Intelligent identification and control method and system for cruise ship fire risk

Abstract

The invention discloses a cruise ship fire risk intelligent identification and control method and system, and particularly relates to the technical field of fire intelligent identification, and the system comprises a data collection module, an image data processing module, a binarization processing module, a segmentation fusion module, a smoke discrimination module, a fire type discrimination module, a linkage control module and an automatic control module. The data acquisition module is used for installing sensors with different performances in a cruise ship to carry out omnibearing monitoring on data, and the image data processing module is used for receiving data information of the data acquisition module and preprocessing image data. According to the invention, through the smoke discrimination module and the linkage control module, intelligent prediction and early warning, disaster response and linkage control are taken as means, the size and the type of a cruise ship fire are correctly judged and accurately identified, manpower and fire fighting equipment are effectively configured, and a reasonable fire extinguishing tactics and a decision scheme are rapidly formulated.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent fire identification, and more specifically, to a method and system for intelligent identification and control of cruise ship fire risks. Background Art

[0002] Fire is one of the most dangerous accidents that can easily occur on cruise ships. As important working and living cabins on cruise ships, the engine room, pump room, hangar, electromechanical control room and cabins contain flammable materials such as diesel, kerosene, cables and mattresses, with a large fire source load. Once a fire occurs, due to the closed and restricted characteristics of the cabin, heat and smoke spread quickly, and the fire is easy to expand. The fire-fighting equipment and instruments on cruise ships are very limited and are only suitable for specific types of fires. When the fire develops to different scales, appropriate fire-fighting tactics and methods must be adopted. Correctly judging and accurately identifying the size and type of cabin fires is an important basis and prerequisite for formulating reasonable fire-fighting decision-making plans and taking the best fire-fighting actions.

[0003] Existing inventions are mainly devoted to the identification of open flames, smoldering fires and no fire in solid materials. The identification of fuel fires, which are most likely to occur inside cruise ships, is still not well developed. At present, the fire sensors installed on most cruise ships only have alarm and early detection functions. The size and type of fire identification mainly rely on manual fire detection and the commander's own experience. The identification takes a long time, the accuracy of judgment is low, and it will threaten the life safety of fire detectors, making it impossible for commanders to formulate effective fire extinguishing plans in a short time, missing the best time to extinguish the fire. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a cruise ship fire risk intelligent identification and control method and system, which solves the problems raised in the above-mentioned background technology through a smoke discrimination module.

[0005] To achieve the above object, the present invention provides the following technical solution: specifically comprising the following steps:

[0006] S1. Arrange sensors with different monitoring performance at different monitoring points in the cabin to achieve all-round monitoring of data;

[0007] S2, preprocessing the collected image information data;

[0008] S3, performing binarization processing on the image data;

[0009] S4, performing segmentation and fusion processing on the image data;

[0010] S5. Determine the occurrence of fire by the irregularity of the smoke area and the rate of change of the smoke area;

[0011] S6. Perform status assessment based on sensor data and identify the fire type;

[0012] S7, output alarm signal to control the equipment in the automatic control module;

[0013] S8. Implement different fire extinguishing methods according to different fire types through the linkage control module.

[0014] In a preferred embodiment, the system includes a data acquisition module, an image data processing module, a binarization processing module, a segmentation and fusion module, a smoke discrimination module, a fire type discrimination module, a linkage control module and an automatic control module. The data acquisition module installs sensors of different performances inside the cruise ship to perform all-round data monitoring. The image data processing module receives data information from the data acquisition module to pre-process the image data. The binarization module performs binarization processing on the image data. The segmentation and fusion module performs segmentation and fusion processing on the image data. The smoke discrimination module determines the occurrence of a fire by the irregularity of the smoke area and the rate of change of the smoke area. The fire type discrimination module determines the fire type by the characteristic parameters of the cabin fire monitored by the sensors installed in the cruise ship. The linkage control module outputs an alarm signal for the occurrence of a fire and controls the fire extinguishing equipment in the automatic control module. The automatic control module implements different fire extinguishing methods through the control instructions transmitted by the linkage control module.

[0015] In a preferred embodiment, the data acquisition module arranges sensors with different monitoring performance at different monitoring points in the cabin to achieve all-round monitoring of data, uses sensors to detect two parameters: atmospheric relative humidity and air temperature, and analyzes the moisture content of combustibles through the two parameters. A smoke sensor is used to obtain the concentration of smoke and combustibles, and the obtained results are used as analysis indicators for preventing and monitoring fires. An image acquisition sensor is used to obtain the original image of the interior of the cabin, a laser sensor is used to perform a three-dimensional image scan of the interior of the cabin, and a ZigBee self-organizing network structure is used to complete data transmission. The sensor network can monitor the fluctuations of different indicator data to ensure the stability and security of the overall network.

[0016] In a preferred embodiment, the image data processing module obtains pixel features of the cabin interior image acquisition through laser three-dimensional scanning technology, smoothes the edge detail feature points of the acquired image, extracts regional features of the cabin interior image, performs noise reduction on the image, enhances the image by histogram equalization, binarizes the image data, performs image segmentation on the image data by regional generation segmentation, separates the target image of smoke, fire and flame from the background, and extracts features of the flame target image.

[0017] In a preferred embodiment, the binarization processing module divides and distinguishes the gray value of each pixel on the image, sets an original threshold, compares the gray value of each pixel with the original threshold, and the pixel group with a gray value greater than the original threshold in the original image is the detection object, and the detection object is marked as 1, and the pixel group with a gray value less than the original threshold is marked as 0, and a single single-channel color map is obtained. The mathematical formula of the binary image is as follows:

[0018]

[0019] Among them, T is the selected threshold, f(x, y) is the grayscale value of each pixel, and the binarized image divides the pixels into foreground and background. The foreground is the monitored object, and the background is the monitored background.

[0020] In a preferred embodiment, the segmentation and fusion module uses a regional scale segmentation method to perform block fusion processing on the image. In the fire area inside the cruise ship, the 4×4 fire observation sub-block is divided into 4 3×3 sub-blocks with information fused with each other, and the image is transformed to obtain an image gray value reflecting the flame situation of the fire inside the cruise ship, which is calculated as:

[0021] x i = k1[s ij ,...,s in ][x1(yk),...,x n (tk)] T

[0022] Among them, x i is the gray value of the image, k1 is the texture feature of the cruise ship interior image under normal conditions, [s ij ,...,s in ] is the pixel distribution of the local area of ​​the image, [x1(tk),...,x n (tk)] T is the average gray value of the image.

[0023] In a preferred embodiment, the smoke identification module uses the irregularity of smoke shape as the basis for smoke identification, detects the irregularity of shape by using the perimeter and area ratio of the suspected smoke area, and identifies the smoke area. The specific algorithm is as follows:

[0024] C / S>L

[0025] Among them, C and S represent the total number of pixels of the perimeter and area of ​​the extracted smoke area, respectively. L is the critical threshold for distinguishing smoke from objects with regular shapes. The smoke diffusion characteristics are used to improve the accuracy of smoke detection. The change rate of the smoke area over a period of time is calculated. The specific calculation formula is as follows:

[0026]

[0027] Among them, g i+m and g i Respectively represent the total number of pixels in the suspected smoke area in the i+mth image and the ith image in the video image sequence, Y i It is the change in the number of pixels in the suspected smoke area from the i-th frame to the i+m-th frame in the image sequence.

[0028] In a preferred embodiment, the fire type identification module performs state evaluation based on the cabin fire characteristic parameters monitored by the current sensor and the fire state of the current cabin according to the sensor data to obtain the current fire state. The calculation formula is as follows:

[0029] K={U1,U2,L co1 ,L co2 ,L co3 ,B,C,D}

[0030] Among them, U1, U2, L co1 ,L co2 ,L co3 , B are the upper layer temperature, lower layer temperature, CO concentration, CO2 concentration, I2 concentration and shading degree of the flame respectively, and C and D are the size and type of the fire.

[0031] In a preferred embodiment, after receiving the fire alarm signal transmitted by the gateway, the linkage control module uses ZigBee technology to control various linkage facilities and equipment in the automatic control module, monitor the operating status of the equipment in real time, and feed back the information to the control center. The intelligent alarm will promptly warn the real-time situation and location of the fire point. During the fire monitoring process, the sensor receives the signal, converts the sensor status into an electrical signal, and sends the electrical signal to the intelligent node. According to the type of fire, the fire linkage equipment is deployed.

[0032] In a preferred embodiment, the automatic control module is composed of cooling fire extinguishing, nitrogen injection fire extinguishing, aerosol fire extinguishing, wind flow control fire extinguishing and asphyxiation fire extinguishing. The cooling fire extinguishing is composed of a motor, a water tank and a nozzle. When the cooling fire extinguishing is working, a command is sent to the relay through the linkage control module. The intermediate relay receives the action command, receives and amplifies the command, and controls the motor to move. The water pipe is controlled to absorb water from the water tank and spray it to the flame through the nozzle. The asphyxiation fire extinguishing is composed of a gas tank and a solenoid valve. By spraying CO2 and N2 gas to the flame, the oxygen concentration at the flame is reduced to extinguish the fire. For the most commonly used nitrogen injection technology for fire prevention and extinguishing, corresponding online metering and automatic control are performed to achieve online precise monitoring of nitrogen concentration and nitrogen flow rate.

[0033] Technical effects and advantages of the present invention:

[0034] The present invention uses a smoke discrimination module and a linkage control module, intelligent prediction and warning, disaster response, and linkage control as means to correctly judge and accurately identify the size and type of cruise ship fires, effectively allocate manpower and fire-fighting equipment, quickly formulate reasonable fire-fighting tactics and decision-making plans, and maximize the maintenance of cruise ship safety and vitality. It has very important application value and practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a system flow chart of the present invention.

[0036] Figure 2 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Example 1

[0039] This embodiment provides Figure 1 A cruise ship fire risk intelligent identification and control method is shown, which specifically includes the following steps:

[0040] S1. Arrange sensors with different monitoring performance at different monitoring points in the cabin to achieve all-round monitoring of data;

[0041] S2, preprocessing the collected image information data;

[0042] S3, performing binarization processing on the image data;

[0043] S4, performing segmentation and fusion processing on the image data;

[0044] S5. Determine the occurrence of fire by the irregularity of the smoke area and the rate of change of the smoke area;

[0045] S6. Perform status assessment based on sensor data and identify the fire type;

[0046] S7, output alarm signal to control the equipment in the automatic control module;

[0047] S8. Implement different fire extinguishing methods according to different fire types through the linkage control module.

[0048] like Figure 2 The present embodiment provides an intelligent identification and control system for cruise ship fire risk, comprising a data acquisition module, an image data processing module, a binarization processing module, a segmentation and fusion module, a smoke discrimination module, a fire type discrimination module, a linkage control module and an automatic control module. The data acquisition module installs sensors with different performances inside the cruise ship to perform all-round data monitoring. The image data processing module receives data information from the data acquisition module to pre-process the image data. The binarization module performs binarization processing on the image data. The segmentation and fusion module performs segmentation and fusion processing on the image data. The smoke discrimination module determines the occurrence of a fire by the irregularity of a smoke area and the rate of change of the smoke area. The fire type discrimination module discriminates the fire type by the characteristic parameters of a cabin fire monitored by the sensors installed in the cruise ship. The linkage control module outputs an alarm signal for the occurrence of a fire and controls the fire extinguishing equipment in the automatic control module. The automatic control module implements different fire extinguishing methods by the control instructions transmitted by the linkage control module.

[0049] 101. Arrange sensors with different monitoring performance at different monitoring points in the cabin to achieve all-round monitoring of data;

[0050] In this embodiment, what needs to be explained specifically is the data acquisition module. The data acquisition module arranges sensors with different monitoring performance at different monitoring points in the cabin to achieve all-round monitoring of data, uses sensors to detect two parameters, namely atmospheric relative humidity and air temperature, and analyzes the moisture content of combustibles through the two parameters. A smoke sensor is used to obtain the concentration of smoke and combustibles, and the obtained results are used as analysis indicators for preventing and monitoring fires. An image acquisition sensor is used to obtain the original image of the interior of the cabin, a laser sensor is used to perform a three-dimensional image scan of the interior of the cabin, and a ZigBee self-organizing network structure is used to complete data transmission. The sensor network can monitor the fluctuation of different indicator data to ensure the stability and security of the overall network.

[0051] 102. Preprocessing the collected image information data;

[0052] In this embodiment, what needs to be specifically explained is the image data processing module. The image data processing module obtains the pixel features of the image acquisition of the interior of the cabin through the laser three-dimensional scanning technology, smoothes the edge detail feature points of the acquired image, extracts the regional features of the image of the interior of the cabin, performs noise reduction on the image, enhances the image by using histogram equalization, binarizes the image data, performs image segmentation on the image data by using regional generation segmentation, separates the target image of the smoke, fire and flame from the background, and extracts features of the flame target image.

[0053] 103. Perform binarization processing on the image data;

[0054] In this embodiment, what needs to be explained specifically is the binarization processing module, which divides and distinguishes the gray value of each pixel on the image, sets an original threshold, compares the gray value of each pixel with the original threshold, and the pixel group with a gray value greater than the original threshold in the original image is the detection object, the detection object is marked as 1, and the pixel group with a gray value less than the original threshold is marked as 0, and a single single-channel color map is obtained. The mathematical formula of the binary image is as follows:

[0055]

[0056] Among them, T is the selected threshold, f(x, y) is the grayscale value of each pixel, and the binarized image divides the pixels into foreground and background. The foreground is the monitored object, and the background is the monitored background.

[0057] 104. Perform segmentation and fusion processing on the image data;

[0058] In this embodiment, the segmentation and fusion module specifically needs to be explained. The segmentation and fusion module uses a regional scale segmentation method to perform block fusion processing on the image. In the fire area inside the cruise ship, the 4×4 fire observation sub-block is divided into 4 3×3 sub-blocks with information fused with each other, and the image is transformed to obtain an image gray value reflecting the flame situation of the fire inside the cruise ship, which is calculated as:

[0059] x i = k1[s ij ,...,s in ][x1(tk),...,x n (tk)] T

[0060] Among them, x i is the gray value of the image, k1 is the texture feature of the cruise ship interior image under normal conditions, [sij ,...,s in ] is the pixel distribution of the local area of ​​the image, [x1(tk),...,x n (tk)] T is the average gray value of the image.

[0061] 105. Determine the occurrence of fire by the shape and rate of change of smoke;

[0062] In this embodiment, the smoke identification module specifically needs to be explained. The smoke identification module uses the irregularity of the smoke shape as the basis for smoke identification. By using the perimeter and area ratio of the suspected smoke area to detect the irregularity of the shape, the smoke area is identified. The specific algorithm is as follows:

[0063] C / S>L

[0064] Among them, C and S represent the total number of pixels of the perimeter and area of ​​the extracted smoke area, respectively. L is the critical threshold for distinguishing smoke from objects with regular shapes. The smoke diffusion characteristics are used to improve the accuracy of smoke detection. The change rate of the smoke area over a period of time is calculated. The specific calculation formula is as follows:

[0065]

[0066] Among them, g i+m and g i Respectively represent the total number of pixels in the suspected smoke area in the i+mth image and the ith image in the video image sequence, Y i It is the change in the number of pixels in the suspected smoke area from the i-th frame to the i+m-th frame in the image sequence.

[0067] 106. Conduct status assessment based on sensor data and identify fire types;

[0068] In this embodiment, the fire type identification module specifically needs to be explained. The fire type identification module performs state evaluation based on the cabin fire characteristic parameters monitored by the current sensor and the fire state of the current cabin according to the sensor data to obtain the current fire state. The calculation formula is as follows:

[0069] K={U1,U2,L co1 ,L co2 ,L co3 ,B,C,D}

[0070] Among them, U1, U2, L co1 ,L co2 ,L co3, B are the upper layer temperature, lower layer temperature, CO concentration, CO2 concentration, O2 concentration and shading degree of the flame respectively, and C and D are the size and type of the fire.

[0071] 107. Output alarm signals to control the equipment in the automatic control module;

[0072] In this embodiment, what needs to be explained specifically is the linkage control module. After receiving the fire alarm signal transmitted by the gateway, the linkage control module uses ZigBee technology to control various linkage facilities and equipment in the automatic control module, monitor the operating status of the equipment in real time, and feed back the information to the control center. The intelligent alarm will promptly warn the real-time situation and location of the fire point. During the fire monitoring process, the sensor receives the signal, converts the sensor status into an electrical signal, and sends the electrical signal to the intelligent node. According to the type of fire, the fire linkage equipment is deployed.

[0073] 108. Implement different fire extinguishing methods according to different fire types through linkage control modules;

[0074] In this embodiment, what needs to be explained specifically is the automatic control module, which is composed of cooling fire extinguishing, nitrogen injection fire extinguishing, aerosol fire extinguishing, wind flow control fire extinguishing and asphyxiation fire extinguishing. The cooling fire extinguishing is composed of a motor, a water tank and a nozzle. When the cooling fire extinguishing is working, a command is sent to the relay through the linkage control module. The intermediate relay receives the action command, receives and amplifies the command, and controls the motor to move, and controls the water pipe to absorb water from the water tank and spray it to the flame through the nozzle. The asphyxiation fire extinguishing is composed of a gas tank and a solenoid valve, which sprays co2 and N2 gas to the flame to reduce the oxygen concentration at the flame and thus extinguish the fire. For the most commonly used nitrogen injection technology for fire prevention and extinguishing, corresponding online metering and automatic control are performed to achieve online precise monitoring of nitrogen concentration and nitrogen flow rate.

[0075] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0076] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A cruise ship fire risk intelligent identification and control method, characterized by: The following steps are involved: S1. Arrange sensors with different monitoring performance at different monitoring points in the cabin to achieve all-round monitoring of data; S2, preprocessing the collected image information data; S3, performing binarization processing on the image data; S4, performing segmentation and fusion processing on the image data; S5. Determine the occurrence of fire by the irregularity of the smoke area and the rate of change of the smoke area; S6. Perform status assessment based on sensor data and identify the fire type; S7, output alarm signal to control the equipment in the automatic control module; S8. Implement different fire extinguishing methods according to different fire types through the linkage control module.

2. According to claim 1, the cruise ship fire risk intelligent identification and control system is characterized by: The system comprises a data acquisition module, an image data processing module, a binarization processing module, a segmentation and fusion module, a smoke discrimination module, a fire type discrimination module, a linkage control module and an automatic control module. The data acquisition module installs sensors with different performances inside the cruise ship to perform all-round data monitoring. The image data processing module receives data information from the data acquisition module to pre-process the image data. The binarization module performs binarization processing on the image data. The segmentation and fusion module performs segmentation and fusion processing on the image data. The smoke discrimination module determines the occurrence of a fire by the irregularity of the smoke area and the rate of change of the smoke area. The fire type discrimination module discriminates the fire type by the characteristic parameters of the cabin fire monitored by the sensors installed in the cruise ship. The linkage control module outputs an alarm signal for the occurrence of a fire and controls the fire extinguishing equipment in the automatic control module. The automatic control module implements different fire extinguishing methods by the control instructions transmitted by the linkage control module.

3. The cruise ship fire risk intelligent identification and control system according to claim 2 is characterized by: The data acquisition module arranges sensors with different monitoring performance at different monitoring points in the cabin to achieve all-round monitoring of data. Sensors are used to detect two parameters, namely atmospheric relative humidity and air temperature. The moisture content of combustibles is analyzed through the two parameters. Smoke sensors are used to obtain the concentrations of smoke and combustibles. The obtained results are used as analytical indicators for preventing and monitoring fires. Image acquisition sensors are used to obtain original images of the interior of the cabin. Laser sensors are used to perform three-dimensional image scanning of the interior of the cabin. ZigBee self-organizing network structure is used to complete data transmission.

4. The cruise ship fire risk intelligent identification and control system according to claim 2 is characterized by: The image data processing module obtains pixel features of the cabin interior image acquisition through laser three-dimensional scanning technology, smoothes the edge detail feature points of the acquired image, extracts regional features of the cabin interior image, performs noise reduction on the image, enhances the image by using histogram equalization, binarizes the image data, performs image segmentation on the image data by using regional generation segmentation, separates the target image of smoke, fire and flame from the background, and extracts features of the flame target image.

5. The cruise ship fire risk intelligent identification and control system according to claim 4 is characterized by: The binarization processing module divides and distinguishes the grayscale value of each pixel on the image, sets the original threshold, and compares the grayscale value of each pixel with the original threshold. The pixel group with a grayscale value greater than the original threshold in the original image is the detection object, and the detection object is marked as 1, and the pixel group with a grayscale value less than the original threshold is marked as 0, and a single single-channel color map is obtained. The mathematical formula of the binary image is as follows: Among them, T is the selected threshold, f(x, y) is the grayscale value of each pixel, and the binarized image divides the pixels into foreground and background. The foreground is the monitored object, and the background is the monitored background.

6. The cruise ship fire risk intelligent identification and control system according to claim 4 is characterized by: The segmentation and fusion module uses a regional scale segmentation method to perform block fusion processing on the image. In the fire area inside the cruise ship, the 4×4 fire observation sub-block is divided into 4 3×3 sub-blocks with information fused with each other, and the image is transformed to obtain an image gray value reflecting the flame situation of the fire inside the cruise ship, which is calculated as: x i =j1[s ij ,...,s in ][x1(t-k),...,x n (t-k)] T Among them, x i is the gray value of the image, k1 is the texture feature of the cruise ship interior image under normal conditions, [s ij ,...,s in ] is the pixel distribution of the local area of ​​the image, [x1(tk),...,x n (tk)] T is the average gray value of the image.

7. The cruise ship fire risk intelligent identification and control system according to claim 2 is characterized by: The smoke identification module uses the irregularity of smoke shape as the basis for smoke identification, detects the irregularity of shape by using the perimeter and area ratio of the suspected smoke area, and identifies the smoke area. The specific algorithm is as follows: C / S>L Among them, C and S represent the total number of pixels of the perimeter and area of ​​the extracted smoke area, respectively. L is the critical threshold for distinguishing smoke from objects with regular shapes. The smoke diffusion characteristics are used to improve the accuracy of smoke detection. The change rate of the smoke area over a period of time is calculated. The specific calculation formula is as follows: Among them, g i+m and g i Respectively represent the total number of pixels in the suspected smoke area in the i+mth image and the ith image in the video image sequence, Y i It is the change in the number of pixels in the suspected smoke area from the i-th frame to the i+m-th frame in the image sequence.

8. The cruise ship fire risk intelligent identification and control system according to claim 2 is characterized by: The fire type identification module evaluates the state of the cabin fire according to the cabin fire characteristic parameters monitored by the current sensor and the fire state of the current cabin, and obtains the current fire state according to the sensor data. The calculation formula is as follows: K={U1,U2,L co1 ,L co2 ,L co3 ,B,C,D} Among them, U1, U2, L co1 ,L co2 ,L co3 , B are the upper layer temperature, lower layer temperature, CO concentration, CO2 concentration, O2 concentration and shading degree of the flame respectively, and C and D are the size and type of the fire.

9. The cruise ship fire risk intelligent identification and control system according to claim 2 is characterized by: After receiving the fire alarm signal transmitted by the gateway, the linkage control module uses ZigBee technology to control various linkage facilities and equipment in the automatic control module, monitor the operating status of the equipment in real time, and feed back the information to the control center. The intelligent alarm will promptly warn the real-time situation and location of the fire point. During the fire monitoring process, the sensor receives the signal, converts the sensor status into an electrical signal, and sends the electrical signal to the intelligent node. According to the type of fire, the fire linkage equipment is deployed.

10. The cruise ship fire risk intelligent identification and control system according to claim 2, characterized in that: The automatic control module is composed of cooling fire extinguishing, nitrogen injection fire extinguishing, aerosol fire extinguishing, wind flow control fire extinguishing and asphyxiation fire extinguishing. The cooling fire extinguishing is composed of a motor, a water tank and a nozzle. When the cooling fire extinguishing is working, a command is sent to the relay through the linkage control module. The intermediate relay receives the action command, receives and amplifies the command, and controls the motor to move. The water pipe is controlled to absorb water from the water tank and spray it to the flame through the nozzle. The asphyxiation fire extinguishing is composed of a gas tank and a solenoid valve. By spraying CO2 and N2 gas to the flame, the oxygen concentration at the flame is reduced to extinguish the fire. For the most commonly used nitrogen injection technology for fire prevention and extinguishing, corresponding online metering and automatic regulation are performed to achieve online precise monitoring of nitrogen concentration and nitrogen flow rate.

Images