Deep learning method for fire risk monitoring and early warning

Abstract

The invention relates to a deep learning method for fire risk monitoring and early warning, and belongs to the technical field of fire safety. Through integration of various sensors (such as an infrared imager, a smoke detector and a temperature sensor), the method realizes omnibearing perception of a monitoring area, and significantly improves the accuracy and reliability of fire monitoring. A convolutional neural network (CNN) and a long and short term memory network (LSTM) are adopted, so that the system can effectively reduce false alarm and missing alarm, monitors key parts in real time, collects multiple parameters such as temperature and voltage, and dynamically evaluates the fire risk through intelligent analysis. In addition, an efficient communication link between the early warning system and other safety systems is established, and it is ensured that emergency measures can be taken quickly when the fire risk is detected. The system also has the functions of target tracking and behavior recognition so as to judge the authenticity and severity of the fire. Through modular design and environmental adaptability optimization, the system still operates stably in various complex environments.

Description

Technical Field

[0001] This invention belongs to the field of fire safety technology, specifically a fire risk monitoring and early warning system and method. By introducing multi-sensor fusion technology, deep learning models and intelligent analysis algorithms, this invention aims to improve the accuracy and reliability of fire monitoring, effectively reduce false alarms and missed alarms, and thus better protect people's lives and property. Background Art

[0002] The severity of fire threats: Fire poses a serious threat to people's lives and property safety. Traditional fire monitoring systems have significant defects in practical applications.

[0003] Limitations of traditional fire monitoring systems: Traditional systems often have problems with false alarms and missed alarms, resulting in the inability to respond promptly and effectively when a real fire occurs, or causing unnecessary panic and waste of resources when there is no fire.

[0004] Disadvantages of single sensors: Single-type sensors, such as smoke detectors, are easily interfered by environmental factors or affected by their own limitations, resulting in false alarms. At the same time, missed alarms may cause fires to go undetected in the early stages, missing the best time to put out the fire.

[0005] Application prospects of emerging technologies: With the development of science and technology, emerging technologies such as multi-sensor fusion technology and deep learning models provide new possibilities for solving the problems of false alarms and missed alarms in fire monitoring.

[0006] Deficiencies of existing systems: There is currently no complete system that can effectively integrate multi-sensor fusion technology, deep learning and intelligent analysis technology and apply it to actual fire monitoring and early warning systems. Summary of the invention

[0007] The present invention uses a variety of different types of sensors, including infrared imagers, smoke detectors, temperature sensors, etc. These sensors work together to achieve all-round perception of the monitoring area. When any sensor detects an abnormal signal, the system will compare and verify the data with the data of other sensors. For example, when the smoke detector detects smoke, the temperature sensor simultaneously detects an abnormal increase in temperature, and the infrared imager captures the changes in thermal imaging. The system can more accurately judge the risk of fire and reduce the problem of false alarms or missed alarms that may be caused by a single sensor.

[0008] The present invention uses deep learning technology, especially convolutional neural network (CNN) and long short-term memory network (LSTM) to identify fire images. By designing a reasonable network structure and optimizing parameters, the system can learn fire characteristics from a large number of real and simulated fire images, including the shape, color, dynamic changes of flames, and the form of smoke, thereby improving the accuracy of fire detection and reducing the false alarm rate.

[0009] The present invention monitors the key parts of the monitoring area (such as battery packs, thermal management systems, electrical control systems, etc.) in real time, collects multiple key parameters (such as temperature, voltage, current, smoke concentration, etc.), and performs intelligent analysis based on historical data and real-time monitoring data. The system dynamically evaluates the fire risk by analyzing the changing trends of these data, and promptly issues an early warning signal when an abnormality is detected. For example, by analyzing the temperature change rate and voltage fluctuations, combined with the changes in smoke concentration, the system can more accurately judge the fire risk.

[0010] The present invention builds an efficient communication link between the early warning system and other safety systems (such as fire protection systems and monitoring systems). Once a fire risk is detected, the system can quickly send out a warning signal through the communication network and initiate corresponding emergency measures, such as opening the valve of the fire protection equipment and adjusting the angle of the monitoring camera to focus on the fire area, ensuring timely response measures and improving the efficiency of fire prevention and control.

[0011] The present invention uses target tracking technology to continuously track fire targets and uses behavior recognition technology to determine the authenticity and severity of the fire. For example, the system can determine the direction of movement and spread speed of the flame, as well as the flickering frequency of the flame, the diffusion mode of the smoke and other characteristics through image analysis, further improving the accuracy of the system and reducing the probability of false alarms.

[0012] In order to ensure that the system can still work normally in complex environments, the present invention optimizes the environmental adaptability design of the sensor. For example, in response to environmental factors such as high temperature, humidity, and dust, a high temperature resistant and moisture-proof design is adopted, or a filter device is equipped for the sensor to reduce the impact of environmental changes on sensor performance. This design ensures that the system can still operate reliably in various harsh environments and reduces false alarms.

[0013] The present invention has formulated a regular maintenance and calibration plan for the system to ensure the long-term stable operation of the system. The sensors are cleaned regularly, the battery power and performance are checked, and the response time of the system is verified and calibrated. For example, the sensors are cleaned and calibrated monthly, and the entire system is fully inspected and maintained every six months to ensure that the system can issue an early warning in time when a fire occurs.

[0014] In order to improve the efficiency of the system, the present invention provides professional user training courses. The training content covers the working principle, operation method, common fault handling and other aspects of the system. Through regular training and practical exercises, operators can master the operation of the system and fault response skills, thereby reducing false alarms caused by operational errors.

[0015] The present invention has data collection and analysis functions, and can automatically collect sensor detection data, warning records, and environmental parameters during system operation. By conducting in-depth analysis of these data, the system can continuously optimize the warning algorithm, adjust parameters, and improve performance. For example, by analyzing false alarms over a period of time, finding out the causes and adjusting the algorithm, the system can be ensured to continuously improve over time.

[0016] The present invention further optimizes the multi-parameter early warning system, combining multiple parameters such as sound, image, and gas to comprehensively monitor fire risks. When the sound sensor detects abnormal sounds, the image sensor captures images of suspected fires, and the gas sensor detects changes in the concentration of related gases, the system analyzes these parameters to improve the timeliness and reliability of early warning.

[0017] The system can predict the trend of fire occurrence based on historical monitoring data, using big data analysis and machine learning technology. For example, by analyzing the fluctuations and patterns of historical monitoring data, potential fire risks can be identified in advance. This function can help users perform preventive maintenance and take appropriate protective measures before a fire occurs, thereby improving the initiative of fire prevention and control.

[0018] The present invention adopts modular design, and each functional module (such as sensor module, communication module, analysis module, etc.) can be customized and expanded according to user needs. This design enables the system to flexibly adapt to monitoring scenarios of different scales, facilitates users to upgrade or adjust the system according to actual conditions, and enhances the versatility and adaptability of the system.

[0019] Through these newly added technical solutions, the present invention not only enhances the practicability of the system, but also enables it to have stronger expansion capabilities, can better adapt to different usage environments and needs, and improves the comprehensiveness and reliability of fire monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of deep learning model training and fire feature identification in the present invention; Figure 2 It is a system architecture diagram of the fire risk monitoring and early warning system of the present invention; Figure 3 It is a comprehensive analysis and warning flow chart of the multi-parameter warning system in the present invention. DETAILED DESCRIPTION

[0021] The present invention uses a variety of different types of sensors, including infrared imagers, smoke detectors, temperature sensors, etc. These sensors work together to achieve all-round perception of the monitoring area. When any sensor detects an abnormal signal, the system will compare and verify the data with the data of other sensors to reduce the false positives or false negatives that may be caused by a single sensor.

[0022] The present invention uses deep learning technology, especially convolutional neural network (CNN) and long short-term memory network (LSTM), to identify fire images. By designing a reasonable network structure and optimizing parameters, the system can learn fire characteristics from a large number of real and simulated fire images, improve the accuracy of fire detection and reduce the false alarm rate.

[0023] The present invention monitors the key parts of the monitoring area in real time, collects multiple key parameters, and performs intelligent analysis based on historical data and real-time monitoring data. The system dynamically evaluates the fire risk by analyzing the changing trends of these data, and issues early warning signals in a timely manner when an abnormality is detected.

[0024] The present invention builds an efficient communication link between the early warning system and other safety systems. Once a fire risk is detected, the system can quickly send out a warning signal through the communication network and initiate corresponding emergency measures.

[0025] The present invention uses target tracking technology to continuously track fire targets and uses behavior recognition technology to determine the authenticity and severity of the fire. The system can determine the direction of movement and spread speed of the flame, as well as the flickering frequency of the flame, the diffusion mode of the smoke and other characteristics through image analysis, further improving the accuracy of the system and reducing the probability of false alarms.

[0026] The present invention optimizes the design of the sensor for environmental adaptability. For example, in view of environmental factors such as high temperature, humidity, and dust, a high temperature resistant and moisture-proof design is adopted, or a filter device is provided for the sensor to reduce the impact of environmental changes on the sensor performance.

[0027] The present invention has formulated a regular maintenance and calibration plan for the system to ensure long-term stable operation of the system. The sensors are regularly cleaned, the battery power and performance are checked, and the response time of the system is verified and calibrated.

[0028] The present invention further optimizes the multi-parameter early warning system, combining multiple parameters such as sound, image, and gas to comprehensively monitor fire risks. When the sound sensor detects abnormal sounds, the image sensor captures images of suspected fires, and the gas sensor detects changes in the concentration of related gases, the system analyzes these parameters to improve the timeliness and reliability of early warning.

[0029] The present invention has data collection and analysis functions, and can automatically collect sensor detection data, warning records, and environmental parameters during system operation. By conducting in-depth analysis of these data, the system can continuously optimize the warning algorithm, adjust parameters, and improve performance.

[0030] The above embodiments describe in detail the implementation steps and technical details of the present invention to achieve the purpose of fire risk monitoring and early warning. These embodiments can be adjusted and optimized according to actual application scenarios and requirements.

Claims

1. A deep learning method for fire risk monitoring and early warning, which integrates the fusion technology of multiple sensors. The sensors include but are not limited to infrared imagers, smoke detectors, temperature sensors, etc. These sensors can achieve all-round perception of the monitoring area through mutual cooperation, thereby improving the accuracy and reliability of fire monitoring.

2. According to the method described in claim 1, when any sensor detects an abnormal signal, the system will automatically compare and verify the data with the data of other sensors. This process not only reduces the risk of false alarms or missed alarms caused by a single sensor, but also improves the credibility of the overall monitoring through cross-validation of multi-sensor data, ensuring timely detection of potential fire risks.

3. According to the method of claim 1 or 2, the deep learning model specifically adopts a convolutional neural network (CNN) and a long short-term memory network (LSTM). The convolutional neural network can effectively identify flame features in images, including shape, color and dynamic changes, while the long short-term memory network can process fire image sequences and analyze the development trend of fires. This combination enables the system to learn from a large number of real and simulated fire images, improve the accuracy of fire detection and significantly reduce the false alarm rate.

4. According to the method described in claim 1, the system can monitor the key parts in the monitoring area in real time, including battery packs, thermal management systems and electrical control systems. By collecting multiple key parameters such as temperature, voltage, current and smoke concentration, and performing intelligent analysis based on historical data and real-time monitoring data, the system can dynamically evaluate the fire risk. This evaluation process combines data analysis of multiple sensors and can issue early warning signals in time when an abnormality is detected, thereby buying valuable time for emergency response.

5. According to the method described in claim 1, an efficient communication link is established between the system and other safety systems (such as fire protection systems and monitoring systems). When the system detects a fire risk, it can quickly send out an early warning signal through the communication network and automatically initiate corresponding emergency measures, such as opening the valve of the fire protection equipment and adjusting the angle of the monitoring camera to focus on the fire area. This rapid response mechanism significantly improves the efficiency of fire prevention and control and reduces potential losses.

6. According to the method described in claim 1, an efficient communication link is established between the system and other safety systems (such as fire protection systems and monitoring systems). When the system detects a fire risk, it can quickly send out an early warning signal through the communication network and automatically initiate corresponding emergency measures, such as opening the valve of the fire protection equipment and adjusting the angle of the monitoring camera to focus on the fire area. This rapid response mechanism significantly improves the efficiency of fire prevention and control and reduces potential losses.

7. According to the method described in claim 1, the sensor is designed to be environmentally adaptable to ensure that it can still work stably under harsh environmental conditions such as high temperature, humidity, and dust. For example, in order to cope with high temperature environments, the sensor adopts high temperature resistant materials, moisture-proof design and filtering devices to reduce the impact of environmental factors on sensor performance. This design concept ensures that the system maintains efficient operation in various complex environments and reduces the occurrence of false alarms.

8. According to the method of claim 1, the system has formulated a regular maintenance and calibration plan to ensure the long-term stable operation of the system, which includes regular cleaning of sensors, checking battery power and performance, and verifying and calibrating the response time of the system. For example, sensors are maintained monthly and fully tested and evaluated every six months. This systematic maintenance process ensures the reliability of the monitoring system at critical moments.

9. According to the method described in claim 1, the system has the function of automatic data collection and analysis, and can automatically collect sensor detection data, warning records and environmental parameters during the operation of the system. By deeply analyzing these data, the system can continuously optimize the warning algorithm and adjust parameters to improve the performance of the system. For example, the system can analyze historical false alarm data, find out the causes of false alarms and adjust the algorithm in time, so that the system can continue to improve over time.

10. According to the method described in claim 1, the system further optimizes the multi-parameter early warning mechanism, and comprehensively monitors the fire risk by combining multiple parameters such as sound, image, and gas. When the sound sensor detects abnormal sound, the image sensor captures an image of suspected fire, and the gas sensor detects changes in the concentration of related gases, the system can integrate this information for analysis to improve the timeliness and reliability of the early warning. This multi-dimensional monitoring capability greatly enhances the early warning effect of the system and enables timely response in the early stages of a fire.

Images