Distributed intelligent fire and disaster prevention early warning system and method

Abstract

The invention relates to a distributed intelligent fire prevention and disaster prevention early warning system and method, and aims to realize early detection, early warning and quick response of a fire disaster through an advanced monitoring technology, a data analysis algorithm, an automatic fire extinguishing device and a safety network communication function. The system includes a plurality of modules. The monitoring module integrates a temperature sensor, a smoke sensor, a flame sensor and a high-resolution camera so as to monitor fire characteristic data in real time. And the data processing center adopts a machine learning algorithm to preprocess and analyze the data, and predicts the possibility of fire occurrence. The automatic fire extinguishing system is provided with an autonomous navigation technology and a fire positioning module, and fire extinguishing operation can be automatically executed when a fire source is detected. The system has the beneficial effects that the accuracy of fire detection is improved, early warning and timely response are realized, automatic fire extinguishing response is realized, the safety and adaptive capacity of the system are enhanced, the expansibility and compatibility of the system are improved, and the maintenance cost and the false alarm rate are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of fire prevention and control, and in particular to a distributed intelligent fire prevention and disaster prevention early warning system and method. Background Art

[0002] In modern society, with the acceleration of urbanization and the increase in population density, fire safety has become an important issue in the field of public safety. Fire not only poses a huge threat to people's lives and property, but also has an adverse impact on the environment and social stability. Therefore, the development of efficient fire warning and fire extinguishing systems is a key link in improving fire safety management capabilities.

[0003] Traditional fire monitoring systems mainly rely on single or small number of monitoring devices such as smoke detectors and temperature sensors. Although these devices can detect the occurrence of fire to a certain extent, they have the following limitations:

[0004] Limited detection range: Traditional sensors can usually only monitor a limited area around them, making it difficult to achieve large-area coverage.

[0005] High false alarm rate: Traditional fire warning systems are prone to false alarms due to the inability to accurately distinguish between fire smoke and other interference factors (such as smoke, dust, etc.).

[0006] Long response time: Traditional fire monitoring systems often require human intervention to acknowledge alarms and take action, which can delay valuable firefighting time.

[0007] Insufficient data processing capabilities: Traditional systems have limitations in processing and analyzing large amounts of real-time data, making it difficult to use historical and real-time data for deep learning and pattern recognition.

[0008] Poor system integration: Lack of effective integration and coordination between various security systems leads to waste of resources and inefficiency.

[0009] Encryption and security issues: Traditional systems often ignore encryption and security during data transmission and processing, making them vulnerable to external attacks and data leaks.

[0010] High maintenance cost: Regular manual inspection and maintenance are required, which increases long-term operating costs.

[0011] Poor environmental adaptability: In complex or changing environments, such as mines, oil fields and other areas with complex terrain, the mobility and adaptability of traditional systems face challenges.

[0012] In order to overcome the above problems, it is imperative to develop a distributed intelligent fire and disaster prevention early warning system that integrates advanced monitoring technology, data analysis algorithms, automatic fire extinguishing devices, and secure network communication functions. Summary of the invention

[0013] In order to solve the problems of the prior art, the present invention provides a distributed intelligent fire prevention and disaster early warning system and method, which aims to provide a wider monitoring coverage, reduce the false alarm rate, shorten the response time, and improve the data processing capability and system integration. At the same time, it ensures the security of data transmission, reduces maintenance costs, and enhances environmental adaptability to meet the high standards of fire safety management in modern society.

[0014] In order to solve the above technical problems, the present invention is implemented by the following technical solutions: In the first aspect, a distributed intelligent fire prevention and disaster prevention early warning system and method comprises:

[0015] An information management platform to coordinate and manage the various components of the system;

[0016] a data processing center connected to the information management platform and configured to receive and process data from the monitoring modules;

[0017] Monitoring module, including temperature sensor, smoke sensor, flame sensor and image monitor, used to monitor fire characteristics in the environment in real time;

[0018] Output modules, including fire isolation doors, alarms, and sprinklers, for performing preventive and suppression actions when a fire is detected;

[0019] Client, used to receive warning information and remotely control the system;

[0020] The automatic fire extinguishing system includes a position moving mechanism, a fire situation positioning module, a data transmission module, a fire extinguishing module and a self-positioning module, which are used to automatically locate the fire source and perform fire extinguishing;

[0021] The Internet police platform transmits data with the information management platform to ensure system security.

[0022] The information management platform should include multiple functional modules such as data reception, processing, analysis and display. It should be able to receive data from the monitoring module in real time, perform preliminary processing and storage, and perform fire warning analysis according to the preset algorithm;

[0023] By deploying high-performance server clusters and configuring sufficient storage space and computing power to support real-time processing and storage of large amounts of data, we develop or integrate advanced fire warning algorithms to quickly analyze received data, identify fire characteristics, and generate warning information.

[0024] In a specific embodiment of the first aspect, the data processing center is further configured to use a machine learning algorithm to analyze the monitoring data to predict the possibility of fire occurrence.

[0025] Data collection and preprocessing:

[0026] Data collection: Through the temperature sensor, smoke sensor, flame sensor, image monitor and other devices in the monitoring module, the fire characteristic data in the environment is collected in real time.

[0027] Data preprocessing: Clean, denoise, and format the collected raw data to ensure data quality and consistency. The preprocessing process may include steps such as removing outliers, filling missing values, and normalizing data.

[0028] Machine learning model selection and training:

[0029] Model selection: According to the specific needs of fire warning, select appropriate machine learning algorithms, such as logistic regression, support vector machine (SVM), random forest, deep learning, etc. Deep learning algorithms (such as convolutional neural network CNN, recurrent neural network RNN) perform well in processing image and video data and are suitable for image monitor data.

[0030] Model training: Use historical fire data and normal environment data as training sets to train the selected machine learning model. During the training process, the prediction accuracy and generalization ability of the model are improved by adjusting model parameters and optimizing algorithms.

[0031] Real-time data analysis and prediction:

[0032] Real-time data access: The data collected by the monitoring module in real time is connected to the data processing center to ensure the timeliness and continuity of the data.

[0033] Data analysis and prediction: Use the trained machine learning model to analyze the real-time access data and predict the possibility of fire. The model will make a comprehensive judgment based on the fire characteristics in the data (such as abnormal temperature, increased smoke concentration, flames, etc.) and give a corresponding warning level.

[0034] Warning information release:

[0035] When the machine learning model predicts that the possibility of a fire exceeds the preset threshold, the data processing center will generate early warning information and release it to relevant personnel in a timely manner through information management platforms, clients and other channels.

[0036] In a specific implementation of the first aspect, the position movement mechanism of the automatic fire extinguishing system includes a multi-axis mobile platform for autonomous navigation in a complex environment.

[0037] Multi-axis mobile platform design:

[0038] Structural selection: Multi-axis mobile platforms usually adopt wheeled, tracked or legged structures to adapt to different terrains and environments. For example, in complex environments such as mines and oil fields, tracked platforms are highly favored due to their good obstacle-crossing ability and stability.

[0039] Power system: Equipped with an efficient motor or engine as the power source, it provides sufficient driving force and torque for the platform to ensure stable movement in various terrains.

[0040] Drive and control: Use advanced drive technology and control systems to achieve multi-axis coordinated motion and precise positioning. The control system may include components such as sensors, controllers, and actuators to sense environmental information, plan motion paths, and execute motion instructions.

[0041] Autonomous navigation technology:

[0042] Map construction and positioning: Use SLAM (simultaneous localization and mapping) technology or pre-loaded map information to achieve autonomous positioning of the platform in complex environments. Use sensors (such as lidar, cameras, etc.) to collect environmental data in real time, build a three-dimensional map of the surrounding environment, and determine the position of the platform in the map.

[0043] Path planning: Based on the location of the fire and the surrounding environment information, a path planning algorithm (such as A* algorithm, Dijkstra algorithm, etc.) is used to plan an optimal fire-fighting path for the platform. The path planning algorithm will consider multiple factors such as terrain, obstacles, distance, etc. to ensure that the platform can reach the fire source safely and quickly.

[0044] Obstacle avoidance and decision-making: During the movement, the platform will monitor the changes in the surrounding environment in real time through sensors, and make decisions and react according to the obstacle avoidance algorithm. When encountering obstacles, the platform will adjust the movement path or take other obstacle avoidance measures to ensure that it can reach the destination smoothly.

[0045] Fire extinguishing module integration:

[0046] The fire extinguishing module (such as water gun, foam sprayer, etc.) is integrated into the multi-axis mobile platform and connected to the platform through pipelines and control systems. When the platform reaches the vicinity of the fire source, the fire extinguishing module will start working according to the instructions and spray the fire extinguishing agent to extinguish the fire.

[0047] The fire extinguishing module may also be equipped with an automatic sensing and regulation system that can automatically adjust the spray angle and flow rate according to the size and intensity of the fire source to improve the fire extinguishing effect.

[0048] In a specific embodiment of the first aspect, the image monitor includes a high-resolution camera configured to capture images and identify flames and smoke through an image recognition algorithm.

[0049] High-resolution camera configuration:

[0050] Hardware selection: Select cameras with high resolution, high sensitivity and low noise as the core components of the image monitor. These cameras should have sufficient pixel count and frame rate to ensure that clear and coherent image sequences can be captured.

[0051] Installation location: According to the characteristics of the monitoring area and the fire risk level, the installation location of the high-resolution camera should be reasonably arranged. The camera should be able to cover key areas, such as flammable material storage areas, electrical equipment dense areas, etc., to ensure comprehensive monitoring of potential fire risks.

[0052] Parameter setting: Set the camera parameters, including exposure time, white balance, contrast, etc., to optimize image quality and adapt to different lighting conditions.

[0053] Image recognition algorithm development:

[0054] Algorithm selection: Advanced image recognition algorithms, such as convolutional neural networks (CNNs) in deep learning algorithms, are used to analyze and process images captured by the camera. These algorithms can automatically identify the characteristics of flames and smoke, such as color, shape, texture, etc.

[0055] Model training: Use a large amount of flame and smoke image data to train the algorithm model. By continuously adjusting and optimizing model parameters, the algorithm's recognition accuracy and robustness for flames and smoke can be improved.

[0056] Real-time processing: Deploy the trained algorithm model into the system to process and analyze the images captured by the camera in real time. When the algorithm identifies flames or smoke, the early warning mechanism is triggered immediately.

[0057] System Integration and Testing:

[0058] Integrate high-resolution cameras and image recognition algorithms into the distributed intelligent fire and disaster prevention early warning system, and seamlessly connect with other components such as the information management platform and data processing center.

[0059] Carry out comprehensive tests on the system, including functional tests, performance tests, stability tests, etc. to ensure that the system can operate stably and accurately identify flames and smoke.

[0060] In a specific implementation of the first aspect, the output module further includes an emergency communication system for notifying relevant personnel in case of fire.

[0061] In a specific implementation of the first aspect, the cyber police platform is configured to implement an encryption protocol to protect system data from unauthorized access.

[0062] In a second aspect, a distributed intelligent fire prevention and disaster prevention early warning method comprises the following steps:

[0063] Step 1: Use the monitoring module to monitor the fire characteristics in the environment in real time;

[0064] Step 2: Transmit monitoring data to the data processing center;

[0065] Step 3: Use the data processing center to analyze monitoring data and determine fire risks;

[0066] Step 4: When it is detected that the fire risk is higher than the first threshold and lower than the second threshold, the automatic fire extinguishing system is controlled to automatically locate the fire source and perform the fire extinguishing operation by outputting a signal;

[0067] Step 5: When it is detected that the fire risk is higher than the second threshold, the output module performs prevention and suppression actions;

[0068] Step 6: Send warning information to users through the client;

[0069] Step 7: Transmit relevant fire data to the online police platform for feedback or alarm.

[0070] In a specific embodiment of the second aspect, the method further includes the step of using a machine learning algorithm to predict the occurrence of fire.

[0071] In a specific implementation of the second aspect, the automatic fire extinguishing system uses its own positioning module and position movement mechanism to autonomously navigate to the fire source location.

[0072] In a specific implementation of the second aspect, the output module notifies relevant personnel through an emergency communication system in the event of a fire.

[0073] The beneficial effects of the present invention are:

[0074] 1. By integrating temperature sensors, smoke sensors, flame sensors and high-resolution cameras, the system can monitor fire characteristics in real time in multiple dimensions. Compared with single sensor systems, this multi-parameter monitoring method significantly improves the accuracy and reliability of fire detection; achieve early warning and timely response: combined with machine learning algorithms, the system can analyze historical data and newly monitored data to predict the possibility of fire. This allows the system to issue an early warning before the fire actually occurs, providing users with enough time to take preventive measures or evacuate, significantly improving the safety of people and property;

[0075] 2. The automatic fire extinguishing system uses advanced positioning technology and mobile platforms to quickly and accurately locate the source of fire and automatically perform fire extinguishing operations without human intervention. This not only shortens the response time, but also effectively controls the fire in the early stages of the fire and reduces the scope of damage; the system's autonomous navigation technology and self-positioning module enable the automatic fire extinguishing system to have good mobility and obstacle avoidance strategies in complex environments, ensuring that it can still work efficiently in changing terrain and emergency situations;

[0076] 3. The online police platform uses advanced encryption protocols, such as AES, to ensure the security of data transmission within the system, prevent data leakage and unauthorized access, and ensure the integrity and privacy of warning information; the client design allows users to easily receive and manage warning information, and can also remotely control the system, providing a convenient interactive method and enhancing the user experience;

[0077] 4. Because the system is based on a distributed architecture, its modular design makes it easy to add new sensors, upgrade existing components or integrate additional functions in the future, maintaining the system's cutting-edge and adaptability; by using high-resolution cameras and image recognition algorithms, the system can reduce false alarms caused by dust, smoke or small animal activities. At the same time, the system's automation reduces dependence on manual inspection and maintenance, saving long-term operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic diagram of the overall system structure of the present invention.

[0079] Figure 2 It is a schematic diagram of the internal structure of the output module of the present invention.

[0080] Figure 3 It is a schematic diagram of the internal structure of the monitoring module of the present invention.

[0081] Figure 4 It is a schematic diagram of the internal structure of the fire location module of the present invention.

[0082] Figure 5 It is a schematic diagram of the internal structure of the automatic fire extinguishing system of the present invention.

[0083] Figure 6 It is a schematic diagram of scenario simulation of the present invention.

[0084] Figures 1 to 6In: 1. Data processing center; 2. Monitoring module; 3. Information management platform; 4. Automatic fire extinguishing system; 5. Client; 6. Output module; 7. Internet police platform; 21. Temperature sensor; 22. Smoke sensor; 23. Flame sensor; 24. Image monitor; 41. Position moving mechanism; 42. Fire location module; 43. Data transmission module; 44. Self-positioning module; 45. Fire extinguishing mechanism; 46. Control processor; 421. Temperature sensor; 422. Flame sensor; 423. Camera; 71. Fire isolation door; 72. Alarm; 73. Sprinkler. DETAILED DESCRIPTION

[0085] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0086] like Figure 1-6 A distributed intelligent fire prevention and disaster prevention early warning system and method is shown, including: an information management platform for coordinating and managing various components of the system; a data processing center, connected to the information management platform and configured to receive and process data from a monitoring module; a monitoring module, including a temperature sensor, a smoke sensor, a flame sensor and an image monitor, for real-time monitoring of fire characteristics in the environment; an output module, including a fire isolation door, an alarm and a sprinkler, for performing preventive and suppressive actions when a fire is detected; a client, for receiving early warning information and remotely controlling the system; an automatic fire extinguishing system, including a position moving mechanism, a fire location module, a data transmission module, a fire extinguishing module and a self-positioning module, for automatically locating the source of fire and performing fire extinguishing; a network alarm platform, which transmits data with the information management platform to ensure system safety.

[0087] The data processing center is further configured to analyze the monitoring data using a machine learning algorithm to predict the possibility of a fire. The position movement mechanism of the automatic fire extinguishing system includes a multi-axis mobile platform for autonomous navigation in a complex environment. The image monitor includes a high-resolution camera configured to capture images and identify flames and smoke through an image recognition algorithm. The output module further includes an emergency communication system for notifying relevant personnel in the event of a fire. The cyber police platform is configured to implement an encryption protocol to protect system data from unauthorized access.

[0088] A distributed intelligent fire prevention and disaster prevention early warning method comprises the following steps: step 1: using a monitoring module to monitor fire characteristics in the environment in real time; step 2: transmitting monitoring data to a data processing center; step 3: using the data processing center to analyze the monitoring data and determine the fire risk; step 4: when it is detected that the fire risk is higher than a first threshold and lower than a second threshold, by outputting a signal, controlling an automatic fire extinguishing system to automatically locate the fire source and perform a fire extinguishing operation;

[0089] Step 5: When it is detected that the fire risk is higher than the second threshold, the output module performs prevention and suppression actions; Step 6: Send warning information to the user through the client; Step 7: Transmit relevant fire data to the online police platform for feedback or alarm.

[0090] The method further includes the step of using a machine learning algorithm to predict the occurrence of a fire. The automatic fire extinguishing system uses a self-positioning module and a position movement mechanism to autonomously navigate to the location of the fire source. The output module notifies relevant personnel through an emergency communication system in the event of a fire.

[0091] Embodiment 1: The design aims to use advanced monitoring technology and data analysis algorithms to achieve real-time monitoring, early warning and automatic fire extinguishing of fire characteristics. The system consists of the following parts:

[0092] Information management platform: This platform is the core of the system. It uses a high-performance server cluster to ensure sufficient data processing capabilities and storage space. The specific configuration includes 8 CPU cores, 32GB memory and 1TB SSD storage space, ensuring the rapid processing and access of large amounts of data. The platform can receive data from the monitoring module, perform preliminary processing and storage, and analyze the data according to the preset algorithm to generate fire warning information.

[0093] Data Processing Center: The data processing center connected to the information management platform uses machine learning algorithms, such as random forests, to conduct in-depth analysis of the collected monitoring data to predict the possibility of fire. This center is responsible for data preprocessing, such as cleaning, denoising, formatting, missing value filling and data normalization.

[0094] Monitoring module: This module includes a variety of sensors, such as temperature sensor (-40℃~125℃ measurement range, ±0.5℃ accuracy), smoke sensor (sensitivity level 1), flame sensor (response time ≤5 seconds) and high-resolution camera (4000×3000 pixels). These devices work together to monitor and record fire characteristic data in the environment in real time.

[0095] Output module: When a fire risk is detected, the output module is responsible for executing prevention and suppression actions. It includes fire isolation doors (fire resistance limit ≥ 3 hours), alarms (sound pressure level ≥ 80dB) and sprinklers (flow rate ≥ 30L / min) to ensure rapid response in emergency situations.

[0096] Client: As the user interface of the system, the client enables users to receive warning information and remotely monitor and control the system.

[0097] Automatic fire extinguishing system: Equipped with a multi-axis mobile platform (maximum speed 1m / s) and a fire location module (positioning accuracy ±1m), the automatic fire extinguishing system can autonomously navigate to the fire source when necessary and use the fire extinguishing module (spraying distance ≥20m) for precise fire extinguishing.

[0098] Internet Police Platform: In order to protect the security of data transmission, the Internet Police Platform implements the AES encryption protocol to prevent unauthorized access and data leakage.

[0099] Embodiment 2: The method achieves early detection and response measures of fire through a series of steps.

[0100] Use monitoring modules: First, monitoring modules deployed in key areas continuously monitor environmental parameters, including temperature and humidity sensors (temperature range -40℃~125℃, humidity range 0%~100%, accuracy ±3%RH), smoke sensors (detection range 0.001~20%obs / m) and flame sensors (response time ≤3 seconds). These sensors collect fire characteristic data in real time, such as temperature, smoke concentration and flame brightness.

[0101] Data transmission and processing: The monitored data is then transmitted to a data processing center, which uses deep learning algorithms, such as convolutional neural networks (CNN), to pre-process and analyze the data. This step is to determine the fire risk, that is, to assess the probability of a fire occurring.

[0102] Automatic fire extinguishing operation: If the fire risk is higher than the first threshold but lower than the second threshold, the data processing center will send a signal to the automatic fire extinguishing system, which will automatically locate the fire source and perform fire extinguishing operations.

[0103] Prevention and suppression actions: When the fire risk exceeds the second threshold, indicating that the fire may be more serious, the output module will be activated to perform prevention and suppression actions, such as activating fire isolation doors and sprinklers to prevent the fire from spreading.

[0104] Warning message sending: When a high fire risk is detected, the client will receive a warning message so that the user can take action. At the same time, the emergency communication system will be activated to notify relevant personnel.

[0105] Data transmission and feedback: Finally, all relevant fire data will be transmitted to the online police platform for further analysis and feedback, or alarm as needed.

[0106] In this approach, each step works closely together to ensure the efficiency and timeliness of fire warning and response measures. The automation and intelligent design of the system greatly improves the ability of fire safety management, reduces human errors, and improves the speed and efficiency of fire response.

[0107] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A distributed intelligent fire and disaster prevention early warning system, characterized in that: include: An information management platform to coordinate and manage the various components of the system; a data processing center connected to the information management platform and configured to receive and process data from the monitoring modules; Monitoring module, including temperature sensor, smoke sensor, flame sensor and image monitor, used to monitor fire characteristics in the environment in real time; Output modules, including fire isolation doors, alarms, and sprinklers, for performing preventive and suppression actions when a fire is detected; Client, used to receive warning information and remotely control the system; The automatic fire extinguishing system includes a position moving mechanism, a fire situation positioning module, a data transmission module, a fire extinguishing module and a self-positioning module, which are used to automatically locate the fire source and perform fire extinguishing; The Internet police platform transmits data with the information management platform to ensure system security.

2. A distributed intelligent fire and disaster prevention early warning system according to claim 1, characterized in that: The data processing center is further configured to analyze the monitoring data using machine learning algorithms to predict the likelihood of fire occurrence.

3. A distributed intelligent fire and disaster prevention early warning system according to claim 1, characterized in that: The position movement mechanism of the automatic fire extinguishing system includes a multi-axis mobile platform for autonomous navigation in complex environments.

4. A distributed intelligent fire and disaster prevention early warning system according to claim 1, characterized in that: The image detector includes a high-resolution camera configured to capture images and identify flames and smoke through image recognition algorithms.

5. A distributed intelligent fire and disaster prevention early warning system according to claim 1, characterized in that: The output module further includes an emergency communication system for notifying relevant personnel in the event of a fire.

6. A distributed intelligent fire and disaster prevention early warning system according to claim 1, characterized in that: The cyber police platform is configured to implement encryption protocols to protect system data from unauthorized access.

7. A distributed intelligent fire prevention and disaster prevention early warning method, characterized in that: The following steps are involved: Step 1: Use the monitoring module to monitor the fire characteristics in the environment in real time; Step 2: Transmit monitoring data to the data processing center; Step 3: Use the data processing center to analyze monitoring data and determine fire risks; Step 4: When it is detected that the fire risk is higher than the first threshold and lower than the second threshold, the automatic fire extinguishing system is controlled to automatically locate the fire source and perform the fire extinguishing operation by outputting a signal; Step 5: When it is detected that the fire risk is higher than the second threshold, the output module performs prevention and suppression actions; Step 6: Send warning information to users through the client; Step 7: Transmit relevant fire data to the online police platform for feedback or alarm.

8. A distributed intelligent fire prevention and disaster prevention early warning method according to claim 7, characterized in that: Further included is the step of using a machine learning algorithm to predict the occurrence of fires.

9. A distributed intelligent fire prevention and disaster prevention early warning method according to claim 7, characterized in that: The automatic fire extinguishing system uses its own positioning module and position movement mechanism to autonomously navigate to the fire source.

10. A distributed intelligent fire prevention and disaster prevention early warning method according to claim 7, characterized in that: The output module notifies relevant personnel through the emergency communication system in the event of a fire.

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