CN112419691A - Fire-fighting monitoring system for ship - Google Patents
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- G08B25/08—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using communication transmission lines
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Abstract
The invention belongs to the field of ship fire protection monitoring, and particularly discloses a fire protection monitoring system for a ship, which comprises monitoring transmission equipment, a monitoring platform and a fire alarm subsystem, wherein the monitoring transmission equipment is connected with the monitoring platform and is used for acquiring monitoring data of the ship and transmitting the monitoring data to the monitoring platform, the monitoring data comprises position data, image data, smoke sensing data, temperature data, flame data and gas data, the monitoring platform processes, screens and analyzes the data and transmits an analysis result to the fire alarm subsystem through an Ethernet, and the fire alarm subsystem carries out fire alarm and fire coefficient early warning according to the monitoring data. The monitoring system can more quickly, accurately and comprehensively monitor the positions of the ship, such as the outside cabin, the inside cabin, the engine room and the like, and has high monitoring and early warning accuracy and low false alarm rate.
Description
Technical Field
The invention relates to the field of ship fire fighting monitoring, in particular to a fire fighting monitoring system for a ship.
Background
A ship is a man-made vehicle that operates primarily in geographic water. In addition, a civil ship is generally called a ship, a military ship is called a ship, and a small-sized ship is called a boat or a boat, which is collectively called a ship or a boat. The interior mainly comprises a containment space, a support structure and a drainage structure, with a propulsion system using an external or self-contained energy source. The appearance is generally favorable for overcoming the streamline envelope of the fluid resistance, the materials are continuously updated along with the technological progress, the early materials are natural materials such as wood, bamboo, hemp and the like, and the modern materials are mostly steel, aluminum, glass fiber, acrylic and various composite materials.
As the ship is used as a water transport tool, the ship is a relatively independent flowing place, the difficulty of obtaining rescue awards after a fire disaster occurs is high, hot smoke generated by the fire disaster can spread quickly in a cabin due to the water tightness characteristic of the ship, the development of the fire disaster is accelerated, meanwhile, the safety of personnel is greatly threatened, and due to the limitation of self functions, the internal space of the ship is narrow, the equipment concentration environment is complex, and the personnel evacuation and fire disaster suppression are difficult after the fire disaster occurs. As the electrical equipment in the cabin is numerous, a large number of combustible and explosive articles such as fuel oil, lubricating oil and the like exist. It follows that ships sailing at sea are often catastrophic in the event of a fire. The traditional ship engine room fire monitoring system mainly depends on temperature and smoke sensors, has high false fire alarm rate and has great limitation.
Disclosure of Invention
The present invention is directed to a fire monitoring system for a ship to solve the above problems of the background art.
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides a fire control monitored control system for boats and ships, including control transmission equipment, monitor platform and fire alarm subsystem, wherein, control transmission equipment connects monitor platform, it is used for gathering the monitoring data of boats and ships, and transmit monitoring data to monitor platform, and monitoring data includes position data, image data, smog sensing data, temperature data, flame data and gas data, handle data by monitor platform, the screening, the analysis passes through ethernet transmission with the analysis result to fire alarm subsystem, carry out fire alarm and fire coefficient early warning by fire alarm subsystem according to monitoring data.
Preferably, the monitoring transmission equipment comprises an in-cabin environment acquisition module, an out-cabin environment acquisition module and an equipment cabin acquisition module, wherein the out-cabin environment acquisition module is a deck lookout camera, the equipment cabin acquisition module comprises an array type temperature sensor unit, an array type smoke sensor unit and a cabin monitoring camera, wherein the array type temperature sensor unit is used for acquiring the temperature of each equipment temperature monitoring point in the equipment cabin, the array type smoke sensor unit is used for acquiring smoke detection information of the smoke monitoring points in the cabin, and the cabin monitoring camera is used for acquiring a video image sequence on each video monitoring point in the cabin.
Preferably, the cabin environment acquisition module is used for acquiring the environment information of the cabin of the ship, the cabin environment acquisition module comprises a plurality of acquisition units, a mounting plate, a side plate and a mounting structure, the acquisition units comprise a smoke sensor, a temperature sensor, a flame sensor, a monitoring camera and a gas sensor, and the acquisition units are all fixed on the mounting plate; the end portion, far away from the acquisition unit, of the mounting plate is connected with a side plate, a sliding groove is formed in the end face of the side plate, and a mounting structure is arranged on the outer side of the side plate.
Preferably, the mounting structure comprises a mounting bottom plate, a first clamping plate, a second clamping plate and a connecting column, the mounting bottom plate is fixed on the bulkhead through bolts, and a limiting clamping edge is arranged at the side end of the mounting bottom plate; the first clamping plate and the second clamping plate are arranged in a mirror image mode relative to the bolt, the first clamping plate is arranged on the second clamping plate, the end part of the first clamping plate is in sliding connection with the sliding groove in the side plate, and the end part of the second clamping plate is fixedly connected with the side plate; the middle part of the first clamping plate is provided with a through hole, and the end parts of the first clamping plate and the second clamping plate are provided with limiting clamping blocks matched with the limiting clamping edges; the connecting column is fixed on the second clamping plate, a spring is sleeved on the connecting column, and the end part of the connecting column penetrates through the through hole and is connected with a stop block.
Preferably, the monitoring platform is used for acquiring monitoring data, the monitoring platform includes a data processing module and a data fusion analysis module, wherein the data processing module includes a data processing unit for processing sensor data and an image processing unit for processing image information, and a processing procedure of the image processing unit includes: and performing image preprocessing on an image with noise in the image to obtain a characteristic image, performing image texture boundary matching on the characteristic image, and extracting the characteristic image after the image texture boundary matching through characteristic points to obtain flame texture characteristic data.
Preferably, the image preprocessing comprises: the method comprises the steps of carrying out segmentation and sub-image block identification on acquired image information to obtain sub-image blocks, extracting features of the sub-image blocks based on a deep convolutional neural network model, obtaining flame information image features through integration, and obtaining abnormal flame information image feature images through pre-classification.
Preferably, the deep convolutional neural network model specifically includes: firstly, according to the recognition result of the sub-image blocks, a corresponding deep convolution neural network model is adopted, the features of the sub-image blocks are extracted by setting a plurality of layers of convolution and pooling layers in the model, then the features of each sub-image block are integrated by setting different weighting parameters to obtain image features, and then, pre-classification is carried out according to the image features to obtain abnormal images. The model for pre-classification is a stable model obtained by training a classification model according to the existing mark data.
Preferably, smoothing and denoising of the image are required before segmentation and sub-image block recognition, the smoothing or denoising of the image is also an averaging process of pixel gray scale, an algorithm used in the process is a convolution operation on pixels in an image field, and the purpose is to reduce or filter the influence of noise and improve the quality of the image, the smoothing process of the image is also a very important work in the image processing technology, the quality of the image directly influences the work of restoration, segmentation, feature extraction, image recognition and the like of the image, the image smoothing method can be respectively processed in a frequency domain and a spatial domain, and the noise is mainly reduced or removed in the spatial domain through methods such as mean filtering, domain averaging, median filtering, multi-image averaging and the like.
Preferably, the data fusion analysis module adopts a multi-source data fusion technology and fuses the flame information image feature image, the temperature information and the smoke information data to obtain fusion features, and in the information fusion process, standardization, fusion and pretreatment are carried out on the features to screen out fusion features which have influence on the final diagnosis result, classification analysis is carried out on the fusion features through an SVM classifier, and the analysis result is output.
Preferably, the fire alarm subsystem acquires the analysis result, and performs fire alarm and fire coefficient early warning according to the analysis result, and the fire coefficient is based on the weight of the analysis result.
Compared with the prior art, the invention has the beneficial effects that:
the monitoring system can more quickly, accurately and comprehensively monitor the positions of the ship, such as the outside cabin, the inside cabin, the engine room and the like, data are monitored through monitoring transmission equipment distributed at the positions of the outside cabin, the inside cabin, the engine room and the like, the data are transmitted to a remote monitoring platform through the Ethernet, and the data are processed and analyzed through the monitoring platform, so that a better monitoring effect is realized, the monitoring early warning accuracy is high, and the false alarm rate is low.
Drawings
FIG. 1 is a block diagram of the present invention in its entirety;
FIG. 2 is a block diagram of an extravehicular environment acquisition module of the present invention;
fig. 3 is a schematic diagram of a specific installation structure of the in-cabin environment acquisition module according to the present invention.
In the figure: 1. monitoring a transmission device; 101. an in-cabin environment acquisition module; 101a, an acquisition unit; 101b, a mounting plate; 101c, side plates; 101d, a chute; 101e, mounting a bottom plate; 101f, a first clamping plate; 101g, a second clamping plate; 101h, connecting columns; 102. an extravehicular environment acquisition module; 102a, an array type temperature sensor unit; 102b, an array smoke sensor unit; 102c, an engine room monitoring camera; 103. an equipment cabin acquisition module; 2. a monitoring platform; 201. a data processing module; 201a, a data processing unit; 201b, an image processing unit; 202. a data fusion analysis module; 3. and a fire alarm subsystem.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "vertical", "upper", "lower", "horizontal", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Example 1: referring to fig. 1-2, the present invention provides a technical solution: the utility model provides a fire control monitored control system for boats and ships, including control transmission equipment 1, monitoring platform 2 and fire alarm subsystem 3, wherein, control transmission equipment 1 connects monitoring platform 2, it is used for gathering the monitoring data of boats and ships, and transmit monitoring data to monitoring platform 2, and monitoring data includes position data, image data, smog sensing data, temperature data, flame data and gas data, handle data by monitoring platform 2, the screening, the analysis and with analysis result through ethernet transmission to fire alarm subsystem 3, carry out fire alarm and fire coefficient early warning by fire alarm subsystem 3 according to monitoring data.
In this embodiment, the monitoring transmission device 1 includes an intra-cabin environment acquisition module 101, an extra-cabin environment acquisition module 102, and an equipment cabin acquisition module 103, where the extra-cabin environment acquisition module 102 is a watch camera on a deck, and the equipment cabin acquisition module 102 includes an array temperature sensor unit 102a, an array smoke sensor unit 102b, and a cabin monitoring camera 102c, where the array temperature sensor unit 102a is used to acquire the temperature of each equipment temperature monitoring point in the equipment cabin, the array smoke sensor unit 102b is used to acquire smoke detection information of the smoke monitoring point in the cabin, and the cabin monitoring camera 102c is used to acquire a video image sequence on each video monitoring point in the cabin.
In this embodiment, the monitoring platform 2 is configured to obtain monitoring data, the monitoring platform 2 includes a data processing module 201 and a data fusion analysis module 202, where the data processing module 201 includes a data processing unit 201a configured to process sensor data and an image processing unit 201b configured to process image information, and a processing procedure of the image processing unit 201b includes: and performing image preprocessing on an image with noise in the image to obtain a characteristic image, performing image texture boundary matching on the characteristic image, and extracting the characteristic image after the image texture boundary matching through characteristic points to obtain flame texture characteristic data.
In the present embodiment, the image preprocessing includes: the method comprises the steps of carrying out segmentation and sub-image block identification on acquired image information to obtain sub-image blocks, extracting features of the sub-image blocks based on a deep convolutional neural network model, obtaining flame information image features through integration, and obtaining abnormal flame information image feature images through pre-classification.
In this embodiment, the deep convolutional neural network model specifically includes: firstly, according to the recognition result of the sub-image blocks, a corresponding deep convolution neural network model is adopted, the features of the sub-image blocks are extracted by setting a plurality of layers of convolution and pooling layers in the model, then the features of each sub-image block are integrated by setting different weighting parameters to obtain image features, and then, pre-classification is carried out according to the image features to obtain abnormal images. The model for pre-classification is a stable model obtained by training a classification model according to the existing mark data.
In this embodiment, smoothing and denoising of an image are required before segmentation and sub-image block recognition, the smoothing or denoising of an image is also an averaging process of pixel gray scale, an algorithm used in the method is a convolution operation performed on pixels in an image field for the purpose of reducing or filtering the influence of noise and improving the quality of the image, the smoothing of an image is also a very important work in an image processing technology, the quality of the image directly influences the restoration, segmentation, feature extraction, image recognition and other works of the image, the image smoothing method can be respectively performed in a frequency domain and a spatial domain, and the noise is mainly reduced or removed in the spatial domain by methods such as mean filtering, domain averaging, median filtering, multi-image averaging and the like.
In this embodiment, the data fusion analysis module 202 adopts a multi-source data fusion technology and fuses the flame information image feature image, the temperature information, and the smoke information data to obtain fusion features, and in the information fusion process, standardizes, fuses, and preprocesses each feature to screen out fusion features having influence on the final diagnosis result, and performs classification analysis on the fusion features through an SVM classifier, and outputs the analysis result.
In this embodiment, the fire alarm subsystem 3 obtains the analysis result, and performs fire alarm and fire coefficient early warning according to the analysis result, and the fire coefficient is based on the weight of the analysis result.
Example 2: referring to fig. 3, the present invention provides a technical solution: a fire-fighting monitoring system for ships comprises a monitoring transmission device 1, a monitoring platform 2 and a fire alarm subsystem 3, wherein the monitoring transmission device 1 is connected with the monitoring platform 2 and is used for acquiring monitoring data of ships and transmitting the monitoring data to the monitoring platform 2, the monitoring transmission device 1 comprises an in-cabin environment acquisition module 101, an out-cabin environment acquisition module 102 and an equipment cabin acquisition module 103, the in-cabin environment acquisition module 101 is used for environment information of a ship cabin, the in-cabin environment acquisition module 101 comprises an acquisition unit 101a, a mounting plate 101b, a side plate 101c and a mounting structure, the acquisition unit 101a is provided with a plurality of acquisition units 101a, the acquisition units comprise a smoke sensor, a temperature sensor, a flame sensor, a monitoring camera and a gas sensor, and the acquisition units 101a are all fixed on the mounting plate 101 b; the end part of the mounting plate 101b far away from the acquisition unit 101a is connected with a side plate 101c, the end face of the side plate 101c is provided with a sliding groove 101d, and the outer side of the side plate 101c is provided with a mounting structure. The mounting structure comprises a mounting bottom plate 101e, a first clamping plate 101f, a second clamping plate 101g and a connecting column 101h, wherein the mounting bottom plate 101e is fixed on a bulkhead through bolts, and a limiting clamping edge is arranged at the side end of the mounting bottom plate 101 e; the first clamping plate 101f and the second clamping plate 101g are arranged in a mirror image mode relative to the bolts, the first clamping plate 101f is arranged on the second clamping plate 101g, the end part of the first clamping plate 101f is in sliding connection with the sliding groove 101d on the side plate 101c, and the end part of the second clamping plate 101g is fixedly connected with the side plate 101 c; the middle part of the first clamping plate 101f is provided with a through hole, and the end parts of the first clamping plate 101f and the second clamping plate 101g are provided with limiting clamping blocks matched with the limiting clamping edges; the connecting column 101h is fixed on the second clamping plate 101g, a spring is sleeved on the connecting column 101h, and the end part of the connecting column 101h penetrates through the through hole and is connected with a stop block.
In this embodiment, the acquisition unit 101a integrated with each sensor is disposed on the mounting plate 101b, and the mounting plate 101b is designed to be easy to mount and dismount, so that the mounting is easier.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. The utility model provides a fire control monitored control system for boats and ships, a serial communication port, including control transmission equipment (1), monitoring platform (2) and fire alarm subsystem (3), wherein, control transmission equipment (1) are connected monitoring platform (2), it is used for gathering the monitoring data of boats and ships, and with monitoring data transmission to monitoring platform (2), and monitoring data includes position data, image data, smog sensing data, temperature data, flame data and gas data, handle, filter, the analysis is carried out data by monitoring platform (2), the analysis and passes through ethernet transmission to fire alarm subsystem (3) with the analysis result, carry out fire alarm and fire coefficient early warning by fire alarm subsystem (3) according to monitoring data.
2. A fire monitoring system for a marine vessel as claimed in claim 1, wherein: the monitoring transmission equipment (1) comprises an in-cabin environment acquisition module (101), an out-of-cabin environment acquisition module (102) and an equipment cabin acquisition module (103), wherein the out-of-cabin environment acquisition module (102) is a deck observation camera, the equipment cabin acquisition module (102) comprises an array type temperature sensor unit (102 a), an array type smoke sensor unit (102 b) and a cabin monitoring camera (102 c), wherein the array type temperature sensor unit (102 a) is used for acquiring the temperature of each equipment temperature monitoring point in the equipment cabin, the array type smoke sensor unit (102 b) is used for acquiring smoke detection information of the smoke monitoring point in the cabin, and the cabin monitoring camera (102 c) is used for acquiring a video image sequence on each video monitoring point in the cabin.
3. A fire monitoring system for a marine vessel as claimed in claim 2, wherein: the cabin environment acquisition module (101) is used for acquiring environment information of a cabin of a ship, the cabin environment acquisition module (101) comprises acquisition units (101 a), a mounting plate (101 b), a side plate (101 c) and a mounting structure, the acquisition units (101 a) are provided with a plurality of smoke sensors, temperature sensors, flame sensors, monitoring cameras and gas sensors, and the acquisition units (101 a) are all fixed on the mounting plate (101 b); the end portion, far away from the acquisition unit (101 a), of the mounting plate (101 b) is connected with a side plate (101 c), a sliding groove (101 d) is formed in the end face of the side plate (101 c), and a mounting structure is arranged on the outer side of the side plate (101 c).
4. A fire monitoring system for a marine vessel as claimed in claim 3, wherein: the mounting structure comprises a mounting bottom plate (101 e), a first clamping plate (101 f), a second clamping plate (101 g) and a connecting column (101 h), the mounting bottom plate (101 e) is fixed on a bulkhead through bolts, and a limiting clamping edge is arranged at the side end of the mounting bottom plate (101 e); the first clamping plate (101 f) and the second clamping plate (101 g) are arranged in a mirror image mode relative to the bolts, the first clamping plate (101 f) is arranged on the second clamping plate (101 g), the end part of the first clamping plate is in sliding connection with the sliding groove (101 d) in the side plate (101 c), and the end part of the second clamping plate (101 g) is fixedly connected with the side plate (101 c); the middle part of the first clamping plate (101 f) is provided with a through hole, and the end parts of the first clamping plate (101 f) and the second clamping plate (101 g) are provided with limiting clamping blocks matched with the limiting clamping edges; the connecting column (101 h) is fixed on the second clamping plate (101 g), a spring is sleeved on the connecting column (101 h), and the end part of the connecting column (101 h) penetrates through the through hole and is connected with a stop block.
5. A fire monitoring system for a marine vessel as claimed in claim 1, wherein: the monitoring platform (2) is used for acquiring monitoring data, the monitoring platform (2) comprises a data processing module (201) and a data fusion analysis module (202), wherein the data processing module (201) comprises a data processing unit (201 a) used for processing sensor data and an image processing unit (201 b) used for processing image information, and the processing process of the image processing unit (201 b) comprises: and performing image preprocessing on an image with noise in the image to obtain a characteristic image, performing image texture boundary matching on the characteristic image, and extracting the characteristic image after the image texture boundary matching through characteristic points to obtain flame texture characteristic data.
6. A fire monitoring system for a marine vessel as claimed in claim 5, wherein: the image preprocessing comprises: the method comprises the steps of carrying out segmentation and sub-image block identification on acquired image information to obtain sub-image blocks, extracting features of the sub-image blocks based on a deep convolutional neural network model, obtaining flame information image features through integration, and obtaining abnormal flame information image feature images through pre-classification.
7. A fire monitoring system for a marine vessel as claimed in claim 5, wherein: the data fusion analysis module (202) adopts a multi-source data fusion technology and fuses the flame information image feature image, the temperature information and the smoke information data to obtain fusion features, and in the information fusion process, standardization, fusion and pretreatment are carried out on the features to screen out fusion features which have influence on the final diagnosis result, classification analysis is carried out on the fusion features through an SVM classifier, and the analysis result is output.
8. A fire monitoring system for a marine vessel as claimed in claim 1, wherein: and the fire alarm subsystem (3) acquires the analysis result, and carries out fire alarm and fire coefficient early warning according to the analysis result, wherein the fire coefficient is based on the weight of the analysis result.
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| CN202011393849.1A CN112419691A (en) | 2020-12-03 | 2020-12-03 | Fire-fighting monitoring system for ship |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113395491A (en) * | 2021-06-11 | 2021-09-14 | 上海海事大学 | Remote monitoring and alarming system for marine engine room |
| CN114520834A (en) * | 2022-02-18 | 2022-05-20 | 湖南省军合科技有限公司 | Electromechanical drive control data acquisition system for ships |
| CN114666370A (en) * | 2022-03-30 | 2022-06-24 | 广东永耀消防安全技术有限公司 | SaaS intelligent fire-fighting monitoring platform based on Internet of things technology |
| CN116311765A (en) * | 2022-12-26 | 2023-06-23 | 深圳市将帅科技有限公司 | Alarm method, system and device with flame induction |
| CN118015779A (en) * | 2024-04-08 | 2024-05-10 | 南通惠江海洋科技有限公司 | Ship fire monitoring system |
| CN118195110A (en) * | 2024-03-14 | 2024-06-14 | 北京中卓时代消防装备科技有限公司 | Fire real-time evacuation path planning method and system based on deep learning |
| CN119049207A (en) * | 2024-11-04 | 2024-11-29 | 长春工程学院 | Disaster emergency alarm method and alarm device based on artificial intelligence |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090315722A1 (en) * | 2008-06-20 | 2009-12-24 | Billy Hou | Multi-wavelength video image fire detecting system |
| CN102298816A (en) * | 2011-05-17 | 2011-12-28 | 杭州电子科技大学 | Fire early warning method for marine engine room based on multi-source fusion |
| CN105931411A (en) * | 2016-06-14 | 2016-09-07 | 广州东亚保安服务有限公司 | Firefighting remote monitoring and early warning platform of multistoried building and realization method of platform |
| CN209657454U (en) * | 2019-01-28 | 2019-11-19 | 北京工业职业技术学院 | Coal mine fire identification system |
| CN209803961U (en) * | 2019-06-26 | 2019-12-17 | 成都航空职业技术学院 | A fire alarm device for a building |
| CN210428225U (en) * | 2019-11-08 | 2020-04-28 | 展翔海事(大连)有限责任公司 | A ship monitoring system |
-
2020
- 2020-12-03 CN CN202011393849.1A patent/CN112419691A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090315722A1 (en) * | 2008-06-20 | 2009-12-24 | Billy Hou | Multi-wavelength video image fire detecting system |
| CN102298816A (en) * | 2011-05-17 | 2011-12-28 | 杭州电子科技大学 | Fire early warning method for marine engine room based on multi-source fusion |
| CN105931411A (en) * | 2016-06-14 | 2016-09-07 | 广州东亚保安服务有限公司 | Firefighting remote monitoring and early warning platform of multistoried building and realization method of platform |
| CN209657454U (en) * | 2019-01-28 | 2019-11-19 | 北京工业职业技术学院 | Coal mine fire identification system |
| CN209803961U (en) * | 2019-06-26 | 2019-12-17 | 成都航空职业技术学院 | A fire alarm device for a building |
| CN210428225U (en) * | 2019-11-08 | 2020-04-28 | 展翔海事(大连)有限责任公司 | A ship monitoring system |
Cited By (12)
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| CN113395491A (en) * | 2021-06-11 | 2021-09-14 | 上海海事大学 | Remote monitoring and alarming system for marine engine room |
| CN114520834A (en) * | 2022-02-18 | 2022-05-20 | 湖南省军合科技有限公司 | Electromechanical drive control data acquisition system for ships |
| CN114520834B (en) * | 2022-02-18 | 2024-03-01 | 湖南省军合科技有限公司 | Ship electromechanical driving control data acquisition system |
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| CN114666370B (en) * | 2022-03-30 | 2024-03-19 | 云南恒品科技有限公司 | A SaaS intelligent fire monitoring platform based on Internet of Things technology |
| CN116311765A (en) * | 2022-12-26 | 2023-06-23 | 深圳市将帅科技有限公司 | Alarm method, system and device with flame induction |
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| CN118195110B (en) * | 2024-03-14 | 2024-08-16 | 北京中卓时代消防装备科技有限公司 | Fire real-time evacuation path planning method and system based on deep learning |
| CN118015779A (en) * | 2024-04-08 | 2024-05-10 | 南通惠江海洋科技有限公司 | Ship fire monitoring system |
| CN118015779B (en) * | 2024-04-08 | 2024-06-14 | 南通惠江海洋科技有限公司 | Ship fire monitoring system |
| CN119049207A (en) * | 2024-11-04 | 2024-11-29 | 长春工程学院 | Disaster emergency alarm method and alarm device based on artificial intelligence |
| CN119049207B (en) * | 2024-11-04 | 2025-02-11 | 长春工程学院 | A disaster emergency alarm method and alarm device based on artificial intelligence |
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