Intelligent fireproof door control method, system and device based on artificial intelligence and medium

Abstract

The invention relates to an intelligent fireproof door control method, system and device based on artificial intelligence and a medium, and the method comprises the steps: obtaining equipment data and sensing data of an intelligent fireproof door in real time, carrying out the feature extraction of the sensing data, and obtaining a feature data set; performing evaluation processing on the environment data according to the feature data set to obtain corresponding environment evaluation information, and performing state analysis on the equipment data to obtain corresponding fireproof door state data; transmitting the environment evaluation information and the fireproof door state data to a preset control model, performing instruction analysis on the fireproof door state data through the control model to obtain a corresponding control instruction, and generating a corresponding instruction task based on the environment evaluation information in combination with the control instruction; the intelligent fireproof door is controlled according to the instruction task, feedback information of the intelligent fireproof door is obtained, model parameter optimization is conducted on the control model according to the feedback information, and the optimized control model is obtained. The method can accurately identify the tiny signs at the initial stage of the fire.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent fire doors, and in particular to an intelligent fire door control method, system, device and medium based on artificial intelligence. Background Art

[0002] In modern buildings, smart fire doors, as an important fire protection facility, are widely used in various places to improve the level of fire safety. Traditional fire door control systems often rely on simple sensor signals to trigger switch actions, lacking the ability to intelligently analyze environmental changes and equipment status. At the same time, existing smart fire doors have limited ability to collect and analyze environmental data and cannot accurately identify subtle signs of fire in the early stages. Summary of the invention

[0003] The main purpose of the present invention is to provide an artificial intelligence-based intelligent fire door control method, system, device and medium, which can improve the analysis ability of environmental data and accurately identify subtle signs in the early stages of a fire.

[0004] To achieve the above object, the present invention provides an intelligent fire door control method based on artificial intelligence, comprising: Acquire device data and sensor data of the intelligent fire door in real time, perform feature extraction on the sensor data, and obtain a feature data set; The environmental data is evaluated and processed according to the characteristic data set to obtain corresponding environmental evaluation information, and the equipment data is analyzed in status to obtain corresponding fire door status data; The environmental assessment information and the fire door status data are transmitted to a preset control model, the fire door status data is subjected to instruction analysis by the control model to obtain corresponding control instructions, and a corresponding instruction task is generated based on the environmental assessment information and the control instructions; The intelligent fire door is controlled according to the instruction task, and feedback information of the intelligent fire door is obtained. Model parameters of the control model are optimized according to the feedback information to obtain the optimized control model.

[0005] Furthermore, the real-time acquisition of device data and sensor data of the intelligent fire door, feature extraction of the sensor data, and obtaining a feature data set include: Acquire external raw data collected by external sensors deployed around the intelligent fire door in real time, and pre-process the external raw data to obtain the corresponding sensor data; Acquire the original internal data of the intelligent fire door, pre-process the original internal data, and obtain the device data; According to a preset classification rule, the sensor data is categorized and integrated to obtain corresponding environmental data, and initial feature calculation is performed on the sensor data to obtain the feature data set; The environmental data is calculated according to the environmental type to obtain smoke concentration data, temperature data, flame data and airflow number.

[0006] Furthermore, the evaluation process is performed according to the feature data set to obtain corresponding environmental evaluation information, including: Reading the characteristic data set to obtain a first smoke concentration change rate, a first temperature rise rate, and a first thermal radiation change amount; Performing deviation analysis on the first smoke concentration change rate according to the smoke concentration data and the airflow data to obtain corresponding smoke deviation data, performing deviation analysis on the first temperature rise rate according to the temperature data and the airflow data to obtain corresponding temperature deviation data, performing deviation analysis on the first thermal radiation change amount according to the temperature data, the flame data and the airflow data to obtain corresponding thermal radiation deviation data; Determine whether the smoke deviation data, the temperature deviation data, and the thermal radiation deviation data meet the preset deviation threshold requirements; when any one of the smoke deviation data, the temperature deviation data, and the thermal radiation deviation data does not meet the deviation threshold requirements, calculate the risk characteristics based on the smoke deviation data, the temperature deviation data, and the thermal radiation deviation data combined with the environmental data to obtain a corrected characteristic data set; An environmental assessment is performed on the intelligent fire door based on the environmental data and the modified characteristic data set to obtain the environmental assessment information.

[0007] Further, the state analysis of the device data to obtain corresponding fire door state data includes: Read the device data by type to obtain the working status of the fire door and the motor status data; Perform status analysis on the working status of the fire door to obtain the switch status and lock status of the fire door; Perform health assessment on motor status data to obtain motor health status information; The switch status, locking status and motor health status information are integrated to obtain the fire door status data.

[0008] Further, the command analysis of the fire door status data by the control model to obtain corresponding control instructions, and generating corresponding command tasks based on the environmental assessment information and the control instructions, includes: The input environment assessment information and the fire door status data are formatted through the input layer of the control model to obtain an environment assessment matrix, the switch status, the locking status and the motor health status information; Comprehensively analyzing the switch state, the locking state and the motor health state information through the double-layer convolution layer and the recurrent neural network of the control model to obtain corresponding instruction features, performing instruction matching according to the instruction features to obtain corresponding control instructions, and performing task analysis on the control instructions according to the environmental assessment matrix to obtain the instruction task; The instruction task is outputted through the output layer of the control model.

[0009] Furthermore, the double-layer convolution layer and the recurrent neural network of the control model comprehensively analyze the switch state, the locking state and the motor health status information to obtain corresponding instruction features, perform instruction matching according to the instruction features to obtain corresponding control instructions, and perform task analysis on the control instructions according to the environmental assessment matrix to obtain the instruction task, including: Inputting the switch state and the lock state into a first convolutional layer, performing feature recognition on the switch state and the lock state through the first convolutional layer, and obtaining corresponding fire door control features; Inputting the motor health status information into the second convolutional layer, performing feature recognition on the motor health status information through the second convolutional layer to obtain corresponding fire door motor features; Inputting the environmental assessment matrix into a recurrent neural network, performing risk assessment on the environmental assessment matrix through the recurrent neural network, and obtaining a corresponding environmental risk level; The fire door control feature and the fire door motor feature are input into the activation function layer, and the fire door control feature and the fire door motor feature are processed nonlinearly by the activation function layer to obtain the corresponding nonlinear fire door control feature and nonlinear fire door motor feature; The nonlinear fire door control features and the nonlinear fire door motor features are input into the pooling layer, and the nonlinear fire door control features and the nonlinear fire door motor features are globally pooled by the pooling layer to obtain the comprehensive instruction pooling features; Perform command matching on the comprehensive command pooling features to obtain the corresponding control commands, and associate the control commands with the environmental risk level; Task analysis is performed on the control instructions based on the environmental risk level to obtain the instruction tasks.

[0010] Further, controlling the intelligent fire door according to the instruction task, obtaining feedback information of the intelligent fire door, optimizing model parameters of the control model according to the feedback information, and obtaining the optimized control model, comprises: Controlling the intelligent fire door to execute the command according to the command task, and obtaining the feedback information sent by the intelligent fire door after executing the command task; Analyze and process the feedback information to obtain the current fire door status, fire door execution result, fire door execution time and fire door abnormality; Performing an expected analysis on the command task to obtain a corresponding expected execution threshold, performing an error analysis on the current fire door state, fire door execution result, and fire door execution time based on the expected execution threshold to obtain corresponding state error, execution error, and time error, performing an abnormal analysis on the abnormal situation of the fire door to obtain abnormal information data; Based on the abnormal information data, the model parameters of the state error, execution error and time error are analyzed to obtain the corresponding optimization parameters; The control model is optimized based on the optimization parameters to obtain the optimized control model.

[0011] The present invention also provides an intelligent fire door control system based on artificial intelligence, which is applied to any of the above-mentioned intelligent fire door control methods based on artificial intelligence, comprising: An acquisition module, the acquisition module is used to acquire device data and sensor data of the intelligent fire door in real time, perform feature extraction on the sensor data, and obtain a feature data set; An analysis module, the analysis module is used to evaluate and process the environmental data according to the characteristic data set to obtain corresponding environmental evaluation information, and to perform status analysis on the equipment data to obtain corresponding fire door status data; a processing module, the processing module being used to transmit the environmental assessment information and the fire door status data to a preset control model, perform instruction analysis on the fire door status data through the control model to obtain corresponding control instructions, and generate corresponding instruction tasks based on the environmental assessment information and the control instructions; A control module, wherein the control module is used to control the intelligent fire door according to the instruction task, and obtain feedback information of the intelligent fire door, and optimize the model parameters of the control model according to the feedback information to obtain the optimized control model.

[0012] The present invention also provides an intelligent fire door control device based on artificial intelligence, comprising: Memory, used to store programs; A processor is used to execute the program to implement each step of the above-mentioned intelligent fire door control method based on artificial intelligence.

[0013] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0014] The artificial intelligence-based intelligent fire door control method, system, device and medium provided by the present invention have the following beneficial effects: Through in-depth analysis of sensor data, various signs of the early stage of fire, such as abnormal temperature rise and increased smoke concentration, can be more accurately identified, so as to achieve early warning and rapid response. Through comprehensive analysis of environmental data and feature data sets, the safety status of the current environment can be more accurately assessed, and then a reasonable response strategy can be formulated to ensure that the most appropriate response can be made quickly when a fire occurs. Perform status analysis on equipment data, effectively monitor the working status of fire doors, timely discover potential fault hazards, and take preventive measures to ensure that fire doors are always in good working condition. Combine environmental assessment information with fire door status data, generate command tasks through intelligent control models, so that fire doors can automatically adjust their operating modes in different environments, and improve the flexibility and adaptability of the system. According to the actual feedback information of the fire door, the parameters of the control model are continuously optimized, so that the system can continue to learn and improve, continuously improve its performance over time, and better adapt to complex and changing usage environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the intelligent fire door control method based on artificial intelligence provided by the present invention; Figure 2 It is a structural diagram of the intelligent fire door control system based on artificial intelligence provided by the present invention; Figure 3 It is a structural diagram of the intelligent fire door control device based on artificial intelligence provided by the present invention.

[0016] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. 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.

[0018] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0019] Reference Figure 1 As shown, the present invention provides an intelligent fire door control method based on artificial intelligence, comprising: Step S1: acquiring device data and sensor data of the intelligent fire door in real time, performing feature extraction on the sensor data, and obtaining a feature data set; Step S2: evaluating and processing the environmental data according to the characteristic data set to obtain corresponding environmental evaluation information, and performing status analysis on the equipment data to obtain corresponding fire door status data; Step S3: transmitting the environmental assessment information and the fire door status data to a preset control model, performing instruction analysis on the fire door status data through the control model to obtain corresponding control instructions, and generating corresponding instruction tasks based on the environmental assessment information and the control instructions; Step S4: Control the intelligent fire door according to the instruction task, obtain feedback information of the intelligent fire door, optimize the model parameters of the control model according to the feedback information, and obtain the optimized control model.

[0020] Based on the above steps, the detailed steps are as follows: Step S1: Collect data in real time from hardware devices such as fire door controllers and motor drive units, such as motor current, voltage, temperature, etc. Collect data from sensors installed around the fire door, such as smoke sensors, temperature sensors, flame detectors, airflow sensors, etc.

[0021] Remove invalid, erroneous or inconsistent data points from the collected data and convert the data into a unified format for subsequent processing. Ensure that the timestamps of all sensor data are synchronized.

[0022] Extract the trend of smoke concentration from the smoke sensor data, extract the trend of temperature over time from the temperature sensor data, and identify the presence or absence of flame from the flame detector data.

[0023] Combining the above characteristics, a feature data set is formed to characterize the current environmental risk level.

[0024] Step S2: Evaluate the risk level of the current environment based on the extracted risk feature data to determine whether there is a fire or other emergency situation, such as smoke density exceeding the safety threshold, sudden temperature rise, or flame detection, etc. Output an environmental assessment information, including the risk level, the probability of emergency occurrence, and recommended response measures.

[0025] Check whether the hardware components of the fire door are operating normally, such as whether the motor is overheated, whether there is wear, etc. Confirm whether the functions of the fire door are complete, such as whether the door can open and close smoothly, whether the sensor is accurate, etc.

[0026] All equipment status information is integrated to form the fire door status data of the fire door.

[0027] Step S3: The environmental assessment information and the fire door status data are transmitted to a preset control model, and the fire door status data is subjected to instruction analysis by the control model. Based on the environmental assessment information, it is determined whether it is necessary to change the status of the fire door (for example, to close or keep it open) to reduce the risk.

[0028] Based on the results of the fire door status command analysis and the environmental assessment command analysis, specific control commands are generated. For example, when smoke concentration rises, the fire door may need to be closed immediately; if the temperature is normal and there are no other emergencies, the current state is maintained.

[0029] The control instructions are combined with the environmental assessment information to generate a series of specific instruction tasks, including adjusting the position of fire doors, activating the alarm system, notifying relevant personnel, etc.

[0030] Step S4: Collect feedback information after the fire door executes instructions, including actual door status changes, sensor data changes, etc., compare the expected control effect with the actual feedback information, and evaluate the performance of the control model.

[0031] Based on the evaluation results, the parameters in the control model are adjusted to improve its accuracy and responsiveness.

[0032] Use new data and feedback information to retrain the control model to make it more adaptable to the current environment and equipment characteristics. Apply the optimized control model to subsequent control processes to achieve closed-loop control and continuously iterate and improve the control strategy.

[0033] The intelligent fire door control method based on artificial intelligence provided by the present invention can more accurately identify various signs of the early stage of fire, such as abnormal temperature increase, increased smoke concentration, etc., by conducting in-depth analysis of sensor data, thereby achieving early warning and rapid response. By comprehensively analyzing environmental data and feature data sets, the safety status of the current environment can be more accurately assessed, and then a reasonable response strategy can be formulated to ensure that the most appropriate response can be made quickly when a fire occurs. The equipment data is analyzed in status, the working status of the fire door is effectively monitored, potential fault hazards are discovered in time, and preventive measures are taken to ensure that the fire door is always in good working condition. The environmental assessment information is combined with the fire door status data, and the command task is generated through the intelligent control model, so that the fire door can automatically adjust its operating mode in different environments, thereby improving the flexibility and adaptability of the system. The parameters of the control model are continuously optimized according to the actual feedback information of the fire door, so that the system can continue to learn and improve, continuously improve its performance over time, and better adapt to the complex and changing use environment.

[0034] In one embodiment, the device data and sensor data of the intelligent fire door are acquired in real time, and features are extracted from the sensor data to obtain a feature data set, including: Acquire external raw data collected by external sensors (such as smoke sensors, temperature sensors, flame detectors, airflow sensors, etc.) deployed around smart fire doors in real time, and pre-process the collected external raw data, such as removing noise, smoothing signals, normalizing data range, etc., to obtain usable sensor data.

[0035] The original internal data of the intelligent fire door (such as the opening and closing status of the door, the working status of the motor, etc.) is obtained, and the original internal data is also pre-processed to obtain the device data.

[0036] The sensor data is categorized and integrated according to the preset classification rules to obtain the corresponding environmental data, and the initial features of the sensor data are calculated to obtain the feature data set.

[0037] The environmental data is subjected to environmental type calculation to obtain smoke concentration data, temperature data, flame data and airflow data.

[0038] This embodiment can quickly and accurately identify environmental changes by real-time acquisition and preprocessing of sensor data, thereby accelerating the response speed of the intelligent fire door to emergency situations and reducing reaction time. By real-time monitoring of environmental conditions (such as smoke concentration, temperature, presence of flames, etc.), potential safety hazards can be discovered in a timely manner, allowing the fire door to take appropriate measures in the shortest possible time to protect the safety of personnel and property.

[0039] In one embodiment, based on the environmental data in the previous embodiment, an evaluation process is performed according to the feature data set to obtain corresponding environmental evaluation information, including: The characteristic data set is read to obtain a first smoke concentration change rate, a first temperature rise rate, and a first thermal radiation change amount.

[0040] A deviation analysis is performed on the first smoke concentration change rate according to the smoke concentration data and the airflow data to obtain corresponding smoke deviation data.

[0041] A deviation analysis is performed on the first temperature rise rate according to the temperature data and the airflow data to obtain corresponding temperature deviation data.

[0042] A deviation analysis is performed on the first thermal radiation variation according to the temperature data, the flame data and the airflow data to obtain corresponding thermal radiation deviation data.

[0043] Determine whether the smoke deviation data, temperature deviation data and thermal radiation deviation data meet the preset deviation threshold requirements. When any of the smoke deviation data, temperature deviation data and thermal radiation deviation data do not meet the deviation threshold requirements, perform risk characteristic calculation based on the smoke deviation data, temperature deviation data and thermal radiation deviation data combined with environmental data to obtain a corrected characteristic data set.

[0044] Conduct environmental assessment of the smart fire door based on environmental data and modified feature data sets to obtain environmental assessment information. The assessment includes safety level determination, risk trend prediction, etc. The environmental assessment information is output and will serve as the basis for subsequent decision-making.

[0045] This embodiment can quickly and accurately identify environmental changes by performing real-time analysis on feature data sets, thereby speeding up the response of intelligent fire doors to emergency situations and reducing reaction time. Real-time monitoring of environmental conditions (such as smoke concentration, temperature, thermal radiation, etc.) can promptly detect potential safety hazards, allowing fire doors to take appropriate measures in the shortest possible time to protect personnel and property safety. By analyzing deviation data, intelligent analysis can be performed based on environmental data to make more accurate decisions, such as quickly closing fire doors when a fire occurs to prevent the spread of fire. By integrating and analyzing deviation data with environmental data, it is possible to better adapt to different environmental conditions and make effective responses even in complex environments.

[0046] In one embodiment, the device data is analyzed to obtain corresponding fire door status data, including: Read the device data by type to obtain the working status of the fire door and the motor status data; Analyze the working status data of the fire door to determine whether the fire door is currently open or closed, and obtain the switch status of the fire door. Analyze whether the fire door is locked, that is, determine whether the fire door can be opened manually or automatically, and obtain the locking status of the fire door.

[0047] Conduct health assessment on motor status data, analyze the motor's working performance, determine whether the motor has abnormalities or potential problems, evaluate whether the motor is working normally, whether there is overheating, abnormal vibration, increased noise, etc., and obtain motor health status information.

[0048] The switch status, lock status and motor health status information are integrated to obtain the fire door status data.

[0049] This embodiment can quickly and accurately identify the state changes of the fire door by monitoring the working state and motor state of the fire door in real time, thereby accelerating the response speed of the intelligent fire door to emergency situations and reducing the reaction time. Real-time monitoring of the state of the fire door can promptly discover potential safety hazards, allowing the fire door to take appropriate measures in the shortest time to protect the safety of personnel and property. By analyzing the state data of the fire door, the system can perform intelligent analysis based on the current state and make more accurate decisions, such as quickly closing the fire door when a fire occurs to prevent the fire from spreading.

[0050] In one embodiment, the control model is used to perform command analysis on the fire door status data to obtain corresponding control commands, and corresponding command tasks are generated based on the environmental assessment information and the control commands, including: The input layer of the control model converts the input environment assessment information and fire door status data into a format to obtain the environment assessment matrix, switch status, lock status and motor health status information. The fire door status data includes the switch status (whether it is open), lock status (whether it is locked) and motor health status (whether the motor is working properly). The environment assessment matrix contains information about the environment that is directly related to the control strategy of the fire door, such as smoke density, temperature level, etc.

[0051] Through the double-layer convolutional layer and recurrent neural network of the control model, the switch state, locking state and motor health status information are comprehensively analyzed to obtain the corresponding instruction features. The instructions are matched according to the instruction features to obtain the corresponding control instructions. The control instructions are task analyzed according to the environmental assessment matrix to obtain the instruction tasks. The instruction tasks include specific actions such as closing the door, sounding the alarm, and notifying relevant personnel.

[0052] Output the instruction task through the output layer of the control model.

[0053] This embodiment uses real-time monitoring and intelligent analysis to quickly identify abnormal conditions in the environment (such as fire) and take immediate measures, such as closing the fire door to prevent the fire from spreading. The control model can make more accurate decisions based on historical data and current environmental conditions, avoiding the misjudgment or slow response problems that may occur in traditional manual control or simple rule-driven systems. At the same time, the control model not only pays attention to the switch status of the fire door itself, but also considers its locking status and motor health status, which means that the system can detect problems and take preventive measures before any potential failure occurs.

[0054] In one embodiment, the switch state, lock state and motor health state information are comprehensively analyzed by the double-layer convolution layer and recurrent neural network of the control model to obtain the corresponding instruction features, and the instruction matching is performed according to the instruction features to obtain the corresponding control instructions, and the control instructions are task analyzed according to the environmental assessment matrix to obtain the instruction tasks, including: The switch state and the lock state are input into the first convolutional layer, and the switch state and the lock state are feature recognized by the first convolutional layer to obtain the corresponding fire door control features; The motor health status information is input into the second convolutional layer, and the motor health status information is feature recognized by the second convolutional layer to obtain the corresponding fire door motor features; The environmental assessment matrix is ​​input into the recurrent neural network, and the risk assessment of the environmental assessment matrix is ​​performed through the recurrent neural network to obtain the corresponding environmental risk level; The fire door control feature and the fire door motor feature are input into the activation function layer, and the fire door control feature and the fire door motor feature are processed nonlinearly by the activation function layer to obtain the corresponding nonlinear fire door control feature and nonlinear fire door motor feature; The nonlinear fire door control features and the nonlinear fire door motor features are input into the pooling layer, and the nonlinear fire door control features and the nonlinear fire door motor features are globally pooled by the pooling layer to obtain the comprehensive instruction pooling features; Perform command matching on the comprehensive command pooling features to obtain the corresponding control commands, and associate the control commands with the environmental risk level; The control instructions are task analyzed based on the environmental risk level to obtain the instruction tasks.

[0055] In this embodiment, by using a double-layer convolutional layer and a recurrent neural network for feature extraction and risk assessment, the system can more quickly identify the state changes and environmental risk levels of fire doors, thereby improving response speed and accuracy. By using deep learning technology, more accurate decisions can be made based on the control characteristics, motor characteristics and environmental risk levels of fire doors to ensure that the optimal control strategy is adopted in various situations. Through the input of the environmental assessment matrix and the assessment of the risk level, its behavior can be flexibly adjusted according to different environmental conditions, so as to better adapt to complex environmental changes. Through the processing of multi-layer neural networks, the system can reduce the probability of false alarms and misoperations, and improve the stability and reliability of the system. By real-time monitoring of the health status of the motor, maintenance personnel can discover potential problems earlier and take preventive measures to reduce the occurrence of equipment failures and reduce maintenance costs.

[0056] In one embodiment, the intelligent fire door is controlled according to the instruction task, and feedback information of the intelligent fire door is obtained. The model parameters of the control model are optimized according to the feedback information to obtain the optimized control model, including: Control the intelligent fire door to execute the command according to the command task, and obtain the feedback information sent by the intelligent fire door after executing the command task; The feedback information is parsed and processed to obtain the current fire door status (whether the fire door is currently closed, locked, etc.), fire door execution results (for example, whether the fire door is successfully closed, opened, etc.), fire door execution time (the time required to execute the command) and fire door abnormalities (any abnormalities or failures encountered during the execution of the command).

[0057] Perform expectation analysis on the instruction task to obtain the corresponding expected execution threshold, perform error analysis on the current fire door status, fire door execution result, and fire door execution time based on the expected execution threshold, and obtain the corresponding status error, execution error, and time error. Perform exception analysis on abnormal conditions of fire doors to obtain exception information data.

[0058] Based on the abnormal information data, the model parameters of the state error, execution error and time error are analyzed to obtain the corresponding optimization parameters; The control model is optimized based on the optimization parameters to obtain an optimized control model.

[0059] This embodiment can better adapt to different environmental conditions and respond effectively even in complex environments by analyzing feedback information and optimizing model parameters. Using feedback information for error analysis can reduce safety hazards caused by false alarms or misoperation and improve the stability and reliability of the system. Real-time equipment data can help maintenance personnel understand the working status of fire doors, facilitate timely maintenance and overhaul, and ensure that the equipment is always in the best working condition. Through intelligent analysis, the normal state of fire doors can be maintained without the need for emergency measures, avoiding unnecessary energy waste. Feedback information is used for model parameter optimization, which means that the control system will become more intelligent and efficient over time and can better adapt to new challenges that may arise in the future.

[0060] Reference Figure 2 As shown, the present invention also provides an intelligent fire door control system based on artificial intelligence, which is applied to any of the above-mentioned intelligent fire door control methods based on artificial intelligence, including: The acquisition module is used to obtain the equipment data and sensor data of the intelligent fire door in real time, extract features from the sensor data, and obtain a feature data set; An analysis module is used to evaluate and process the environmental data according to the characteristic data set to obtain corresponding environmental evaluation information, and to perform status analysis on the equipment data to obtain corresponding fire door status data; A processing module, the processing module is used to transmit the environmental assessment information and the fire door status data to a preset control model, perform instruction analysis on the fire door status data through the control model, obtain corresponding control instructions, and generate corresponding instruction tasks based on the environmental assessment information combined with the control instructions; The control module is used to control the intelligent fire door according to the instruction task and obtain feedback information of the intelligent fire door, optimize the model parameters of the control model according to the feedback information, and obtain the optimized control model.

[0061] The artificial intelligence-based intelligent fire door control system provided by the present invention can more accurately identify various signs of the early stage of fire, such as abnormal temperature increase, increased smoke concentration, etc., by conducting in-depth analysis of sensor data, thereby achieving early warning and rapid response. By comprehensively analyzing environmental data and feature data sets, the safety status of the current environment can be more accurately assessed, and then a reasonable response strategy can be formulated to ensure that the most appropriate response can be made quickly when a fire occurs. The equipment data is analyzed in status, the working status of the fire door is effectively monitored, potential fault hazards are discovered in time, and preventive measures are taken to ensure that the fire door is always in good working condition. The environmental assessment information is combined with the fire door status data, and the command task is generated through the intelligent control model, so that the fire door can automatically adjust its operating mode in different environments, thereby improving the flexibility and adaptability of the system. The parameters of the control model are continuously optimized according to the actual feedback information of the fire door, so that the system can continue to learn and improve, continuously improve its performance over time, and better adapt to the complex and changing use environment.

[0062] Reference Figure 3 As shown, the present invention also provides an intelligent fire door control device based on artificial intelligence, comprising: Memory, used to store programs; A processor is used to execute the program to implement each step of the above-mentioned intelligent fire door control method based on artificial intelligence.

[0063] In this embodiment, the processor and the memory may be connected via a bus or other means. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0064] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0065] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0066] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent fire door control method based on artificial intelligence, characterized in that: include: Acquire device data and sensor data of the intelligent fire door in real time, perform feature extraction on the sensor data, and obtain a feature data set; Evaluate and process the environmental data according to the characteristic data set to obtain corresponding environmental evaluation information, and perform status analysis on the equipment data to obtain corresponding fire door status data; The environmental assessment information and the fire door status data are transmitted to a preset control model, the fire door status data is subjected to instruction analysis by the control model to obtain corresponding control instructions, and a corresponding instruction task is generated based on the environmental assessment information and the control instructions; The intelligent fire door is controlled according to the instruction task, and feedback information of the intelligent fire door is obtained. Model parameters of the control model are optimized according to the feedback information to obtain the optimized control model.

2. The intelligent fire door control method based on artificial intelligence according to claim 1 is characterized in that: The real-time acquisition of device data and sensor data of the intelligent fire door, and feature extraction of the sensor data to obtain a feature data set include: Acquire external raw data collected by external sensors deployed around the intelligent fire door in real time, and pre-process the external raw data to obtain the corresponding sensor data; Acquire the original internal data of the intelligent fire door, pre-process the original internal data, and obtain the device data; According to a preset classification rule, the sensor data is categorized and integrated to obtain corresponding environmental data, and initial feature calculation is performed on the sensor data to obtain the feature data set; The environmental data is calculated according to the environmental type to obtain smoke concentration data, temperature data, flame data and airflow number.

3. The intelligent fire door control method based on artificial intelligence according to claim 2 is characterized in that: The step of performing evaluation processing according to the feature data set to obtain corresponding environmental evaluation information includes: Reading the characteristic data set to obtain a first smoke concentration change rate, a first temperature rise rate, and a first thermal radiation change amount; Performing deviation analysis on the first smoke concentration change rate according to the smoke concentration data and the airflow data to obtain corresponding smoke deviation data, performing deviation analysis on the first temperature rise rate according to the temperature data and the airflow data to obtain corresponding temperature deviation data, performing deviation analysis on the first thermal radiation change amount according to the temperature data, the flame data and the airflow data to obtain corresponding thermal radiation deviation data; Determine whether the smoke deviation data, the temperature deviation data, and the thermal radiation deviation data meet the preset deviation threshold requirements; when any one of the smoke deviation data, the temperature deviation data, and the thermal radiation deviation data does not meet the deviation threshold requirements, calculate the risk characteristics based on the smoke deviation data, the temperature deviation data, and the thermal radiation deviation data combined with the environmental data to obtain a corrected characteristic data set; An environmental assessment is performed on the intelligent fire door based on the environmental data and the modified characteristic data set to obtain the environmental assessment information.

4. The intelligent fire door control method based on artificial intelligence according to claim 1 is characterized in that: The state analysis of the device data to obtain corresponding fire door state data includes: Read the device data by type to obtain the working status of the fire door and the motor status data; Perform status analysis on the working status of the fire door to obtain the switch status and lock status of the fire door; Perform health assessment on motor status data to obtain motor health status information; The switch status, locking status and motor health status information are integrated to obtain the fire door status data.

5. The intelligent fire door control method based on artificial intelligence according to claim 4 is characterized in that: The method of performing instruction analysis on the fire door status data through the control model to obtain corresponding control instructions, and generating corresponding instruction tasks based on the environmental assessment information and the control instructions, includes: The input environment assessment information and the fire door status data are formatted through the input layer of the control model to obtain an environment assessment matrix, the switch status, the locking status and the motor health status information; Comprehensively analyzing the switch state, the locking state and the motor health state information through the double-layer convolution layer and the recurrent neural network of the control model to obtain corresponding instruction features, performing instruction matching according to the instruction features to obtain corresponding control instructions, and performing task analysis on the control instructions according to the environmental assessment matrix to obtain the instruction task; The instruction task is outputted through the output layer of the control model.

6. The intelligent fire door control method based on artificial intelligence according to claim 5 is characterized in that: The double-layer convolution layer and the recurrent neural network of the control model are used to comprehensively analyze the switch state, the locking state and the motor health state information to obtain corresponding instruction features, perform instruction matching according to the instruction features to obtain corresponding control instructions, and perform task analysis on the control instructions according to the environmental assessment matrix to obtain the instruction task, including: Inputting the switch state and the lock state into a first convolutional layer, performing feature recognition on the switch state and the lock state through the first convolutional layer, and obtaining corresponding fire door control features; Inputting the motor health status information into the second convolutional layer, performing feature recognition on the motor health status information through the second convolutional layer to obtain corresponding fire door motor features; Inputting the environmental assessment matrix into a recurrent neural network, performing risk assessment on the environmental assessment matrix through the recurrent neural network, and obtaining a corresponding environmental risk level; The fire door control feature and the fire door motor feature are input into the activation function layer, and the fire door control feature and the fire door motor feature are processed nonlinearly by the activation function layer to obtain the corresponding nonlinear fire door control feature and nonlinear fire door motor feature; The nonlinear fire door control features and the nonlinear fire door motor features are input into the pooling layer, and the nonlinear fire door control features and the nonlinear fire door motor features are globally pooled by the pooling layer to obtain the comprehensive instruction pooling features; Perform command matching on the comprehensive command pooling features to obtain the corresponding control commands, and associate the control commands with the environmental risk level; Task analysis is performed on the control instructions based on the environmental risk level to obtain the instruction tasks.

7. The intelligent fire door control method based on artificial intelligence according to claim 1 is characterized in that: The step of controlling the intelligent fire door according to the instruction task, obtaining feedback information of the intelligent fire door, and optimizing the model parameters of the control model according to the feedback information to obtain the optimized control model includes: Controlling the intelligent fire door to execute the command according to the command task, and obtaining the feedback information sent by the intelligent fire door after executing the command task; Analyze and process the feedback information to obtain the current fire door status, fire door execution result, fire door execution time and fire door abnormality; Performing an expected analysis on the command task to obtain a corresponding expected execution threshold, performing an error analysis on the current fire door state, fire door execution result, and fire door execution time based on the expected execution threshold to obtain corresponding state error, execution error, and time error, performing an abnormal analysis on the abnormal situation of the fire door to obtain abnormal information data; Based on the abnormal information data, the model parameters of the state error, execution error and time error are analyzed to obtain the corresponding optimization parameters; The control model is optimized based on the optimization parameters to obtain the optimized control model.

8. An intelligent fire door control system based on artificial intelligence, applied to any one of the intelligent fire door control methods based on artificial intelligence in claims 1-7, characterized in that: include: An acquisition module, the acquisition module is used to acquire device data and sensor data of the intelligent fire door in real time, perform feature extraction on the sensor data, and obtain a feature data set; An analysis module, the analysis module is used to evaluate and process the environmental data according to the characteristic data set to obtain corresponding environmental evaluation information, and to perform status analysis on the equipment data to obtain corresponding fire door status data; a processing module, the processing module being used to transmit the environmental assessment information and the fire door status data to a preset control model, perform instruction analysis on the fire door status data through the control model to obtain corresponding control instructions, and generate corresponding instruction tasks based on the environmental assessment information and the control instructions; A control module, wherein the control module is used to control the intelligent fire door according to the instruction task, and obtain feedback information of the intelligent fire door, and optimize the model parameters of the control model according to the feedback information to obtain the optimized control model.

9. An intelligent fire door control device based on artificial intelligence, characterized in that: include: Memory, used to store programs; A processor is used to execute the program to implement the various steps of an artificial intelligence-based intelligent fire door control method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 7.

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