CN109785572B - Fire-fighting early warning method and system based on neural network - Google Patents
Fire-fighting early warning method and system based on neural network Download PDFInfo
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Abstract
The invention discloses a fire-fighting early warning method and a fire-fighting early warning system based on a neural network, wherein the fire-fighting prediction method comprises the following steps: early warning data acquisition: acquiring temperature and smoke data of each monitoring point in real time through a data acquisition node; and (3) data analysis step: guiding the collected monitoring point data into a fire-fighting prediction model, further performing learning training on the model, and analyzing the change trend of temperature and smoke data; early warning judgment: and judging whether to carry out early warning prompting or not according to the data analysis result of the prediction model. Through analyzing the ambient temperature of each monitoring point and the trend of change of the temperature of the position where the fire easily occurs, whether the temperature change of the monitoring points can reach the preset or early warning temperature in the next time period or not is predicted, the temperature of the possible fire is predicted in advance, the fire can be early warned in time, rescue workers can have time to reach the fire, and the loss caused by the fire is reduced.
Description
Technical Field
The invention relates to the technical field of intelligent fire safety, in particular to a fire early warning method and system based on a neural network.
Background
At present, the existing fire safety alarm is carried out or alarms when or after a fire disaster happens, and if the alarm is carried out after the fire disaster happens, although rescue workers can be informed to arrive at the scene in time to control the fire disaster, the rescue workers need a certain time when arriving at the scene of the fire disaster, and the fire disaster can cause a certain loss in the time; therefore, the early warning for fire safety is far more important than the warning after the fire happens.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a fire-fighting early warning method and system based on a neural network, which can prevent the loss caused by the existing alarm after a fire disaster happens by early warning of fire-fighting safety accidents.
The purpose of the invention is realized by the following technical scheme: a fire-fighting early warning method based on a neural network comprises the following steps:
early warning data acquisition: acquiring temperature and smoke data of each monitoring point in real time through a data acquisition node;
and (3) data analysis step: guiding the collected monitoring point data into a fire-fighting prediction model, further performing learning training on the model, and analyzing the change trend of temperature and smoke data;
early warning judgment: and judging whether to carry out early warning prompting or not according to the data analysis result of the prediction model.
Before fire-fighting early warning is carried out through the fire-fighting early warning method based on the neural network, historical data of each monitoring point is acquired through the early warning data acquisition step to carry out machine learning and establish a fire-fighting prediction model.
The data collected by the data collecting node comprises the steps of collecting the ambient temperature of each monitoring point in real time through a temperature sensor, collecting the central temperature of a position where a fire easily occurs in each monitoring point in real time through a thermal infrared imager, and collecting the ambient smoke concentration of each monitoring point in real time through a smoke sensor.
The analysis of the variation trend of the temperature and smoke data comprises the analysis of the temperature increasing variation trend and the smoke concentration increasing variation trend collected from each monitoring point.
The analysis of the trend of the temperature increasing includes the following contents:
at time T1To T2Randomly selecting a plurality of time points t within a time period1、t2、---、tgObtaining the environmental temperature f acquired by the temperature sensor at the corresponding time point11、f12、---、fngAnd the central temperature F of the fire-prone position acquired by the thermal infrared imager at the corresponding time point11、F12、---、Fig；
Fitting the environmental temperature data corresponding to each time point and the central temperature data of the position where the fire easily occurs to obtain T1To T2A change trend function of the environmental temperature in a time period and a change trend function of the central temperature at a position where a fire easily occurs;
and predicting the change conditions of the ambient temperature in the next time period and the central temperature at the position where the fire easily occurs according to the change trend functions of the respective temperatures.
The analysis of the smoke concentration increasing change trend comprises the following contents:
at time T1To T2Randomly selecting a plurality of time points t within a time period1、t2、---、tgObtaining the environmental smoke concentration C collected by the smoke sensor at the corresponding time point11、C12、---、Cng；
Fitting the environmental smoke concentration data corresponding to each time point to obtain T1To T2A trend function of environmental smoke concentration over a period of time;
and predicting the change condition of the environmental smoke concentration in the next time period according to the change trend function of the smoke concentration.
The data analysis step further comprises analyzing the boundary variations of the temperature and smoke data, which comprises the following:
according to the change conditions of the environmental temperature collected by the temperature sensor and the highest temperature of the central temperature of the position where the fire easily occurs collected by the thermal infrared imager, the relation between the environmental temperature fire early warning and the central temperature preset temperature of the position where the fire easily occurs is analyzed in real time;
and analyzing the relation with the fire early warning preset smoke concentration in real time according to the change condition of the highest concentration of the environmental smoke concentration acquired by the smoke sensor.
The early warning judgment step comprises the following steps:
judging whether the environmental temperature reaches the fire early warning preset temperature of the environmental temperature in the next time period or not according to the predicted change condition of the environmental temperature in the next time period;
judging whether the central temperature of the position where the fire easily occurs reaches a fire early warning preset temperature of the position where the fire easily occurs in the next time period or not according to the predicted change condition of the central temperature of the position where the fire easily occurs in the next time period;
judging whether the environmental smoke concentration reaches the fire disaster early warning preset concentration of the environmental smoke concentration in the next time period or not according to the predicted change condition of the environmental smoke concentration in the next time period;
and if any one condition in the steps reaches the preset condition of fire early warning, early warning prompt is carried out.
A fire-fighting early-warning system based on a neural network fire-fighting early-warning method comprises data acquisition nodes and a data analysis processing module, wherein the data acquisition nodes are used for acquiring data of monitoring points in real time, and the data analysis processing module is used for receiving the data acquired by the data acquisition nodes, analyzing and judging results.
The data acquisition node comprises a temperature sensor for acquiring environmental temperature data of each monitoring point, a thermal infrared imager for acquiring central temperature data of a position where a fire easily occurs at each monitoring point and a smoke sensor for acquiring environmental smoke concentration data of each monitoring point.
The invention has the beneficial effects that: a fire-fighting early warning method and a fire-fighting early warning system based on a neural network are characterized in that the environmental temperature of each monitoring point and the change trend of the temperature at the position where a fire easily occurs are analyzed, whether the temperature change reaches the preset or early warning temperature in the next time period is predicted, the temperature at which the fire possibly occurs is predicted in advance, the fire can be early warned in time, rescue workers can have time to reach the fire, and the loss caused by the fire is reduced.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it should be noted that the terms "upper", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings or orientations or positional relationships that the products of the present invention conventionally use, which are merely for convenience of description and simplification of description, but do not indicate or imply that the devices or elements referred to 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," and "connected" are to be construed broadly, e.g., as meaning 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 in specific cases to those skilled in the art.
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a fire-fighting early warning method based on a neural network, the fire-fighting prediction method comprises the following steps:
s1, early warning data acquisition: acquiring temperature and smoke data of each monitoring point in real time through a data acquisition node;
s2, data analysis: guiding the collected monitoring point data into a fire-fighting prediction model, further performing learning training on the model, and analyzing the change trend of temperature and smoke data;
s3, early warning judgment: and judging whether to carry out early warning prompting or not according to the data analysis result of the prediction model.
Furthermore, before fire early warning is performed by a fire early warning method based on a neural network, historical data of each monitoring point is acquired through the early warning data acquisition step to perform machine learning and establish a fire prediction model.
Further, the fire prediction model is learning trained by one of a convolutional neural network, a cyclic neural network, or a time-recursive neural network.
Further, the data collected by the data collecting node comprises the steps of collecting the environmental temperature of each monitoring point in real time through a temperature sensor, collecting the central temperature of the position where a fire easily occurs in each monitoring point in real time through a thermal infrared imager, and collecting the environmental smoke concentration of each monitoring point in real time through a smoke sensor.
Furthermore, the places which are easy to cause fire in each monitoring point include but are not limited to a power distribution room, a motor room, a place with a socket or a dense circuit (such as a centralized charging place of a battery car), a connection place of electrical equipment and a circuit wiring, and any place which can generate open fire or generate a circuit short circuit; and the ignition point data of various materials in the places where the fire easily occurs are led into a fire-fighting early warning model in advance, thermal imaging is carried out on the places where the fire easily occurs through a thermal infrared imager, and the temperature change conditions of the various materials in the thermal imaging image are analyzed in real time.
The analysis of the variation trend of the temperature and smoke data comprises the analysis of the temperature increasing variation trend and the smoke concentration increasing variation trend collected from each monitoring point.
The analysis of the trend of the temperature increasing includes the following contents:
a1 at time T1To T2Randomly selecting a plurality of time points t within a time period1、t2、---、tgObtaining the environmental temperature f acquired by the temperature sensor at the corresponding time point11、f12、---、fngAnd the central temperature F of the fire-prone position acquired by the thermal infrared imager at the corresponding time point11、F12、---、Fig；
A2, fitting the environmental temperature data corresponding to each time point with the central temperature data of the position where the fire easily occurs to obtain T1To T2A change trend function of the environmental temperature in a time period and a change trend function of the central temperature at a position where a fire easily occurs;
and A3, predicting the change situation of the environment temperature in the next time period and the center temperature where the fire easily occurs according to the change trend function of the respective temperatures.
The analysis of the smoke concentration increasing change trend comprises the following contents:
b1 at time T1To T2Randomly selecting a plurality of time points t within a time period1、t2、---、tgObtaining the environmental smoke concentration C collected by the smoke sensor at the corresponding time point1、C2、---、Cng；
B2, fitting the environmental smoke concentration data corresponding to each time point to obtain T1To T2A trend function of environmental smoke concentration over a period of time;
and B3, predicting the change situation of the environmental smoke concentration in the next time period according to the change trend function of the smoke concentration.
Wherein g represents T1To T2The number of the selected time points in the time period is a positive integer greater than 2; n represents the number of environment temperature or environment smoke concentration monitoring in the monitoring point, and the value of n is a positive integer greater than 2; and i represents the number of central temperature monitoring at the position where the fire easily occurs in the monitoring points, and the value of i is a positive integer greater than 2.
The data analysis step further comprises analyzing the boundary variations of the temperature and smoke data, which comprises the following:
according to the change conditions of the environmental temperature collected by the temperature sensor and the highest temperature of the central temperature of the position where the fire easily occurs collected by the thermal infrared imager, the relation between the environmental temperature fire early warning and the central temperature preset temperature of the position where the fire easily occurs is analyzed in real time;
and analyzing the relation with the fire early warning preset smoke concentration in real time according to the change condition of the highest concentration of the environmental smoke concentration acquired by the smoke sensor.
And if the highest environmental temperature collected by the temperature sensor is greater than the preset fire early warning environmental temperature data, or the highest central temperature of a position where a fire easily occurs, collected by the thermal infrared imager, is greater than the preset fire early warning temperature data, or the highest environmental smoke concentration data collected by the smoke sensor is greater than the preset fire early warning smoke concentration data, early warning prompt is carried out.
Furthermore, the preset fire early warning temperature and the preset fire early warning smoke concentration in each monitoring point are different according to different monitoring point environments; the preset fire early warning environment temperature of the relatively closed environment such as a power distribution room, a motor room and the like is higher than that of the relatively ventilated environment such as a passageway or a larger space.
The early warning judgment step comprises the following steps:
judging whether the environmental temperature reaches the fire early warning preset temperature of the environmental temperature in the next time period or not according to the predicted change condition of the environmental temperature in the next time period;
judging whether the central temperature of the position where the fire easily occurs reaches a fire early warning preset temperature of the central temperature of the position where the fire easily occurs in the next time period or not according to the predicted change condition of the central temperature of the position where the fire easily occurs in the next time period;
judging whether the environmental smoke concentration reaches the fire disaster early warning preset concentration of the environmental smoke concentration in the next time period or not according to the predicted change condition of the environmental smoke concentration in the next time period;
and if any one condition in the steps reaches the preset condition of fire early warning, early warning prompt is carried out.
The preset fire early warning temperature of each part which is easy to cause fire in each monitoring point is set differently according to different materials; for example, the fire early warning preset environment temperature of the closed environment can be set to be 48-55 ℃, and the fire early warning preset environment temperature of the ventilation environment can be set to be 42-50 ℃; for example, the preset temperature (the central temperature of the preset thermal imaging image) of fire early warning at the socket or the dense part of the electric wire can be set to be 220-230 ℃, because the main component of the common electric wire and cable sheath is PVC, and the ignition temperature is about 256 ℃.
The fire early warning preset concentration of the environmental smoke concentration in each monitoring point can be set between 0.7% obs/m and 15% obs/m according to the ventilation condition of the environment, and the fire early warning preset concentration can play a role of early warning compared with the common smoke concentration early warning setting range between 5% obs/m and 15% obs/m; moreover, the smoke concentration of 0.7% obs/m can be monitored, more time can be taken for subsequent accident treatment, and the smoke concentration of 5% obs/m indicates that a fire safety accident may occur.
For example, selecting a time point every 5 minutes in a period of 14:30-15:00, selecting 5 time points of 14:35, 14:40, 14:45, 14:50 and 14:55 in total, and obtaining an environmental temperature of a certain monitoring point, wherein the environmental temperatures of the time points in the period are respectively 30 ℃, 31 ℃, 33 ℃, 35 ℃ and 37 ℃; through analysis of the change trend of the environmental temperature of the monitoring point, the environmental temperature of the monitoring point is predicted to reach the preset fire early warning environmental temperature minimum threshold value of 42 ℃ after about 10-15 minutes, and early warning can be sent out in advance to allow relevant personnel to check on the spot to determine the alarm condition, namely, the fire directly gives an alarm to fire fighting.
Or obtaining the central temperatures of the parts (such as a power distribution room) which are easy to generate fire in a certain monitoring point, wherein the central temperatures are respectively 35 ℃, 37 ℃, 39 ℃, 42 ℃ and 45 ℃; through analyzing the central temperature variation trend of the power distribution room, the central temperature of the power distribution room is predicted to reach the lowest threshold value of the central temperature of the position where a preset person easily breaks out a fire in about 5 minutes, and early warning can be sent out in advance to allow related personnel to check the temperature on the spot to determine the condition of an alarm and directly give an alarm to fire fighting.
Or obtaining the environmental concentrations of 0.1% obs/m, 0.15% obs/m, 0.25% obs/m, 0.38% obs/m and 0.5% obs/m corresponding to each time point in the time period of the environmental smoke concentration of a certain monitoring point, predicting that the environmental smoke concentration of the point reaches the minimum threshold value of 0.7% obs/m of the fire early warning preset environmental smoke concentration after about 5-10 minutes through analyzing the change trend of the environmental smoke concentration of the monitoring point, and sending out early warning in advance to allow related personnel to check on site to determine the situation of alarm, wherein the fire directly alarms to the fire.
A fire-fighting early-warning system based on a neural network fire-fighting early-warning method comprises data acquisition nodes and a data analysis processing module, wherein the data acquisition nodes are used for acquiring data of monitoring points in real time, and the data analysis processing module is used for receiving the data acquired by the data acquisition nodes, analyzing and judging results.
The data acquisition node comprises a temperature sensor for acquiring environmental temperature data of each monitoring point, a thermal infrared imager for acquiring central temperature data of a position where a fire easily occurs at each monitoring point and a smoke sensor for acquiring environmental smoke concentration data of each monitoring point.
Furthermore, a fire-fighting prediction model is embedded in the data analysis processing module to realize the prediction analysis of fire-fighting safety accidents.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (4)
1. A fire-fighting early warning method based on a neural network is characterized in that: the fire-fighting early warning method comprises the following steps:
early warning data acquisition: acquiring temperature and smoke data of each monitoring point in real time through a data acquisition node;
and (3) data analysis step: guiding the collected monitoring point data into a fire-fighting prediction model, further performing learning training on the model, and analyzing the change trend of temperature and smoke data;
early warning judgment: judging whether to carry out early warning prompting or not according to the data analysis result of the prediction model;
the data acquired by the data acquisition nodes comprise the steps of acquiring the environmental temperature of each monitoring point in real time through a temperature sensor, acquiring the central temperature of a position where a fire easily occurs in each monitoring point in real time through a thermal infrared imager, and acquiring the environmental smoke concentration of each monitoring point in real time through a smoke sensor; the parts which are easy to cause fire in the monitoring points comprise a distribution room, a motor room, a socket or a circuit dense part, and a connection part of electrical equipment and circuit wiring, ignition point data of various materials in the parts which are easy to cause fire are led into a fire-fighting early warning model in advance, thermal imaging is carried out on the parts which are easy to cause fire through a thermal infrared imager, and the temperature change conditions of the various materials in a thermal imaging image are analyzed in real time;
the analysis of the variation trend of the temperature and smoke data comprises the analysis of the temperature increasing variation trend collected by each monitoring point and the analysis of the smoke concentration increasing variation trend;
the analysis of the trend of the temperature increasing includes the following contents:
randomly selecting a plurality of time points T1, T2, F-and tg in a time period from T1 to T2 to obtain ambient temperatures F11, F12, F-and F ng collected by the temperature sensors corresponding to the time points and central temperatures F11, F12, F-and F-of the fire-prone positions collected by the thermal infrared imager corresponding to the time points;
fitting the environmental temperature data corresponding to each time point and the central temperature data of the position where the fire easily occurs to obtain a change trend function of the environmental temperature in a time period from T1 to T2 and a change trend function of the central temperature of the position where the fire easily occurs;
predicting the change conditions of the environment temperature in the next time period and the central temperature at the position where the fire easily occurs according to the change trend functions of the respective temperatures;
the analysis of the smoke concentration increasing change trend comprises the following steps:
randomly selecting a plurality of time points T1, T2, T-g and T2 within the time period from T1 to T2 to obtain the environmental smoke concentrations C11, C12, T-g and T Cng collected by the smoke sensor at the corresponding time points;
fitting the environmental smoke concentration data corresponding to each time point to obtain a change trend function of the environmental smoke concentration in a time period from T1 to T2;
predicting the change condition of the environmental smoke concentration in the next time period according to the change trend function of the smoke concentration;
the data analysis step further comprises analyzing the boundary variations of the temperature and smoke data, which comprises the following:
according to the change conditions of the environmental temperature collected by the temperature sensor and the highest temperature of the central temperature of the position where the fire easily occurs collected by the thermal infrared imager, the relation between the environmental temperature and the fire early warning preset temperature and the relation between the central temperature of the position where the fire easily occurs and the preset temperature are analyzed in real time; the fire early warning temperature and the fire early warning smoke concentration preset in each monitoring point are different according to different monitoring point environments;
analyzing the relation with the fire early warning preset smoke concentration in real time according to the change condition of the highest concentration of the environmental smoke concentration acquired by the smoke sensor;
the early warning judgment step comprises the following steps:
judging whether the environmental temperature reaches the fire early warning preset temperature of the environmental temperature in the next time period or not according to the predicted change condition of the environmental temperature in the next time period;
judging whether the central temperature of the position where the fire easily occurs reaches a fire early warning preset temperature of the central temperature of the position where the fire easily occurs in the next time period or not according to the predicted change condition of the central temperature of the position where the fire easily occurs in the next time period; the system comprises a plurality of monitoring points, a plurality of sockets or wire-intensive places and a plurality of monitoring points, wherein the preset fire early warning temperature of each place where a fire easily occurs in each monitoring point is set to be different according to different materials, the preset fire early warning environment temperature of a closed environment is set to be 48-55 ℃, the preset fire early warning environment temperature of a ventilation environment is set to be 42-50 ℃, and the preset fire early warning temperature of the socket or wire-intensive places is set to be 220-230;
judging whether the environmental smoke concentration reaches the fire disaster early warning preset concentration of the environmental smoke concentration in the next time period or not according to the predicted change condition of the environmental smoke concentration in the next time period; the fire early warning preset concentration of the environmental smoke concentration in each monitoring point can be set between 0.7% obs/m and 15% obs/m according to the ventilation condition of the environment;
and if any condition in the early warning judgment steps reaches the preset condition of fire early warning, carrying out early warning prompt.
2. A fire-fighting early warning method based on a neural network as claimed in claim 1, wherein: before fire-fighting early warning is carried out through the fire-fighting early warning method based on the neural network, historical data of each monitoring point is acquired through the early warning data acquisition step to carry out machine learning and establish a fire-fighting prediction model.
3. A fire-fighting early warning system based on a neural network fire-fighting early warning method according to any one of claims 1 to 2, characterized in that: the monitoring system comprises a data acquisition node for acquiring data of each monitoring point in real time and a data analysis processing module for receiving the data acquired by the data acquisition node, analyzing and judging results.
4. A fire-fighting early warning system based on a neural network fire-fighting early warning method according to claim 3, characterized in that: the data acquisition node comprises a temperature sensor for acquiring environmental temperature data of each monitoring point, a thermal infrared imager for acquiring central temperature data of a position where a fire easily occurs at each monitoring point and a smoke sensor for acquiring environmental smoke concentration data of each monitoring point.
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| 基于模糊神经网络的智能火灾报警***;夏文智;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20081215(第12期);第5-47页 * |
| 夏文智.基于模糊神经网络的智能火灾报警***.《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》.2008,(第12期), * |
| 无线传感器网络中火灾监测***的研究与实现;孙毅辉;《中国优秀硕士学位论文全文数据库 信息科技辑》;20121015(第10期);第6-51页 * |
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