty increases, and the risk coefficient should also increase accordingly. Then, the ratio of the gas diffusion area to the predetermined area is calculated, and the initial harmful gas risk coefficient of the evacuation passage is compensated according to the result, and the final harmful gas risk coefficient of the evacuation passage is output. Finally, according to the overall harmful gas risk coefficient and the harmful gas risk coefficient of the evacuation passage, the fitness of the smoke exhaust plan is evaluated to obtain the first smoke exhaust fitness, which is added to the multiple smoke exhaust fitness values. Among them, the higher the smoke exhaust fitness, the more effective the smoke exhaust plan is, which can reduce the concentration of harmful gases in the fire and ensure the safety of personnel. And the smoke exhaust fitness is negatively correlated with the overall harmful gas risk coefficient and the harmful gas risk coefficient of the evacuation passage, that is, when the overall harmful gas risk coefficient and the harmful gas risk coefficient of the evacuation passage are low, it means that the smoke exhaust plan is more effective and the smoke exhaust fitness is higher; while when these risk coefficients are high, it indicates that the effect of the smoke exhaust plan is poor and the smoke exhaust fitness is low. Then, the most suitable smoke exhaust plan for the current fire situation is judged to achieve the optimal fire emergency response, ensure the safe evacuation of personnel and reduce the health risks brought by the fire.

[0043] Further, step S450 further includes using the regulation parameter threshold as the optimization space and the predetermined limit concentration in the evacuation passage as the constraint, and using the genetic algorithm to optimize the smoke exhaust linkage control plan according to the multiple smoke exhaust fitness values until the predetermined convergence condition is reached, and outputting the plan with the maximum smoke exhaust fitness as the optimal smoke exhaust linkage control plan.

[0044] Preferably, taking the regulation parameter thresholds (control parameters of various smoke exhaust devices in the smoke exhaust system, such as the opening degree of natural ventilation windows, the wind speed of mechanical smoke exhaust devices, the pressure difference of differential pressure smoke exhaust devices, etc.) as the optimization space, that is, the value ranges of these control parameters, to explore different combinations of control parameters to find the optimal smoke exhaust control plan. Then, taking the predetermined limit concentration in the evacuation passage as the constraint, that is, the harmful gas concentration in the evacuation passage cannot exceed a certain safety limit value, to ensure that during a fire, the harmful gas concentration in the evacuation passage does not pose a threat to the safe evacuation of personnel. Then, based on the genetic algorithm, optimize the smoke exhaust linkage control plan for multiple smoke exhaust fitness values. Specifically, based on the current smoke exhaust fitness value (that is, the effect of the simulated smoke exhaust result), select the plans with higher fitness values for "reproduction", and "cross" the selected multiple smoke exhaust control plans, that is, exchange their regulation parameters to generate new smoke exhaust plans. Make a small amount of random changes to the crossed smoke exhaust plans to generate new mutant individuals to avoid falling into a local optimal solution. Then, evaluate the fitness of the newly generated smoke exhaust plans, select the plans with higher fitness values to retain, and form a new population. After multiple generations of iteration until the predetermined convergence condition is reached (for example, the fitness value no longer changes significantly after a certain number of iterations, or the set time limit is reached), finally output the plan with the maximum smoke exhaust fitness as the optimal smoke exhaust linkage control plan, which can minimize the harmful gas concentration, meet the safety concentration limit in the evacuation passage, and at the same time optimize the coordinated operation of various smoke exhaust devices to ensure the safety of personnel and effective smoke exhaust in the entire building during a fire, thereby improving the evacuation safety and emergency response efficiency.

[0045] Step S500, perform smoke exhaust control on the target building in the predetermined future time zone according to the optimal smoke exhaust linkage control plan.

[0046] Preferably, the optimal smoke exhaust linkage control scheme includes the optimal control parameters and scheduling strategies for multiple smoke exhaust devices (such as natural ventilation windows, mechanical smoke exhaust devices, differential pressure smoke exhaust devices). Applying the optimal smoke exhaust linkage control scheme to the actual fire emergency response, the smoke exhaust control of the target building in the predetermined future time zone is carried out, including adopting different strategies for smoke exhaust control at different development stages of the fire. That is, mainly relying on natural ventilation in the initial stage of the fire. As the fire spreads and the harmful gases increase, gradually start the mechanical smoke exhaust devices and adjust the working state of the differential pressure smoke exhaust devices. And according to the optimal smoke exhaust linkage control scheme, in the predetermined future time zone, automatically adjust the state of the smoke exhaust device according to the fire dynamic simulation and real-time monitoring data. For example, start or close the natural ventilation window to utilize the natural wind for smoke exhaust; start the mechanical smoke exhaust devices (such as fans, smoke exhaust pipes, etc.) to strongly discharge the smoke; adjust the differential pressure smoke exhaust device to maintain the negative pressure in the evacuation passage and prevent the smoke from entering the evacuation area; thereby effectively reducing the concentration of harmful gases in the evacuation passage, improving the safety of personnel evacuation, and ensuring that personnel can escape from the building smoothly and safely when a fire occurs.

[0047] In the above text, reference is made to Figure 1 The intelligent fire linkage control method for avoiding fire risks according to the embodiments of the present invention is described in detail. Next, reference will be made to Figure 2 Describe the intelligent fire linkage control system for avoiding fire risks according to the embodiments of the present invention.

[0048] The intelligent fire linkage control system for avoiding fire risks according to the embodiments of the present invention is used to solve the technical problems existing in the prior art, such as the fire alarm being difficult to adjust according to different building structures and fire scenarios, unable to predict the diffusion paths of fires and harmful gases in real time, resulting in the lack of pertinence of the emergency linkage control scheme and the lack of timeliness and accuracy of the fire emergency response. It achieves the technical effects of improving the timeliness and accuracy of the fire emergency response, reducing the risk of harmful gases in the evacuation passage and the overall fire safety risk. As Figure 2 shown, the intelligent fire linkage control system for avoiding fire risks includes: a fire dynamic simulator construction module 10, a combustible distribution acquisition module 20, a harmful gas prediction distribution generation module 30, a smoke exhaust linkage control scheme optimization module 40, and a smoke exhaust control module 50.

[0049] The fire dynamic simulator construction module 10 is used to simulate and construct a fire dynamic simulator of the target building based on the building structure characteristics, evacuation passage layout, and combustible distribution information of the target building; the coverage combustible distribution acquisition module 20 is used to, when a fire occurs, use a multi-sensor alarm device to obtain real-time monitoring parameters at fixed points, combine with the fire dynamic simulator to perform fire simulation in a predetermined future time zone, and obtain the coverage combustible distribution; the harmful gas prediction distribution generation module 30 is used to predict the diffusion of harmful gases in a predetermined future time zone based on the real-time monitoring parameters and the coverage combustible distribution, and generate a harmful gas prediction distribution; the smoke exhaust linkage control scheme optimization module 40 is used to, based on the harmful gas prediction distribution, with the predetermined limit concentration in the evacuation passage as a constraint, with the goal of minimizing the overall harmful gas risk and the harmful gas risk in the evacuation passage, combine with the smoke exhaust device layout to optimize the smoke exhaust linkage control scheme, and output the optimal smoke exhaust linkage control scheme; the smoke exhaust control module 50 is used to perform smoke exhaust control of the target building in the predetermined future time zone according to the optimal smoke exhaust linkage control scheme.

[0050] Next, the specific configuration of the fire dynamic simulator construction module 10 will be described in detail. The fire dynamic simulator construction module 10 further includes: The combustible distribution information includes the type, location, scale of combustibles, and the characteristics of harmful gases generated during combustion, where the characteristics of harmful gases include the type of harmful gases and the concentration of harmful gases generated per unit time.

[0051] Next, the specific configuration of the harmful gas prediction distribution generation module 30 will be described in detail. The harmful gas prediction distribution generation module 30 further includes: With the building structure characteristics and evacuation passage layout as conditional constraints, use big data to retrieve fire cases, collect a sample monitoring parameter set and a sample combustible distribution set, and label the harmful gas distribution in a predetermined historical time zone to obtain a sample harmful gas distribution set, where the time interval between the predetermined historical time zone and the predetermined future time zone is the same; Use the sample monitoring parameter set and the sample combustible distribution set as inputs, and the sample harmful gas distribution set as supervision, train the BP neural network until convergence, generate a harmful gas diffusion prediction model, and use the harmful gas diffusion prediction model to predict the diffusion of harmful gases in a predetermined future time zone based on the real-time monitoring parameters and the coverage combustible distribution, and output the harmful gas prediction distribution.

[0052] Next, the specific configuration of the smoke exhaust linkage control scheme optimization module 40 will be described in detail. The smoke exhaust linkage control scheme optimization module 40 further includes: The smoke exhaust device layout includes a number of natural ventilation windows, a number of mechanical smoke exhaust devices, and a number of differential pressure smoke exhaust devices, where each smoke exhaust device is marked with a position coordinate.

[0053] Next, the specific configuration of the smoke exhaust linkage control scheme optimization module 40 will be further described in detail. The smoke exhaust linkage control scheme optimization module 40 further includes: randomly generating a plurality of simulated smoke exhaust linkage control schemes based on the regulation parameter thresholds of the plurality of natural ventilation windows, a plurality of mechanical smoke exhaust devices, and a plurality of differential pressure smoke exhaust devices; simulating and constructing a smoke exhaust dynamic simulation plug-in based on the building structure characteristics and evacuation passage layout of the target building, and rendering the predicted distribution of harmful gases to the smoke exhaust dynamic simulation plug-in to generate a real-time smoke exhaust dynamic simulation plug-in; in the real-time smoke exhaust dynamic simulation plug-in, respectively performing simulated smoke exhaust according to the plurality of simulated smoke exhaust linkage control schemes to obtain a plurality of simulated smoke exhaust results; screening the plurality of simulated smoke exhaust results with a predetermined limit concentration in the evacuation passage as a constraint to obtain a plurality of qualified simulated smoke exhaust results, and performing an overall harmful gas risk and evacuation passage harmful gas risk analysis on the plurality of qualified simulated smoke exhaust results to evaluate and determine a plurality of smoke exhaust fitness degrees; optimizing the smoke exhaust linkage control scheme according to the regulation parameter thresholds and the plurality of smoke exhaust fitness degrees, and outputting the optimal smoke exhaust linkage control scheme.

[0054] Next, the specific configuration of the smoke exhaust linkage control scheme optimization module 40 will be further described in detail. The smoke exhaust linkage control scheme optimization module 40 further includes: randomly selecting a first qualified simulated smoke exhaust result, where the first qualified simulated smoke exhaust result includes a first non-evacuation passage simulated smoke exhaust result and a first evacuation passage simulated smoke exhaust result, where the first non-evacuation passage simulated smoke exhaust result includes the type of harmful gas and the concentration of harmful gas, and the first evacuation passage simulated smoke exhaust result includes the gas diffusion area, the type of gas, and the gas concentration; performing weight configuration according to the type of harmful gas, and performing weighted calculation on the concentration of harmful gas according to the weight configuration result to obtain an overall harmful gas risk coefficient; performing weighted calculation on the gas concentration according to the weight configuration result to obtain an initial evacuation passage harmful gas risk coefficient, and compensating the initial evacuation passage harmful gas risk coefficient according to the gas diffusion area to output an evacuation passage harmful gas risk coefficient; evaluating and obtaining a first smoke exhaust fitness degree according to the overall harmful gas risk coefficient and the evacuation passage harmful gas risk coefficient, and adding it to the plurality of smoke exhaust fitness degrees, where the smoke exhaust fitness degree is negatively correlated with the overall harmful gas risk coefficient and the evacuation passage harmful gas risk coefficient.

[0055] Next, the specific configuration of the smoke exhaust linkage control scheme optimization module 40 will be further described in detail. The smoke exhaust linkage control scheme optimization module 40 further includes: using the regulation parameter thresholds as the optimization space and the predetermined limit concentration in the evacuation passage as a constraint, and using the genetic algorithm to optimize the smoke exhaust linkage control scheme according to the plurality of smoke exhaust fitness degrees until a predetermined convergence condition is reached, and outputting the scheme with the maximum smoke exhaust fitness degree as the optimal smoke exhaust linkage control scheme.

[0056] The intelligent fire-fighting linkage control system for avoiding fire risks provided by the embodiments of the present invention can execute the intelligent fire-fighting linkage control method for avoiding fire risks provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0057] Although various references are made to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0058] The above specific implementation manners do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. An intelligent fire linkage control method for avoiding fire risks, characterized in that: Methods include: Based on the building structure characteristics, evacuation channel layout and combustible distribution information of the target building, a fire dynamic simulator of the target building is constructed; When a fire occurs, a multi-sensor alarm device is used to obtain real-time monitoring parameters at a fixed point, and a fire simulation is performed in a predetermined future time zone in combination with the fire dynamic simulator to obtain the distribution of covered combustibles; According to the real-time monitoring parameters and the distribution of covered combustibles, a harmful gas diffusion prediction is performed in a predetermined future time zone to generate a predicted distribution of harmful gases; Based on the predicted distribution of harmful gases, taking the predetermined limit concentration in the evacuation channel as a constraint, minimizing the overall harmful gas risk and the harmful gas risk in the evacuation channel as a goal, optimizing the smoke exhaust linkage control scheme in combination with the layout of the smoke exhaust device, and outputting the optimal smoke exhaust linkage control scheme; The smoke exhaust control of the target building in the predetermined future time zone is performed according to the optimal smoke exhaust linkage control scheme.

2. The intelligent fire linkage control method for avoiding fire risks according to claim 1 is characterized in that: The combustible material distribution information includes the type, location, scale and harmful gas characteristics generated during combustion of the combustible material, wherein the harmful gas characteristics include the type of harmful gas and the concentration of the harmful gas generated per unit time.

3. The intelligent fire linkage control method for avoiding fire risks according to claim 1 is characterized in that: According to the real-time monitoring parameters and the distribution of covered combustibles, the harmful gas diffusion prediction is performed in a predetermined future time zone to generate the predicted distribution of harmful gases, including: Taking the building structure characteristics and evacuation channel layout as conditional constraints, big data is used to retrieve fire cases, collect sample monitoring parameter sets and sample combustible distribution sets, and annotate the distribution of harmful gases in a predetermined historical time zone to obtain a sample harmful gas distribution set, wherein the time interval between the predetermined historical time zone and the predetermined future time zone is the same; Taking the sample monitoring parameter set and the sample combustible distribution set as input and the sample harmful gas distribution set as supervision, training the BP neural network until convergence to generate a harmful gas diffusion prediction model; The harmful gas diffusion prediction model is used to predict the harmful gas diffusion in a predetermined future time zone according to the real-time monitoring parameters and the distribution of covered combustibles, and the predicted distribution of harmful gases is output.

4. The intelligent fire linkage control method for avoiding fire risks according to claim 1 is characterized in that: The smoke exhaust device layout includes a plurality of natural ventilation windows, a plurality of mechanical smoke exhaust devices and a plurality of pressure difference smoke exhaust devices, wherein each smoke exhaust device is marked with a position coordinate.

5. The intelligent fire linkage control method for avoiding fire risks according to claim 4 is characterized in that: Based on the predicted distribution of harmful gases, with the predetermined limit concentration in the evacuation channel as a constraint, with the goal of minimizing the overall harmful gas risk and the harmful gas risk in the evacuation channel, the smoke exhaust linkage control scheme is optimized in combination with the layout of the smoke exhaust device, and the optimal smoke exhaust linkage control scheme is output, including: Randomly generate multiple simulated smoke exhaust linkage control schemes based on the control parameter thresholds of the plurality of natural ventilation windows, the plurality of mechanical smoke exhaust devices and the plurality of pressure difference smoke exhaust devices; Constructing a smoke exhaust dynamic simulation plug-in based on the architectural structure characteristics and evacuation channel layout of the target building, and rendering the predicted distribution of harmful gases to the smoke exhaust dynamic simulation plug-in to generate a real-time smoke exhaust dynamic simulation plug-in; In the real-time smoke exhaust dynamic simulation plug-in, simulated smoke exhaust is performed respectively according to the multiple simulated smoke exhaust linkage control schemes to obtain multiple simulated smoke exhaust results; Taking the predetermined limit concentration in the evacuation channel as a constraint, the multiple simulated smoke exhaust results are screened to obtain multiple qualified simulated smoke exhaust results, and the overall harmful gas risk and the evacuation channel harmful gas risk analysis are performed on the multiple qualified simulated smoke exhaust results to evaluate and determine multiple smoke exhaust adaptability; The smoke exhaust linkage control scheme is optimized according to the control parameter threshold and multiple smoke exhaust adaptability, and the optimal smoke exhaust linkage control scheme is output.

6. The intelligent fire linkage control method for avoiding fire risks according to claim 5 is characterized in that: The overall harmful gas risk and the harmful gas risk of the evacuation passage are analyzed for the multiple qualified simulated smoke exhaust results, and multiple smoke exhaust adaptability is evaluated and determined, including: Randomly select a first qualified simulated smoke exhaust result, wherein the first qualified simulated smoke exhaust result includes a first non-evacuation channel simulated smoke exhaust result and a first evacuation channel simulated smoke exhaust result, wherein the first non-evacuation channel simulated smoke exhaust result includes a harmful gas type and a harmful gas concentration, and the first evacuation channel simulated smoke exhaust result includes a gas diffusion area, a gas type, and a gas concentration; Perform weight configuration according to the type of harmful gas, and perform weighted calculation on the concentration of harmful gas according to the weight configuration result to obtain the overall harmful gas risk coefficient; Performing weighted calculation on the gas concentration according to the weight configuration result to obtain the initial evacuation channel harmful gas risk coefficient, compensating the initial evacuation channel harmful gas risk coefficient according to the gas diffusion area, and outputting the evacuation channel harmful gas risk coefficient; A first smoke exhaust fitness is obtained according to the overall harmful gas risk coefficient and the evacuation channel harmful gas risk coefficient, and is added to the multiple smoke exhaust fitnesses, wherein the smoke exhaust fitness is negatively correlated with the overall harmful gas risk coefficient and the evacuation channel harmful gas risk coefficient.

7. The intelligent fire linkage control method for avoiding fire risks according to claim 5 is characterized in that: Optimizing a smoke exhaust linkage control scheme according to the control parameter threshold and multiple smoke exhaust adaptability, and outputting the optimal smoke exhaust linkage control scheme, including: The control parameter threshold is used as the optimization space, the predetermined limit concentration in the evacuation channel is used as the constraint, and a genetic algorithm is used to optimize the smoke exhaust linkage control scheme according to the multiple smoke exhaust fitnesses until the predetermined convergence condition is reached. The scheme with the largest smoke exhaust fitness is output as the optimal smoke exhaust linkage control scheme.

8. An intelligent fire linkage control system to avoid fire risks, characterized in that: The system is used to implement the intelligent fire linkage control method for avoiding fire risks according to any one of claims 1 to 7, and the system includes: A fire dynamic simulator construction module is used to simulate and construct a fire dynamic simulator of a target building based on the building structure characteristics, evacuation channel layout and combustible distribution information of the target building; A module for acquiring distribution of covered combustibles is used to obtain real-time monitoring parameters at a fixed point using a multi-sensor alarm device when a fire occurs, and to perform fire simulation in a predetermined future time zone in combination with the fire dynamic simulator to obtain distribution of covered combustibles; A harmful gas prediction distribution generation module, used to predict the diffusion of harmful gases in a predetermined future time zone according to the real-time monitoring parameters and the distribution of covered combustibles, and generate a harmful gas prediction distribution; A smoke exhaust linkage control scheme optimization module is used to optimize the smoke exhaust linkage control scheme based on the predicted distribution of harmful gases, with the predetermined limit concentration in the evacuation channel as a constraint, with the goal of minimizing the overall harmful gas risk and the harmful gas risk in the evacuation channel, combined with the layout of the smoke exhaust device, and output the optimal smoke exhaust linkage control scheme; The smoke exhaust control module is used to perform smoke exhaust control of the target building in the predetermined future time zone according to the optimal smoke exhaust linkage control scheme.

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