Intelligent fire extinguishing system based on Internet of Things and artificial intelligence technology

Abstract

The invention relates to the technical field of fire fighting systems, and discloses an intelligent fire fighting system based on the Internet of Things and artificial intelligence technology, and the system can timely discover fire hazards and perform early warning by monitoring and intelligently analyzing the fire fighting safety of a building in real time, thereby effectively preventing the occurrence of a fire. When a fire occurs, the system can quickly transfer fire extinguishing power and automatically generate and optimize an optimal fire extinguishing and escape strategy, and the escape strategy guides personnel to escape through the guide module, so that the efficiency and safety of fire extinguishing rescue and personnel evacuation are improved; besides, the intelligent fire-fighting monitoring platform provides a visual user interface, supports real-time monitoring and decision-making command of firefighters, and realizes comprehensive control of a fire scene. The limitation of a traditional fire safety means in complex scenes is overcome, intelligence, automation and high efficiency of fire safety work are achieved, and safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fire fighting systems, and in particular to a smart fire fighting system based on Internet of Things and artificial intelligence technologies. Background Art

[0002] Fire safety, as a vital link in production and life, has always received extensive attention from all walks of life. With the accelerated advancement of urbanization, complex scenes such as large factories, office buildings, and high-rise buildings continue to emerge. These places are not only densely populated, but also complex in structure, posing unprecedented challenges to fire safety work.

[0003] Traditional fire safety measures mainly rely on manual inspections and basic firefighting facilities, such as fire extinguishers and fire hydrants. However, when faced with large and complex scenes, this traditional fire safety measure seems to be inadequate. On the one hand, manual inspections are difficult to achieve full coverage and real-time monitoring, and it is easy to miss potential fire hazards; on the other hand, traditional firefighting facilities often require manual operation when a fire occurs, with slow response speed and difficulty in responding flexibly according to the specific situation at the fire scene.

[0004] Especially in large factories and high-rise buildings, due to the complex building structure and high mobility of personnel, once a fire occurs, the fire often spreads rapidly, bringing great difficulties to personnel evacuation and fire fighting and rescue. Therefore, how to achieve fire safety monitoring, early warning and corresponding treatment in complex scenes, and at the same time guide evacuation safely and efficiently, has become an important problem that needs to be solved urgently. For this purpose, a smart fire protection system based on the Internet of Things and artificial intelligence technology is proposed. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a smart fire fighting system based on the Internet of Things and artificial intelligence technology to solve the background technical problems.

[0006] To achieve the above object, the present invention provides the following technical solution: a smart fire fighting system based on the Internet of Things and artificial intelligence technology, the system comprising: Fire information monitoring module: Deploy sensors and cameras based on the Internet of Things technology in the building. The sensors include fire smoke sensors, fire temperature sensors, and toxic or asphyxiating gas monitoring sensors. The sensors and cameras are used to monitor the interior of the building in real time to obtain comprehensive fire information. Firefighting force management module: used to obtain and manage the types, quantities, locations and status information of firefighting equipment deployed in the building, ensuring that sufficient firefighting forces can be quickly mobilized to put out the fire when a fire occurs; Guidance module: Deploy a lighting system based on IoT technology on the walls or ground of the internal passages of the building, and intelligently control the on and off of the lights to form a clear escape route and guide people to escape safely; Intelligent analysis module: Analyzes the fire information obtained by the fire information monitoring module based on artificial intelligence algorithms, including: Real-time analysis of sensor monitoring data to promptly detect fire hazards and issue early warnings and alarms; Predict the development trend of fire and determine the degree of fire risk; Analyze camera surveillance images to obtain personnel distribution information; According to the fire scene conditions, the information provided by the fire fighting force management module and the distribution of personnel, the best fire fighting plan and escape strategy are automatically generated and optimized.

[0007] Smart fire monitoring platform: As the core of the system, it integrates the functions of the above modules, receives and processes the data and information of each module, provides an intuitive user interface, displays the fire scene situation, personnel distribution, fire-fighting force distribution and escape strategy information, and supports firefighters to conduct real-time monitoring, decision-making and command, and take over control through the platform.

[0008] Preferably, the intelligent analysis module also includes automatically generating and optimizing the best rescue strategy according to the fire scene conditions and personnel distribution, and the rescue strategy is displayed in the smart fire monitoring platform.

[0009] Preferably, the smart fire monitoring platform is equipped with a lighting system of a control guidance module based on the escape strategy and rescue strategy generated by the intelligent analysis module to form lighting guidance that conforms to the escape route and rescue strategy.

[0010] Preferably, the lighting system includes red light, green light and yellow light, the escape strategy controls the green light and red light to light up, the green light represents the escape route, the rescue strategy controls the yellow light and red light to light up, the yellow light represents the rescue route, and the red light represents the dangerous route.

[0011] Preferably, the guidance module also includes a loudspeaker based on the Internet of Things technology deployed inside the building, which uses the smart fire monitoring platform and the loudspeaker to voice-control and direct the trapped people on site to escape and extinguish the fire, and voice-control and direct the fire rescue forces on site to carry out rescue. The early warning notifies the fire situation in a timely manner through the loudspeaker.

[0012] Preferably, the smart fire monitoring platform adopts 5G network for signal transmission to achieve high-speed, low-latency data transmission, ensure the real-time and accuracy of fire information, and improve the efficiency of fire command and rescue.

[0013] Preferably, the alarm information is communicated based on the fire risk level to ensure that the alarm information communicated is consistent with the fire level so that rescue equipment and personnel meet rescue needs.

[0014] Preferably, the smart fire monitoring platform remotely controls the fire-fighting equipment based on the fire-fighting force management module through the fire-fighting plan. When the intelligent analysis module detects a fire or issues an early warning, based on the predicted fire development trend and fire risk level, the fire-fighting equipment is controlled to automatically or according to the instructions of personnel to remotely start or shut down the fire-fighting equipment inside the building to extinguish the fire and prevent the spread of fire.

[0015] Preferably, the smart fire monitoring platform is also connected to a mobile application, which can receive fire warnings, escape instructions, fire extinguishing plans and rescue strategy information pushed by the smart fire monitoring platform in real time, providing instant safety guidance and rescue support for on-site personnel.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention monitors and intelligently analyzes building fire safety in real time. The system can detect fire hazards in time and issue early warnings, effectively preventing fires from happening. When a fire occurs, the system can quickly mobilize firefighting forces, automatically generate and optimize the best firefighting and escape strategies. The escape strategy guides people to escape through the guidance module, improving the efficiency and safety of firefighting and rescue and personnel evacuation. In addition, the smart fire monitoring platform provides an intuitive user interface, supports real-time monitoring and decision-making command by firefighters, and achieves comprehensive control of the fire scene. It overcomes the limitations of traditional fire safety methods in complex scenarios, and also realizes the intelligence, automation and efficiency of fire safety work, improving safety.

[0017] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a control diagram of the smart fire protection system based on the Internet of Things and artificial intelligence technology of the present invention; Figure 2 This is a flowchart of the smart fire protection system based on the Internet of Things and artificial intelligence technology of the present invention; DETAILED DESCRIPTION

[0019] 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 technical field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1-2 , a smart fire protection system based on Internet of Things and artificial intelligence technology, the system includes: a fire protection information monitoring module, a fire extinguishing force management module, a guidance module, an intelligent analysis module and a smart fire protection monitoring platform.

[0021] 1. Hardware deployment Fire information monitoring module Sensor deployment: When deploying fire smoke sensors, fire temperature sensors, and toxic or asphyxiating gas monitoring sensors in a building, a distributed layout is adopted to ensure that each key area (such as corridors, stairwells, computer rooms, warehouses, etc.) is covered by at least one sensor; the sensors are connected to the central processor through low-power wireless communication protocols such as Zigbee or LoRa to achieve real-time data transmission.

[0022] Camera deployment: HD cameras are installed in main passages, entrances and exits, key equipment areas, etc., and are powered by PoE (Power over Ethernet) to ensure 24-hour uninterrupted monitoring. The cameras support night vision and can automatically identify abnormal behaviors (such as smoke, flames, crowds, etc.).

[0023] Fire fighting force management module Equipment information entry: Through RFID (Radio Frequency Identification) technology, a unique identifier is assigned to each fire-fighting equipment (such as fire extinguishers, fire hydrants, automatic sprinkler systems, gas fire extinguishing systems, etc.), and its type, quantity, location and status information are entered into the smart fire monitoring platform. The fire-fighting force management module can control the switch of fire-fighting equipment with electronic control (such as top sprinkler systems).

[0024] Condition monitoring: Some firefighting equipment (such as automatic sprinkler systems) are equipped with sensors to monitor key parameters such as water pressure and water volume in real time to ensure that the equipment is in good condition.

[0025] Boot Module Lighting system: LED light strips or point light sources are deployed along the building walls or the ground. The lighting system is connected to the smart fire monitoring platform via Wi-Fi. The lights include yellow, green and red lights.

[0026] Speaker system: Smart speakers are installed on each floor and key areas to support voice broadcasting and command playback; the speaker system is connected to the smart fire monitoring platform through the IP network to achieve remote control.

[0027] 2. Real-time fire monitoring and early warning Intelligent analysis module Step 1: Fire hazard identification: Use machine learning algorithms (such as support vector machines (SVM) and random forests (RF)) to analyze sensor data in real time and establish a fire characteristic model. When the monitoring data exceeds the preset threshold, a warning or alarm is triggered. Specifically: Fire characteristic model establishment Algorithm input: sensor data (smoke density, temperature, gas concentration, etc.); Algorithm output: Fire risk threshold; Taking the SVM model as an example, its formula is expressed as:

[0028] in, , , are support vectors and their labels and features, is the kernel function and b is the bias term.

[0029] Early warning and alarm: Set a risk threshold. When the RiskScore exceeds the threshold, a warning or alarm is triggered. The early warning directly broadcasts fire information through the speaker system; and real-time feedback is provided to the smart fire monitoring platform.

[0030] Step 2: Fire development trend prediction: Use deep learning algorithms (such as LSTM networks) to combine historical fire data and current monitoring data to predict the speed of fire spread and the areas that may be affected. Specifically: Time series data: collect fire-related time series data, such as temperature, smoke concentration, flame intensity, etc. These data will be used as input to the LSTM network and preprocess the data; LSTM network structure Input layer: receives preprocessed time series data.

[0031] LSTM layer: contains multiple LSTM units, each of which can capture long-term dependencies in time series. The internal structure of the LSTM unit includes input gate, forget gate, output gate and cell state.

[0032] Fully connected layer: The output of the LSTM layer is further processed by a fully connected layer (also called a dense layer) to generate the final prediction results.

[0033] Forward propagation: For each time step t, the LSTM unit performs the following operations; Forget gate: determines which information to discard from the cell state. The formula is:

[0034] in, is the output of the forget gate, is the sigmoid function, and are the weight and bias of the forget gate, is the hidden state at the previous time step, is the input for the current time step.

[0035] Input gate: determines what new information will be added to the cell state. The formula is:

[0036]

[0037] in, is the output of the input gate, is a candidate cell state.

[0038] Cell state update: Combine the output of the forget gate and the input gate to update the cell state. The formula is:

[0039] Output gate: Based on the cell state, decide what information to output. The formula is:

[0040]

[0041] in, is the output of the output gate, is the hidden state at the current time step.

[0042] Loss function calculation: Calculate the difference between the predicted result and the actual value using an appropriate loss function (such as mean squared error MSE).

[0043] Back propagation and weight update: According to the gradient of the loss function, the weights and biases of the LSTM network are updated through the back propagation algorithm.

[0044] After the training is completed, the trained LSTM network is used to predict new time series data to obtain the fire spread speed and the possible affected areas.

[0045] Step 3: Fire risk assessment: Based on sensor data information, the fire risk level is assessed, and alarm information is communicated based on the fire risk level to ensure that the alarm information communicated is consistent with the fire severity, so that rescue equipment and personnel meet rescue needs, and avoid waste of firefighting resources and insufficient equipment and personnel affecting rescue. Specifically; The fire risk assessment formula is:

[0046] Among them, f is a nonlinear function obtained through machine learning training and is used to comprehensively assess fire risks.

[0047] Based on the prediction of the fire development trend in step 2 and according to the degree of fire risk, the fire-fighting equipment is remotely started or shut down to ensure that the fire can be extinguished in time when the fire point is small.

[0048] Step 4: Personnel distribution analysis: Using images captured by the camera, we use target detection algorithms (such as YOLO and Faster R-CNN) to identify and count people, and combine them with the building floor plan to generate a personnel distribution heat map. Specifically: Image capture: Use cameras to capture images of the interior of a building.

[0049] Object detection: Use object detection algorithms (such as YOLO, Faster R-CNN) to identify people in images.

[0050] Algorithm output: personnel location and number; Generate a heat map of personnel distribution; map personnel locations onto the floor plan in combination with the building floor plan; and use a heat map algorithm to generate a heat map of personnel distribution.

[0051] Step 5: Optimal strategy generation: Based on the above analysis results, heuristic search algorithms (such as A* algorithm, Dijkstra algorithm) or reinforcement learning algorithms are used to automatically generate and optimize the best fire extinguishing plan, escape strategy and rescue strategy. Specifically: Optimal path search algorithm:

[0052] Among them, g(n) is the actual cost from the starting point to the current node n, and h(n) is the estimated cost from the current node n to the end point (heuristic function). The optimal path search algorithm finds the optimal path from the starting point to the end point by continuously comparing and updating the g(n)+h(n) value of the node.

[0053] Step 6: Based on the generated fire extinguishing plan, staff and firefighters can quickly find firefighting equipment to perform fire extinguishing operations; Based on the generated escape strategy, the lighting system is controlled to start, the green light in the lighting system is used to guide the escape route, and the red light is used to warn the roads that cannot be escaped. At the same time, the speaker system is controlled to broadcast the escape information; Based on the generated rescue strategy, the lighting system is controlled to start, and the yellow light in the lighting system is used to guide the rescue road. Because firefighters wear equipment and can safely pass through roads that are more dangerous than ordinary people, they choose a closer road to enter the building to rescue the trapped people when it is safe. Red light is also used to warn of wrong rescue roads and unsafe roads.

[0054] 3. Functional realization of smart fire monitoring platform Smart fire monitoring platform Data integration and processing: The platform adopts a microservice architecture, and each module exchanges data through RESTful API; it uses big data processing frameworks (such as Apache Kafka and Spark) to achieve real-time data collection, cleaning, storage, and analysis.

[0055] User interface: Use Web front-end technologies (such as React and Vue.js) to develop an intuitive user interface that displays information such as the fire scene, personnel distribution, firefighting force distribution, escape strategy, and rescue strategy. The interface supports touch operation, which facilitates firefighters to make decisions quickly; At the same time, controllable fire-fighting equipment (such as top sprinkler system) can be remotely started or shut down to extinguish fire and prevent fire from spreading; Through the intelligent fire monitoring platform and loudspeaker voice control, the trapped people on the scene are commanded to escape and extinguish the fire, and the fire rescue forces on the scene are commanded to carry out rescue by voice control; Staff and firefighters can also modify the generated escape and rescue strategies through the smart fire monitoring platform to avoid temporary debris or unidentified dangers on the road for safe rescue and escape, and optimize the escape and rescue strategies by modifying the path.

[0056] Mobile applications: Develop mobile applications based on native development frameworks (such as Flutter and React Native), supporting iOS and Android systems. The application receives fire warnings, escape instructions, fire extinguishing plans, and rescue strategy information pushed by the platform in real time through the WebSocket protocol, and provides voice prompts and map navigation functions.

[0057] 4. 5G network applications and remote control Data transmission: The smart fire monitoring platform uses 5G network for data transmission, taking advantage of the high bandwidth and low latency characteristics of 5G to ensure the real-time and accuracy of fire information.

[0058] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A smart fire fighting system based on Internet of Things and artificial intelligence technology, characterized in that: The system comprises: Fire information monitoring module: Deploy sensors and cameras based on the Internet of Things technology in the building. The sensors include fire smoke sensors, fire temperature sensors, and toxic or asphyxiating gas monitoring sensors. The sensors and cameras are used to monitor the interior of the building in real time to obtain comprehensive fire information. Firefighting force management module: used to obtain and manage the types, quantities, locations and status information of firefighting equipment deployed in the building, ensuring that sufficient firefighting forces can be quickly mobilized to put out the fire when a fire occurs; Guidance module: Deploy a lighting system based on IoT technology on the walls or ground of the internal passages of the building, and intelligently control the on and off of the lights to form a clear escape route and guide people to escape safely; Intelligent analysis module: Analyzes the fire information obtained by the fire information monitoring module based on artificial intelligence algorithms, including: Real-time analysis of sensor monitoring data to promptly detect fire hazards and issue early warnings and alarms; Predict the development trend of fire and determine the degree of fire risk; Analyze camera surveillance images to obtain personnel distribution information; Automatically generate and optimize the best fire-fighting plan and escape strategy based on the fire scene situation, information provided by the fire-fighting force management module, and personnel distribution; Smart fire monitoring platform: As the core of the system, it integrates the functions of the above modules, receives and processes the data and information of each module, provides an intuitive user interface, displays the fire scene situation, personnel distribution, fire-fighting force distribution and escape strategy information, and supports firefighters to conduct real-time monitoring, decision-making and command, and take over control through the platform.

2. According to claim 1, a smart fire fighting system based on the Internet of Things and artificial intelligence technology is characterized in that: The intelligent analysis module also includes automatically generating and optimizing the best rescue strategy based on the fire scene conditions and personnel distribution. The rescue strategy is displayed on the smart fire monitoring platform.

3. According to claim 2, a smart fire fighting system based on the Internet of Things and artificial intelligence technology is characterized in that: The smart fire monitoring platform generates escape strategies and rescue strategies based on the intelligent analysis module, and has a lighting system that controls the guidance module to form lighting guidance that conforms to the escape route and rescue strategy.

4. According to claim 3, a smart fire fighting system based on the Internet of Things and artificial intelligence technology is characterized in that: The lighting system includes red light, green light and yellow light. The escape strategy controls the green light and red light to light up, and the green light represents the escape route. The rescue strategy controls the yellow light and red light to light up, and the yellow light represents the rescue route, and the red light represents the dangerous route.

5. According to claim 1, the intelligent fire fighting system based on the Internet of Things and artificial intelligence technology is characterized in that: The guidance module also includes a loudspeaker based on the Internet of Things technology deployed inside the building. Based on the smart fire monitoring platform and the loudspeaker voice control command, the trapped people on site are directed to escape and extinguish the fire, and the fire rescue forces on site are directed to carry out rescue by voice control. The early warning notifies the fire situation in time through the loudspeaker.

6. The intelligent fire fighting system based on the Internet of Things and artificial intelligence technology according to claim 5 is characterized in that: The smart fire monitoring platform uses 5G network for signal transmission to achieve high-speed, low-latency data transmission, ensure the real-time and accuracy of fire information, and improve the efficiency of fire command and rescue.

7. The intelligent fire fighting system based on the Internet of Things and artificial intelligence technology according to claim 1 is characterized in that: The alarm information is communicated based on the fire risk level, ensuring that the alarm information communicated is consistent with the fire level so that rescue equipment and personnel meet rescue needs.

8. The intelligent fire fighting system based on the Internet of Things and artificial intelligence technology according to claim 1 is characterized in that: The smart fire monitoring platform remotely controls the fire-fighting equipment based on the fire-fighting force management module through the fire-fighting plan. When the intelligent analysis module detects a fire or issues an early warning, it controls the fire-fighting equipment automatically or according to the instructions of personnel based on the predicted fire development trend and fire risk level, and remotely starts or shuts down the fire-fighting equipment inside the building to extinguish the fire and prevent the spread of fire.

9. The intelligent fire fighting system based on the Internet of Things and artificial intelligence technology according to claim 2 is characterized in that: The smart fire monitoring platform is also connected to a mobile application, which can receive fire warnings, escape instructions, fire extinguishing plans and rescue strategy information pushed by the smart fire monitoring platform in real time, providing on-site personnel with immediate safety guidance and rescue support.

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