Large-space intelligent active fire extinguishing system

Abstract

The invention relates to the technical field of fire detection and fire extinguishing, and discloses a large-space intelligent active fire extinguishing system which comprises a sound wave imaging module used for emitting ultrasonic waves in a target space, receiving echoes, analyzing the position of a fire source and generating three-dimensional fire source positioning data; the intelligent optical fiber sensing network is deployed in each area of the space and is used for collecting temperature, pressure and humidity information in real time and sending the collected environmental parameter data to the central control system; and the central control system is in two-way communication with the sound wave imaging module and the intelligent optical fiber sensing network and receives fire source positioning data and environment monitoring data provided by the sound wave imaging module and the intelligent optical fiber sensing network. Through the multi-modal fusion scheme of the intelligent optical fiber sensing technology and the sound wave imaging technology, the high-precision real-time monitoring effect on a fire source and the environment is achieved, accurate positioning and dynamic tracking of the position of the fire source are achieved, and powerful data support is provided for fire extinguishing decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of fire detection and fire extinguishing, and in particular to a large-space intelligent active fire extinguishing system. Background Art

[0002] While existing fire detection and extinguishing systems can provide a certain degree of response in certain application scenarios, they often suffer from the following deficiencies. First, traditional fire detection systems often rely on a single temperature sensor or smoke sensor to detect the fire source. This approach struggles to accurately locate the fire source in the early stages of a fire, especially in large spaces, and often results in false alarms or missed alarms. Traditional technologies are insufficient for rapid response in the early stages of a fire and are unable to provide real-time, accurate fire source data in complex environments.

[0003] Existing fire-fighting systems generally rely on manual intervention or automated execution based on preset rules, lacking flexible, intelligent adjustment mechanisms. Traditional methods often require fixed, pre-set firefighting strategies for varying fire scenarios, making them incapable of addressing the challenges posed by changing fire environments. For example, factors like wind speed fluctuations and the spread of fire sources can affect firefighting effectiveness, but traditional systems cannot adjust strategies in real time. For complex fires in large spaces, traditional firefighting methods are often imprecise, resulting in wasted resources and delayed extinguishing efforts.

[0004] Existing drone technologies for firefighting are typically simplistic, often serving only as auxiliary reconnaissance tools. While drones can provide real-time video surveillance, they lack the ability to effectively coordinate with ground-based firefighting systems, limiting their effectiveness in complex fire environments. The lack of precise flight path planning and firefighting strategy optimization limits the effectiveness of drones in carrying out their missions, hindering their ability to flexibly and efficiently locate fire sources and precisely extinguish fires. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a large-space intelligent active fire extinguishing system, which solves the problem that existing fire extinguishing systems generally rely on manual intervention or automatic execution based on preset rules, and lack flexible intelligent adjustment.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a large space intelligent active fire extinguishing system, comprising: Acoustic imaging module, used to transmit ultrasonic waves in the target space and receive echoes, analyze the location of the fire source and generate three-dimensional fire source positioning data; An intelligent fiber optic sensor network is deployed in various areas of the space to collect temperature, pressure, and humidity information in real time and send the collected environmental parameter data to the central control system; The central control system communicates bidirectionally with the acoustic imaging module and the intelligent fiber optic sensor network, receives the fire source location data and environmental monitoring data provided by them, and integrates and analyzes them through the built-in artificial intelligence algorithm to generate dynamic fire extinguishing strategies; Automatic fire extinguishing device, connected to the central control system, selects the corresponding fire extinguishing method and releases the fire extinguishing medium in a targeted manner according to the received fire extinguishing strategy; The drone collaborative system works in conjunction with the central control system to perform real-time monitoring tasks above the fire scene, assist in confirming the location of the fire source, and conduct aerial auxiliary fire fighting according to the instructions of the central control system.

[0007] Preferably, the acoustic imaging module includes multiple ultrasonic transmitting units and receiving units, which are arranged in an array at multiple fixed points, and their relative positions are known. They are used to synchronously transmit ultrasonic pulse signals and receive echo signals, and calculate echo path length information through propagation time difference.

[0008] Preferably, the acoustic imaging module includes the following steps to achieve fire source location: Synchronously transmitting an ultrasonic pulse signal of a predetermined frequency and waveform; Receive the echo and record the receiving time; Calculate the propagation time difference Δt and combine it with the speed of sound c to derive the propagation distance d = c·Δt; The three-dimensional coordinates of the fire source are inverted based on multiple sets of path distance constraints.

[0009] Preferably, the intelligent optical fiber sensing network includes distributed fiber Bragg grating sensors for collecting temperature, humidity and pressure parameters, and converting them into digital signals for uploading to a central control system.

[0010] Preferably, the sensor has a redundant layout structure, which supports stable and continuous data acquisition and transmission under high temperature and high pressure environments, ensuring the real-time and reliability of fire scene monitoring.

[0011] Preferably, the central control system integrates a multimodal data fusion algorithm for jointly analyzing data from the acoustic imaging module and the optical fiber sensing network to generate fire source location results and dynamic fire extinguishing plans; The data fusion process is based on a weighted fusion model: F(x,y,z)=α·S(x,y,z)+β·G(x,y,z); Among them, F(x, y, z) is the fused fire source space estimation value; S(x, y, z) is the fire source position estimation value obtained by the acoustic imaging module; G(x, y, z) is the position estimation value obtained by the optical fiber sensing network through thermal field interpolation; α is the acoustic wave data weight coefficient, satisfying α+β=1, where α∈[0,1], β is the optical fiber data weight coefficient, satisfying β∈[0,1]; (x, y, z) is the three-dimensional space coordinate.

[0012] Preferably, the automatic fire extinguishing device includes: Rotary positioning nozzle subsystem, used to adjust the spray direction according to space target instructions; Multiple types of fire extinguishing agent injection units, equipped with gas, dry powder and water-based modules; Select the control module to control the switching of fire extinguishing agent channels and execute fire extinguishing instructions; Feedback monitoring device is used to collect parameters such as injection pressure and residual medium and feed them back to the central control system.

[0013] Preferably, the fire extinguishing agent injection parameters are adjusted in real time based on the following model: Where D is the spray distance; v is the spray velocity of the fire extinguishing agent; θ is the spray elevation angle; g is the acceleration of gravity, which is 9.81 m / s 2 H is the height difference between the nozzle and the target fire source, sinθ is the y-coordinate of the intersection of the terminal side of angle θ and the unit circle, and cosθ is the x-coordinate of the intersection of the terminal side of angle θ and the unit circle; The central control system adjusts the parameters v and θ in real time according to the target coordinates (x, y, z) to ensure that the fire extinguishing agent hits the fire source.

[0014] Preferably, the UAV collaborative system includes multiple aircraft equipped with infrared thermal imaging modules and wireless communication modules. After receiving instructions from the central control system, the aircraft completes fire scene inspections, fire source verification and aerial auxiliary fire fighting tasks.

[0015] Preferably, the drone has an autonomous return mechanism, which automatically returns to the base station when the battery level is lower than a preset threshold or after completing the inspection mission, and is redeployed to the fire scene according to the central control system after charging is completed.

[0016] The present invention provides a large-space intelligent active fire extinguishing system. It has the following beneficial effects: 1. This invention utilizes a multimodal fusion solution combining intelligent fiber optic sensing and acoustic imaging technologies to achieve high-precision, real-time monitoring of fire sources and the surrounding environment. Compared to traditional single-sensor methods, this invention effectively overcomes the interference of temperature and smoke in complex fire environments, enabling precise positioning and dynamic tracking of fire sources, providing robust data support for firefighting decision-making.

[0017] 2. This invention integrates deep neural networks and reinforcement learning algorithms to provide an intelligent solution for fire source identification and optimization of fire extinguishing strategies. The system can automatically adjust fire extinguishing strategies and paths based on the different fire environments and fire source characteristics. Compared to existing manual or rule-driven fire extinguishing methods, this invention significantly improves fire extinguishing efficiency through adaptive intelligent control, reduces reliance on human intervention, and enhances the system's response capabilities.

[0018] 3. This invention utilizes efficient flight path planning and real-time feedback mechanisms within the UAV collaborative system, ensuring that drones can rapidly respond to fires and execute precise firefighting tasks. Compared to traditional firefighting methods, this invention, through the linkage of drones with a central control system, not only enables rapid location of fire sources but also enables aerial firefighting in challenging environments, significantly improving the flexibility and efficiency of large-scale fire management.

[0019] 4. This invention utilizes a distributed data storage and history management system to comprehensively record and efficiently query firefighting process and environmental data. Compared to traditional static storage solutions, this invention improves data utilization efficiency through intelligent data processing and real-time storage, providing powerful data support for subsequent fault diagnosis, performance optimization, and security audits, ensuring the system's continued operation and reliability in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. 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 making creative efforts are within the scope of protection of the present invention.

[0022] Please see the attached Figure 1 The embodiment of the present invention provides a large-space intelligent active fire extinguishing system, comprising: Acoustic imaging module, used to transmit ultrasonic waves in the target space and receive echoes, analyze the location of the fire source and generate three-dimensional fire source positioning data; An intelligent fiber optic sensor network is deployed in various areas of the space to collect temperature, pressure, and humidity information in real time and send the collected environmental parameter data to the central control system; The central control system communicates bidirectionally with the acoustic imaging module and the intelligent fiber optic sensor network, receives the fire source location data and environmental monitoring data provided by them, and integrates and analyzes them through the built-in artificial intelligence algorithm to generate dynamic fire extinguishing strategies; Automatic fire extinguishing device, connected to the central control system, selects the corresponding fire extinguishing method and releases the fire extinguishing medium in a targeted manner according to the received fire extinguishing strategy; The drone collaborative system works in conjunction with the central control system to perform real-time monitoring tasks above the fire scene, assist in confirming the location of the fire source, and conduct aerial auxiliary fire fighting according to the instructions of the central control system.

[0023] Module 1: Acoustic Imaging Module In this embodiment, the acoustic imaging module is used to achieve three-dimensional positioning and imaging identification of fire sources in a large-space fire environment. Its core principle is based on the characteristics of ultrasonic wave propagation in the air, combined with a multi-point sensor array deployed in space, to achieve accurate capture and analysis of fire source reflection signals.

[0024] The acoustic imaging module preferably includes a plurality of ultrasonic transmitting units and ultrasonic receiving units, each of which is distributed in an array at multiple spatial fixed points in the monitored area. Its position is known during installation and it has geometric stability of relative positioning, which facilitates subsequent collaborative calculation of multi-point data.

[0025] During actual operation, each ultrasonic transmitting unit synchronously emits an acoustic pulse signal with a predetermined frequency and waveform into space. Preferably, this acoustic signal has good directionality and penetration, suitable for propagation and reflection in large, open or semi-open spaces. Fire sources or high-temperature areas in the acoustic wave propagation path can create significant acoustic reflection interfaces due to factors such as temperature disturbances and changes in air density.

[0026] After the reflected echo reaches the receiving unit, its arrival time is recorded independently by each receiving unit. Based on this time delay, the system calculates the echo propagation time and thus obtains the sound wave propagation path length. The propagation distance D can be calculated using the following relationship: Where D represents the one-way distance of the sound wave from the transmitting unit to the reflection point and then back to the receiving unit, Δt is the total time difference between the emission and reception of the sound wave, and c is the speed of sound in air. It should be noted that the speed of sound parameter can be dynamically adjusted according to changes in ambient temperature and humidity in specific applications or corrected in real time by a pre-set correction model of the system to ensure the accuracy of distance calculation.

[0027] Multiple receiving units are set up within the entire monitoring space. The system can simultaneously obtain multiple sets of path distance data. Combined with the known position coordinates of the receiving units, the three-dimensional coordinates of the fire source can be inverted using the multilateral measurement principle and spatial positioning algorithm. This process is completed by solving the following optimization problem: In the above formula, X f is the fire source position vector to be estimated, X i is the known spatial position of the i-th receiving unit, ||XX i || is the actual space distance, Δt i is the sound wave propagation time difference recorded by the receiving unit, n is the number of receiving units, is the value of X when it reaches the minimum value, and n is the number of receiving units.

[0028] By solving this minimization problem, the system can obtain the fire source spatial coordinate solution that minimizes the sum of squared propagation path errors, thereby achieving high-precision fire source spatial positioning.

[0029] It's worth noting that to ensure the stability and availability of acoustic propagation data, the system can be configured with a time synchronization module or a reference signal module in actual deployments to ensure consistent data timestamps between transmitting and receiving units, preventing synchronization errors from affecting positioning results. Furthermore, a coded identifier can be preferably introduced into the transmitted signal to enhance signal recognizability in complex acoustic environments.

[0030] In addition, to improve the system's adaptability to dynamic fire sources or multiple fire sources, the acoustic imaging module can be set up with periodic polling or continuous pulse mode, so that the system can obtain the dynamic change process of the fire source in real time, and then provide the central control system with trend prediction and strategy update.

[0031] The acoustic imaging module is connected to the central control system via a data bus or wireless communication. The module transmits location data to the central control system in the form of structured coordinates or image data. The central control system further integrates and analyzes this location information with data from the intelligent fiber optic sensor network to improve the accuracy and completeness of fire source determination.

[0032] In integrated applications, the acoustic imaging module, as a key front-end sensing component of the fire extinguishing system of the present invention, not only provides accurate spatial basic data for the formulation of fire extinguishing strategies, but also plays a supporting role in tasks such as fire source verification and spread path judgment.

[0033] Module 2: Intelligent Fiber Optic Sensor Networks In this embodiment, the intelligent fiber-optic sensor network serves as a key environmental sensing subsystem within the large-space intelligent active fire extinguishing system of the present invention, enabling continuous monitoring of multiple areas and parameters within a large space. This module utilizes distributed fiber-optic sensing units to achieve high-density sensing of key parameters such as ambient temperature, humidity, and pressure. This multidimensional environmental data is then reported in real time to a central control system via a highly reliable transmission link, providing data support for fire source identification, trend assessment, and firefighting decision-making.

[0034] The intelligent optical fiber sensing network preferably adopts fiber Bragg grating (FBG) sensor technology, which has the characteristics of sensitive response, strong anti-electromagnetic interference ability, and long-distance transmission. It is particularly suitable for long-distance and continuous parameter collection in large space environments with complex structures such as industrial buildings, tunnels, factories, and airport terminals.

[0035] In terms of specific structure, the FBG sensor uses a grating structure with periodic refractive index changes in the optical fiber. In response to changes in external environmental parameters (such as temperature or strain), the reflected wavelength will drift accordingly. The relationship between this wavelength drift Δλ and the change in environmental parameters can be established by the following expression: Δλ=k T ΔT+k ε ·Δε; Where Δλ is the change in the optical fiber reflection center wavelength in nanometers, ΔT is the change in ambient temperature in degrees Celsius, Δ is the change in the axial strain of the optical fiber in dimensionless units, and k T k is the sensitivity coefficient of the optical fiber to temperature changes, ε is the sensitivity coefficient of the optical fiber to strain changes.

[0036] In this embodiment, FBG sensors are preferably packaged in different types of optical fiber array structures, including planar and linear arrangements, to accommodate deployment requirements within different building spaces. Planar arrangements are suitable for installation on rooftops, suspended ceilings, or ceilings, enabling dense sampling of the upper thermal field. Linear arrangements are suitable for installation along walls or ventilation ducts, monitoring heat conduction paths or airflow propagation trajectories.

[0037] To achieve simultaneous sensing of multiple environmental parameters, each FBG sensor unit in the system can be configured with multiple grating points, forming a fiber multi-point array. Wavelength division multiplexing technology enables parallel transmission of data from multiple sensing points within the same fiber channel. The optical fiber signal is converted into an electrical signal by a photoelectric demodulation module and then uploaded to the central control system in real time by the data acquisition system.

[0038] During fiber optic network operation, the system continuously collects data on changes in optical fiber reflection wavelengths and uses a temperature-wavelength mapping model to restore the ambient temperature at each monitoring point. To improve the accuracy of the system's modeling of temperature field distribution in complex fire environments, the central control system uses a thermal field interpolation algorithm to spatially restore discrete temperature point data, forming a continuous temperature field data map.

[0039] Thermal field interpolation can be calculated using a weighted average model, and its core expression is: Where: T(x,y,z) is the interpolated temperature value at the target spatial point (x,y,z); T i is the measured temperature value of the i-th sensing point; w i is the weight factor for the interpolation of the target point by the i-th sensor point, which can preferably be allocated according to functions such as Euclidean distance or inverse distance squared; m is the number of sensor points involved in the interpolation calculation.

[0040] The interpolation model can be used in conjunction with the spatial coordinate grid to establish a three-dimensional thermal field distribution map to assist in determining the potential location, development trend and impact area of ​​the fire source, and serve as one of the input data for fusion analysis with the positioning results of the acoustic imaging module.

[0041] To ensure the system's continued operation in harsh environments such as high temperature, high pressure, or toxic fumes, the fiber optic sensing units in this embodiment preferably utilize industrial-grade fiber optic sensors with high-temperature-resistant coatings, fire-resistant protective layers, or metal sheathed enclosures. Furthermore, to enhance system stability in the event of a single point of failure, the fiber optic layout incorporates a redundant structure, such as a dual-ring or ring hybrid topology, allowing data transmission to continue through alternative paths even if any node fails.

[0042] Data collected by the fiber optic sensor is uploaded to the central control system via a communication interface module. Data upload methods include Ethernet, wired serial port, direct fiber connection, etc., and can also be integrated into the industrial control network through a fiber-optic OPC (OLE for Process Control) gateway to achieve unified scheduling and integration with other modules.

[0043] Based on real-time environmental data such as temperature, humidity, and pressure, the central control system can issue early warnings for abnormal trends in specific areas. For example, if the temperature in a certain area continues to rise and there is a significant temperature gradient in the surrounding area, the system will automatically mark the area as a suspected high-risk area and conduct a collaborative comparison with the positioning results of the acoustic imaging module.

[0044] Module 3: Central control system.

[0045] In this embodiment, the central control system serves as the core decision-making and coordination module of the large-space intelligent active fire extinguishing system of the present invention. It undertakes the main functions of receiving, integrating, processing and generating response strategies for various types of sensor information, and is a key component for realizing the intelligent and autonomous operation of the system.

[0046] The central control system establishes a two-way communication connection with the acoustic imaging module and the intelligent fiber optic sensor network through the communication interface. The connection can be implemented based on Ethernet, serial bus, wireless communication protocol or industrial control bus. During operation, the system continuously receives fire source location information and environmental monitoring data from the above modules, and standardizes and encapsulates them in a unified data structure for subsequent fusion analysis and model processing.

[0047] For fire source identification, the central control system preferably integrates a multimodal data fusion algorithm module. This module is used to spatially cross-validate the fire source coordinate estimates provided by the acoustic imaging module with the high-temperature ranges derived from thermal field interpolation by the intelligent fiber optic sensor network, thereby improving the robustness and accuracy of fire source location. The fusion process integrates multi-source information using a linear weighted model, which is mathematically expressed as follows: X fused =α·X acoustic +β·X thermal ; Where: X fused =(x f ,y f ,z f ) is the estimated coordinate of the fire source space after fusion; X acoustic =(x a ,y a ,z a ) is the fire source location coordinate output by the acoustic imaging module; X thermal =(x t ,y t ,z t ) is the estimated coordinate of the high-temperature zone obtained after the optical fiber thermal field inversion; α is the credibility weight coefficient of the acoustic imaging module, which satisfies 0≤α≤1; β is the weight coefficient of the optical fiber thermal field estimation, which satisfies β=1-α; the weight coefficient can be dynamically set based on the stability of real-time data, the adaptive parameter adjustment algorithm or the training model output.

[0048] Based on fusion positioning, the central control system further intelligently predicts and responds to fire development trends. In this embodiment, a deep neural network (DNN) model is preferably used as a fire risk identifier. This model uses historical and real-time sensor data as input and outputs a classification and risk score for the current fire source status. Input features include, but are not limited to, temperature distribution, acoustic signal waveform characteristics, thermal field gradient, and smoke diffusion rate.

[0049] The basic structure of the DNN model includes an input layer, several hidden layers, and an output layer. The input feature vector is represented as: x = [x1, x2, ..., x n ]; The fire risk score output calculated by the system is: Among them, P fire is the probability or risk score of fire, ranging from [0, 1], x i is the i-th input eigenvalue, w i is the model weight of the corresponding feature; b is the bias term, σ(·) is the activation function, preferably Sigmoid or ReLU function, x usually represents an n-dimensional vector, and n is the dimension of the vector.

[0050] The above models can be trained through offline data, or dynamically updated and retrained based on field monitoring data to maintain adaptability to different spatial structures and fire types.

[0051] In terms of firefighting strategy formulation, the central control system further integrates a reinforcement learning (RL) algorithm module. This module is used to automatically search for the optimal response strategy under different fire source spatial states and environmental conditions. Its core calculation model is: Among them, Q(s,a): the expected cumulative return that can be obtained by taking action a in state s, R(s,a): the immediate reward of performing action a in the current state s, s′: the new state transferred to after action a is executed, a′: all optional actions in the new state s′, γ: discount factor, which controls the influence of future rewards on the current strategy, and its value is between (0, 1).

[0052] The state variables may include the spatial location of the fire source, the fire level, the current state of fire extinguishing resources, etc., and the action variables represent executable fire extinguishing operation combinations (such as which group of sprinklers to start, which fire extinguishing agent to select, etc.).

[0053] During the continuous interaction with the system environment, the reinforcement learning module optimizes the strategy through the feedback mechanism of state-action return, so that the system can form an autonomous and adaptive decision-making path when facing different types of fires or fire development.

[0054] The fire extinguishing strategy information output by the system will be structured and encoded and sent to the automatic fire extinguishing device and drone collaborative system, including parameters such as the type of fire extinguishing agent, target area coordinates, release intensity, and execution time window, to ensure that the strategy can be accurately responded to by the execution module.

[0055] The data fusion process is based on a weighted fusion model: F(x,y,z)=α·S(x,y,z)+β·G(x,y,z); Among them, F(x, y, z) is the estimated value of the fire source space after fusion; S(x, y, z) is the estimated value of the fire source position obtained by the acoustic imaging module; G(x, y, z) is the position estimate obtained by the optical fiber sensing network through thermal field interpolation; α is the acoustic wave data weight coefficient, satisfying α+β=1, where α∈[0,1], β is the optical fiber data weight coefficient, satisfying β∈[0,1]; (x, y, z) is the three-dimensional space coordinate Preferably, the central control system can also integrate an anomaly detection module, a model confidence assessment mechanism and a manual intervention interface to support fault-tolerant judgment, alarm prompts and manual takeover in special circumstances.

[0056] Module 4: Automatic fire extinguishing device In this embodiment, the automatic fire extinguishing device and response execution system serve as key execution modules of the large-scale intelligent active fire extinguishing system. They are primarily responsible for automatically executing fire extinguishing operations based on the fire extinguishing strategy generated by the central control system. This module is designed to ensure rapid and accurate response and extinguishing actions when a fire occurs, ensuring the system's efficiency and reliability.

[0057] The automatic fire extinguishing system is composed of multiple fire extinguishing units, including but not limited to automatic sprinklers, gas fire extinguishing systems, and mobile drone fire extinguishing units. The selection and layout of fire extinguishing units will be dynamically adjusted based on fire risk assessments and real-time monitoring data for different spatial environments. Each fire extinguishing unit maintains real-time data communication with the central control system to receive and respond to instructions from the central control system.

[0058] Regarding the control strategy for fire extinguishing devices, the central control system uses an optimization algorithm to calculate the optimal fire extinguishing strategy based on the fire source location, fire growth trend, and environmental parameters of the target area. This strategy involves several aspects: extinguishing agent selection, extinguishing device activation sequence, extinguishing agent release amount, extinguishing agent release location, and release duration. This fire extinguishing strategy preferably utilizes a multi-objective optimization algorithm to comprehensively consider fire extinguishing effectiveness and resource utilization efficiency.

[0059] The fire extinguishing strategy calculation formula can be expressed as: Among them, S represents the optimal fire extinguishing strategy, S i represents the i-th candidate strategy, E j (S i ) represents the fire extinguishing effect evaluation function, described in the strategy S i The fire extinguishing effect of the next j-th target area, wj represents the weight coefficient of the jth target area, C k (S i ) indicates that the fire extinguishing device is in strategy S i The resource consumption under the above conditions preferably includes fire extinguishing dosage, energy consumption, etc., λ is the trade-off factor between resource consumption and fire extinguishing effect, m represents the number of target areas, that is, the total number of areas where the fire extinguishing effect needs to be evaluated, and n represents the number of resource consumption types, such as fire extinguishing agent dosage, energy consumption, equipment load, and other consumption factors. Indicates that among all candidate strategies S i In the equation, find the strategy that minimizes the entire objective function (the part in brackets), which is the optimal fire extinguishing strategy.

[0060] Through the above optimization calculations, the system can select the optimal strategy to achieve the best balance between fire extinguishing effect and resource utilization.

[0061] During execution, the control system of the automatic fire extinguishing system activates the corresponding fire extinguishing unit according to the instructions of the central control system. For automatic sprinkler systems, the control system accurately calculates the spraying time and spraying volume to ensure sufficient coverage of the spraying area and avoid damage to the equipment caused by excess water. For gas fire extinguishing systems, the system automatically adjusts the gas type (such as carbon dioxide, nitrogen, etc.) and release concentration based on the type of fire source and environmental characteristics to ensure effective fire extinguishing while avoiding hazards to personnel health and equipment safety.

[0062] In the application of drone firefighting units, the system generates a drone flight path and firefighting strategy based on precise fire source location and real-time environmental monitoring data. Using real-time feedback data and image recognition technology, the drone determines the specific location and severity of the fire, adjusting its flight altitude, speed, and extinguishing agent release accordingly. During its mission, the drone utilizes autonomous navigation and obstacle avoidance technology to ensure efficient and accurate arrival at the fire source and firefighting operations in complex environments.

[0063] The response time of automatic fire extinguishing devices and the response execution system directly impacts fire extinguishing efficiency. To this end, the response system in this embodiment utilizes a real-time scheduling and early warning mechanism. Once the central control system issues a fire extinguishing command, the system prioritizes the fire extinguishing device closest to the fire source and in the most appropriate condition. If a fire extinguishing device malfunctions or is unable to respond in a timely manner, the system automatically switches to another available device to ensure unimpeded fire extinguishing operations. Furthermore, the system can adjust its strategy based on changes in the fire source and feedback during the fire extinguishing process, thereby improving the adaptability and reliability of fire extinguishing operations.

[0064] The fire extinguishing agent injection parameters are adjusted in real time based on the following models: Where D is the spray distance; v is the spray velocity of the fire extinguishing agent; θ is the spray elevation angle; g is the acceleration of gravity, which is 9.81 m / s 2 H is the height difference between the nozzle and the target fire source, sinθ is the y-coordinate of the intersection of the terminal side of angle θ and the unit circle, and cosθ is the x-coordinate of the intersection of the terminal side of angle θ and the unit circle; The central control system adjusts the parameters v and θ in real time according to the target coordinates (x, y, z) to ensure that the fire extinguishing agent hits the fire source.

[0065] To monitor the firefighting process, the system is equipped with a real-time monitoring module that displays key indicators such as the operating status of each firefighting device, the remaining amount of extinguishing agent, and the health status of the equipment. The central control system dynamically adjusts the firefighting strategy based on this monitoring data to ensure maximum firefighting effectiveness.

[0066] Preferably, to enhance the system's fault tolerance and redundancy, the automatic fire extinguishing system can be configured with multiple backup systems, such as backup fire sprinklers, backup drones, or backup gas fire extinguishing systems. These backup systems will automatically activate when the primary system fails or cannot meet fire extinguishing needs, ensuring the continuity and reliability of fire extinguishing operations.

[0067] Module 5: UAV Collaborative System In this embodiment, the drone collaborative system serves as one of the key execution modules of the large-scale intelligent active fire extinguishing system of the present invention. It is primarily responsible for coordinating with the central control system to perform real-time monitoring of the airspace above the fire scene and provide aerial assistance in firefighting according to the central control system's instructions. This module leverages the drone's aerial patrol and real-time data transmission capabilities to rapidly locate the fire source in its early stages, enabling efficient fire source identification and real-time monitoring. This provides accurate fire information and real-time data support for firefighting decision-making.

[0068] The drone collaboration system consists of multiple drones, each equipped with different sensor modules and fire-fighting equipment. By integrating high-precision thermal imaging sensors, high-definition cameras, lidar, and other equipment, the system can provide comprehensive, three-dimensional monitoring of the fire scene, collecting and transmitting real-time information such as fire scene images, temperature distribution, and smoke concentration. This information is transmitted to the central control system via a communication link for real-time analysis and judgment.

[0069] Real-time monitoring and fire source location: The drone uses its thermal imaging sensor to scan the fire scene and obtain thermal radiation information from the fire source. The temperature changes of the fire source detected by the thermal imaging sensor can be processed using the following formula: T fire =ΔT·κ; Among them, T firerepresents the temperature of the fire source; ΔT is the temperature difference between the fire source and the surrounding environment; κ is the emissivity of the fire source material, which is usually a known constant.

[0070] By analyzing the thermal radiation intensity of different areas, the central control system can accurately locate the fire source and integrate it with the real-time data of other modules in the system through coordinate calibration to provide support for the formulation of subsequent fire extinguishing strategies.

[0071] Aerial assisted firefighting: After confirming the location of the fire source, the drone will quickly fly to the fire source area according to the instructions issued by the central control system to perform aerial fire fighting tasks. The fire fighting operations may include: spraying gas fire extinguishing agent, spraying dry powder fire extinguishing agent, etc. The drone fire fighting device calculates the amount of fire extinguishing agent released using the following formula: Among them, M extinguish Indicates the amount of extinguishing agent released; A fire is the area of ​​the fire source; ρ extinguish is the density of the extinguishing agent; V disp is the spraying speed of the extinguishing agent; t release is the release time of the fire extinguishing agent.

[0072] UAV collaborative flight and path planning: During firefighting missions, multiple drones can work together, using precise path planning algorithms to ensure each drone efficiently covers the target area. This path planning algorithm dynamically calculates the drone's flight capabilities, fire source location, wind speed, and other factors. Optimization techniques such as AI or genetic algorithms are preferably employed to achieve the shortest flight path and optimal coverage over the fire.

[0073] The basic goal of UAV flight path planning is to minimize flight time and energy consumption while avoiding obstacles or other safety risks that may exist during flight. The path planning model can be described by the following optimization function: Among them, P opt is the optimal path; f time (P) is the flight time of the path; f energy (P) is the energy consumption of the path; α is the weight factor of time and energy consumption, which is dynamically adjusted according to the actual situation. It means finding the parameter value that minimizes the objective function among all candidate parameters P.

[0074] Data feedback and system linkage: During flight, the drones transmit real-time data (including thermal images, flight data, and extinguisher release status) to the central control system via wireless communication. This data is analyzed and processed in real time by the central control system, which adjusts the drone's flight path and firefighting strategy based on the fire's development. Furthermore, the drone collaborative system supports a variety of emergency response plans, such as rapid adjustment of flight paths and firefighting strategies in response to wind speed fluctuations or fire expansion.

[0075] Fault detection and redundancy mechanisms: To ensure the high reliability of the drone collaboration system in complex fire environments, each drone is equipped with multiple redundant systems, including built-in battery health monitoring, flight stability testing, and communication link monitoring. If a drone malfunctions, the central control system automatically dispatches other drones to take over, ensuring uninterrupted firefighting operations.

[0076] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A large space intelligent active fire extinguishing system, characterized in that: include: Acoustic imaging module, used to transmit ultrasonic waves in the target space and receive echoes, analyze the location of the fire source and generate three-dimensional fire source positioning data; An intelligent fiber optic sensor network is deployed in various areas of the space to collect temperature, pressure, and humidity information in real time and send the collected environmental parameter data to the central control system; The central control system communicates bidirectionally with the acoustic imaging module and the intelligent fiber optic sensor network, receives the fire source location data and environmental monitoring data provided by them, and integrates and analyzes them through the built-in artificial intelligence algorithm to generate dynamic fire extinguishing strategies; Automatic fire extinguishing device, connected to the central control system, selects the corresponding fire extinguishing method and releases the fire extinguishing medium in a targeted manner according to the received fire extinguishing strategy; The drone collaborative system works in conjunction with the central control system to perform real-time monitoring tasks above the fire scene, assist in confirming the location of the fire source, and conduct aerial auxiliary fire fighting according to the instructions of the central control system.

2. A large space intelligent active fire extinguishing system according to claim 1, characterized in that: The acoustic imaging module includes multiple ultrasonic transmitting units and receiving units, which are arranged in an array at multiple fixed points with known relative positions. They are used to synchronously transmit ultrasonic pulse signals and receive echo signals, and calculate echo path length information through the propagation time difference.

3. A large space intelligent active fire extinguishing system according to claim 1, characterized in that: The acoustic imaging module includes the following steps to achieve fire source location: Synchronously transmitting an ultrasonic pulse signal of a predetermined frequency and waveform; Receive the echo and record the receiving time; Calculate the propagation time difference Δt and combine it with the speed of sound c to derive the propagation distance d = c·Δt; The three-dimensional coordinates of the fire source are inverted based on multiple sets of path distance constraints.

4. A large space intelligent active fire extinguishing system according to claim 1, characterized in that: The intelligent optical fiber sensing network includes distributed optical fiber Bragg grating sensors for collecting temperature, humidity and pressure parameters, and converting them into digital signals and uploading them to the central control system.

5. The large space intelligent active fire extinguishing system according to claim 1, characterized in that: The sensor has a redundant layout structure, supports stable and continuous data acquisition and transmission in high temperature and high pressure environments, and ensures the real-time and reliability of fire scene monitoring.

6. A large space intelligent active fire extinguishing system according to claim 1, characterized in that: The central control system integrates a multimodal data fusion algorithm for jointly analyzing data from the acoustic imaging module and the fiber optic sensor network to generate fire source location results and dynamic fire extinguishing plans; The data fusion process is based on a weighted fusion model: F(x,y,z)=α·S(x,y,z)+β·G(x,y,z); Among them, F(x, y, z) is the fused fire source space estimation value; S(x, y, z) is the fire source position estimation value obtained by the acoustic imaging module; G(x, y, z) is the position estimation value obtained by the optical fiber sensing network through thermal field interpolation; α is the acoustic wave data weight coefficient, satisfying α+β=1, where α∈[0,1], β is the optical fiber data weight coefficient, satisfying β∈[0,1]; (x, y, z) is the three-dimensional space coordinate.

7. The large space intelligent active fire extinguishing system according to claim 1, characterized in that: The automatic fire extinguishing device comprises: Rotary positioning nozzle subsystem, used to adjust the spray direction according to space target instructions; Multiple types of fire extinguishing agent injection units, equipped with gas, dry powder and water-based modules; Select the control module to control the switching of fire extinguishing agent channels and execute fire extinguishing instructions; Feedback monitoring device is used to collect parameters such as injection pressure and residual medium and feed them back to the central control system.

8. The large space intelligent active fire extinguishing system according to claim 1, characterized in that: The fire extinguishing agent injection parameters are adjusted in real time based on the following model: Where D is the spray distance; v is the spray velocity of the fire extinguishing agent; θ is the spray elevation angle; g is the acceleration of gravity, which is 9.81 m / s 2 H is the height difference between the nozzle and the target fire source, sinθ is the y-coordinate of the intersection of the terminal side of angle θ and the unit circle, and cosθ is the x-coordinate of the intersection of the terminal side of angle θ and the unit circle; The central control system adjusts the parameters v and θ in real time according to the target coordinates (x, y, z) to ensure that the fire extinguishing agent hits the fire source.

9. The large space intelligent active fire extinguishing system according to claim 1, characterized in that: The UAV collaborative system includes multiple aircraft equipped with infrared thermal imaging modules and wireless communication modules. After receiving instructions from the central control system, the aircraft completes fire scene inspections, fire source verification and aerial auxiliary fire fighting tasks.

10. The large space intelligent active fire extinguishing system according to claim 1, characterized in that: The drone has an autonomous return mechanism, which will automatically return to the base station when the battery level is lower than a preset threshold or after completing the inspection mission, and will be redeployed to the fire scene according to the central control system after charging is completed.

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