Microwave induction linkage fire extinguishing device response system and method and electronic equipment

Abstract

The invention discloses a microwave induction linkage fire extinguishing device response system and method and electronic equipment, and relates to the technical field of fire emergency rescue, the microwave induction linkage fire extinguishing device response system comprises a microwave induction module which is deployed in a monitoring area and is used for emitting microwaves, receiving reflection signals and transmitting the reflection signals to an intelligent analysis module; the environment sensing module and the microwave sensing module are deployed in a monitoring area at the same time, and environment information of wind speed, wind direction and humidity data is collected in real time and transmitted to the fire extinguishing control module; the intelligent analysis module is used for receiving the reflected signal of the microwave induction module and extracting spectrum features from the reflected signal by adopting a fast Fourier transform algorithm; by adopting the microwave induction and intelligent analysis technology, quick response and accurate positioning at the initial stage of a fire can be realized, and the rescue efficiency is remarkably improved; a deep learning algorithm is introduced to optimize microwave signal analysis, so that a high-precision fire source identification function can be kept in a complex environment, and the false alarm rate and the missing report rate are reduced.

Description

Technical Field

[0001] This invention relates to the field of fire emergency rescue technology, specifically to a microwave induction linkage fire extinguishing device response system, method, and electronic equipment. Background Technology

[0002] In the field of fire emergency rescue, the ability to respond to early fires within seconds and pinpoint their location directly determines the effectiveness of disaster control. Currently, mainstream building, shopping mall, and industrial fire protection systems generally rely on two types of technologies.

[0003] For example, in fire emergency rescue environments, one type of technology uses smoke detection, which triggers an alarm based on particle concentration, but requires waiting for the smoke to spread to the detector location, resulting in an inherent physical delay; another type uses infrared thermal sensing technology, which judges the fire situation by the rate of temperature rise, but has a high false alarm rate in high-temperature environments or under non-fire source heat radiation interference.

[0004] The existing technology has the following shortcomings: the smoke concentration needs to reach the threshold and the temperature needs to break through the thermal equilibrium state, which causes the fire to spread to the middle stage before triggering the alarm, resulting in a certain lag and thus missing the golden intervention window; Dust / steam environments are prone to triggering false alarms from smoke detectors; thermal radiation from high-temperature equipment interferes with the positioning of infrared sensors; poor environmental interference resistance results in frequent false alarms and wasted resources. Traditional detectors only provide area alarms for floors or zones, and cannot pinpoint the coordinates of fire sources in complex spaces in real time. This difficulty in accurate location leads to delays in precise fire suppression.

[0005] In recent years, multi-sensor fusion solutions, such as the combined measurement and judgment of smoke, temperature, and CO concentration from multiple sensors, have improved reliability. However, they rely too heavily on backend data fusion algorithms, leading to increased decision latency and soaring hardware costs, making large-scale deployment difficult. Therefore, the fire protection field urgently needs a microwave induction-linked fire extinguishing device response system, method, and electronic equipment that can bypass the smoke / temperature accumulation stage, achieve millisecond-level non-contact fire detection, improve resistance to interference from dust, high temperature, and steam environments, accurately identify fires, and automatically extinguish them.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a response system, method, and electronic device for a microwave induction-linked fire extinguishing device. This invention solves the problems in the background art by employing microwave induction and intelligent analysis technology, introducing deep learning algorithms to optimize microwave signal analysis, and further introducing deep learning algorithms to optimize microwave signal analysis.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a microwave induction linkage fire extinguishing device response system, including a microwave induction module: deployed in the monitoring area, used to emit microwaves and receive reflected signals, and transmit them to an intelligent analysis module; Environmental sensing module: Deployed in the monitoring area at the same time as the microwave sensing module, it collects environmental information such as wind speed, wind direction and humidity data in real time and transmits it to the fire extinguishing control module; Intelligent analysis module: Receives the reflected signal from the microwave induction module, uses the fast Fourier transform algorithm to extract spectral features from the reflected signal, calculates the spectral phase difference based on the spectral features obtained from whether there is a fire, calculates the three-dimensional coordinate information of the fire source based on the spectral phase difference, and then transmits it to the fire extinguishing control module. Fire extinguishing control module: Receives the three-dimensional coordinate information of the fire source from the intelligent analysis module, combines it with the environmental information received from the environmental perception module, and generates a spray vector command; Fire control communication module: Synchronizes the three-dimensional coordinate information of the fire source, the fire level and the spray vector command to the fire control center and the target fire extinguishing device, and is used to command the terminal to control the target fire extinguishing device to automatically extinguish the fire.

[0009] Optionally, the intelligent analysis module includes a signal analysis unit, a fire source localization unit, and a deep learning unit, wherein the signal analysis unit is used to extract spectral features from the reflected signal by performing a Fast Fourier Transform (FFT). The fire source location unit calculates the three-dimensional coordinates of the fire source based on the spectral phase difference. The deep learning unit is a pre-trained convolutional neural network (CNN) model, with spectral feature vectors as input and fire source probability values ​​as output. The fire alarm is triggered by identifying a fire using a built-in dynamic threshold comparator. Set up an online update mechanism for the CNN model to periodically optimize the CNN weights by learning from newly added reflection signal data.

[0010] Optionally, the spectral feature vector includes the dominant frequency amplitude, Doppler frequency shift, and spectral entropy value.

[0011] Optionally, the injection vector command generation logic steps are as follows: Using the coordinates of the fire source as the target point, calculate the azimuth and elevation angles from the base of the fire extinguishing device to the target point. The coordinates of the fire source are defined as (x, y, z), the azimuth angle is denoted as α, and the elevation angle is denoted as β. The injection angle is dynamically adjusted based on wind speed and direction, where the wind speed is calibrated as v. w The wind direction is defined as θ w The formula for calculating the dynamic correction injection angle is α'=α+k1·v w ·cos(θw ), and β'=β+k2·v w ·sin(θ w In the formula, α' represents the azimuth angle of the dynamic correction injection, k1 represents the drag compensation coefficient of the azimuth angle, β' represents the pitch angle of the dynamic correction injection, and k2 represents the drag compensation coefficient of the pitch angle. The water flow rate of the target fire extinguishing device is adjusted based on humidity. The formula for adjusting the water flow rate is Q′=Q0·(1+0.02(50-RH)), and RH≤80%. In the formula, Q′ represents the adjusted water flow rate, Q0 represents the base water flow rate, and RH represents the humidity.

[0012] Optionally, the microwave sensing module adopts a 24GHz frequency modulated continuous wave radar with a spatial resolution ≤0.5m and a refresh rate ≥10Hz, and the number of microwave sensing modules is not less than three, forming a positioning array.

[0013] Optionally, the intelligent analysis module fuses the data collected by the microwave sensing and environmental sensing modules using the Time Difference of Arrival (TDOA) algorithm to obtain the fire source location coordinates and improve the fire source location accuracy. The fire control communication module adopts dual-link communication of LoRa and 5G. The LoRa link transmits the coordinates of the fire source and the fire level; the 5G link transmits the control command stream of the target fire extinguishing device.

[0014] Optionally, the steps for setting up an online update mechanism for the CNN model in the deep learning unit are as follows: New feature vectors and manually verified labels are collected every 24 hours; The weights of the fully connected layers in a CNN are dynamically adjusted using the cross-entropy loss function, where the expression for the cross-entropy loss function is loss = -∑x i ·logP i In the formula, loss represents the cross-entropy loss function, x i Let P be the true label of the i-th manually verified label. i This is represented as the CNN model outputting the probability value of the fire source based on the newly added feature vector.

[0015] The response method for microwave induction-linked fire extinguishing devices includes the following steps: S1. Microwave signal acquisition and analysis: The microwave induction module is used to transmit swept microwaves, capture the reflected signals and extract the spectral feature vectors. S2. Intelligent fire source identification: Input the spectral feature vector into the pre-trained neural network CNN model, output the fire source probability value, set the judgment accuracy threshold, and compare and analyze the fire source probability value output by CNN with the judgment accuracy threshold. S3. Dynamic fire extinguishing strategy generation: When the fire source probability value output by the CNN is not less than the judgment accuracy threshold, the fire source coordinates obtained from the intelligent analysis module and the environmental data obtained from the environmental perception module are combined to calculate the spray correction angle and adaptive water flow rate in the fire extinguishing strategy. S4. Command and control execution of fire extinguishing: The fire control communication module sends control commands for spray correction angle and adaptive water flow to the target fire extinguishing device. The target fire extinguishing device executes the fire extinguishing strategy and synchronizes the fire information to the fire control center.

[0016] An electronic device for responding to a microwave induction-linked fire extinguishing device includes: a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-mentioned microwave induction-linked fire extinguishing device response system: the electronic device integrates a microwave radar front end and a target fire extinguishing device drive interface to form an embedded response terminal.

[0017] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention designs a microwave sensing module, an environmental sensing module, an intelligent analysis module, a fire extinguishing control module, and a fire control communication module. The intelligent analysis module uses a fast Fourier transform algorithm to extract spectral features from reflected signals, enabling precise capture of subtle changes within the monitoring area. Even small initial fires can be detected promptly, significantly improving the accuracy and sensitivity of fire detection. The intelligent analysis module calculates the spectral phase difference based on the spectral features obtained from the presence or absence of a fire, and accurately calculates the three-dimensional coordinates of the fire source based on this phase difference. This allows for the determination of the fire source's exact location in a very short time, providing accurate target positioning for subsequent fire extinguishing efforts and effectively shortening the fire response time. In short, by employing microwave sensing and intelligent analysis technology, this invention enables rapid response and precise positioning in the early stages of a fire, significantly improving rescue efficiency. By deploying environmental sensing modules and microwave sensing modules simultaneously in the monitoring area, environmental information such as wind speed, wind direction, and humidity can be collected in real time. Combined with the introduction of deep learning algorithms to optimize microwave signal analysis, automatic fire situation judgment, automatic fire source location, and automatic generation of fire extinguishing strategies are realized. This provides a more comprehensive understanding of the actual situation at the fire scene and enables high-precision fire source identification even in complex environments, reducing false alarms and missed alarms. At the same time, it provides more scientific guidance for fire extinguishing devices to perform fire extinguishing operations. It also incorporates dynamic fire suppression strategy adjustment and environmental perception modules. Through synchronous information transmission, the fire control center can coordinate multiple target fire suppression devices to work together based on the fire situation, avoiding chaos and waste of resources during the fire suppression process. This not only ensures more intelligent fire suppression operations but also improves the timeliness and accuracy of fire suppression, reduces the exposure time of firefighters in dangerous environments, protects the lives of firefighters, and reduces water waste. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a block diagram of the response system of the microwave induction linkage fire extinguishing device of the present invention.

[0020] Figure 2 This is a flowchart of the response method of the microwave induction linkage fire extinguishing device of the present invention.

[0021] Figure 3 This is a diagram of the information reporting link for the microwave induction linkage fire extinguishing device of the present invention. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0023] Example 1 This invention provides, for example Figure 1 The microwave induction linkage fire extinguishing device response system shown includes a microwave induction module: deployed in the monitoring area, it is used to emit microwaves and receive reflected signals, and transmit them to the intelligent analysis module, which is used to accurately capture subtle changes in the monitoring area, detect small fires in the early stage in a timely manner, and improve the accuracy and sensitivity of fire detection. Environmental sensing module: Deployed in the monitoring area at the same time as the microwave sensing module, it collects environmental information such as wind speed, wind direction and humidity data in real time and transmits it to the fire extinguishing control module; Intelligent analysis module: Receives the reflected signal from the microwave induction module, uses the fast Fourier transform algorithm to extract spectral features from the reflected signal, calculates the spectral phase difference based on the spectral features obtained from whether there is a fire, and calculates the three-dimensional coordinate information of the fire source based on the spectral phase difference. Then it is transmitted to the fire extinguishing control module, which can determine the specific location of the fire source in a very short time, providing accurate target positioning for subsequent fire extinguishing work and effectively shortening the fire extinguishing response time. Fire extinguishing control module: Receives the three-dimensional coordinate information of the fire source from the intelligent analysis module, combines it with the environmental information received from the environmental perception module, and generates a spray vector command; Fire control communication module: Synchronizes the three-dimensional coordinate information of the fire source, the fire level and the spray vector command to the fire control center and the target fire extinguishing device, and is used to command the terminal to control the target fire extinguishing device to automatically extinguish the fire.

[0024] Specifically, the intelligent analysis module includes a signal analysis unit, a fire source localization unit, and a deep learning unit. The signal analysis unit is used to extract spectral features from the reflected signal by performing a Fast Fourier Transform (FFT). The fire source location unit calculates the three-dimensional coordinates of the fire source based on the spectral phase difference. The deep learning unit is a pre-trained convolutional neural network (CNN) model, with spectral feature vectors as input and fire source probability values ​​as output. The fire alarm is triggered by identifying a fire using a built-in dynamic threshold comparator. Set up an online update mechanism for the CNN model to periodically optimize the CNN weights by learning from newly added reflection signal data.

[0025] Specifically, the spectral feature vector includes the dominant frequency amplitude, Doppler frequency shift, and spectral entropy value.

[0026] Specifically, the logic steps for generating the injection vector command are as follows: Using the coordinates of the fire source as the target point, calculate the azimuth and elevation angles from the base of the fire extinguishing device to the target point. The coordinates of the fire source are defined as (x, y, z), the azimuth angle is denoted as α, and the elevation angle is denoted as β. The injection angle is dynamically adjusted based on wind speed and direction, where the wind speed is calibrated as v. w The wind direction is defined as θ w The formula for calculating the dynamic correction injection angle is α'=α+k1·v w ·cos(θ w ), and β'=β+k2·v w ·sin(θ w In the formula, α' represents the azimuth angle of the dynamic correction injection, k1 represents the drag compensation coefficient of the azimuth angle, β' represents the pitch angle of the dynamic correction injection, and k2 represents the drag compensation coefficient of the pitch angle. The water flow rate of the target fire extinguishing device is adjusted based on humidity. The formula for adjusting the water flow rate is Q′=Q0·(1+0.02(50-RH)), and RH≤80%. In the formula, Q′ represents the adjusted water flow rate, Q0 represents the base water flow rate, and RH represents the humidity.

[0027] Specifically, the microwave sensing module uses a 24GHz frequency modulated continuous wave radar with a spatial resolution ≤0.5m and a refresh rate ≥10Hz, and the number of microwave sensing modules is not less than three, forming a positioning array.

[0028] Specifically, the intelligent analysis module uses the Time Difference of Arrival (TDOA) algorithm to fuse the data collected by the microwave sensing and environmental sensing modules in order to obtain the fire source location coordinates and improve the fire source location accuracy.

[0029] Specifically, the fire control communication module adopts dual-link communication of LoRa and 5G. The LoRa link transmits the coordinates of the fire source and the fire level, while the 5G link transmits the control command stream of the target fire extinguishing device.

[0030] Specifically, the steps for setting up the online update mechanism for the CNN model in the deep learning unit are as follows: New feature vectors and manually verified labels are collected every 24 hours; The weights of the fully connected layers in a CNN are dynamically adjusted using the cross-entropy loss function, where the expression for the cross-entropy loss function is loss = -∑x i ·logP i In the formula, loss represents the cross-entropy loss function, x i Let P be the true label of the i-th manually verified label. i This is represented as the CNN model outputting the probability value of the fire source based on the newly added feature vector.

[0031] Example 2 This invention provides, for example Figure 2 The microwave induction-linked fire extinguishing device response method shown includes the following steps: S1. Microwave signal acquisition and analysis: The microwave induction module is used to transmit swept microwaves, capture the reflected signals and extract the spectral feature vectors. S2. Intelligent fire source identification: Input the spectral feature vector into the pre-trained neural network CNN model, output the fire source probability value, set the judgment accuracy threshold, and compare and analyze the fire source probability value output by CNN with the judgment accuracy threshold. S3. Dynamic fire extinguishing strategy generation: When the fire source probability value output by the CNN is not less than the judgment accuracy threshold, the fire source coordinates obtained from the intelligent analysis module and the environmental data obtained from the environmental perception module are combined to calculate the spray correction angle and adaptive water flow rate in the fire extinguishing strategy. S4. Command and control execution of fire extinguishing: The fire control communication module sends control commands for spray correction angle and adaptive water flow to the target fire extinguishing device. The target fire extinguishing device executes the fire extinguishing strategy and synchronizes the fire information to the fire control center.

[0032] Example 3 This invention provides, for example Figure 3 The electronic device shown is a response device for a microwave induction-linked fire extinguishing device. The electronic device integrates a microwave radar front-end and a target fire extinguishing device drive interface to form an embedded response terminal.

[0033] The microwave induction linkage fire extinguishing device response method provided in this embodiment of the invention is implemented through the above-mentioned microwave induction linkage fire extinguishing device response system. For details of the specific method and process of the microwave induction linkage fire extinguishing device response method, please refer to the above-mentioned embodiment of the microwave induction linkage fire extinguishing device response system, which will not be repeated here.

[0034] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0035] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0036] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0037] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0038] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A microwave induction-linked fire extinguishing device response system, characterized in that, Includes a microwave sensing module: deployed in the monitoring area, used to emit microwaves and receive reflected signals, which are then transmitted to the intelligent analysis module; Environmental sensing module: Deployed in the monitoring area at the same time as the microwave sensing module, it collects environmental information such as wind speed, wind direction and humidity data in real time and transmits it to the fire extinguishing control module; Intelligent analysis module: Receives the reflected signal from the microwave induction module, uses the fast Fourier transform algorithm to extract spectral features from the reflected signal, calculates the spectral phase difference based on the spectral features obtained from whether there is a fire, calculates the three-dimensional coordinate information of the fire source based on the spectral phase difference, and then transmits it to the fire extinguishing control module. Fire extinguishing control module: Receives the three-dimensional coordinate information of the fire source from the intelligent analysis module, combines it with the environmental information received from the environmental perception module, and generates a spray vector command; Fire control communication module: Synchronizes the three-dimensional coordinate information of the fire source, the fire level and the spray vector command to the fire control center and the target fire extinguishing device, and is used to command the terminal to control the target fire extinguishing device to automatically extinguish the fire.

2. The microwave induction linkage fire extinguishing device response system according to claim 1, characterized in that, The intelligent analysis module includes a signal analysis unit, a fire source localization unit, and a deep learning unit. The signal analysis unit is used to extract spectral features from the reflected signal by performing a Fast Fourier Transform (FFT). The fire source location unit calculates the three-dimensional coordinates of the fire source based on the spectral phase difference. The deep learning unit is a pre-trained convolutional neural network (CNN) model, with spectral feature vectors as input and fire source probability values ​​as output. The fire alarm is triggered by identifying a fire using a built-in dynamic threshold comparator. Set up an online update mechanism for the CNN model to periodically optimize the CNN weights by learning from newly added reflection signal data.

3. The microwave induction-linked fire extinguishing device response system according to claim 2, characterized in that, The spectral feature vector includes the dominant frequency amplitude, Doppler frequency shift, and spectral entropy value.

4. The microwave induction linkage fire extinguishing device response system according to claim 3, characterized in that, The logic steps for generating the injection vector command are as follows: Using the coordinates of the fire source as the target point, calculate the azimuth and elevation angles from the base of the fire extinguishing device to the target point. The coordinates of the fire source are defined as (x, y, z), the azimuth angle is denoted as α, and the elevation angle is denoted as β. The injection angle is dynamically adjusted based on wind speed and direction, where the wind speed is calibrated as v. w The wind direction is defined as θ w The formula for calculating the dynamic correction injection angle is α'=α+k1·v w ·cos(θ w ), and β'=β+k2·v w sin(θ) w In the formula, α' represents the azimuth angle of the dynamic correction injection, k1 represents the drag compensation coefficient of the azimuth angle, β' represents the pitch angle of the dynamic correction injection, and k2 represents the drag compensation coefficient of the pitch angle. The water flow rate of the target fire extinguishing device is adjusted based on humidity. The formula for adjusting the water flow rate is Q′=Q0·(1+0.02(50-RH)), and RH≤80%. In the formula, Q′ represents the adjusted water flow rate, Q0 represents the base water flow rate, and RH represents the humidity.

5. The microwave induction-linked fire extinguishing device response system according to claim 4, characterized in that, The microwave sensing module uses a 24GHz frequency modulated continuous wave radar with a spatial resolution ≤0.5m and a refresh rate ≥10Hz. The number of microwave sensing modules is not less than three, forming a positioning array.

6. The microwave induction-linked fire extinguishing device response system according to claim 5, characterized in that, The intelligent analysis module fuses the data collected by the microwave sensing and environmental sensing modules through the Time Difference of Arrival (TDOA) algorithm to obtain the fire source location coordinates and improve the fire source location accuracy. The fire control communication module adopts dual-link communication of LoRa and 5G. The LoRa link transmits the coordinates of the fire source and the fire level; the 5G link transmits the control command stream of the target fire extinguishing device.

7. The microwave induction-linked fire extinguishing device response system according to claim 6, characterized in that, The steps for setting up the online update mechanism for the CNN model in the deep learning unit are as follows: New feature vectors and manually verified labels are collected every 24 hours; The weights of the fully connected layers in a CNN are dynamically adjusted using the cross-entropy loss function, where the expression for the cross-entropy loss function is loss = -∑x i ·logP i In the formula, loss represents the cross-entropy loss function, x i Let P be the true label of the i-th manually verified label. i This is represented as the CNN model outputting the probability value of the fire source based on the newly added feature vector.

8. A microwave induction-linked fire extinguishing device response method, implemented by the microwave induction-linked fire extinguishing device response system according to any one of claims 1-8, characterized in that, The steps include the following: S1. Microwave signal acquisition and analysis: The microwave induction module is used to transmit swept microwaves, capture the reflected signals and extract the spectral feature vectors. S2. Intelligent fire source identification: Input the spectral feature vector into the pre-trained neural network CNN model, output the fire source probability value, set the judgment accuracy threshold, and compare and analyze the fire source probability value output by CNN with the judgment accuracy threshold. S3. Dynamic fire extinguishing strategy generation: When the fire source probability value output by the CNN is not less than the judgment accuracy threshold, the fire source coordinates obtained from the intelligent analysis module and the environmental data obtained from the environmental perception module are combined to calculate the spray correction angle and adaptive water flow rate in the fire extinguishing strategy. S4. Command and control execution of fire extinguishing: The fire control communication module sends control commands for spray correction angle and adaptive water flow to the target fire extinguishing device. The target fire extinguishing device executes the fire extinguishing strategy and synchronizes the fire information to the fire control center.

9. An electronic device responding to a microwave induction-linked fire extinguishing device, comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of the microwave induction linkage fire extinguishing device response system as described in any one of claims 1 to 7.

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