Intelligent control method and system for smoke exhaust fire damper

Abstract

The invention relates to the technical field of fire fighting, in particular to an intelligent control method and system for a smoke exhaust fireproof valve, and the method comprises the steps of initial state setting, fire signal detection, smoke exhaust fireproof valve control and state monitoring and feedback. Compared with the prior art which only depends on temperature and particulate matter concentration data to carry out fire monitoring and control and has the defects of insufficient monitoring on internal air pressure change of the smoke exhaust fireproof valve and inflexible control response, the scheme is characterized in that an air pressure sensor is additionally arranged in a data acquisition module; an air pressure data processing step based on a sliding window average value method is introduced into the control module in a targeted manner, and the opening and closing degree of the smoke exhaust fireproof valve is adjusted by combining a proportion, integral and differential control algorithm, so that the scheme can monitor the air pressure change in the smoke exhaust fireproof valve in real time, improve the accuracy of fire early warning, and improve the safety of the fire. And opening and closing of the smoke exhaust fireproof valve can be dynamically adjusted according to air pressure changes, and the stability of smoke exhaust efficiency and the sensitivity of control response are ensured.

Description

Technical Field

[0001] The present invention relates to the field of fire protection technology, and in particular to an intelligent control method and system for a smoke exhaust fire damper. Background Art

[0002] In the prior art, the intelligent control system of smoke exhaust fire dampers mainly relies on temperature and particle concentration data for fire monitoring and control. Although this solution can achieve early warning and control of fire to a certain extent, it has obvious shortcomings. Specifically, the prior art lacks real-time monitoring of the changes in air pressure inside the smoke exhaust fire damper, and the change in air pressure is an important feature when a fire occurs, which can reflect the spread speed of the fire and the diffusion of smoke. Therefore, relying solely on temperature and particle concentration data for fire monitoring and control may result in early warnings that are not timely or accurate enough.

[0003] In addition, the existing technology often uses a relatively simple control algorithm to control the opening and closing degree of smoke exhaust fire dampers, which lacks flexibility and dynamic adjustment capabilities. This may result in the opening and closing degree of smoke exhaust fire dampers failing to meet actual needs when a fire occurs, thereby affecting the smoke exhaust efficiency and the sensitivity of the control response.

[0004] In summary, the existing technology has obvious deficiencies in fire monitoring and control of smoke exhaust fire damper intelligent control systems, and a more comprehensive, accurate and flexible solution is needed to overcome these limitations. Summary of the invention

[0005] In order to overcome the problems raised in the above background technology, the present invention proposes an intelligent control method and system for smoke exhaust and fire dampers.

[0006] The technical solution of the present invention is: a smoke exhaust fire damper intelligent control method, comprising the following steps: S11: Initial status setting: according to the design specifications, ensure that the status of all smoke exhaust fire dampers is correctly set, and monitor the status of all smoke exhaust fire dampers; S12: Fire signal detection, monitoring fire signals in real time through installed sensors and detectors, and sending alarm signals when fire signals are detected; S13: smoke exhaust fire damper control, according to the detected fire signal, start the preset control strategy to control the smoke exhaust fire damper; S14: Status monitoring and feedback: real-time monitoring of the status of smoke exhaust and fire dampers, and when an abnormality is detected, an alarm signal is immediately issued and fault diagnosis is performed.

[0007] Preferably, in accordance with the design specifications, when ensuring that the status of all smoke exhaust fire dampers are correctly set and monitoring the status of all smoke exhaust fire dampers, the following steps are included: S21: System startup and self-test: After the smoke exhaust and fire dampers are installed, the system starts up and performs a self-test to check that the sensors, actuators, and communication networks are in normal working condition. S22: Check the status of all smoke exhaust fire dampers to see if they are in the open and closed states according to the design specifications. S23: Data setting and calibration, setting the thresholds of various parameters and calibrating the data of various sensors, wherein the calibration method adopted is to compare the sensor data in the same area and modify the sensor data according to the average value of the sensor data.

[0008] Preferably, the following steps are included when real-time monitoring of fire signals is performed by installed sensors and detectors and sending alarm signals when fire signals are detected: S31: Data preprocessing, in which the data from sensors and detectors are preprocessed. The preprocessing methods used include data cleaning, data denoising and data normalization; S32: feature extraction, extracting features from the preprocessed features, wherein the extracted features include a rate of change of smoke concentration and a rate of change of temperature; S33: Algorithm application, using a preset algorithm model to calculate and analyze the extracted feature data, and output the current scene mode with the highest probability, where the scene modes include normal scenes, smoke scenes and fire scenes.

[0009] Preferably, when the extracted feature data is calculated and analyzed using a preset algorithm model, the loss function of the algorithm model used is: ; Among them, y is the true value, is the predicted value, is the threshold parameter.

[0010] Preferably, when the extracted feature data is calculated and analyzed using a preset algorithm model, the optimization function of the algorithm model used is: ; in, is the parameter of the tth iteration, is the learning rate, is a constant, is the corrected momentum estimate, is the momentum estimate of the corrected squared gradient, where , ,in, , ;in, is the momentum estimate of the gradient, Momentum hyperparameter, is the current gradient, is the momentum estimate of the squared gradient, is the RMSprop hyperparameter, A smoke exhaust fire damper intelligent control system, comprising: Data acquisition module, used to detect the gas around and inside the smoke exhaust fire damper using sensors and detectors; Communication module, used to realize network communication between data acquisition module, control module, feedback module and linkage module, and realize network communication between control module and other systems; The control module is used to process and analyze the data collected by the data acquisition module and generate a control signal according to the analysis results; An execution module, used to drive the blades in the smoke channel inside the smoke exhaust fire damper to rotate according to the signal of the control module; The feedback module is used to monitor the working status of the smoke exhaust fire damper in real time and feed back the working status of the smoke exhaust fire damper to the control module in real time.

[0011] Preferably, the data acquisition module includes: A11: Temperature sensor, used to detect the temperature of the gas around and inside the smoke exhaust fire damper; A12: Particle concentration detector, used to monitor the particle concentration of the gas around and inside the smoke exhaust fire damper; A13: Air pressure sensor, used to monitor the air pressure in the smoke channel inside the smoke exhaust fire damper.

[0012] Preferably, when the control module processes and analyzes the data collected by the data acquisition module and generates a control signal according to the analysis result, the control module processes and analyzes the data of the air pressure sensor through the following steps: S41: data reception and preprocessing, receiving data collected by the air pressure sensor through the communication module, and preprocessing the received data; S42: further processing the pre-processed data of the air pressure sensor, wherein a sliding window average method is used to calculate the change in the air pressure of the smoke channel inside the smoke exhaust fire damper; S43: According to the change of the air pressure of the smoke passage inside the smoke exhaust fire damper, the opening and closing degree of the smoke exhaust fire damper is adjusted, wherein the principle formula is: ; in, express The adjustment amount of the opening and closing degree of the smoke exhaust fire damper at each moment; Represents the proportionality factor, which is used to determine the error Adjustment amount the extent of direct impact; express The difference between the target air flow rate and the actual air flow rate at the moment, that is, the difference between the actual air pressure and the normal air pressure; Represents the integral coefficient, which is used to eliminate static errors; express Error accumulation at any moment; Represents the differential coefficient, which is used to reduce dynamic errors and oscillations; express Error in time The rate of change.

[0013] Preferably, the data acquisition module further includes a microphone, which is used to collect sound information of the environment where the smoke exhaust fire damper is located. When the control module processes and analyzes the data collected by the data acquisition module and generates a control signal according to the analysis result, the control module processes and analyzes the data of the microphone through the following steps: S51: Data screening, screening the audio data, and removing the data with energy lower than the set threshold; S52: data matching, matching and querying the audio data of the set characters in the filtered audio data, wherein the set characters include fire, run and rescue; S53: further analyzing, performing text recognition on the matched audio data and data within a certain period of time before and after the audio data, and analyzing the recognized text; S54: Generate a control signal according to the analysis result and the preset judgment logic.

[0014] Preferably, the feedback module includes a state monitoring sensor, which is used to monitor the opening and closing degree of the smoke exhaust fire damper. When the feedback module monitors the working state of the smoke exhaust fire damper in real time and feeds back the working state of the smoke exhaust fire damper to the control module in real time, the following steps are included: S61: When the control module generates a control instruction and sends it to the execution module, the feedback module records the control instruction generated by the control module; S62: the feedback module accumulates and calculates the recorded control instructions, and determines the theoretical opening and closing degree of the smoke exhaust fire damper according to the initial state of the smoke exhaust fire damper; S63: The actual opening and closing degree of the smoke exhaust fire damper is collected in real time through the status monitoring sensor; S64: Compare the theoretical opening and closing degree of the smoke exhaust fire damper with the actual opening and closing degree, and send it to the control module when an error occurs.

[0015] Beneficial effects of the present invention: 1. Compared with the prior art that only relies on temperature and particle concentration data for fire monitoring and control, there are shortcomings such as insufficient monitoring of air pressure changes inside the smoke exhaust fire damper and inflexible control response. This solution adds an air pressure sensor in the data acquisition module, and specifically introduces an air pressure data processing step based on the sliding window average method in the control module, as well as an algorithm combining proportional, integral, and differential control to adjust the opening and closing degree of the smoke exhaust fire damper. This solution can not only monitor the air pressure changes inside the smoke exhaust fire damper in real time and improve the accuracy of fire warning, but also dynamically adjust the opening and closing of the smoke exhaust fire damper according to the air pressure changes, effectively avoiding static errors and dynamic errors, ensuring the stability of smoke exhaust efficiency and the sensitivity of control response, thereby significantly improving the overall performance and reliability of the smoke exhaust fire damper intelligent control system; 2. Compared with the existing technology that only relies on gas sensor data for fire monitoring and control, lacks effective use of environmental sound information, which may lead to the disadvantage that fire warning is not timely and comprehensive. This solution innovatively adds a microphone to the data acquisition module to collect sound information of the environment where the smoke exhaust fire damper is located, and adds processing and analysis steps for microphone data in the control module, including data screening, data matching, further text recognition and analysis, and generation of control signals based on analysis results and preset judgment logic. This solution not only broadens the dimension of fire monitoring and can capture the unique sound characteristics of the fire scene, but also improves the accuracy and timeliness of fire warning through text recognition and analysis, thereby realizing more comprehensive and intelligent fire monitoring and control, and significantly improving the warning capability and response speed of the smoke exhaust fire damper intelligent control system; 3. Compared with the prior art in which the feedback module simply feeds back the working status of the smoke exhaust fire damper and lacks the shortcomings of accurate verification of the execution of control instructions and error correction, this solution introduces a state monitoring sensor in the feedback module and designs a complete feedback process including control instruction recording, theoretical opening and closing degree calculation, actual opening and closing degree collection and error comparison. This solution can not only monitor the working status of the smoke exhaust fire damper in real time, but also accurately verify the execution of the control instructions. By comparing the theoretical opening and closing degree with the actual opening and closing degree, the errors in the execution process can be discovered and corrected in time, thereby ensuring the high-precision operation and reliability of the smoke exhaust fire damper intelligent control system and significantly improving the overall performance and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Shown is a flow chart of the intelligent control method of smoke exhaust fire damper of the present invention; Figure 2 Shown is a schematic diagram of the structure of the smoke exhaust fire damper intelligent control system of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0018] Embodiment 1 See also Figure 1 The present invention provides an embodiment: a smoke exhaust fire damper intelligent control method, comprising the following steps: S11: Initial status setting: according to the design specifications, ensure that the status of all smoke exhaust fire dampers is correctly set, and monitor the status of all smoke exhaust fire dampers; S12: Fire signal detection, monitoring fire signals in real time through installed sensors and detectors, and sending alarm signals when fire signals are detected; S13: smoke exhaust fire damper control, according to the detected fire signal, start the preset control strategy to control the smoke exhaust fire damper; S14: Status monitoring and feedback: real-time monitoring of the status of smoke exhaust and fire dampers, and when an abnormality is detected, an alarm signal is immediately issued and fault diagnosis is performed.

[0019] As described above, when in use, first, according to the building design fire protection specifications, ensure that all smoke exhaust fire dampers are correctly installed and initialized, and use the sensor network to monitor the opening and closing status of all valves in real time; then, detect the fire signal in real time through the smoke detector and temperature sensor, and immediately send a fire alarm signal once the smoke concentration exceeds 50ppm or the temperature exceeds 70°C; then, according to the preset control strategy, if the fire occurs on a certain floor, the smoke exhaust fire dampers on this floor and the adjacent floors will be automatically opened for smoke exhaust, and the fire dampers in other non-related areas will be closed to prevent the spread of fire; finally, the system continuously monitors the working status of each smoke exhaust fire damper. Once it is found that the valve fails to open or close as instructed, an audible and visual alarm will be immediately issued, and the cause of the fault will be analyzed through the built-in fault diagnosis algorithm, such as electrical fault or mechanical jam, so as to facilitate timely maintenance.

[0020] The present invention uses an intelligent control method to ensure that the initial state of the smoke exhaust and fire damper system is correct and continuously monitored, and can detect fire signals in real time and respond quickly. It can accurately control the smoke exhaust and fire dampers according to preset strategies to effectively exhaust smoke and block fire. At the same time, it continuously monitors the valve status, promptly discovers and alarms abnormalities, and quickly diagnoses faults, thereby significantly improving the intelligence level of building fire prevention and control and the efficiency of emergency response.

[0021] Preferably, in accordance with the design specifications, when ensuring that the status of all smoke exhaust fire dampers are correctly set and monitoring the status of all smoke exhaust fire dampers, the following steps are included: S21: System startup and self-test: After the smoke exhaust and fire dampers are installed, the system starts up and performs a self-test to check that the sensors, actuators, and communication networks are in normal working condition. S22: Check the status of all smoke exhaust fire dampers to see if they are in the open and closed states according to the design specifications. S23: Data setting and calibration, setting the thresholds of various parameters and calibrating the data of various sensors, wherein the calibration method adopted is to compare the sensor data in the same area and modify the sensor data according to the average value of the sensor data.

[0022] As described above, the present invention ensures the normal operation of sensors, actuators and communication networks through comprehensive self-checking at system startup, and then accurately checks the status of smoke exhaust fire dampers to ensure that they are in an open or closed state in accordance with the design specifications. At the same time, by setting parameter thresholds and using the method of comparing and calibrating sensor data in the area, the data accuracy is effectively improved, providing a solid and reliable foundation for subsequent fire signal detection and intelligent control of smoke exhaust fire dampers, and significantly enhancing the stability and reliability of the building fire prevention and control system.

[0023] Preferably, the following steps are included when real-time monitoring of fire signals is performed by installed sensors and detectors and sending alarm signals when fire signals are detected: S31: Data preprocessing, in which the data from sensors and detectors are preprocessed. The preprocessing methods used include data cleaning, data denoising and data normalization; S32: feature extraction, extracting features from the preprocessed features, wherein the extracted features include a rate of change of smoke concentration and a rate of change of temperature; S33: Algorithm application, using a preset algorithm model to calculate and analyze the extracted feature data, and output the current scene mode with the highest probability, where the scene modes include normal scenes, smoke scenes and fire scenes.

[0024] As described above, the present invention effectively improves the accuracy and reliability of sensor and detector data through a data preprocessing step, and then extracts key features from the processed data, such as the smoke concentration change rate and the temperature change rate, and then uses advanced algorithm models to conduct in-depth analysis of these features. It can accurately judge the current scene mode and promptly distinguish smoke or fire scenes from normal states, thereby quickly sending alarm signals, greatly improving the sensitivity and accuracy of fire monitoring, and providing strong support for early fire warning and timely response.

[0025] Preferably, when the extracted feature data is calculated and analyzed using a preset algorithm model, the loss function of the algorithm model used is: ; Among them, y is the true value, is the predicted value, is the threshold parameter.

[0026] Preferably, when the extracted feature data is calculated and analyzed using a preset algorithm model, the optimization function of the algorithm model used is: ; in, is the parameter of the tth iteration, is the learning rate, is a constant, is the corrected momentum estimate, is the momentum estimate of the corrected squared gradient, where , ,in, , ;in, is the momentum estimate of the gradient, Momentum hyperparameter, is the current gradient, is the momentum estimate of the squared gradient, is the RMSprop hyperparameter.

[0027] As described above, the present invention can accurately measure the difference between the predicted value and the true value of the algorithm model through a carefully designed loss function, and combined with the adopted optimization function (such as a variant that integrates momentum estimation and RMSprop characteristics), effectively adjust the model parameters in the iteration process, which not only accelerates the training process, but also improves the model's analysis accuracy and generalization ability for feature data, thereby ensuring the accuracy and stability of fire scene pattern recognition, and providing more reliable decision support for the fire warning system.

[0028] Embodiment 2 See also Figure 2 The present invention provides an embodiment: an intelligent control system for smoke exhaust and fire dampers, comprising: Data acquisition module, used to detect the gas around and inside the smoke exhaust fire damper using sensors and detectors; Communication module, used to realize network communication between data acquisition module, control module, feedback module and linkage module, and realize network communication between control module and other systems; The control module is used to process and analyze the data collected by the data acquisition module and generate a control signal according to the analysis results; An execution module, used to drive the blades in the smoke channel inside the smoke exhaust fire damper to rotate according to the signal of the control module; The feedback module is used to monitor the working status of the smoke exhaust fire damper in real time and feed back the working status of the smoke exhaust fire damper to the control module in real time.

[0029] As described above, the system first uses a data acquisition module to monitor the gas around and inside the smoke exhaust fire damper in real time using a highly sensitive smoke sensor and temperature sensor. The operating temperature range of the sensor is 0°C to 280°C, which can accurately detect the smoke concentration and temperature changes in the environment. Subsequently, the communication module ensures real-time network communication between the data acquisition module, the control module, the feedback module and the linkage module, and keeps the communication between the control module and external systems such as the fire alarm system unblocked. After receiving the data from the data acquisition module, the control module immediately processes and analyzes it quickly, and uses advanced algorithm models to judge the fire risk. Once a sharp increase in smoke concentration or an abnormal increase in temperature (such as the temperature reaching the preset threshold of 70°C or 280°C) is detected, the control module immediately generates a control signal. After receiving the signal, the execution module quickly drives the blades inside the smoke exhaust fire damper to rotate to open or close the smoke exhaust channel. At the same time, the feedback module continuously monitors the working status of the smoke exhaust fire damper, including the position and rotation speed of the blades, and feeds these real-time data back to the control module to ensure that the system can accurately grasp the operation of the smoke exhaust fire damper and make adjustments when necessary. The entire system not only ensures efficient and accurate fire prevention and control, but also guarantees the stability and reliability of the system.

[0030] Preferably, the data acquisition module includes: A11: Temperature sensor, used to detect the temperature of the gas around and inside the smoke exhaust fire damper; A12: Particle concentration detector, used to monitor the particle concentration of the gas around and inside the smoke exhaust fire damper; A13: Air pressure sensor, used to monitor the air pressure in the smoke channel inside the smoke exhaust fire damper.

[0031] Preferably, when the control module processes and analyzes the data collected by the data acquisition module and generates a control signal according to the analysis result, the control module processes and analyzes the data of the air pressure sensor through the following steps: S41: data reception and preprocessing, receiving data collected by the air pressure sensor through the communication module, and preprocessing the received data; S42: further processing the pre-processed data of the air pressure sensor, wherein a sliding window average method is used to calculate the change in the air pressure of the smoke channel inside the smoke exhaust fire damper; S43: According to the change of the air pressure of the smoke passage inside the smoke exhaust fire damper, the opening and closing degree of the smoke exhaust fire damper is adjusted, wherein the principle formula is: ; in, express The adjustment amount of the opening and closing degree of the smoke exhaust fire damper at each moment; Represents the proportionality factor, which is used to determine the error Adjustment amount the extent of direct impact; express The difference between the target air flow rate and the actual air flow rate at the moment, that is, the difference between the actual air pressure and the normal air pressure; Represents the integral coefficient, which is used to eliminate static errors; express Error accumulation at any moment; Represents the differential coefficient, which is used to reduce dynamic errors and oscillations; express Error in time The rate of change.

[0032] As described above, compared with the existing technology that only relies on temperature and particle concentration data for fire monitoring and control, there are shortcomings such as insufficient monitoring of air pressure changes inside the smoke exhaust fire damper and inflexible control response. This solution adds an air pressure sensor in the data acquisition module, and specifically introduces an air pressure data processing step based on the sliding window average method in the control module, as well as a PID algorithm that combines proportional (P), integral (I), and differential (D) control to adjust the opening and closing degree of the smoke exhaust fire damper. This solution can not only monitor the air pressure changes inside the smoke exhaust fire damper in real time and improve the accuracy of fire warning, but also dynamically adjust the opening and closing of the smoke exhaust fire damper according to the air pressure changes, effectively avoiding static errors and dynamic errors, ensuring the stability of smoke exhaust efficiency and the sensitivity of control response, thereby significantly improving the overall performance and reliability of the smoke exhaust fire damper intelligent control system.

[0033] Preferably, the data acquisition module further includes a microphone, which is used to collect sound information of the environment where the smoke exhaust fire damper is located. When the control module processes and analyzes the data collected by the data acquisition module and generates a control signal according to the analysis result, the control module processes and analyzes the data of the microphone through the following steps: S51: Data screening, screening the audio data, and removing the data with energy lower than the set threshold; S52: data matching, matching and querying the audio data of the set characters in the filtered audio data, wherein the set characters include fire, run and rescue; S53: further analyzing, performing text recognition on the matched audio data and data within a certain period of time before and after the audio data, and analyzing the recognized text; S54: Generate a control signal according to the analysis result and the preset judgment logic.

[0034] As mentioned above, compared with the existing technology that only relies on gas sensor data for fire monitoring and control, lacks effective use of environmental sound information, which may lead to the disadvantage that fire warning is not timely and comprehensive. This solution innovatively adds a microphone to the data acquisition module to collect sound information of the environment where the smoke exhaust fire damper is located, and adds processing and analysis steps for microphone data in the control module, including data screening, data matching, further text recognition and analysis, and generation of control signals based on analysis results and preset judgment logic. This solution not only broadens the dimension of fire monitoring and can capture the unique sound characteristics of the fire scene (such as calls for help, fire alarms, etc.), but also improves the accuracy and timeliness of fire warnings through text recognition and analysis, thereby achieving more comprehensive and intelligent fire monitoring and control, and significantly improving the warning capability and response speed of the smoke exhaust fire damper intelligent control system.

[0035] Preferably, the feedback module includes a state monitoring sensor, which is used to monitor the opening and closing degree of the smoke exhaust fire damper. When the feedback module monitors the working state of the smoke exhaust fire damper in real time and feeds back the working state of the smoke exhaust fire damper to the control module in real time, the following steps are included: S61: When the control module generates a control instruction and sends it to the execution module, the feedback module records the control instruction generated by the control module; S62: the feedback module accumulates and calculates the recorded control instructions, and determines the theoretical opening and closing degree of the smoke exhaust fire damper according to the initial state of the smoke exhaust fire damper; S63: The actual opening and closing degree of the smoke exhaust fire damper is collected in real time through the status monitoring sensor; S64: Compare the theoretical opening and closing degree of the smoke exhaust fire damper with the actual opening and closing degree, and send it to the control module when an error occurs.

[0036] As described above, compared with the shortcomings of the prior art in which the feedback module only simply feeds back the working status of the smoke exhaust fire damper and lacks accurate verification of the execution of the control instructions and error correction, this solution introduces a state monitoring sensor in the feedback module and designs a complete feedback process including control instruction recording, theoretical opening and closing degree calculation, actual opening and closing degree collection and error comparison. This solution can not only monitor the working status of the smoke exhaust fire damper in real time, but also accurately verify the execution of the control instructions. By comparing the theoretical opening and closing degree with the actual opening and closing degree, it can timely discover and correct errors in the execution process, thereby ensuring the high-precision operation and reliability of the smoke exhaust fire damper intelligent control system and significantly improving the overall performance and safety of the system.

[0037] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. A smoke exhaust fire damper intelligent control method; characterized in that: The following steps are included: S11: Initial status setting: according to the design specifications, ensure that the status of all smoke exhaust fire dampers is correctly set, and monitor the status of all smoke exhaust fire dampers; S12: Fire signal detection, monitoring fire signals in real time through installed sensors and detectors, and sending alarm signals when fire signals are detected; S13: smoke exhaust fire damper control, according to the detected fire signal, start the preset control strategy to control the smoke exhaust fire damper; S14: Status monitoring and feedback: real-time monitoring of the status of smoke exhaust and fire dampers, and when an abnormality is detected, an alarm signal is immediately issued and fault diagnosis is performed.

2. According to claim 1, a smoke exhaust fire damper intelligent control method is characterized in that: In order to ensure that the status of all smoke exhaust fire dampers are correctly set according to the design specifications and monitor the status of all smoke exhaust fire dampers, the following steps are included: S21: System startup and self-test: After the smoke exhaust and fire dampers are installed, the system starts up and performs a self-test to check that the sensors, actuators, and communication networks are in normal working condition. S22: Check the status of all smoke exhaust fire dampers to see if they are in the open and closed states according to the design specifications. S23: Data setting and calibration, setting the thresholds of various parameters and calibrating the data of various sensors, wherein the calibration method adopted is to compare the sensor data in the same area and modify the sensor data according to the average value of the sensor data.

3. The intelligent control method of smoke exhaust fire damper according to claim 2 is characterized in that: When the installed sensors and detectors monitor the fire signal in real time and send the alarm signal when the fire signal is detected, the following steps are included: S31: Data preprocessing, preprocessing the data from the sensors and detectors, wherein the preprocessing methods used include data cleaning, data denoising and data normalization; S32: feature extraction, extracting features from the preprocessed features, wherein the extracted features include a rate of change of smoke concentration and a rate of change of temperature; S33: Algorithm application, using a preset algorithm model to calculate and analyze the extracted feature data, and output the current scene mode with the highest probability, where the scene modes include normal scenes, smoke scenes and fire scenes.

4. The intelligent control method of smoke exhaust fire damper according to claim 3 is characterized in that: When the preset algorithm model is used to calculate and analyze the extracted feature data, the loss function of the algorithm model used is: ； Among them, y is the true value, is the predicted value, is the threshold parameter.

5. The intelligent control method of smoke exhaust fire damper according to claim 4 is characterized in that: When the extracted feature data is calculated and analyzed using the preset algorithm model, the optimization function of the algorithm model used is: ； in, is the parameter of the tth iteration, is the learning rate, is a constant, is the corrected momentum estimate, is the momentum estimate of the corrected squared gradient, where , ,in, , ;in, is the momentum estimate of the gradient, Momentum hyperparameter, is the current gradient, is the momentum estimate of the squared gradient, is the RMSprop hyperparameter.

6. An intelligent control system for smoke exhaust fire dampers, used in an intelligent control method for smoke exhaust fire dampers according to any one of claims 1 to 5, characterized in that: Included are: Data acquisition module, used to detect the gas around and inside the smoke exhaust fire damper using sensors and detectors; Communication module, used to realize network communication between data acquisition module, control module, feedback module and linkage module, and realize network communication between control module and other systems; The control module is used to process and analyze the data collected by the data acquisition module and generate a control signal according to the analysis results; An execution module, used to drive the blades in the smoke channel inside the smoke exhaust fire damper to rotate according to the signal of the control module; The feedback module is used to monitor the working status of the smoke exhaust fire damper in real time and feed back the working status of the smoke exhaust fire damper to the control module in real time.

7. The intelligent control system for smoke exhaust and fire dampers according to claim 6 is characterized in that: The data acquisition module includes: A11: Temperature sensor, used to detect the temperature of the gas around and inside the smoke exhaust fire damper; A12: Particle concentration detector, used to monitor the particle concentration of the gas around and inside the smoke exhaust fire damper; A13: Air pressure sensor, used to monitor the air pressure in the smoke channel inside the smoke exhaust fire damper.

8. The intelligent control system for smoke exhaust and fire dampers according to claim 7 is characterized in that: When the control module processes and analyzes the data collected by the data acquisition module and generates a control signal according to the analysis result, the control module processes and analyzes the data of the air pressure sensor through the following steps: S41: data reception and preprocessing, receiving data collected by the air pressure sensor through the communication module, and preprocessing the received data; S42: further processing the pre-processed data of the air pressure sensor, wherein a sliding window average method is used to calculate the change in the air pressure of the smoke channel inside the smoke exhaust fire damper; S43: According to the change of the air pressure of the smoke passage inside the smoke exhaust fire damper, the opening and closing degree of the smoke exhaust fire damper is adjusted, wherein the principle formula is: ； in, express The adjustment amount of the opening and closing degree of the smoke exhaust fire damper at each moment; Represents the proportionality factor, which is used to determine the error Adjustment amount the extent of direct impact; express The difference between the target air flow rate and the actual air flow rate at the moment, that is, the difference between the actual air pressure and the normal air pressure; Represents the integral coefficient, which is used to eliminate static errors; express Error accumulation at any moment; Represents the differential coefficient, which is used to reduce dynamic errors and oscillations; express Error in time The rate of change.

9. The intelligent control system for smoke exhaust and fire dampers according to claim 8 is characterized in that: The data acquisition module also includes a microphone, which is used to collect sound information of the environment where the smoke exhaust fire damper is located. When the control module processes and analyzes the data collected by the data acquisition module and generates a control signal according to the analysis result, the control module processes and analyzes the data of the microphone through the following steps: S51: Data screening, screening the audio data, and removing the data with energy lower than the set threshold; S52: data matching, matching and querying the audio data of the set characters in the filtered audio data, wherein the set characters include fire, run and rescue; S53: further analyzing, performing text recognition on the matched audio data and data within a certain period of time before and after the audio data, and analyzing the recognized text; S54: Generate a control signal according to the analysis result and the preset judgment logic.

10. The intelligent control system for smoke exhaust and fire dampers according to claim 9, characterized in that: The feedback module includes a state monitoring sensor, which is used to monitor the opening and closing degree of the smoke exhaust fire damper. The feedback module monitors the working state of the smoke exhaust fire damper in real time and feeds back the working state of the smoke exhaust fire damper to the control module in real time, including the following steps: S61: When the control module generates a control instruction and sends it to the execution module, the feedback module records the control instruction generated by the control module; S62: the feedback module accumulates and calculates the recorded control instructions, and determines the theoretical opening and closing degree of the smoke exhaust fire damper according to the initial state of the smoke exhaust fire damper; S63: The actual opening and closing degree of the smoke exhaust fire damper is collected in real time through the status monitoring sensor; S64: Compare the theoretical opening and closing degree of the smoke exhaust fire damper with the actual opening and closing degree, and send it to the control module when an error occurs.

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