Intelligent fire linkage control method and system for avoiding fire risks

Abstract

The present invention discloses an intelligent fire linkage control method and system for avoiding fire risks, which relates to the technical field related to fire linkage control. The method includes: simulating and constructing a fire dynamic simulator; obtaining real-time monitoring parameters and obtaining the distribution of covered combustibles; predicting the diffusion of harmful gases in a predetermined future time zone and generating a predicted distribution of harmful gases; 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; and performing smoke exhaust control of the target building in the predetermined future time zone. The method solves the technical problems existing in the prior art, such as the difficulty in adjusting fire alarms to different building structures and fire scenarios, the inability to predict the diffusion paths of fire and harmful gases in real time, resulting in a lack of pertinence in emergency linkage control schemes and insufficient timeliness and accuracy in fire emergency responses. The method achieves the technical effect 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.

Description

Technical Field

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

[0002] With the increasing number of complex building structures such as high-rise buildings and large commercial complexes, the functional layouts within buildings are becoming increasingly diverse, leading to an increase in the risk and complexity of fires. During fire emergency response, traditional fire linkage control systems primarily rely on preset fixed linkage logic and simple sensor alarm mechanisms. These systems typically rely on basic sensing devices such as smoke detectors and temperature sensors, combined with simple logical judgments to control the linkage of firefighting equipment such as smoke exhaust and sprinklers. These systems struggle to dynamically adjust to different building structures and fire scenarios, and are unable to predict fire development trends and the diffusion paths of hazardous gases in real time. Furthermore, they lack comprehensive consideration of key factors such as the specific building structure, evacuation route layout, and combustible material distribution. This results in a lack of optimization of emergency linkage control solutions, an inability to accurately respond to different fire scenarios, and a lack of timely and accurate emergency response. This increases the risk of hazardous gases in evacuation routes and overall fire safety risks, and fails to effectively reduce the risk of casualties and property damage.

[0003] Therefore, in the current relevant technologies, there are technical problems such as fire alarms are difficult to adjust to different building structures and fire scenarios, and the diffusion paths of fire and harmful gases cannot be predicted in real time, resulting in a lack of targeted emergency linkage control plans and insufficiently timely and accurate fire emergency responses. Summary of the Invention

[0004] This application solves the technical problems in the existing technology that fire alarms are difficult to adjust to different building structures and fire scenarios, and the diffusion paths of fire and harmful gases cannot be predicted in real time, resulting in a lack of targeted emergency linkage control solutions and insufficient timeliness and accuracy in fire emergency responses, by providing an intelligent fire linkage control method and system for avoiding fire risks. This achieves the technical effect 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] The present application provides an intelligent fire linkage control method for avoiding fire risks, the method comprising: simulating and constructing a fire dynamic simulator of a target building based on the architectural structure characteristics, evacuation channel layout and combustible material distribution information of the target building; when a fire occurs, using a multi-sensor alarm device to obtain real-time monitoring parameters at a fixed point, and combining the fire dynamic simulator to perform a fire simulation in a predetermined future time zone to obtain the distribution of covered combustible materials; predicting the diffusion of harmful gases in a predetermined future time zone based on the real-time monitoring parameters and the distribution of covered combustible materials, and generating a predicted distribution of harmful gases; based on the predicted distribution of harmful gases, with a predetermined limiting concentration in the evacuation channel as a constraint, and with the goal of minimizing the overall harmful gas risk and the harmful gas risk in the evacuation channel, optimizing the smoke exhaust linkage control scheme in combination with the smoke exhaust device layout, 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 also performs the following processing: the combustible material distribution information includes the type, location, scale and harmful gas characteristics generated during combustion, wherein the harmful gas characteristics include the type of harmful gas and the concentration of harmful gas generated per unit time.

[0007] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks also performs the following processing: taking the building structure characteristics and evacuation channel layout as conditional constraints, big data is used to retrieve fire cases, sample monitoring parameter sets and sample combustible distribution sets are collected, and the harmful gas distribution in the predetermined historical time zone is marked to obtain the sample harmful gas distribution set, wherein the time interval of 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, the BP neural network is trained until convergence to generate a harmful gas diffusion prediction model; using the harmful gas diffusion prediction model, the harmful gas diffusion prediction in the predetermined future time zone is performed according to the real-time monitoring parameters and the covering combustible distribution, and the harmful gas prediction distribution is output.

[0008] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks also performs the following processing: the smoke exhaust device layout includes several natural ventilation windows, several mechanical smoke exhaust equipment and several pressure difference smoke exhaust equipment, wherein each smoke exhaust device is marked with a location coordinate.

[0009] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks also performs the following processing: randomly generating multiple simulated smoke exhaust linkage control schemes based on the control parameter thresholds of the several natural ventilation windows, several mechanical smoke exhaust equipment and several pressure difference smoke exhaust equipment; constructing a smoke exhaust dynamic simulation plug-in based on the architectural structure characteristics and evacuation channel layout simulation 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 according to the multiple simulated smoke exhaust linkage control schemes to obtain multiple simulated smoke exhaust results; based on 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 evacuation channel harmful gas risk analysis are performed on the multiple qualified simulated smoke exhaust results to evaluate and determine multiple smoke exhaust adaptabilities; optimizing the smoke exhaust linkage control scheme according to the control parameter thresholds and multiple smoke exhaust adaptabilities, and outputting the optimal smoke exhaust linkage control scheme.

[0010] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks also performs the following processing: randomly selecting 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; performing weight configuration according to the harmful gas type, and performing weighted calculation on the harmful gas concentration 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 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; evaluating the first smoke exhaust fitness based on the overall harmful gas risk coefficient and the evacuation channel harmful gas risk coefficient, and adding it 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.

[0011] In a possible implementation, the intelligent fire linkage control method for avoiding fire risks also performs the following processing: using the control parameter threshold as the optimization space, and the predetermined limit concentration in the evacuation channel as the constraint, using a genetic algorithm, optimizing the smoke exhaust linkage control scheme according to the multiple smoke exhaust fitnesses until the predetermined convergence conditions are reached, and outputting the scheme with the largest smoke exhaust fitness 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, which is used to simulate and construct a fire dynamic simulator of the target building based on the architectural structure characteristics, evacuation channel layout and combustible material distribution information of the target building; a coverage combustible material distribution acquisition module, which is used to use a multi-sensor alarm device to obtain real-time monitoring parameters at a fixed point when a fire occurs, and perform fire simulation in a predetermined future time zone in combination with the fire dynamic simulator to obtain the coverage combustible material distribution; a harmful gas prediction distribution generation module, which 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 material distribution, and generate a harmful gas prediction distribution; a smoke exhaust linkage control scheme optimization module, which is used to optimize the smoke exhaust linkage control scheme based on the harmful gas prediction distribution, with a 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, and with the layout of the smoke exhaust device, to output the optimal smoke exhaust linkage control scheme; a smoke exhaust control module, which 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.

[0013] The intelligent fire linkage control method and system proposed in this application for avoiding fire risks is intended to simulate and construct a fire dynamic simulator; obtain real-time monitoring parameters and the distribution of covered combustibles; predict the diffusion of harmful gases in a predetermined future time zone and generate a predicted distribution of harmful gases; optimize the smoke exhaust linkage control scheme based on the layout of the smoke exhaust device and output the optimal smoke exhaust linkage control scheme; and perform smoke exhaust control for target buildings in the predetermined future time zone. This solves the technical problems in the existing technology that fire alarms are difficult to adjust for different building structures and fire scenarios, and that the diffusion paths of fire and harmful gases cannot be predicted in real time, resulting in a lack of pertinence in emergency linkage control schemes and insufficient timeliness and accuracy in fire emergency responses. It achieves the technical effect of improving the timeliness and accuracy of fire emergency responses, reducing the risk of harmful gases in evacuation routes, and reducing overall fire safety risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A flow chart of the intelligent fire linkage control method for avoiding fire risks provided in an embodiment of the present application.

[0016] Figure 2Schematic diagram of the structure of an intelligent fire linkage control system for avoiding fire risks provided in an embodiment of the present application.

[0017] Explanation of the reference numerals: fire dynamic simulator construction module 10 , combustible material distribution acquisition module 20 , harmful gas prediction distribution generation module 30 , smoke exhaust linkage control scheme optimization module 40 , smoke exhaust control module 50 . DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are 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 skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The embodiment of the present application provides an intelligent fire linkage control method to avoid fire risks, such as Figure 1 As shown, the method includes:

[0022] Step S100 , based on the architectural structure characteristics, evacuation passage layout and combustible material distribution information of the target building, a fire dynamic simulator of the target building is simulated and constructed.

[0023] Step S100 further includes 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.

[0024] Preferably, based on the actual physical properties and fire behavior characteristics of the building, computer modeling and simulation technology is used to create a virtual model that can dynamically simulate the development of fire and the diffusion process of harmful gases, that is, a fire dynamic simulator of the target building. The fire dynamic simulator can predict the fire spread path, changes in harmful gas concentrations and their potential impact on personnel evacuation and safety under different fire scenarios. Specifically, the architectural structural characteristics of the target building include the number of floors of the building, room layout, materials of walls and partitions, location of doors and windows, ventilation system and opening size, etc., which determine the propagation path, speed and temperature distribution of flames and smoke, and affect the development dynamics of the fire; the evacuation channel layout includes the location of the safety exit, the width and length of the evacuation channel, the layout of stairs and elevators, etc., which determines the feasibility and efficiency of personnel evacuation and is also the key to the optimization of the smoke exhaust linkage control strategy. Key parameters; combustible material distribution information includes combustible material type (such as wood, plastic, textile, paper, etc., different materials have different ignition points and combustion characteristics), location and scale (accurately calibrate the distribution of combustibles in the building, determine the potential combustion source of the fire and the direction of fire spread), and harmful gas characteristics produced during combustion, among which harmful gas characteristics include harmful gas type (such as carbon monoxide, hydrogen cyanide, etc.) and the concentration of harmful gas produced per unit time; combined with this information, simulation tools such as computational fluid dynamics (CFD) and fire dynamics models (such as FDS) are used to simulate the temperature field, smoke diffusion, harmful gas concentration distribution, etc. of the fire at different time nodes, and build a fire dynamic simulator for the target building, which can reflect the dynamic changes of fire development and harmful gas diffusion in real time, and continuously calibrate the model based on the data obtained by fire scene sensors to improve the accuracy of the prediction.

[0025] Step S200: 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.

[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, real-time monitoring of environmental data at the fire scene, and combined with a fire dynamic simulator to predict and analyze future fire scenes, and obtain the distribution of covered combustibles. Specifically, sensor types may include: temperature sensors, which monitor temperature changes in the fire area to help identify the location of the fire and the development of the fire; smoke sensors, which detect the smoke concentration in the air to determine the spread of the fire; harmful gas sensors (such as CO, CO2, HCN, SO2, etc.), which monitor the concentration of harmful gases in the air and capture changes in toxic gases produced by the fire in real time; PM sensors, which measure the concentration of particulate matter in the air and evaluate the degree of pollution caused by the fire; wind speed sensors, which monitor wind speed and determine the impact of airflow changes on the spread of fire during the fire. The acquired real-time monitoring data is then input into a fire dynamic simulator to carry out fire simulation in a predetermined future time zone, i.e., to simulate and predict the fire in the predetermined future time zone, including simulating the spread speed and temperature distribution of the fire at different time nodes, predicting the concentration distribution of harmful gases (such as CO, HCN, etc.) during the fire, and then calculating the impact of harmful gases on the evacuation path and safe area of ​​personnel. Based on the distribution of combustible materials in the building and combined with the fire simulator, the speed and direction of fire spread are predicted, and then the scope of fire spread in the future time zone, the diffusion of harmful gases and the impact on personnel evacuation are determined. Finally, the distribution of covered combustible materials is generated to more accurately judge the dangerous area when the fire occurs and predict the fire behavior, thereby providing a more intelligent and accurate fire control strategy and optimizing the smoke exhaust linkage control and evacuation path planning.

[0027] Step S300 , predicting the diffusion of harmful gases in a predetermined future time zone based on the real-time monitoring parameters and the distribution of covered combustibles, and generating a predicted distribution of harmful gases.

[0028] Preferably, by using real-time monitoring data (such as temperature, smoke, harmful gas concentration, etc.) and combustible material distribution information inside the building, combined with the predictive 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., in future time zones is predicted, and the concentration distribution map of these harmful gases at different time points (i.e., the predicted distribution of harmful gases) is generated. Specifically, the environmental parameters of the fire scene, temperature, smoke concentration, combustible gas concentration (such as CO, CO2, HCN, etc.), as well as the building layout and combustible material distribution information are input into the fire dynamic simulator to simulate the generation of harmful gases during the fire. and diffusion process, in which different types of combustibles release different types and concentrations of harmful gases during combustion. The generated harmful gases will diffuse to the surroundings, and the diffusion of harmful gases is affected by many factors such as fire intensity, building structure, and ventilation conditions. Then, a model is constructed to predict the concentration changes of gases at different time nodes and spatial positions and the diffusion of harmful gases, and then the predicted distribution of harmful gases is determined, which is used to show the concentration changes of harmful gases at the fire scene at different time nodes after the fire occurs. It also includes the harmful gas concentrations in various areas, such as whether evacuation passages and important exit areas are affected by high concentrations of gases, thereby improving the intelligence and responsiveness of the entire fire linkage control system.

[0029] Furthermore, step S300 also includes step S310, using the building structure characteristics and evacuation channel layout as conditional constraints, performing fire case retrieval using big data, collecting sample monitoring parameter sets and sample combustible distribution sets, and marking the harmful gas distribution in the predetermined historical time zone to obtain the sample harmful gas distribution set, wherein the time interval between the predetermined historical time zone and the predetermined future time zone is the same; step S320, using 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, and generating a harmful gas diffusion prediction model; step S330, using the harmful gas diffusion prediction model, performing harmful gas diffusion prediction in the predetermined future time zone based on the real-time monitoring parameters and the covered combustible distribution, and outputting the harmful gas prediction distribution.

[0030] Preferably, the structural characteristics of the target building (such as the number of floors, room layout, evacuation channel settings, etc.) and the evacuation channel layout (including exits, stairs, corridors, etc.) are used as conditions, and big data technology is combined to retrieve existing fire cases. These cases provide monitoring data and fire development patterns under real fire scenes, and sample monitoring parameter sets and sample combustible distribution sets are collected. The sample monitoring parameter set includes real-time monitoring data under different fire conditions, such as temperature, smoke concentration, and concentrations of various harmful gases (such as CO, HCN, etc.). The sample combustible distribution set records the distribution of combustibles under different fire scenes, including the type, location, scale, etc. of combustibles. The distribution of harmful gases in the predetermined historical time zone is then marked, that is, the historical fire data is traced back, the distribution of harmful gases at that time is marked, and the sample harmful gas distribution set is obtained, and the time interval of the predetermined historical time zone and the predetermined future time zone is the same.

[0031] Preferably, the collected sample monitoring parameter set and sample combustible distribution set are used as input data, and the historical time zone harmful gas distribution set is used as supervision data, and the BP (back propagation) neural network is used for training to capture the complex relationship between the input data and the diffusion of harmful gases. 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 signal of the 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 distribution of harmful gases; finally, the harmful gas diffusion prediction model is used to predict the diffusion of harmful gases, that is, the current real-time monitoring data and the combustible distribution information in the building are input to predict the diffusion of harmful gases in the predetermined future time zone, including gas type, concentration distribution and diffusion path, etc., to form a harmful gas prediction distribution, which helps to determine which areas need priority evacuation or smoke exhaust control, thereby significantly improving the accuracy and safety of fire emergency response.

[0032] Step S400, 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, and combining the layout of the smoke exhaust device to optimize the smoke exhaust linkage control scheme, and output the optimal smoke exhaust linkage control scheme.

[0033] Step S400 further includes 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.

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

[0035] Preferably, the goal is to minimize the overall harmful gas risk and the harmful gas risk in the evacuation channel. The overall harmful gas risk refers to the impact of the harmful gas concentration in all areas inside the building on the safety of personnel, and the harmful gas risk in the evacuation channel focuses on the harmful gas risk in the evacuation channel, ensuring that personnel can avoid exposure to high concentrations of harmful gases during the evacuation process, especially the risk of oxygen deficiency and poisoning, that is, simultaneously reducing the concentration of harmful gases in the entire building and the concentration of harmful gases in the evacuation channel, thereby reducing the harm of fire to human health and life safety; then the smoke exhaust device layout is combined to optimize the smoke exhaust linkage control scheme, wherein the smoke exhaust device layout includes several different types of smoke exhaust equipment, namely several natural ventilation windows (exhausting smoke by opening windows or natural ventilation holes), several mechanical smoke exhaust equipment (such as smoke exhaust fans, which forcibly exhaust smoke by mechanical means) and several pressure difference smoke exhaust equipment (by setting a pressure difference in the evacuation channel to maintain negative pressure in the area and prevent smoke from entering the evacuation channel). Each smoke exhaust device is marked with a location coordinate 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 and stop and power output of the smoke exhaust equipment according to the concentration of harmful gases at the time of 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 two aspects of optimization by controlling the linkage of the smoke exhaust equipment, namely, minimizing the concentration of harmful gases, and reducing the concentration of harmful gases in the building as much as possible through the reasonable scheduling of the smoke exhaust devices, especially in the evacuation passage; ensuring the safety of the personnel evacuation passage, ensuring that the smoke exhaust system can effectively remove the harmful gases in the evacuation passage, and ensuring that personnel can evacuate safely; and finally outputting the optimal smoke exhaust linkage control scheme, including the start and stop timing of the smoke exhaust equipment, the operating status of the fan and the opening and closing status of the smoke exhaust passage, etc., and can be adjusted in real time according to the fire situation to ensure that the concentration of harmful gases in the building is effectively controlled during the fire, especially when the crowd is evacuated, keeping the air in the evacuation passage fresh and safe, and effectively reducing the fire risk in the fire.

[0037] Furthermore, step S400 also includes step S410, randomly generating multiple simulated smoke exhaust linkage control schemes based on the control parameter thresholds of the several natural ventilation windows, several mechanical smoke exhaust devices and several pressure difference smoke exhaust devices; step S420, constructing a smoke exhaust dynamic simulation plug-in based on the architectural structure characteristics and evacuation channel layout simulation 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, in the real-time smoke exhaust dynamic simulation plug-in, performing simulated smoke exhaust according to the multiple simulated smoke exhaust linkage control schemes respectively to obtain multiple simulated smoke exhaust results; step S440, screening the multiple simulated smoke exhaust results based on the predetermined limit concentration in the evacuation channel as a constraint to obtain multiple qualified simulated smoke exhaust results, and performing overall harmful gas risk and evacuation channel harmful gas risk analysis on the multiple qualified simulated smoke exhaust results, and evaluating and determining multiple smoke exhaust adaptabilities; step S450, optimizing the smoke exhaust linkage control scheme based on the control parameter thresholds and multiple smoke exhaust adaptabilities, and outputting the optimal smoke exhaust linkage control scheme.

[0038] Preferably, when a fire occurs, according to the different types of multiple smoke exhaust devices (such as natural ventilation windows, mechanical smoke exhaust equipment, pressure difference smoke exhaust equipment), their control parameter thresholds (such as opening, wind speed, pressure, etc.) are set to randomly generate multiple simulated smoke exhaust linkage control schemes, covering different possible operating conditions and fire response requirements, and then simulate and construct a smoke exhaust dynamic simulation plug-in to simulate how the smoke exhaust system operates during the fire process, especially how to guide and remove smoke according to the building structure and the layout of the evacuation passage during the development of the fire. Specifically, the smoke exhaust dynamic simulation plug-in is modeled according to the structure of the target building (such as the number of floors, door and window locations, partition settings, etc.) and the design of the evacuation passage (such as corridors, stairs, exits, etc.), thereby providing 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 changes of harmful gases under each scheme, and then obtains multiple simulated smoke exhaust results, including the changes in the concentration of harmful gases in the evacuation channel, the removal of smoke, etc., showing the effects of different smoke exhaust schemes. Then, according to the predetermined limit concentration of the evacuation channel, the smoke exhaust results that can ensure that the concentration of harmful gases is lower than the safety limit are screened out as multiple qualified simulated smoke exhaust results, that is, schemes that can ensure the safe evacuation of personnel; and then the overall harmful gas risk (analyzing the harmful gas concentrations in each area of ​​the entire building to ensure the fire) of the multiple qualified simulated smoke exhaust results is analyzed. The system evaluates the adaptability of each scheme based on multiple simulated smoke exhaust results, including how much the scheme can reduce the concentration of harmful gases inside the building, especially the safety of personnel during evacuation. Ultimately, it outputs the optimal smoke exhaust linkage control scheme, which not only maximizes the reduction of harmful gas concentrations inside the building, but also ensures the safety of personnel in the evacuation passages, thereby significantly improving the efficiency and accuracy of fire emergency response.

[0040] Furthermore, step S440 also includes step S441, randomly selecting 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 the harmful gas type and harmful gas concentration, and the first evacuation channel simulated smoke exhaust result includes the gas diffusion area, gas type and gas concentration; step S442, performing weight configuration according to the harmful gas type, 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 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; step S444, evaluating the overall harmful gas risk coefficient and the evacuation channel harmful gas risk coefficient to obtain a first smoke exhaust fitness, and adding it 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.

[0041] Preferably, one is randomly selected from multiple qualified simulated smoke exhaust results as the first qualified simulated smoke exhaust result, including the first non-evacuation channel simulated smoke exhaust result and the first evacuation channel simulated smoke exhaust result, wherein the first non-evacuation channel simulated smoke exhaust result includes the harmful gas type (such as CO, CO2, HCN, etc.) and the harmful gas concentration (the harmful gas concentration at different time and space points), and the first evacuation channel simulated smoke exhaust result includes the gas diffusion area (the spatial range of harmful gas diffusion), gas type (specific harmful gas type) and gas concentration (gas concentration in these areas); different types of harmful gases have different degrees of harm to personnel, for example, CO (carbon monoxide) is a more 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 assigned to each harmful gas to reflect the severity of its harm, and then the harmful gas concentration is weighted calculated according to the weight configuration result, so as to obtain an overall harmful gas risk coefficient reflecting the overall fire hazard.

[0042] Preferably, according to the gas concentration at each point in the evacuation channel (according to the simulation results), these concentration values ​​are weightedly calculated to obtain the initial evacuation channel harmful gas risk coefficient, and then the initial evacuation channel harmful gas risk coefficient is compensated according to the gas diffusion area. Specifically, the distribution range of the harmful gas in the evacuation channel (gas diffusion area) has an impact on the risk coefficient. If the area where the harmful gas diffuses is larger, 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 evacuation channel harmful gas risk coefficient is compensated according to the result, and the final evacuation channel harmful gas risk coefficient is output. Risk coefficient; Finally, based on the overall harmful gas risk coefficient and the evacuation channel harmful gas risk coefficient, the smoke exhaust scheme's adaptability is evaluated to obtain the first smoke exhaust adaptability, which is then added to multiple smoke exhaust adaptabilities. The higher the smoke exhaust adaptability, the more effective the smoke exhaust scheme is, which can reduce the concentration of harmful gases in the fire and ensure personnel safety. Furthermore, the smoke exhaust adaptability is negatively correlated with the overall harmful gas risk coefficient and the evacuation channel harmful gas risk coefficient. That is, when the overall harmful gas risk coefficient and the evacuation channel harmful gas risk coefficient are low, the smoke exhaust scheme is more effective and the smoke exhaust adaptability is higher. When these risk coefficients are high, the smoke exhaust scheme is less effective and the smoke exhaust adaptability is lower. The most suitable smoke exhaust scheme for the current fire situation is then determined to achieve the most optimized fire emergency response, ensure the safe evacuation of personnel, and reduce health risks caused by the fire.

[0043] Furthermore, step S450 also includes using the control parameter threshold as the optimization space, the predetermined limit concentration in the evacuation channel as the constraint, and using a genetic algorithm to optimize the smoke exhaust linkage control scheme according to the multiple smoke exhaust fitnesses until the predetermined convergence condition is reached, and outputting the scheme with the largest smoke exhaust fitness as the optimal smoke exhaust linkage control scheme.

[0044] Preferably, the control parameter thresholds (control parameters of various smoke exhaust devices in the smoke exhaust system, such as the opening of natural ventilation windows, the wind speed of mechanical smoke exhaust devices, the pressure difference of pressure difference smoke exhaust devices, etc.) are used as the optimization space, that is, the value range of these control parameters, to explore the combination of different control parameters to find the optimal smoke exhaust control scheme, and then the predetermined limit concentration in the evacuation channel is used as a constraint, that is, the concentration of harmful gases in the evacuation channel cannot exceed a certain safety limit, to ensure that when a fire occurs, the concentration of harmful gases in the evacuation channel does not pose a threat to the safe evacuation of personnel; then, based on the genetic algorithm, the smoke exhaust linkage control scheme is optimized 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), the scheme with higher fitness is selected for "breeding", and the selected multiple smoke exhaust control schemes are selected. Perform "crossover", that is, exchange their control parameters to generate new smoke exhaust schemes, make a small amount of random changes to the crossed smoke exhaust schemes, and generate new mutant individuals to avoid falling into the local optimal solution. Then evaluate the fitness of the newly generated smoke exhaust schemes, select the schemes with higher fitness to retain, and form a new population. After multiple generations of iteration, until the predetermined convergence conditions are reached (for example, the fitness value no longer changes significantly after a certain number of iterations, or the set time limit is reached), the scheme with the largest smoke exhaust fitness is finally output as the optimal smoke exhaust linkage control scheme, which can minimize the concentration of harmful gases and meet the safety concentration limit in the evacuation channel. At the same time, it optimizes the coordinated work of various smoke exhaust equipment to ensure the safety of personnel and effective smoke exhaust in the entire building during a fire, thereby improving evacuation safety and emergency response efficiency.

[0045] Step S500 , performing smoke exhaust control of the target building in the predetermined future time zone according to the optimal smoke exhaust linkage control scheme.

[0046] Preferably, the optimal smoke exhaust linkage control scheme includes optimal control parameters and scheduling strategies for multiple smoke exhaust devices (such as natural ventilation windows, mechanical smoke exhaust devices, and pressure differential smoke exhaust devices). The optimal smoke exhaust linkage control scheme is applied to actual fire emergency response to perform smoke exhaust control on target buildings in a predetermined future time zone, including adopting different strategies for smoke exhaust control at different stages of fire development, that is, relying mainly on natural ventilation in the early stage of the fire, and gradually starting mechanical smoke exhaust devices as the fire spreads and harmful gases increase, and adjusting the working state of the pressure differential smoke exhaust devices. In addition, according to the optimal smoke exhaust linkage control scheme, within the predetermined future time zone, the state of the smoke exhaust device is automatically adjusted according to the fire dynamic simulation and real-time monitoring data, for example, starting or closing natural ventilation windows to utilize natural wind to exhaust smoke; starting mechanical smoke exhaust devices (such as fans, smoke exhaust ducts, etc.) to forcefully exhaust smoke; adjusting the pressure differential smoke exhaust devices to maintain negative pressure in the evacuation channel to prevent smoke from entering the evacuation area; thereby effectively reducing the concentration of harmful gases in the evacuation channel, improving the safety of personnel evacuation, and ensuring that personnel can smoothly and safely escape from the building when a fire occurs.

[0047] In the above, refer to Figure 1 The intelligent fire linkage control method for avoiding fire risks according to an embodiment of the present invention is described in detail. Figure 2 An intelligent fire linkage control system for avoiding fire risks according to an embodiment of the present invention is described.

[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 difficulty in adjusting fire alarms for different building structures and fire scenarios, the inability to predict the diffusion paths of fire and harmful gases in real time, resulting in a lack of pertinence in emergency linkage control solutions and insufficient timeliness and accuracy in fire emergency responses. It achieves the technical effect of improving the timeliness and accuracy of fire emergency responses, reducing the risk of harmful gases in evacuation routes and overall fire safety risks. Figure 2 As shown, the intelligent fire linkage control system for avoiding fire risks includes: a fire dynamic simulator construction module 10, a combustible material 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 architectural structure characteristics, evacuation channel layout and combustible material distribution information of the target building; the covered combustible material distribution acquisition module 20 is used to use a multi-sensor alarm device to obtain real-time monitoring parameters at a fixed point when a fire occurs, and perform fire simulation in a predetermined future time zone in combination with the fire dynamic simulator to obtain the covered combustible material distribution; the harmful gas predicted distribution generation module 30 is used to predict the harmful gas diffusion in a predetermined future time zone based on the real-time monitoring parameters and the covered combustible material distribution, and generate a harmful gas predicted distribution; the smoke exhaust linkage control scheme optimization module 40 is used to optimize the smoke exhaust linkage control scheme based on the harmful gas predicted distribution, 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, and with the layout of the smoke exhaust device, to 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] The specific configuration of the fire dynamics simulator construction module 10 will be described in detail below. The fire dynamics simulator construction module 10 further includes: the combustible material distribution information includes the type, location, scale of the combustible material, and the characteristics of the harmful gases generated during combustion, wherein the harmful gas characteristics include the type of harmful gas and the concentration of the harmful gas generated per unit time.

[0051] The specific configuration of the harmful gas prediction distribution generation module 30 will be described in detail below. The harmful gas prediction distribution generation module 30 further includes: taking the building structure characteristics and evacuation channel layout as conditional constraints, performing fire case retrieval with big data, collecting sample monitoring parameter sets and sample combustible distribution sets, and marking the harmful gas distribution in the predetermined historical time zone to obtain the sample harmful gas distribution set, wherein the time interval of the predetermined historical time zone and the predetermined future time zone is the same; taking the sample monitoring parameter set and sample combustible distribution set as input, and taking the sample harmful gas distribution set as supervision, training the BP neural network until convergence, and generating a harmful gas diffusion prediction model; using the harmful gas diffusion prediction model, performing harmful gas diffusion prediction in the predetermined future time zone according to the real-time monitoring parameters and the covering combustible distribution, and outputting the harmful gas prediction distribution.

[0052] The following describes in detail the specific configuration of the smoke exhaust linkage control solution optimization module 40. The smoke exhaust linkage control solution optimization module 40 further includes: 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 location coordinate.

[0053] The specific configuration of the smoke exhaust linkage control scheme optimization module 40 will be described in detail below. The smoke exhaust linkage control scheme optimization module 40 further includes: randomly generating multiple simulated smoke exhaust linkage control schemes based on the control parameter thresholds of the multiple natural ventilation windows, multiple mechanical smoke exhaust devices, and multiple pressure difference smoke exhaust devices; constructing a smoke exhaust dynamic simulation plug-in based on the architectural structure characteristics and evacuation channel layout simulation 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 according to the multiple simulated smoke exhaust linkage control schemes to obtain multiple simulated smoke exhaust results; based on 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 multiple qualified simulated smoke exhaust results are analyzed for overall harmful gas risk and evacuation channel harmful gas risk, and multiple smoke exhaust adaptability is evaluated and determined; based on the control parameter thresholds and multiple smoke exhaust adaptability, the smoke exhaust linkage control scheme is optimized and the optimal smoke exhaust linkage control scheme is output.

[0054] The specific configuration of the smoke exhaust linkage control scheme optimization module 40 will be described in detail below. The smoke exhaust linkage control scheme optimization module 40 further includes: randomly selecting 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; performing weight configuration according to the harmful gas type, performing weighted calculation on the harmful gas concentration 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 channel harmful gas risk coefficient, compensating the initial evacuation channel harmful gas risk coefficient according to the gas diffusion area, and outputting an evacuation channel harmful gas risk coefficient; evaluating a first smoke exhaust fitness based on the overall harmful gas risk coefficient and the evacuation channel harmful gas risk coefficient, and adding it 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.

[0055] The specific configuration of the smoke exhaust linkage control scheme optimization module 40 will be described in detail below. The smoke exhaust linkage control scheme optimization module 40 further includes: using the control parameter threshold as the optimization space and the predetermined limit concentration within the evacuation channel as a constraint, utilizing a genetic algorithm to optimize the smoke exhaust linkage control scheme based on the multiple smoke exhaust fitnesses until a predetermined convergence condition is reached, and outputting the scheme with the highest smoke exhaust fitness as the optimal smoke exhaust linkage control scheme.

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

[0057] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and 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 distinguishing each other and are not used to limit the scope of protection of the present invention.

[0058] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. An intelligent fire linkage control method for avoiding fire risks, characterized in that: Methods include: Based on the architectural structure characteristics, evacuation route layout and combustible material 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 combined with the fire dynamic simulator, a fire simulation is performed in a predetermined future time zone to obtain the distribution of covered combustibles; Perform harmful gas diffusion prediction in a predetermined future time zone based on the real-time monitoring parameters and the distribution of covered combustibles to generate a predicted distribution of harmful gases; Based on the predicted distribution of harmful gases, with the predetermined limit concentration in the evacuation channel as a constraint, and 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; 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: The harmful gas diffusion prediction is performed in a predetermined future time zone based on the real-time monitoring parameters and the distribution of covered combustible materials to generate a predicted distribution of harmful gases, including: Using the building structure characteristics and evacuation route layout as conditional constraints, fire case retrieval is performed using big data to collect sample monitoring parameter sets and sample combustible material distribution sets, and the distribution of harmful gases in a predetermined historical time zone is annotated to obtain a sample harmful gas distribution set, wherein the time intervals between the predetermined historical time zone and the predetermined future time zone are the same; Taking the sample monitoring parameter set and the sample combustible material 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, and with the goal of minimizing the overall harmful gas risk and the harmful gas risk in the evacuation channel, a 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 generating 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; A smoke exhaust dynamic simulation plug-in is constructed based on the architectural structure characteristics and evacuation channel layout of the target building, and the predicted distribution of harmful gases is rendered 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; The plurality of simulated smoke exhaust results are screened based on a predetermined limit concentration within the evacuation passage to obtain a plurality of qualified simulated smoke exhaust results, and the plurality of qualified simulated smoke exhaust results are analyzed for overall harmful gas risk and evacuation passage harmful gas risk to evaluate and determine a plurality of smoke exhaust adaptability; An optimization of a smoke exhaust linkage control scheme is performed according to the control parameter threshold and a plurality of smoke exhaust adaptabilities, 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: Conduct an overall harmful gas risk analysis and an evacuation channel harmful gas risk analysis on the multiple qualified simulated smoke exhaust results, and evaluate and determine multiple smoke exhaust adaptability, including: Randomly selecting 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 the type and concentration of harmful gases, and the first evacuation channel simulated smoke exhaust result includes the gas diffusion area, gas type, and gas concentration; Perform weight allocation according to the type of harmful gas, and perform weighted calculation on the harmful gas concentration based on the weight allocation 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 based on 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: Taking the control parameter threshold as the optimization space and the predetermined limit concentration in the evacuation channel as the constraint, a genetic algorithm is used to optimize the smoke exhaust linkage control scheme according to the multiple smoke exhaust fitnesses until a 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 that avoids fire risks is characterized by: 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 route layout, and combustible material distribution information of the target building; A module for acquiring the 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 a fire simulation in a predetermined future time zone in combination with the fire dynamic simulator to obtain the distribution of covered combustibles; A harmful gas prediction distribution generation module is used to predict the diffusion of harmful gases in a predetermined future time zone based on 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 for optimizing the smoke exhaust linkage control scheme based on the predicted distribution of harmful gases, with a 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, and in combination with the layout of the smoke exhaust device, to 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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