A fire ventilation control method, device, system and storage medium

Abstract

This invention relates to the field of fire ventilation system technology, specifically a fire ventilation control method, device, system, and storage medium. The control method includes the following steps: fire detection, using fire sensors installed in the building to detect the occurrence of a fire; fire location, determining the specific location of the fire based on feedback information from the fire sensors; and fan control, dynamically adjusting the operating status of the ventilation fans according to the fire location and the structure of the building's ventilation system. This invention, by introducing an intelligent and dynamic control method, achieves rapid response and effective operation of the fire ventilation system during a fire. Combining modern sensor technology, artificial intelligence algorithms, and an Internet of Things platform, the system can monitor the fire situation in real time and react quickly, optimizing ventilation strategies, removing smoke, introducing fresh air, and ensuring air quality and personnel safety within the building.

Description

Technical Field

[0001] The present invention relates to the technical field of fire ventilation systems, and in particular to a fire ventilation control method, device, system and storage medium. Background Art

[0002] With the continuous development of modern urban construction, the height and complexity of buildings are gradually increasing, and the threat of fire to life and property is becoming increasingly serious. Especially in complex structures such as high-rise buildings, commercial complexes and industrial plants, smoke spreads rapidly after a fire, which greatly hinders the evacuation and rescue of personnel. Therefore, how to effectively control and eliminate fire smoke and ensure the air quality and personnel safety in buildings has become an important technical problem that needs to be solved in the field of building fire protection.

[0003] Traditional fire protection systems mainly rely on equipment such as fire alarms, sprinkler systems and fire extinguishers. Although they can slow down the spread of fire to a certain extent, they still have shortcomings in smoke emission and fresh air input after the fire occurs.

[0004] When a fire occurs, the rapid spread of smoke will not only cause suffocation and poisoning of personnel, but also affect the evacuation of personnel and the rescue of firefighters. When a disaster occurs, the design and control of the fire ventilation system inside the building is very important. Existing fire ventilation systems are usually operated by manual control or simple preset programs, and cannot be dynamically adjusted according to the real-time situation of the fire, resulting in the inability to quickly and effectively exhaust smoke and introduce fresh air when a fire occurs. Summary of the invention

[0005] The present invention provides a fire ventilation control method, device, system and storage medium to solve the technical problem that the ventilation control of the existing fire ventilation system is not timely and effective.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] In a first aspect, a fire ventilation control method is provided for use in a building, the control method comprising the following steps:

[0008] Fire detection, using fire sensors installed in buildings to detect the occurrence of fire;

[0009] Fire location: Determine the specific location of the fire based on the feedback information from the fire sensor;

[0010] Fan control, dynamically adjusting the operating status of ventilation fans according to the fire location and the building ventilation system structure to optimize smoke exhaust and fresh air input;

[0011] Air valve control, automatically adjusting the opening and closing status of air valves in the ventilation system according to the development of the fire and the smoke concentration in each area of ​​the building;

[0012] Data feedback: real-time monitoring and feedback of the temperature, smoke concentration and gas composition at the fire scene, and dynamic adjustment of ventilation strategies;

[0013] For personnel evacuation, cooperate with the fire emergency evacuation system to provide evacuation route guidance to personnel through intelligent indicator lights, broadcasting systems or mobile devices.

[0014] In a second aspect, a fire ventilation control device is provided for use in a building, the control device comprising:

[0015] Fire sensor module, used to monitor the occurrence of fire;

[0016] A fire locating module, used to receive and process the data of the fire sensor module to determine the specific location of the fire, wherein the fire locating module is integrated with a processor;

[0017] a fan control module, for adjusting the operation of the ventilation fan according to the calculation result of the processor;

[0018] A damper control module, used for adjusting the opening and closing state of dampers in the ventilation system according to the calculation result of the processor;

[0019] a communication module, used to realize wireless communication between the control device and an external control system; and

[0020] The personnel evacuation device is used to cooperate with the fire emergency evacuation system to provide evacuation route guidance to personnel through intelligent indicator lights, broadcasting systems or mobile devices.

[0021] In a third aspect, a fire ventilation control system is provided for use in a building, wherein the control system comprises the fire ventilation control device as described above, and further comprises:

[0022] Central control server, used to centrally manage and coordinate multiple fire ventilation control devices, record fire ventilation data, and provide data analysis and decision support;

[0023] Wireless communication network, used to achieve real-time data transmission between modules;

[0024] The central control server is configured as follows:

[0025] Dynamically generate ventilation control strategies and adjust the operating status of fans and dampers in real time according to fire trends;

[0026] Generate a fire heat map based on the data from the fire sensor module to visually display the distribution of fire and smoke;

[0027] Record fan and damper operation data during each fire ventilation process and generate ventilation reports; and

[0028] Use data mining algorithms to analyze historical ventilation data and optimize future ventilation strategies.

[0029] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program runs on a processor, the processor executes the fire ventilation control method as described above.

[0030] The beneficial effects of the present invention are:

[0031] The present invention achieves rapid response and effective operation of the fire ventilation system when a fire occurs by introducing an intelligent and dynamic control method. Combined with modern sensor technology, artificial intelligence algorithms and the Internet of Things platform, the system can monitor the fire situation in real time and respond quickly, optimize ventilation strategies, remove smoke, introduce fresh air, and ensure the air quality and personnel safety in the building. At the same time, the intelligent evacuation system provides personalized evacuation route guidance, greatly improving the evacuation efficiency and safety of personnel. Through the present invention, the shortcomings of traditional fire ventilation systems in fire response are solved, and an efficient and reliable fire ventilation control solution is provided, which has broad application prospects.

[0032] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flow chart of a fire ventilation control method in one embodiment of the present invention;

[0034] Figure 2 This is a flow chart of fan control in one embodiment of the present invention;

[0035] Figure 3 This is a flow chart of air valve control in one embodiment of the present invention;

[0036] Figure 4 This is a flow chart of controlling the air valve opening in one embodiment of the present invention;

[0037] Figure 5 A flowchart of adaptive optimization in one embodiment of the present invention;

[0038] Figure 6 is a structural diagram of a control device in one embodiment of the present invention;

[0039] Figure 7 This is a structural diagram of a fire sensor module in one embodiment of the present invention;

[0040] Figure 8 FIG. 4 is an architecture diagram of a control system in one embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. In addition, it should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] The disclosure below provides many different embodiments or examples for implementing different structures of the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. Of course, they are merely examples and are not intended to limit the present invention. In addition, the present invention may repeat reference numbers and / or reference letters in different examples, and such repetition is for the purpose of simplicity and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides examples of various specific models / series, but those of ordinary skill in the art may be aware of the application of other models and / or the use scenarios of other series.

[0043] The present invention provides the following preferred embodiments: Example

[0044] In order to solve the problem in the prior art that the fire ventilation system cannot promptly and effectively exhaust smoke and introduce fresh air when a fire occurs, this embodiment provides a fire ventilation control method, which can realize dynamic and intelligent ventilation control when a fire occurs.

[0045] refer to Figure 1 As shown, the fire ventilation control method of this embodiment includes the following steps:

[0046] S100, fire detection, using fire sensors installed in buildings to detect the occurrence of fire;

[0047] S200, fire location, determining the specific location of the fire based on the feedback information from the fire sensor;

[0048] S300, fan control, dynamically adjusts the operating status of ventilation fans according to the fire location and the building ventilation system structure to optimize smoke exhaust and fresh air input;

[0049] S400, air valve control, automatically adjusts the opening and closing status of air valves in the ventilation system according to the fire development and smoke concentration in each area of ​​the building;

[0050] S500, data feedback, real-time monitoring and feedback of the temperature, smoke concentration and gas composition at the fire scene, dynamic adjustment of ventilation strategy;

[0051] S600, personnel evacuation, cooperate with the fire emergency evacuation system to provide evacuation route guidance to personnel through intelligent indicator lights, broadcasting systems or mobile devices.

[0052] Specifically, in the fire detection stage, fire sensors installed in the building are used to detect the occurrence of fire. These sensors include smoke sensors, temperature sensors and carbon monoxide sensors. The models can be selected from the market with stable performance and high sensitivity, such as Honeywell's smoke sensor HM200, temperature sensor TMP36, carbon monoxide sensor MQ-7, etc. Through these sensors, the occurrence of fire and its early changes can be accurately monitored in real time.

[0053] Furthermore, in the fire location phase, the feedback information from the fire sensor is used to determine the specific location of the fire. The fire location module integrated in the central control system conducts a comprehensive analysis of the sensor data to determine the location of the fire. This process utilizes multi-point data fusion algorithms in the sensor network, such as the Kalman filter algorithm and the Bayesian estimation method, to improve the accuracy and real-time performance of fire location.

[0054] Furthermore, in the fan control stage, the operating status of the ventilation fan is dynamically adjusted according to the fire location and the structure of the building's ventilation system to optimize smoke exhaust and fresh air input. By calculating the fire location and smoke concentration distribution in real time, the fan operating parameters are dynamically adjusted to ensure rapid smoke exhaust and effective fresh air input. Furthermore, artificial intelligence algorithms, such as the LSTM model in deep learning, are used to predict the development trend of the fire, adjust the fan operating status in advance, and prevent the spread of the fire.

[0055] During the damper control stage, the opening and closing status of the dampers in the ventilation system are automatically adjusted according to the development of the fire and the smoke concentration in each area of ​​the building.

[0056] It is important to understand that the data feedback phase monitors and feeds back the temperature, smoke concentration, and gas composition of the fire scene in real time, and dynamically adjusts the ventilation strategy. Through the IoT platform, sensor data is uploaded to the central control server in real time for big data analysis and modeling. For example, the Hadoop framework is used to process large amounts of real-time data, and machine learning algorithms such as random forests or support vector machines are applied for pattern recognition and trend prediction to dynamically optimize ventilation strategies.

[0057] Furthermore, during the evacuation phase, the fire emergency evacuation system can be used to provide evacuation route guidance to personnel through intelligent indicator lights, broadcasting systems or mobile devices. Intelligent indicator lights can use LED display technology, connect to the central control system, and update evacuation route information in real time. The broadcasting system can promptly convey evacuation instructions to personnel in the building through the public broadcasting network. Mobile device applications can develop dedicated fire evacuation apps to provide users with optimal evacuation route recommendations through GPS positioning and wireless communication networks.

[0058] It is understandable that the various stages and features in this embodiment are interconnected to form an integrated intelligent fire ventilation control system. For example, when the fire sensor detects a fire, the system immediately starts the fire location module to determine the specific location, and triggers the fan and air valve control module to adjust the operating status of the ventilation equipment. The data feedback module continuously collects and analyzes on-site data, optimizes the ventilation strategy in real time, and ensures rapid smoke discharge and fresh air input. The personnel evacuation module dynamically adjusts the evacuation path guidance based on real-time data to ensure the safe evacuation of personnel. Example

[0059] In order to solve the problem that the existing fire ventilation system cannot quickly and effectively exhaust smoke and introduce fresh air when a fire occurs, this embodiment further refines the specific steps of fan control so that it can better implement optimized ventilation strategies in fire situations.

[0060] refer to Figure 2 As shown, fan control includes the following steps:

[0061] S301, establish a mathematical model of the building ventilation system, denoted as V=g(L,η,ΔP), where L is the length of the ventilation duct, η is the fan efficiency, and ΔP is the duct pressure drop;

[0062] S302, dynamically calculating the fan operating parameters η and ΔP according to the fire location and smoke concentration distribution to ensure rapid smoke discharge and effective fresh air input;

[0063] S303. Use artificial intelligence algorithms to predict the development trend of fire and adjust the operating status of the fan in advance to prevent the spread of fire.

[0064] In this embodiment, the fan control step first includes establishing a mathematical model of the building ventilation system, denoted as V=g(L,η,ΔP), where L is the length of the ventilation duct, η is the fan efficiency, and ΔP is the duct pressure drop. The establishment of this model needs to take into account the complex ventilation duct layout inside the building and the air flow characteristics in different areas. Through detailed mapping and parameter collection of the actual ventilation system in the building, an accurate mathematical model can be constructed. This model can not only reflect the physical structure of the ventilation system, but also adapt to different fire conditions and smoke diffusion states through parameter adjustment.

[0065] Furthermore, according to the fire location and smoke concentration distribution, the fan operating parameters η and ΔP are dynamically calculated to ensure rapid smoke discharge and effective input of fresh air. When a fire occurs, the system first obtains the smoke concentration distribution data of the specific location of the fire and its surroundings through the sensor network. These data are transmitted wirelessly to the central control system. The system uses a dynamic calculation method to adjust the fan operating parameters in combination with the mathematical model of the ventilation system. It should be understood that by adjusting the fan efficiency η and the duct pressure drop ΔP, the fan air volume and air pressure can be accurately controlled, thereby optimizing smoke discharge and fresh air input. For example, the fan efficiency η can be achieved by adjusting the operating frequency of the motor, while the duct pressure drop ΔP can be controlled by adjusting the opening of the air valve.

[0066] It is understandable that in this embodiment, the artificial intelligence algorithm is used to predict the development trend of the fire and adjust the operating status of the fan in advance to prevent the spread of the fire. Specifically, the system can use an LSTM (long short-term memory) network model based on deep learning to train historical fire data and establish a prediction model for the development trend of the fire. When a fire occurs, the sensor data collected by the system in real time is input into the prediction model, and the model outputs the development trend of the fire in the future. This prediction result is used to adjust the operating status of the fan in advance, such as increasing the speed of the fan to improve the smoke exhaust efficiency, or adjusting the direction of the fan to guide fresh air into the affected area.

[0067] Furthermore, the control algorithm in the system can achieve precise control of the fan operating status. For example, the system can dynamically calculate the optimal fan operating parameters based on the real-time monitored smoke concentration and fire temperature. The calculation process can be optimized using a genetic algorithm, that is, in each time step, multiple possible fan operating parameter combinations are generated based on the current smoke concentration distribution and ventilation system status, and the optimal combination is selected as the operating parameter for the next time step by evaluating the ventilation effect of each combination.

[0068] It should be understood that the control of the ventilation system is not just a simple parameter adjustment, but also involves complex system coordination. In order to achieve the best ventilation effect, this embodiment adopts a distributed control system (DCS), and each fan and damper node has independent control capabilities and is coordinated through a central control system. The central control system communicates with each node through industrial Ethernet or wireless network, sending control instructions and receiving feedback data in real time.

[0069] The benefit of this embodiment is that by introducing an intelligent and dynamic fan control method, rapid response and effective ventilation in fire situations are achieved. This distributed control method not only improves the response speed of the system, but also enhances the reliability and fault tolerance of the system. Example

[0070] According to the fire ventilation control method described in the above-mentioned embodiment, this embodiment further optimizes the specific steps of air valve control so that it can achieve precise air flow control in fire situations.

[0071] As shown in reference 3, in this embodiment, the air valve control includes the following steps:

[0072] S401, using a control algorithm to adjust the air valve opening and closing state in real time according to smoke concentration and temperature;

[0073] S402, controlling the air valve opening, and adjusting the air valve opening through a control algorithm to optimize the air flow in each area of ​​the building;

[0074] S403, automatically adjust the opening and closing status of air valves in different areas according to the real-time data of fire development to ensure effective smoke discharge and prevent its spread;

[0075] The control algorithm formula is as follows:

[0076] ,

[0077] Among them, θ(t) is the air valve opening, e(t) is the smoke concentration error, Kp, Ki, and Kd are control coefficients used to adjust the response speed of the control system.

[0078] Specifically, this embodiment is dedicated to using a control algorithm to adjust the opening and closing state of the air valve in real time according to the smoke concentration and temperature. To achieve this goal, the system monitors the smoke concentration and temperature changes in each area in real time through multiple smoke sensors and temperature sensors installed in the building. The data collected by the sensors is transmitted to the central control system through a wireless communication network. The central control system uses these real-time data and adopts a PID control algorithm to adjust the opening and closing state of the air valve.

[0079] Furthermore, the air valve opening is controlled and adjusted by a control algorithm to optimize the air flow in various areas of the building.

[0080] It is important to understand that the smoke concentration error e(t) is the difference between the current measured smoke concentration and the preset ideal concentration. Through the PID control algorithm, the air valve opening can be dynamically adjusted according to the actual smoke concentration error to ensure that the smoke in each area can be discharged quickly and effectively while introducing fresh air.

[0081] Furthermore, according to the real-time data of the fire development, the opening and closing status of the air valves in different areas are automatically adjusted to ensure the effective discharge of smoke and prevent its spread. Specifically, the central control system determines the spread trend of the fire and the distribution of smoke concentration in each area by comprehensively analyzing the real-time data from different areas. Based on these data, the system uses a distributed control strategy to dynamically adjust the opening and closing status of the air valves in each area. For example, when the smoke concentration in a certain area reaches the warning threshold, the system opens the air valve in that area to increase the smoke exhaust; at the same time, according to the situation in other areas, the air valve opening is adjusted in time to prevent the smoke from spreading to safe areas.

[0082] It is understandable that this embodiment further optimizes the adaptive adjustment mechanism of the air valve control. In each time step, the system dynamically adjusts the air valve opening according to the real-time monitoring data and control algorithm, and records the adjusted air flow state. Through the feedback control mechanism, the control parameters of the air valve are continuously optimized to ensure that the system is always in the best operating state during the fire. The implementation of the adaptive adjustment mechanism can be achieved by online adjustment and optimization of the control parameters through intelligent optimization methods such as genetic algorithms or particle swarm optimization algorithms, thereby improving the robustness and adaptability of the system.

[0083] The benefit of this embodiment is that through the intelligent and dynamic damper control method, rapid response and precise control in the event of a fire are achieved. Combining modern sensor technology, PID control algorithm and adaptive optimization mechanism, the system can monitor the fire situation in real time and respond quickly, optimize the damper opening and closing state, and ensure rapid smoke discharge and fresh air input. This method not only improves the efficiency and reliability of the fire ventilation system, but also enhances the air quality and personnel safety inside the building. Example

[0084] In order to be able to timely and effectively adjust the real-time opening of the air valve, this embodiment further optimizes the control steps of the air valve opening, so that it can achieve accurate air flow control in fire conditions and improve the response speed and stability of the system.

[0085] refer to Figure 4 As shown, the steps of controlling the air valve opening include:

[0086] S4021, data setting, setting the initial air valve opening θ0, adjustment amplitude α, adjustment frequency ω and phase offset φ, and obtaining the current smoke concentration Csmoke(t) and temperature T(t) data;

[0087] S4022, real-time monitoring, continuously monitor the smoke concentration and temperature in different areas of the building, and obtain real-time data through sensors;

[0088] S4023, dynamic adjustment, using real-time data, dynamically adjust the opening of the air valve according to the following formula:

[0089] ,

[0090] Among them, θ(t0) is the real-time opening of the air valve, and F(Csmoke(t), T(t)) is an adaptation function used to adjust the real-time opening of the air valve. The specific form is:

[0091] ,

[0092] Among them, Csmokethreshold and Tthreshold are the trigger thresholds of smoke concentration and temperature respectively, and Csmokemax and Tmax are the maximum smoke concentration and maximum temperature respectively;

[0093] S4024, feedback control, in each time step, adjusting the real-time opening of the air valve according to the real-time opening of the air valve calculated above, and recording the adjusted air flow state;

[0094] S4025, adaptive optimization, according to the air flow state, using an adaptive algorithm to adjust the adjustment amplitude α, the adjustment frequency ω and the phase offset φ to optimize the ventilation effect;

[0095] S4026, Iteration: Repeat the steps of real-time monitoring and adaptive optimization to continuously optimize the real-time opening of the air valve and ventilation strategy during the fire to ensure that the air quality and temperature in the building remain within a safe range.

[0096] In this embodiment, the step of controlling the air valve opening first includes data setting. The initial air valve opening θ0, adjustment amplitude α, adjustment frequency ω and phase offset φ, and obtaining the current smoke concentration Csmoke(t) and temperature T(t) data. The initial air valve opening θ0 is usually set according to the ventilation requirements and design specifications of the building, and the adjustment amplitude α, adjustment frequency ω and phase offset φ are determined through experiments and simulation optimization to ensure that the air valve state can be flexibly adjusted under various fire scenarios.

[0097] Furthermore, in the real-time monitoring stage, the system continuously monitors the smoke concentration and temperature in different areas of the building and obtains real-time data through the sensor network. These sensors include high-precision smoke sensors and temperature sensors, and the models can be selected from products with stable performance and high sensitivity on the market, such as Honeywell's smoke sensor HM200 and temperature sensor TMP36. The data collected by the sensors is transmitted to the central control system through a wireless communication network, and the system determines the development of the fire and the air quality of each area based on these real-time data.

[0098] It should be understood that during the dynamic adjustment phase, the system uses real-time monitoring data to dynamically calculate the real-time opening of the air valve according to the following formula:

[0099] ,

[0100] Among them, θ(t0) is the real-time opening of the air valve, and F(Csmoke(t), T(t)) is an adaptation function used to adjust the real-time opening of the air valve. The specific form is:

[0101] ,

[0102] In this way, the system can dynamically adjust the real-time opening of the air valve according to the real-time monitored smoke concentration and temperature data, ensuring that the smoke can be discharged quickly and the air flow is optimized.

[0103] Furthermore, in the feedback control stage, the system adjusts the actual opening of the air valve according to the air valve opening calculated above in each time step, and records the adjusted air flow state. Through this real-time adjustment and feedback mechanism, the system can continuously monitor and optimize the opening and closing state of the air valve to adapt to the dynamic changes at the fire scene. It should be understood that feedback control not only improves the response speed of the system, but also enhances the stability of the system, avoiding energy waste and uneven air flow caused by excessive opening and closing of the air valve.

[0104] It is understandable that in the adaptive optimization stage, the system uses an adaptive algorithm to adjust the control parameters based on the feedback of air flow data to optimize the ventilation effect. The adaptive algorithm can use intelligent optimization methods such as genetic algorithms or particle swarm optimization algorithms to adjust and optimize the control parameters online to improve the robustness and adaptability of the system. For example, the genetic algorithm simulates the natural selection process, generates multiple parameter combinations, evaluates their ventilation effects, selects the optimal combination for the next generation of parameter adjustment, and gradually improves the system performance.

[0105] The benefit of this embodiment is that through the intelligent and dynamic damper control method, rapid response and precise control in the event of a fire are achieved. Combining modern sensor technology, control algorithms and adaptive optimization mechanisms, the system can monitor the fire situation in real time and respond quickly, optimize the damper opening and closing state, and ensure rapid smoke discharge and fresh air input. This method not only improves the efficiency and reliability of the fire ventilation system, but also enhances the air quality and personnel safety inside the building. Example

[0106] In order to solve the problem in the prior art that the fire ventilation system cannot efficiently and accurately optimize the air valve opening when a fire occurs, this embodiment further refines the adaptive optimization steps so that it can adjust the air valve parameters with minimum error in fire conditions to ensure that the system operates in the best state.

[0107] refer to Figure 5 As shown, the adaptive optimization includes the following steps:

[0108] S40251. Calculate the effect of the current air valve adjustment on smoke concentration and temperature, and evaluate the adjustment effect through the following error function:

[0109] ,

[0110] Among them, Csmokesetpoint and Tsetpoint are the ideal smoke concentration and temperature setting values ​​respectively;

[0111] S40252. According to the size of E(t) in the error function, adjust the adjustment amplitude α, the adjustment frequency ω and the value of the phase shift φ so that they tend to the minimum error.

[0112] In this embodiment, the adaptive optimization step first includes calculating the impact of the current damper adjustment on the smoke concentration and temperature. Specifically, in each time step, the system obtains the smoke concentration and temperature data of each area in the building in real time through the sensor network, and records the air flow state after adjusting the damper opening. In order to evaluate the damper adjustment effect, the system uses the following error function for evaluation:

[0113] ,

[0114] Among them, Csmokesetpoint and Tsetpoint are the ideal smoke concentration and temperature set points, respectively. These set points are usually determined according to the ventilation requirements and safety standards of the building to ensure that the air quality and temperature are controlled within a safe range in the event of a fire. It should be understood that the size of the error function E(t) directly reflects the control effect of the current damper adjustment on the smoke concentration and temperature. The smaller the error, the closer the adjustment is to the ideal state.

[0115] Furthermore, the system dynamically adjusts the adjustment amplitude α, adjustment frequency ω and phase offset φ according to the size of E(t) in the error function, so that it tends to the minimum error. In order to achieve this goal, the system uses intelligent optimization methods such as genetic algorithms or particle swarm optimization algorithms to evaluate and optimize parameter combinations by simulating the natural selection process. Specifically, the system generates multiple different parameter combinations, each of which corresponds to a damper adjustment strategy. By evaluating the error function values ​​of these combinations, the combination with the smallest error is selected as the next generation of parameter combinations, and the adjustment parameters are gradually optimized to reduce system errors.

[0116] It should be understood that the optimization of the adjustment amplitude α, adjustment frequency ω and phase shift φ not only involves minimizing the error of a single time step, but also needs to consider the system stability and response speed during the entire fire period. For example, the adjustment of the adjustment frequency ω needs to ensure the rapid response of the system at different stages of fire development, while the optimization of the adjustment amplitude α needs to take into account the stability of the system to avoid oscillation caused by over-adjustment.

[0117] Furthermore, during the adaptive optimization process, the system can also introduce machine learning algorithms to analyze and model historical fire data. By training deep learning models, such as LSTM networks, the system can predict the development trend of fires and adjust the damper parameters in advance to improve the system's predictability and response efficiency. It is understandable that combining model training with historical data and online adjustment of real-time data can achieve accurate response to complex fire scenarios and ensure that the air quality and temperature in the building are always within a safe range. Example

[0118] In order to solve the problem in the prior art that the fire ventilation control device cannot respond promptly and effectively and guide the evacuation of personnel when a fire occurs, this embodiment further optimizes the structure and function of the fire ventilation control device so that it can achieve precise positioning, intelligent control and effective evacuation in the event of a fire.

[0119] refer to Figure 6 As shown, in this embodiment, the fire ventilation control device includes multiple modules, and each module works in coordination through a processor and a communication system. First, the fire sensor module is used to detect the occurrence of a fire. This module can select a smoke sensor and a temperature sensor with high sensitivity and high reliability on the market, such as Honeywell's HM200 series smoke sensor and TMP36 temperature sensor. The sensor module monitors the smoke concentration and temperature of each area in the building in real time, and transmits the data to the fire location module.

[0120] Furthermore, the fire location module is used to receive and process the data from the fire sensor module to determine the specific location of the fire. The module integrates a high-performance processor, such as an ARM Cortex-M4 or higher processor, to ensure rapid processing of large amounts of sensor data. Through multi-point data analysis and triangulation positioning algorithms, the system can accurately locate the source of the fire, generate a fire location map, and update fire dynamic information in real time.

[0121] It is important to understand that the fan control module adjusts the operating state of the ventilation fan according to the calculation results of the processor. This module is connected to the ventilation fan system in the building and can control the start and stop of the fan and the wind speed adjustment. By monitoring the development of the fire in real time, the system dynamically adjusts the operation mode of the fan to ensure rapid smoke exhaust in the early stage of the fire to prevent smoke from spreading, and introduce fresh air after the fire stabilizes.

[0122] The air valve control module is used to adjust the opening and closing status of the air valves in the ventilation system according to the calculation results of the processor. This module is connected to each air valve in the building and adjusts the air valve opening in real time through control algorithms, such as PID control algorithms, to optimize the air flow in each area. The specific implementation of the air valve control module can ensure accurate control and feedback of the opening and closing of the air valve by using high-precision stepper motors and sensors.

[0123] Furthermore, the communication module is used to realize wireless communication between the control device and the external control system. The module can use wireless communication technologies such as LoRa, Zigbee or Wi-Fi to ensure the real-time and stability of data transmission. Through the communication module, the fire ventilation control device can exchange data with the building's central control system, fire monitoring center and personnel evacuation system to coordinate fire emergency response.

[0124] The personnel evacuation device is used to cooperate with the fire emergency evacuation system to provide evacuation route guidance to personnel through intelligent indicator lights, broadcasting systems or mobile devices. Intelligent indicator lights can use LED display technology to dynamically update the evacuation route according to the fire location. The broadcasting system can guide personnel to evacuate the fire scene quickly and safely through preset voice prompts. Mobile devices can receive real-time evacuation information and navigation guidance by installing a dedicated APP.

[0125] It should be understood that this embodiment can further combine big data and artificial intelligence technology to improve the intelligence level of fire ventilation control devices. For example, the system can use the historical fire data of the building for model training, and improve the accuracy of fire prediction and response through machine learning algorithms such as deep neural network (DNN) or long short-term memory (LSTM) network. Through big data analysis, the system can also optimize fire emergency plans and improve overall emergency response capabilities.

[0126] The benefit of this embodiment is that through the collaborative work of multiple modules and intelligent control, rapid response and precise control in the event of a fire are achieved. Combining modern sensor technology, control algorithms, wireless communication technology and artificial intelligence, the system can monitor the fire situation in real time, accurately locate the source of the fire, and dynamically adjust the ventilation system and air valve opening; through intelligent indicator lights, broadcasting systems and mobile devices, it can effectively guide personnel evacuation and improve the safety of people in the building. Example

[0127] In order to solve the problem in the prior art that the fire ventilation control device cannot fully and accurately detect the fire situation when a fire occurs, this embodiment further optimizes the configuration of the fire sensor module so that it can detect fire in multiple dimensions and multiple parameters, thereby improving the system's early warning capability and response accuracy.

[0128] refer to Figure 7 As shown, in this embodiment, the fire sensor module includes a smoke sensor, a temperature sensor and a carbon monoxide sensor, which are used to monitor the occurrence of fire. The smoke sensor is used to detect the concentration of smoke particles in the air, the temperature sensor is used to monitor the change of ambient temperature, and the carbon monoxide sensor is used to detect the concentration of carbon monoxide gas produced by the fire. These sensors are combined together to fully and accurately sense the occurrence and development of fire.

[0129] Furthermore, the choice of smoke sensor can consider the use of highly sensitive photoelectric smoke sensors, such as Honeywell's HM200 series. This series of sensors has the characteristics of fast response and high accuracy, and can quickly detect the generation of smoke in the early stage of a fire. The temperature sensor can be selected from the TMP36 model, which has the advantages of high accuracy and low power consumption, and is suitable for real-time monitoring of ambient temperature changes. The carbon monoxide sensor can use an electrochemical sensor, such as Figaro's TGS5042, which has high sensitivity and high selectivity and can accurately detect carbon monoxide concentrations.

[0130] It is important to understand that these sensors process and analyze data through an integrated processor. The processor can be a high-performance model, such as the ARM Cortex-M4, to ensure that large amounts of sensor data can be processed quickly and efficiently. The processor is not only responsible for data collection, but also performs multi-parameter comprehensive analysis, determines the specific location and severity of the fire through built-in algorithms, and transmits the results to other modules.

[0131] Furthermore, the data of the fire sensor module is transmitted to the central control system through the communication module. The communication module can use wireless communication technologies such as LoRa, Zigbee or Wi-Fi to ensure the real-time and reliability of data transmission. Through seamless connection with the central control system, the fire sensor module can update the fire data in real time and assist the system to respond quickly.

[0132] Furthermore, after receiving the data from the fire sensor module, the fire location module accurately determines the specific location of the fire through multi-point data analysis and triangulation positioning algorithm. The processor of the positioning module generates a fire location map through efficient calculation and analysis, and updates the fire dynamic information in real time, providing accurate decision-making basis for fan control and air valve control.

[0133] It is important to understand that the fan control module dynamically adjusts the operating status of the ventilation fan based on the calculation results of the fire location module. By controlling the start and stop and wind speed of the fan, the system can quickly remove the smoke generated by the fire, and after the fire is under control, continue to introduce fresh air to maintain the air quality in the building.

[0134] Furthermore, the air valve control module is used to accurately adjust the opening and closing state of the air valve in the ventilation system according to the calculation results of the processor. The module adjusts the air valve opening in real time through a control algorithm, such as a PID control algorithm, to optimize air flow. The specific implementation can use a high-precision stepper motor and feedback sensor to ensure the accuracy and stability of the air valve adjustment.

[0135] Furthermore, the personnel evacuation device provides evacuation route guidance to personnel through intelligent indicator lights, broadcasting systems and mobile devices. The intelligent indicator lights use LED display technology to dynamically update the evacuation route according to the fire location, the broadcasting system guides personnel to evacuate through preset voice prompts, and the mobile devices provide real-time evacuation information and navigation services through a dedicated APP.

[0136] The benefit of this embodiment is that, through multi-sensor comprehensive detection and intelligent control, a comprehensive and accurate response is achieved in the event of a fire. Combining modern sensor technology, efficient processors and wireless communication technology, the system can monitor the fire situation in real time, accurately locate the source of the fire, dynamically adjust the ventilation system and the air valve opening, and effectively guide the evacuation of personnel. Through this embodiment, the fire ventilation control device can provide comprehensive safety protection when a fire occurs, improving the safety and emergency response capabilities inside the building. Example

[0137] In order to solve the problem in the prior art that the fire ventilation control device has insufficient detection of key environmental parameters such as air flow, pressure and humidity when a fire occurs, this embodiment further optimizes the configuration of the fire sensor module so that it can comprehensively and accurately monitor various fire-related environmental parameters, thereby improving fire warning and emergency response capabilities.

[0138] refer to Figure 7 As shown, in this embodiment, the air flow sensor is used to detect the air flow state in the building; the pressure sensor is used to detect the air pressure change in the ventilation duct; and the humidity sensor is used to detect the humidity level of the air in the building to predict the development trend of the fire.

[0139] Specifically, the airflow sensor model can be selected from TSI's 8455 series thermoelectric airflow sensor, which has high sensitivity and fast response characteristics and can monitor the direction and speed of air flow in real time. Through the airflow sensor, the system can analyze the airflow changes caused by the fire, determine the smoke diffusion path, and provide data support for the adjustment of the ventilation system.

[0140] Furthermore, the pressure sensor is used to detect the change of air pressure in the ventilation duct. The choice may be Honeywell's 24PC series pressure sensor, which has high precision and stability and can accurately measure the pressure fluctuation in the ventilation duct. The introduction of the pressure sensor can help the system monitor the operating status of the ventilation system, timely discover and deal with the pipeline blockage or leakage caused by the fire, and ensure the effective operation of the ventilation system.

[0141] It should be understood that humidity sensors are used to detect the humidity level of the air in a building. The choice can be a humidity sensor such as Sensirion's SHT31 series, which has the characteristics of high accuracy and low power consumption. By monitoring humidity changes, the system can predict the development trend of a fire. For example, the humidity will drop sharply at the beginning of a fire. The real-time data of the humidity sensor can assist the fire warning system to judge the potential risk of a fire in advance and optimize ventilation and fire extinguishing strategies.

[0142] Furthermore, the multi-parameter data collected by the fire sensor module is comprehensively analyzed by the integrated processor. The processor can be an ARM Cortex-M4 or higher performance model to ensure fast and efficient processing of large amounts of real-time data. The processor uses a multi-parameter fusion algorithm to comprehensively analyze data such as smoke concentration, temperature, carbon monoxide concentration, air flow velocity, pressure and humidity, generate a fire risk assessment report, and update the fire dynamic information in real time.

[0143] Furthermore, these multi-parameter data are transmitted to the central control system through the communication module. The communication module can use wireless communication technologies such as LoRa, Zigbee or Wi-Fi to ensure the real-time and reliability of data transmission. Through seamless connection with the central control system, the fire sensor module can achieve real-time data sharing and assist the decision-making system to respond quickly.

[0144] Furthermore, after receiving the data transmitted by the processor, the fire location module determines the specific location of the fire through multi-point data analysis and triangulation positioning algorithm. The efficient processor of the positioning module performs complex calculations and data fusion to generate a fire location map, providing accurate decision-making basis for fan control and air valve control.

[0145] It is important to understand that the fan control module dynamically adjusts the operating status of the ventilation fan based on the calculation results of the fire location module. The system quickly removes smoke generated by the fire by controlling the start and stop and wind speed of the fan, and continuously introduces fresh air after the fire is under control to maintain the air quality in the building.

[0146] Furthermore, the air valve control module is used to accurately adjust the opening and closing state of the air valve in the ventilation system according to the calculation results of the processor. This module uses advanced control algorithms, such as PID control algorithms, to adjust the air valve opening in real time and optimize the air flow in each area. The specific implementation can use high-precision stepper motors and feedback sensors to ensure the accuracy and stability of air valve adjustment.

[0147] Furthermore, the personnel evacuation device provides evacuation route guidance to personnel through intelligent indicator lights, broadcasting systems and mobile devices. The intelligent indicator lights use LED display technology to dynamically update the evacuation route according to the fire location, the broadcasting system guides personnel to evacuate through preset voice prompts, and the mobile devices provide real-time evacuation information and navigation services through a dedicated APP.

[0148] Through this embodiment, the fire ventilation control device can provide more comprehensive environmental monitoring and intelligent response in the event of a fire, significantly improving fire warning and emergency handling capabilities. The system can not only accurately monitor and locate fires, but also optimize ventilation and personnel evacuation strategies in real time. Example

[0149] In order to solve the problems in the prior art that the fire ventilation control system lacks centralized management and coordination and cannot perform effective data analysis when facing complex fire situations, this embodiment further optimizes and refines the architecture and functions of the fire ventilation control system so that it can achieve multi-device linkage, real-time data transmission and intelligent decision support.

[0150] refer to Figure 8 As shown, the control system includes the fire ventilation control device as above, and also includes:

[0151] Central control server, used to centrally manage and coordinate multiple fire ventilation control devices, record fire ventilation data, and provide data analysis and decision support;

[0152] Wireless communication network, used to achieve real-time data transmission between modules;

[0153] The central control server is configured as follows:

[0154] Dynamically generate ventilation control strategies and adjust the operating status of fans and dampers in real time according to fire trends;

[0155] Generate a fire heat map based on the data from the fire sensor module to visually display the distribution of fire and smoke;

[0156] Record fan and damper operation data during each fire ventilation process and generate ventilation reports; and

[0157] Use data mining algorithms to analyze historical ventilation data and optimize future ventilation strategies.

[0158] The fire ventilation control system of this embodiment includes multiple fire ventilation control devices, each of which is equipped with the aforementioned fire sensor module, fire location module, fan control module, air valve control module, communication module and personnel evacuation device. These devices are connected to the central control server through a wireless communication network to achieve real-time data transmission and centralized management. The wireless communication network can use technologies such as LoRa, Zigbee or Wi-Fi to ensure the stability and real-time performance of data transmission between modules.

[0159] Furthermore, the central control server, as the core management and coordination center of the system, is equipped with high-performance processors and large-capacity memory to process and store a large amount of real-time fire data. The central control server not only receives real-time data from various fire ventilation control devices, but is also responsible for centralized management and coordination of the operation of multiple devices to ensure that the system can respond quickly and effectively when a fire occurs.

[0160] It is important to understand that the central control server can dynamically generate ventilation control strategies. By analyzing the data from the fire sensor module, the server adjusts the operating status of the fans and dampers in real time to respond to different stages of fire development. For example, in the early stages of a fire, the system can start all smoke exhaust fans to quickly exhaust smoke; when the fire is under control, the server will adjust the operating mode of the fans and dampers to introduce fresh air and maintain the air quality in the building.

[0161] Furthermore, the central control server generates a fire heat map based on the data from the fire sensor module. The heat map visually displays the distribution of fire and smoke, helping firefighters quickly understand the spread trend and severity of the fire. During the heat map generation process, the server uses advanced data visualization technology to convert multi-parameter data into easy-to-understand graphical information, providing important reference for emergency decision-making.

[0162] The central control server also records the fan and damper operation data during each fire ventilation process and generates a detailed ventilation report. The ventilation report includes data such as fan start and stop time, operating time, damper opening and closing status, air flow and pressure changes. By analyzing this data, the system can evaluate the effectiveness of ventilation strategies, identify potential problems, and provide experience accumulation for future fire emergency response.

[0163] It is important to understand that the central control server uses data mining algorithms to analyze historical ventilation data and optimize future ventilation strategies. For example, through cluster analysis and regression analysis, the server can identify typical ventilation patterns for different types of fires, predict the development trend of fires, and formulate corresponding emergency plans in advance. The application of data mining algorithms improves the intelligence level of the system, enabling it to continuously learn and optimize, and improve the efficiency and effectiveness of fire emergency response.

[0164] Furthermore, the system can also combine artificial intelligence technologies, such as deep learning and reinforcement learning, to further improve the intelligence of fire warning and response. By training the neural network model, the system can more accurately identify abnormal signals in the early stage of a fire and improve the timeliness and accuracy of fire warning. At the same time, the reinforcement learning algorithm can help the system continuously optimize decision-making strategies in actual fire response and enhance the system's autonomous learning and adaptability.

[0165] Through this embodiment, the fire ventilation control system realizes multi-device linkage, real-time data transmission and intelligent decision support, and can provide efficient and accurate emergency response when a fire occurs. The system can not only comprehensively monitor and analyze the fire situation, optimize ventilation strategies and personnel evacuation plans, but also continuously improve emergency response capabilities through data mining and artificial intelligence technology. Example

[0166] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is run on a processor, the processor executes a fire ventilation control method, including the integration and optimization of multiple key steps and functional modules, to further optimize the design and function of the program, so that it can run efficiently on the processor and achieve accurate fire ventilation control. The processor can be a high-performance, multi-core architecture model, such as the ARM Cortex-A series or the Intel Core series, to ensure sufficient computing power and response speed when processing large amounts of data.

[0167] Furthermore, the program optimizes the efficiency of data collection, analysis and control command execution through multi-threaded processing technology. It should be understood that multi-threaded processing enables real-time collection and processing of sensor data to be carried out in parallel, improving the overall response speed of the system. The synchronization mechanism between threads ensures the accuracy of data interaction and processing results of each module.

[0168] Furthermore, the computer program of this embodiment includes a data acquisition module, a data analysis module and a control command generation module. The data acquisition module is responsible for acquiring real-time data from various sensors, including parameters such as smoke, temperature, carbon monoxide, airflow, pressure and humidity. After the sensor data is converted by an analog-to-digital converter (ADC), it is preliminarily processed and filtered by the processor to remove noise and outliers to ensure the accuracy and stability of the data.

[0169] Furthermore, the data analysis module uses advanced data processing algorithms to conduct a comprehensive analysis of the collected sensor data. It should be understood that the program uses a multi-parameter fusion algorithm to cross-analyze data such as smoke concentration, temperature changes, carbon monoxide concentration, airflow speed, pressure changes, and humidity levels to generate a fire risk assessment report. The data analysis module also uses machine learning technology to continuously optimize and improve the accuracy of fire prediction. For example, by training the neural network model, the system can more accurately identify weak signals in the early stages of a fire and issue early warnings.

[0170] Furthermore, the control command generation module generates specific control strategies based on the data analysis results. This module uses rule-based decision-making algorithms and optimization algorithms to dynamically adjust the operating status of the ventilation system. Specifically, the control command generation module controls the start and stop of the fan and the wind speed, and adjusts the opening and closing status of the air valve to ensure ventilation needs at different stages of the fire. In the process of generating the control strategy, the program also takes into account the structural characteristics of the building and the distribution of personnel to optimize the ventilation effect and the evacuation path of personnel.

[0171] Furthermore, when the program is running on the processor, it also supports remote transmission and monitoring of real-time data. Through the wireless communication module, the processor transmits the collected and processed data to the central control server for centralized management and further analysis. It should be understood that the program also supports encrypted transmission of data to ensure the security of fire data and control commands during transmission.

[0172] Through this embodiment, the program on the computer-readable storage medium can run efficiently on the processor and execute the fire ventilation control method in real time. The system can not only comprehensively and accurately monitor various environmental parameters related to fire and generate accurate fire risk assessments, but also dynamically adjust the operating status of the ventilation system through intelligent control strategies to improve the efficiency and effectiveness of fire emergency response. Through the program's multi-threaded processing and machine learning technology, the system has a higher response speed and prediction accuracy, ensuring comprehensive safety protection when a fire occurs.

[0173] The beneficial effects of the present invention are specifically embodied in that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A fire ventilation control method for a building, characterized in that: The control method comprises the following steps: Fire detection, using fire sensors installed in buildings to detect the occurrence of fire; Fire location, determining the specific location of the fire based on the feedback information from the fire sensor; Fan control, dynamically adjusting the operating status of ventilation fans according to the fire location and the building ventilation system structure to optimize smoke exhaust and fresh air input; Air valve control, automatically adjusting the opening and closing status of air valves in the ventilation system according to the development of the fire and the smoke concentration in each area of ​​the building; Data feedback: real-time monitoring and feedback of the temperature, smoke concentration and gas composition at the fire scene, and dynamic adjustment of ventilation strategies; Evacuation of personnel: cooperate with the fire emergency evacuation system to provide evacuation route guidance to personnel through intelligent indicator lights, broadcasting systems or mobile devices; The air valve control comprises the following steps: Use control algorithms to adjust the air valve opening and closing status in real time according to smoke concentration and temperature; Controlling the air valve opening, adjusting the air valve opening by the control algorithm to optimize the air flow in various areas of the building; According to the real-time data of fire development, the opening and closing status of air valves in different areas are automatically adjusted to ensure effective smoke discharge and prevent its spread; The control algorithm formula is as follows: ， Among them, θ(t) is the air valve opening, e(t) is the smoke concentration error, Kp, Ki, and Kd are control coefficients used to adjust the response speed of the control system; The step of controlling the air valve opening comprises: Data setting: set the initial air valve opening θ0, adjustment amplitude α, adjustment frequency ω and phase offset φ, and obtain the current smoke concentration Csmoke(t) and temperature T(t) data; Real-time monitoring, continuously monitoring the smoke concentration and temperature in different areas of the building, and obtaining real-time data through the sensors; Dynamic adjustment: using the real-time data, dynamically adjust the opening of the air valve according to the following formula: ， Among them, θ(t0) is the real-time opening of the air valve, and F(Csmoke(t), T(t)) is an adaptation function used to adjust the real-time opening of the air valve. The specific form is: ， Among them, Csmokethreshold and Tthreshold are the trigger thresholds of smoke concentration and temperature respectively, and Csmokemax and Tmax are the maximum smoke concentration and maximum temperature respectively; Feedback control, in each time step, adjusting the real-time opening of the air valve according to the real-time opening of the air valve obtained by the above calculation, and recording the adjusted air flow state; Adaptive optimization, according to the air flow state, using an adaptive algorithm to adjust the adjustment amplitude α, the adjustment frequency ω and the phase offset φ to optimize the ventilation effect; Iteration loop: repeat the steps of real-time monitoring and adaptive optimization, continuously optimize the real-time opening degree and ventilation strategy of the damper during the fire, and ensure that the air quality and temperature in the building remain within a safe range.

2. The fire ventilation control method according to claim 1, characterized in that: The fan control comprises the following steps: A mathematical model of the building ventilation system is established, denoted as V=g(L,η,ΔP), where L is the length of the ventilation duct, η is the fan efficiency, and ΔP is the duct pressure drop; Dynamically calculate the fan operating parameters η and ΔP according to the fire location and smoke concentration distribution to ensure rapid smoke exhaust and effective fresh air input; Artificial intelligence algorithms are used to predict the development trend of fires and adjust the operating status of fans in advance to prevent the spread of fires.

3. The fire ventilation control method according to claim 1, characterized in that: The adaptive optimization comprises the following steps: Calculate the impact of the current damper adjustment on smoke concentration and temperature, and evaluate the adjustment effect through the following error function: ， Among them, Csmokesetpoint and Tsetpoint are the ideal smoke concentration and temperature setting values ​​respectively; According to the size of E(t) in the error function, the adjustment amplitude α, the adjustment frequency ω and the value of the phase shift φ are adjusted to make them tend to the minimum error.

4. A fire ventilation control device for a building, using the fire ventilation control method according to any one of claims 1 to 3, characterized in that: The control device comprises: A fire sensor module for detecting the occurrence of fire; A fire locating module, used to receive and process the data of the fire sensor module to determine the specific location of the fire, wherein the fire locating module is integrated with a processor; a fan control module, for adjusting the operation of the ventilation fan according to the calculation result of the processor; A damper control module, used for adjusting the opening and closing state of dampers in the ventilation system according to the calculation result of the processor; a communication module, used to realize wireless communication between the control device and an external control system; and The personnel evacuation device is used to cooperate with the fire emergency evacuation system to provide evacuation route guidance to personnel through intelligent indicator lights, broadcasting systems or mobile devices.

5. The fire ventilation control device according to claim 4, characterized in that: The fire sensor module includes a smoke sensor, a temperature sensor and a carbon monoxide sensor, and is used to detect the occurrence of a fire.

6. The fire ventilation control device according to claim 5, characterized in that: The fire sensor module also includes: Air flow sensors are used to detect air flow conditions within buildings; Pressure sensors to detect changes in air pressure within ventilation ducts; and Humidity sensors are used to detect humidity levels in the air within a building to predict the development of a fire.

7. A fire ventilation control system for a building, characterized in that: The control system comprises the fire ventilation control device according to any one of claims 4 to 6, and further comprises: Central control server, used to centrally manage and coordinate multiple fire ventilation control devices, record fire ventilation data, and provide data analysis and decision support; Wireless communication network, used to achieve real-time data transmission between modules; The central control server is configured as follows: Dynamically generate ventilation control strategies and adjust the operating status of fans and dampers in real time according to fire trends; Generate a fire heat map based on the data from the fire sensor module to visually display the distribution of fire and smoke; Record fan and damper operation data during each fire ventilation process and generate ventilation reports; and Use data mining algorithms to analyze historical ventilation data and optimize future ventilation strategies.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed on a processor, the processor is enabled to execute the fire ventilation control method according to any one of claims 1 to 3.

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