CN117351638A - Intelligent fire monitoring system and method for passenger rolling ship automobile cabin - Google Patents
Intelligent fire monitoring system and method for passenger rolling ship automobile cabin Download PDFInfo
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- G—PHYSICS
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/12—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions
- G08B17/125—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions by using a video camera to detect fire or smoke
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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Abstract
The invention relates to the technical field of ship fire monitoring, and provides an intelligent fire monitoring system and method for a passenger rolling ship automobile cabin, wherein the intelligent fire monitoring system comprises a fire detection terminal, network communication equipment and a cab monitoring device; the fire detection terminal is fixed on each roll-on and roll-off vehicle; the fire detection terminal transmits detected data around the vehicle to the cab monitoring equipment through the network communication equipment, the cab monitoring equipment takes the measured data of various sensors as an evidence source, and a data fusion algorithm based on DS evidence theory is adopted to judge the current fire. By setting the fire detection terminals for each vehicle, dead angle free collection of surrounding information of the vehicle can be achieved, measurement data of various sensors are used as evidence sources, sensor data fusion is conducted through DS evidence theory, fusion judgment of fires based on different types of data is achieved through a data fusion algorithm based on DS evidence theory, and the fires can be identified rapidly and accurately.
Description
Technical Field
The disclosure relates to the technical field of ship fire monitoring, in particular to an intelligent fire monitoring system and method for a passenger rolling ship in an automobile cabin.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The passenger rolling ship can realize 'people and vehicles together' and becomes the most common transportation mode in the process of cross-sea running of trucks and passengers. During the transportation of a ro-ro passenger ship across the sea, fire accidents are extremely fatal to ship safety and passenger life safety. Because the conditions of the vehicles carried in the cabin are different, and the cargoes in the cabin are various, and in addition, the collision of the vehicles or the friction between the cargoes caused by the jolt of the ship, the fire risk of the vehicles in the cabin is greatly increased. Under the background of continuous development of economy and more frequent personnel circulation, the voyage and the carrying capacity of the rolling passenger ship are greatly increased, and the consequent fire accident of the rolling passenger ship is also continuously increased. In recent years, a plurality of fire accidents of the rolling boats occur worldwide, and serious casualties and economic losses are caused, so that early warning of the fire is a key for ensuring safe transportation of the rolling boats.
The inventor finds that a large number of temperature alarms, smoke alarms and monitoring cameras are arranged in the cabin of the passenger rolling ship and used for detecting and monitoring fire in the cabin, but the alarms and the cameras are generally arranged on the bulkhead, so that the defects of limited detection range, low detection sensitivity, large detection error, delayed detection time and the like exist, and the cabin is covered after the vehicle is fully loaded, so that a large-area monitoring dead angle is caused. Meanwhile, temperature data, smoke detection data and harmful gas detection data belong to different characteristic data, false alarm or false alarm is easy to occur when sensor faults or external disturbance are large, and therefore single characteristic data are directly used as fire judgment basis to identify low accuracy.
Disclosure of Invention
In order to solve the problems, the disclosure provides an intelligent fire monitoring system and method for a passenger rolling ship automobile cabin, which are used for directly monitoring rolling vehicles through a designed fire detection terminal, network communication equipment and a driver's desk monitoring device, and realizing fusion processing of different characteristic data based on a multi-sensor data fusion artificial intelligent processing algorithm to realize intelligent judgment and prediction of fire conditions.
In order to achieve the above purpose, the present disclosure adopts the following technical scheme:
one or more embodiments provide an intelligent fire monitoring system for use in a passenger rolling vessel automobile cabin, comprising a fire detection terminal, a network communication device, and a cab monitoring device;
the fire detection terminal is fixed on each roll-on and roll-off vehicle; the fire detection terminal transmits detected data around the vehicle to the cab monitoring equipment through the network communication equipment, the cab monitoring equipment takes the measured data of various sensors as an evidence source, and a data fusion algorithm based on DS evidence theory is adopted to judge the current fire.
One or more embodiments provide an intelligent fire monitoring method for use in a passenger rolling vessel automobile cabin, comprising the steps of:
acquiring measurement values of various sensors, and converting the measurement values into values of a basic probability distribution function of event occurrence in a sensor measurement value support identification framework;
fusing different characteristic data through DS evidence theory, and synthesizing the values of the basic probability distribution functions of different independent evidence sources into a new basic probability distribution function;
and judging the fire accident according to the final fusion result.
One or more embodiments provide an intelligent fire monitoring method for use in a passenger rolling vessel automobile cabin, comprising the steps of:
setting an alarm threshold value through a fire detection terminal key according to the ambient temperature;
each vehicle is matched with one or more fire detection terminals, the license plate recognition module of the fire detection terminal is used for recognizing the license plate of the corresponding vehicle, the fire detection terminals are bound with the recognized vehicles, and each vehicle can be matched with one or more detection terminals according to the situation;
and sampling the sensing data based on the fire detection terminal in a set sampling period, transmitting the sampling data to the cab monitoring equipment, and judging the current fire by adopting a data fusion algorithm based on DS evidence theory according to an alarm threshold.
Compared with the prior art, the beneficial effects of the present disclosure are:
according to the method and the device, the fire detection terminals are arranged for each vehicle, dead angle-free acquisition of surrounding information of the vehicle can be achieved, measurement data of various sensors are used as evidence sources, sensor data fusion is conducted through DS evidence theory, fusion judgment of fires based on different types of data is achieved through a DS evidence theory-based data fusion algorithm, decision accuracy can be improved, and fire identification can be conducted rapidly.
The advantages of the present disclosure, as well as those of additional aspects, will be described in detail in the following detailed description of embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain and do not limit the disclosure.
FIG. 1 is a schematic diagram of an intelligent fire monitoring system according to embodiment 1 of the present disclosure;
fig. 2 (a) is a first view angle structure diagram of a fire detection terminal of embodiment 1 of the present disclosure;
fig. 2 (b) is a second view angle structure diagram of the fire detection terminal of embodiment 1 of the present disclosure;
fig. 3 is a block diagram of a fire detection terminal of embodiment 1 of the present disclosure;
fig. 4 is a schematic view of a fire detection terminal of embodiment 1 of the present disclosure provided on an automobile;
FIG. 5 is a flow chart of a fire monitoring method of embodiment 2 of the present disclosure;
wherein: 1. the automobile license plate comprises a shell, 2, a display screen, 3, keys, 4, a license plate recognition module, 5, an external temperature sensor, 6, an external smoke sensor, 7, a CO concentration sensor, 8, an automobile body temperature sensor, 9, a magnetic attraction block, 10, a battery compartment, 11 and a wireless communication module.
Detailed Description
The disclosure is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof. It should be noted that, without conflict, the various embodiments and features of the embodiments in the present disclosure may be combined with each other. The embodiments will be described in detail below with reference to the accompanying drawings.
Example 1
In one or more embodiments, as shown in fig. 1 to 4, an intelligent fire monitoring system for use in a passenger rolling ship automobile cabin includes: fire detection terminal, network communication equipment and driver's desk monitoring equipment;
the fire detection terminal is fixed on each roll-on and roll-off vehicle; the fire detection terminal transmits detected data around the vehicle to the cab monitoring equipment through the network communication equipment, the cab monitoring equipment takes the measured data of various sensors as an evidence source, and a data fusion algorithm based on DS evidence theory is adopted to judge the current fire.
In the embodiment, by setting the fire detection terminal for each vehicle, dead angle free collection of surrounding information of the vehicle can be achieved, measurement data of various sensors are used as evidence sources, sensor data fusion is conducted through DS evidence theory, fusion judgment of fires based on different types of data is achieved through a DS evidence theory-based data fusion algorithm, decision accuracy can be improved, and fire identification can be conducted rapidly.
In some embodiments, the fire detection terminal may have a structure as shown in fig. 2 (a), 2 (b) and 3, and includes a housing 1, a main control MCU disposed in the housing 1, a sensor group disposed on the housing 1, and a license plate recognition module 4, where the main control MCU is respectively connected with the sensor group and the license plate recognition module 4 in a communication manner;
the license plate recognition module 4 is configured to recognize a vehicle license plate;
optionally, the sensor group of the fire detection terminal is used for simultaneously monitoring the temperature of the vehicle body, the temperature of the surrounding environment of the vehicle, the smoke concentration and the concentration of harmful gases, and may include an external temperature sensor 5, an external smoke sensor 6, a CO concentration sensor 7, a vehicle body temperature sensor 8 and the like;
an external temperature sensor 5 for measuring the temperature of the surrounding environment of the vehicle;
a vehicle body temperature sensor 8 provided on a side close to the vehicle body for measuring the temperature of the vehicle body;
an external smoke sensor 6 for detecting a smoke concentration around the vehicle;
a CO concentration sensor 7 for detecting the CO concentration.
The fire detection terminal of this embodiment has set up license plate recognition module, can carry out license plate discernment earlier to waiting for discernment vehicle, and behind the data transmission to driver's stand supervisory equipment that sensor group detected, can bind the data that gathers with corresponding vehicle, avoids data confusion, improves data processing efficiency.
Optionally, the shell 1 is made of fireproof and high-temperature-resistant materials, so that the fireproof and high-temperature-resistant functions of the fire detection terminal are realized;
further, a magnetic attraction block 9 is further arranged on the shell 1 of the fire detection terminal and used for fixing the fire detection terminal on a vehicle to be identified.
Further, the fire detection terminal can be further provided with a wireless communication module 11, an alarm, a display screen 2 and a key 3; the wireless communication module 11, the alarm, the display screen 2 and the keys 3 are respectively in communication connection with the main control MCU;
as shown in FIG. 3, each component of the fire detection terminal is arranged on a PCB circuit board, a master control MCU can adopt STM32 series single-chip microcomputer, each component is electrically connected with the master control MCU, and a program is embedded in the master control MCU to realize data calculation and processing.
Optionally, the display screen 2 may be an LED display screen or a liquid crystal display screen, where the display screen 2 is used to display information such as current temperature, smoke concentration, and battery power;
optionally, the key 3 can realize setting of the fire detection terminal;
specifically, the alarm can adopt an audible and visual alarm, and the audible and visual alarm gives an alarm when judging that a fire disaster occurs.
Specifically, the wireless communication module 11 realizes wireless connection between the fire detection terminal and the network communication device.
A battery compartment 10 is arranged in the shell 1 of the fire detection terminal, and a rechargeable battery can be adopted, and comprises a charge-discharge module and a battery energy management module, so as to supply power for the fire detection terminal, and the battery compartment 10 is arranged to facilitate the disassembly and replacement of the battery; and when the fire disaster is judged to occur, the audible and visual alarm gives an alarm.
Further, each vehicle is provided with a fire detection terminal, a plurality of fire detection terminals are arranged in the system, each fire detection terminal is provided with a unique code, and the system has uniqueness so as to realize the correspondence of data.
Optionally, the network communication device includes a gateway and a switch connected in sequence. Wherein the gateway may be a wireless gateway.
Further, the cab monitoring equipment comprises a server and a visual man-machine interaction system;
the server of the cab monitoring equipment is used for realizing data storage and processing;
the visual man-machine interaction system can realize that staff can call monitoring data of all fire detection terminals through a visual interface and information such as cabin positions, vehicle types, cargo types, temperatures, smoke concentrations and the like of the fire detection terminals with alarms.
Further, the server of the cab monitoring device and the loading management system of the rolling passenger ship share data, and the cab monitoring device can display the parking position, the vehicle information, the cargo information and the like of the warning vehicle in the cabin.
Further, the driver's cab monitoring device adopts a DS evidence theory-based data fusion algorithm to judge the current fire, and the method can be implemented in a server of the driver's cab monitoring device and comprises the following steps:
step 1, obtaining measurement values of various sensors, and converting the sensor measurement values into values of a basic probability distribution function for identifying occurrence of an event in a frame;
specifically, by a probability statistical method, the false alarm rate and the false alarm rate of the sensor are calculated by considering the sensor measurement noise and the system noise and counting the historical false alarm times and the false alarm times, and the value of a basic probability distribution function of the occurrence of the event in the sensor measurement numerical support identification frame is obtained; that is, the higher the false alarm rate or false alarm rate, the lower the value of the basic probability distribution function supporting the occurrence of an event in the recognition frame.
Step 2, fusing through DS synthesis rules, and synthesizing the values of the basic probability distribution functions of different mutually independent evidence sources into a new basic probability distribution function;
step 3, judging the fire event according to the final fusion result;
optionally, in step 2, the DS synthesis rule is as follows:
wherein,
where k represents a collision coefficient, the more k tends to 1 to represent the more the different sensor measurement data support for a certain event collides, the more k tends to 0 to represent the more consistent the different sensor measurement data support for a certain event; a is that i Representing mutually independent hypothesized propositions in the recognition framework, and corresponding event types; m is m i (A i ) Representing class i sensor measurement data supporting event recognition under framework A i A basic probability distribution function of event occurrence, wherein i=1, 2, 3; phi represents the empty set.
Determining a fire event identification framework in a passenger rolling ship automobile cabin, wherein the event type is expressed as A= { A 1 ,A 2 ,A 3 Wherein A1 represents that no fire has occurred; a2 represents the occurrence of a dark fire, in which case the temperature does not rise significantly, but a large amount of toxic gases such as CO and smoke are generated; a3 indicates that an open flame is generated, and in this case, the temperature rises sharply, and toxic gases such as CO and smoke are generated.
In this embodiment, the fire detection terminal has 4 kinds of sensors, and the abnormal data of one kind of sensor cannot be used as the basis for judging fire information. For example, the temperature is increased due to the waste heat of an automobile engine, and the concentration of harmful gases such as CO and the like in a ship cabin is increased due to dust emission or tail gas emission caused by automobile running, so that the occurrence of fire in the ship cabin cannot be indicated, and the probability of corresponding fire is low.
In the embodiment, the measurement data of various sensors are used as evidence sources, and the sensor data fusion is carried out through DS evidence theory, so that the accuracy of decision making can be improved.
In order to illustrate the accuracy of the algorithm of the embodiment in identifying fire conditions, simulation verification is performed.
Example 1, the probability of occurrence of no fire, a dark fire, and an open fire under the fire identification framework is shown in table 1 for each sensor measurement data.
Table 1 probability of occurrence of events in example 1
After the synthesis formula, the probability of supporting no fire is calculated to be m ({ A1 }) =0.0002, the probability of supporting dark fire is calculated to be m ({ A2 }) =0.0003, and the probability of supporting open fire is calculated to be m ({ A3 }) =0.9995;
example 1 actually occurred an open flame, and the fusion results were also highly supportive of the occurrence of an open flame.
Example 2, no fire occurred, and the probabilities of each sensor measurement supporting no fire, a dark fire, and an open fire under the fire identification framework are shown in table 2.
Table 2 probability of occurrence of events in example 2
After the synthesis formula, the probability of supporting no fire is calculated to be m ({ A1 }) = 0.8235, the probability of supporting dark fire is calculated to be m ({ A2 }) = 0.1544, and the probability of supporting open fire is calculated to be m ({ A3 }) = 0.0221.
Example 2 is actually no fire, but the probability that the smoke sensor and the CO concentration sensor support the occurrence of a dark fire and an open fire, respectively, is large because the smoke sensor and the CO concentration sensor have large values due to large dust and exhaust emissions when the automobile is driven into the cabin. However, the fusion result still highly supports no fire, which is consistent with the actual situation.
Example 3, a smoldering fire occurred, and the probability of each sensor measurement supporting no fire, a smoldering fire, and an open fire under the fire identification framework, respectively, is shown in table 3.
Table 3 probability of occurrence of events in example 3
After the synthesis formula, the probability of supporting no fire is calculated to be m ({ A1 }) =0.01, the probability of supporting dark fire is calculated to be m ({ A2 }) =0.84, and the probability of supporting open fire is calculated to be m ({ A3 }) =0.15.
Example 3 the occurrence of a dark fire was actually occurred, and at this time, the temperature change inside and outside the vehicle was not obvious, but a large amount of toxic gases such as smoke and CO were generated, and the fusion result highly supported the occurrence of a dark fire, which was consistent with the actual situation.
According to the embodiment, based on information such as vehicle temperature, smog and harmful gas, the fire disaster in the cabin is predicted through the Internet of things and an artificial intelligence algorithm, the early warning, intelligent judgment and accurate positioning of the fire disaster in the cabin of the vehicle are facilitated, and the occurrence of fire accidents of the passenger rolling ship is greatly reduced from the source.
According to the embodiment, fusion processing of different types of data is realized through a simple data fusion algorithm, the fire disaster identification accuracy is ensured, meanwhile, the data processing efficiency is improved, the memory occupation of a processor for processing the data is saved, and the operation efficiency of the system is improved.
Example 2
Based on embodiment 1, the present embodiment provides an intelligent fire monitoring method for a passenger rolling ship in an automobile cabin, as shown in fig. 5, including the following steps:
step 1, setting an alarm threshold value through a fire detection terminal key according to the ambient temperature;
step 2, each vehicle is matched with one or more fire detection terminals, the corresponding vehicle license plate is identified through a fire detection terminal license plate identification module, the fire detection terminals are bound with the identified vehicles, and each vehicle is optionally matched with one or more detection terminals;
and 3, sampling the sensing data based on the fire detection terminal in a set sampling period, transmitting the sampling data to the cab monitoring equipment, and judging the current fire by adopting a DS evidence theory-based data fusion algorithm according to an alarm threshold.
Specifically, the sampled data is sent to a server of the cab monitoring device through the wireless communication module 11 and the network communication device, a data fusion algorithm based on DS evidence theory embedded in the server judges the current fire condition, and judgment information is sent to the fire detection terminal.
The data fusion algorithm based on DS evidence theory is the same as that in embodiment 1, and will not be described here again.
Further, when judging that a fire disaster occurs, the cab monitoring device sends alarm information to the fire disaster detection terminal and alarms through the audible and visual alarm.
Further, when the fire disaster is judged to occur, the data of the passenger rolling ship vehicle loading system is called by the cab monitoring equipment server, and key information such as the parking position of the vehicle in the cabin, the type of the vehicle, the type of the goods, the temperature, the smoke concentration and the like of the fire disaster is displayed on a man-machine interaction interface of the cab monitoring equipment.
Example 3
Based on embodiment 1, the present embodiment provides an intelligent fire monitoring method for use in a passenger rolling ship automobile cabin, which may be implemented in a server of a cab monitoring device, including the following steps:
step 1, obtaining measurement values of various sensors, and converting the sensor measurement values into values of a basic probability distribution function for identifying occurrence of an event in a frame;
specifically, by a probability statistical method, the false alarm rate and the false alarm rate of the sensor are calculated by considering the sensor measurement noise and the system noise and counting the historical false alarm times and the false alarm times, and the value of a basic probability distribution function of the occurrence of the event in the sensor measurement numerical support identification frame is obtained;
step 2, fusing through DS evidence theory, fusing based on DS synthesis rules, and synthesizing the values of the basic probability distribution functions of different mutually independent evidence sources into a new basic probability distribution function;
step 3, judging the fire event according to the final fusion result;
optionally, in step 2, the DS synthesis rule is as follows:
wherein,
where k represents a collision coefficient, the more k tends to 1 to represent the more the different sensor measurement data support for a certain event collides, the more k tends to 0 to represent the more consistent the different sensor measurement data support for a certain event; a is that i Representing mutually independent hypothesized propositions in the recognition framework; m is m i (A i ) Representing class i sensor measurement data supporting event recognition under framework A i A basic probability distribution function of event occurrence, wherein i=1, 2, 3; phi represents the empty set.
Establishing a fire event identification framework in the passenger rolling ship automobile cabin, wherein the framework is expressed as A= { A1, A2, A3}, and A1 represents that no fire occurs; a2 represents the occurrence of a dark fire, in which case the temperature does not rise significantly, but a large amount of toxic gases such as CO and smoke are generated; a3 indicates that an open flame is generated, and in this case, the temperature rises sharply, and toxic gases such as CO and smoke are generated.
The foregoing description of the preferred embodiments of the present disclosure is provided only and not intended to limit the disclosure so that various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
While the specific embodiments of the present disclosure have been described above with reference to the drawings, it should be understood that the present disclosure is not limited to the embodiments, and that various modifications and changes can be made by one skilled in the art without inventive effort on the basis of the technical solutions of the present disclosure while remaining within the scope of the present disclosure.
Claims (10)
1. An intelligent fire monitoring system for a passenger rolling ship in an automobile cabin is characterized in that: the system comprises a fire detection terminal, network communication equipment and a cab monitoring device;
the fire detection terminal is fixed on each roll-on and roll-off vehicle; the fire detection terminal transmits detected data around the vehicle to the cab monitoring equipment through the network communication equipment, the cab monitoring equipment takes the measured data of various sensors as an evidence source, and a data fusion algorithm based on DS evidence theory is adopted to judge the current fire.
2. An intelligent fire monitoring system for use in a passenger ro-ro vehicle cabin according to claim 1, wherein: the current fire condition is judged by adopting a data fusion algorithm based on DS evidence theory, and the method comprises the following steps:
acquiring measurement values of various sensors, and converting the sensor measurement values into values of a basic probability distribution function for identifying occurrence of an event in a frame;
and combining the values of the basic probability distribution functions of different mutually independent evidence sources into a new basic probability distribution function through the combination of DS combining rules.
3. An intelligent fire monitoring system for use in a passenger ro-ro vehicle cabin according to claim 2, wherein: the conversion method of the values of the basic probability distribution functions of the event occurrence in the sensor measurement numerical support identification framework comprises the following steps:
and counting the historical false alarm times and the false alarm times by considering the sensor measurement noise and the system noise, and calculating the false alarm rate and the false alarm rate of the sensor to obtain the value of a basic probability distribution function of event occurrence in a sensor measurement numerical support identification frame.
4. An intelligent fire monitoring system for use in a passenger ro-ro vehicle cabin according to claim 2, wherein:
the DS synthesis rules are as follows:
wherein,
where k represents a collision coefficient, the more k tends to 1 to represent the more the different sensor measurement data support for a certain event collides, the more k tends to 0 to represent the more consistent the different sensor measurement data support for a certain event; a is that i Representing mutually independent hypothesized propositions in the recognition framework; m is m i (A i ) Representing class i sensor measurement data supporting event recognition under framework A i A basic probability distribution function of event occurrence, wherein i=1, 2, 3; phi represents the empty set.
5. An intelligent fire monitoring system for use in a passenger ro-ro vehicle cabin according to claim 1, wherein: the fire detection terminal comprises a shell, a main control MCU arranged in the shell, a sensor group and a license plate recognition module which are arranged on the shell, wherein the main control MCU is respectively in communication connection with the sensor group and the license plate recognition module;
the license plate recognition module is configured to recognize a license plate of a vehicle.
6. An intelligent fire monitoring system for use in a passenger ro-ro vehicle cabin according to claim 5, wherein: the sensor group of the fire detection terminal is used for simultaneously monitoring the temperature of the vehicle body, the temperature of the surrounding environment of the vehicle, the smoke concentration and the concentration of harmful gases, and comprises an external temperature sensor, an external smoke sensor, a CO concentration sensor and a vehicle body temperature sensor;
an external temperature sensor for measuring a temperature of an environment surrounding the vehicle;
the vehicle body temperature sensor is arranged on one side close to the vehicle body and is used for measuring the temperature of the vehicle body;
an external smoke sensor for detecting a smoke concentration around the vehicle;
a CO concentration sensor for detecting CO concentration;
or/and the fire detection terminal is also provided with a magnetic attraction block for fixing the fire detection terminal on the vehicle to be identified;
or/and the fire detection terminal is also provided with a wireless communication module, an alarm, a display screen and keys; the alarm, the display screen and the keys are respectively in communication connection with the main control MCU.
7. An intelligent fire monitoring system for use in a passenger ro-ro vehicle cabin according to claim 1, wherein: each fire detection terminal is provided with a unique code.
8. An intelligent fire monitoring system for use in a passenger ro-ro vehicle cabin according to claim 1, wherein: the cab monitoring equipment comprises a server and a visual man-machine interaction system;
the server of the cab monitoring equipment is used for realizing data storage and processing;
the visual man-machine interaction system is used for realizing that staff can call the monitoring data of all fire detection terminals through a visual interface and the information of the cabin position, the vehicle type, the cargo type, the temperature and the smoke concentration of the fire detection terminal with alarm;
or/and the server of the cab monitoring equipment and the passenger rolling ship load management system share data.
9. An intelligent fire monitoring method for a passenger rolling ship in an automobile cabin is characterized by comprising the following steps:
acquiring measurement values of various sensors, and converting the sensor measurement values into values of a basic probability distribution function for identifying occurrence of an event in a frame;
fusing DS synthesis rules to synthesize values of the basic probability distribution functions of different mutually independent evidence sources into a new basic probability distribution function;
and judging the fire accident according to the final fusion result.
10. An intelligent fire monitoring method for a passenger rolling ship in an automobile cabin is characterized by comprising the following steps:
setting an alarm threshold value through a fire detection terminal key according to the ambient temperature;
each vehicle is matched with one or more fire detection terminals, the license plate recognition module of the fire detection terminal is used for recognizing the license plate of the corresponding vehicle, the fire detection terminals are bound with the recognized vehicles, and each vehicle can be matched with one or more detection terminals according to the situation;
and sampling the sensing data based on the fire detection terminal in a set sampling period, transmitting the sampling data to the cab monitoring equipment, and judging the current fire by adopting a data fusion algorithm based on DS evidence theory according to an alarm threshold.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118015779A (en) * | 2024-04-08 | 2024-05-10 | 南通惠江海洋科技有限公司 | Ship fire monitoring system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118015779A (en) * | 2024-04-08 | 2024-05-10 | 南通惠江海洋科技有限公司 | Ship fire monitoring system |
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