Fixed and mobile cooperative detection system and method for highway tunnel fire

Abstract

The invention relates to the technical field of fire detection, in particular to a highway tunnel fire fixed and mobile cooperative detection system and method, and the method comprises the steps: presetting each monitoring point in a to-be-detected tunnel, shooting an infrared image of each monitoring point in a monitored road section in the tunnel in real time, and dividing the infrared image into each region, obtaining each high-temperature area in each infrared image at each moment; acquiring a temperature change sequence and a trend change coefficient of each high-temperature area, acquiring a temperature drift coefficient of each high-temperature area in combination with the change condition of the temperature values of each high-temperature area and each preset adjacent area in each infrared image at each moment in a previous preset time period, acquiring a time-space characteristic value of each infrared image at each moment, and acquiring a time-space characteristic value of each infrared image at each moment; obtaining a fire possibility coefficient of each high-temperature area, and screening each suspected fire area; and transferring the mobile detection unmanned vehicle to shoot a video for evaluating the fire condition in the to-be-detected tunnel. The invention aims to improve the accuracy of fire monitoring in the highway tunnel.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fire detection, in particular to a fixed and mobile cooperative detection system and method for highway tunnel fire. BACKGROUND

[0002] With the development of society, the rapid increase in the number of highway tunnels has brought new challenges to safety management. Due to the particularity of the tunnel structure, when a disaster occurs, the traditional fire smoke detector sensing technology often responds slowly and is difficult to effectively deal with abnormal vehicle behavior, slow disaster handling response speed, and other problems, making it difficult for personnel to evacuate, and thus threatening tunnel safety management.

[0003] At present, infrared thermal imaging and visual image are often used to identify fire hazards in various parts of the tunnel, so as to quickly judge potential dangers and achieve fire warning. However, for a long highway tunnel, the road section is long and the environment is complex, and the wind speed in the tunnel is fast, so when using traditional methods to monitor the fire in the tunnel using infrared images, temperature drift is likely to occur, which may cause deviation in the judgment of the actual fire point position and affect the accuracy of the monitoring. SUMMARY

[0004] In view of the above, it is necessary to provide a fixed and mobile cooperative detection system and method for highway tunnel fire, which improves the accuracy of fire monitoring in the highway tunnel compared to the traditional highway tunnel fire detection method: In a first aspect, the embodiments of the present application provide a fixed and mobile cooperative detection method for highway tunnel fire, which comprises the following steps: Pre-set each monitoring point in the tunnel to be detected, and real-time capture infrared images of the monitoring section of each monitoring point in the tunnel; Divide each infrared image into regions, obtain each high-temperature region in each infrared image at each time through the distribution of temperature values of all regions in each infrared image at each time, obtain the temperature change sequence of each high-temperature region through the position distribution and temperature value of all high-temperature regions in each infrared image at each time, obtain the trend change coefficient of each high-temperature region through the length and trend change intensity of the temperature change sequence, in combination with the temperature value of each high-temperature region, obtain the temperature drift coefficient of each high-temperature region in combination with the temperature value change of each high-temperature region and its preset neighboring region in each infrared image within a preset period of time, and obtain the space-time characteristic value of each infrared image at each time in combination with the distance from each high-temperature region to the edge of the infrared image in which it is located at the time when the temperature value of each high-temperature region in each infrared image at each time has the maximum mutation, and thus obtain the fire possibility coefficient of each high-temperature region, which is used to screen each suspected fire region from the high-temperature regions in each infrared image at each time; The distance between each mobile detection unmanned vehicle in the tunnel to be detected and each monitoring point corresponding to a suspected fire area is obtained, and the mobile detection unmanned vehicle is mobilized to shoot a video for evaluating the fire condition in the tunnel to be detected.

[0005] In one embodiment, the high-temperature region acquisition process is as follows: A segmentation threshold of temperature values of all regions in all infrared images at each time point is obtained, and a region with a temperature value greater than or equal to the segmentation threshold is regarded as a high-temperature region.

[0006] In one embodiment, the temperature change sequence acquisition process is as follows: A high-temperature region with the minimum temperature value in each infrared image at each time point is recorded as a lowest high-temperature region in each infrared image at each time point, and a center point of each high-temperature region in each infrared image at each time point is connected with a center point of the lowest high-temperature region. All temperature values of high-temperature regions passed by the connecting line are arranged in order of the high-temperature regions to the lowest high-temperature region to form a temperature change sequence of the high-temperature regions in each infrared image at each time point.

[0007] In one embodiment, the trend change coefficient acquisition process is as follows: The length of the temperature change sequence of the high-temperature regions is multiplied by the trend intensity to obtain a product. The trend change coefficient is a sum of the temperature values of the high-temperature regions and the product.

[0008] In one embodiment, the temperature drift coefficient acquisition process is as follows: The difference value of the temperature values of the high-temperature regions between any two adjacent time points in the preset time period is calculated, and the cumulative value of the difference values between all adjacent time points in the preset time period is calculated. The mean value of the difference values between any two time points in the preset time period of the preset near-neighbor regions of the high-temperature regions is calculated, and the cumulative sum of the mean values in the preset time period of all preset near-neighbor regions of the high-temperature regions is calculated. The product value of the trend change coefficient and the cumulative sum is calculated, and the product value is mapped to a first positive number. The temperature drift coefficient is a ratio of the cumulative value and the first positive number.

[0009] In one embodiment, the spatiotemporal feature value acquisition process is as follows: The temperature values of the high-temperature regions in each infrared image at each time point before each time point are arranged in time sequence to obtain a temperature sequence of the high-temperature regions at each time point, and a maximum mutation point in each temperature sequence is obtained. numbering all time points in time sequence; counting the minimum value of the serial number of the time point where the maximum mutation point corresponding to all high-temperature regions in each infrared image at each time point is located; calculating the average value of the distance from the center point of each high-temperature region to the four edges of the infrared image to which it belongs; calculating the cumulative value of the average value corresponding to all high-temperature regions in each infrared image at each time point; The space-time feature value is the ratio of the cumulative value to the minimum value.

[0010] In one embodiment, the process of obtaining the fire possibility coefficient is: mapping the temperature drift coefficient to a second positive number, and the fire possibility coefficient is the ratio of the space-time feature value to the second positive number.

[0011] In one embodiment, the process of obtaining the suspected fire region is: Obtain the segmentation threshold of the fire possibility coefficient of all high-temperature regions in all infrared images at each time point, and regard the high-temperature region with a fire possibility coefficient greater than or equal to the segmentation threshold as a suspected fire region.

[0012] In one embodiment, the mobile detection unmanned vehicle shoots a video of each suspected fire region for evaluating the fire situation in the tunnel to be detected, including: Real-time acquisition of the position of each mobile detection unmanned vehicle in the tunnel to be detected; acquisition of the position of the monitoring point corresponding to each suspected fire region, and recording as the monitoring point of each suspected fire region; Controlling the mobile detection unmanned vehicle closest to the monitoring point of each suspected fire region to go to the position of the monitoring point of each suspected fire region, and then shooting a video of the scene for assisting the staff to determine whether a fire occurs in the tunnel to be detected and whether to start the tunnel fire emergency plan.

[0013] In a second aspect, the embodiments of the present application also provide a fixed and mobile cooperative detection system for highway tunnel fire, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the fixed and mobile cooperative detection method for highway tunnel fire.

[0014] The present application has at least the following beneficial effects: The application can exclude non-fire point areas by extracting high temperature areas, focus on high temperature areas that may have fire hazards, and improve the pertinence of fire monitoring; simulate smoke diffusion paths by using the position distribution and temperature value of the high temperature area, and then obtain the trend change coefficient of the high temperature area by superimposing the temperature value through the length of the smoke diffusion path and the temperature change trend on the smoke diffusion path, reflecting the possibility of each high temperature area being a fire point, and then obtaining the temperature drift coefficient by combining the temperature change of the high temperature area and the temperature change of the neighboring area of the high temperature area, effectively measuring the possibility of each high temperature area being caused by temperature drift; considering that smoke diffusion to non-fire areas may cause misjudgment of the high temperature area caused by smoke as the area where the fire point is located, the space-time characteristic value is obtained by the characteristics of smoke diffusion in the tunnel and the position characteristics of the fire point, which can effectively avoid misjudgment caused by smoke movement, and further improve the accuracy of the position judgment of the fire point; obtain the fire possibility coefficient, which comprehensively evaluates the possibility of fire in each high temperature area, provides a scientific and reliable quantitative index for screening suspected fire areas, helps to accurately select the area most likely to have a fire from numerous high temperature areas, and provides a clear target for subsequent accurate monitoring and emergency treatment; by detecting the distance from the mobile detection unmanned vehicle to the position of each suspected fire area corresponding to the monitoring point, the mobile detection unmanned vehicle can be flexibly dispatched, the on-site video of the suspected fire area can be taken in time, the intuitive and accurate fire situation information can be provided for the staff, which helps to quickly and accurately judge whether a fire has occurred, and timely start the tunnel fire emergency plan, and maximize the reduction of fire loss. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0016] Figure 1 The step flow chart of the fixed and mobile cooperative detection method of the highway tunnel fire provided by an embodiment of the present application is shown in the figure. Figure 2 The screening process schematic diagram of the suspected fire area is shown in the figure. DETAILED DESCRIPTION

[0017] In the description of the embodiments of the present application, the words "exemplary", "or", "for example" are used to mean serving as an example, instance, or illustration, and not to imply any preference or superiority. In fact, the use of the words "exemplary", "or", "for example" is intended to present concepts in a concrete manner.

[0018] Unless otherwise defined, 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 application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. It is understood that unless specifically stated otherwise, "or" as used herein, refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0019] In addition, it should be pointed out that the terms "first", "second" in the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence.

[0020] The specific scheme of the highway tunnel fire fixed and mobile cooperative detection system and method provided by the present application will be specifically described below in combination with the drawings.

[0021] Please refer to Figure 1 which shows the step flow chart of the highway tunnel fire fixed and mobile cooperative detection method provided by an embodiment of the present application, which comprises the following steps: Step 1, preset each monitoring point in the tunnel to be detected, and real-time shoot infrared images of the monitoring road section in the tunnel of each monitoring point.

[0022] For the tunnel to be detected, a fixed infrared thermal imaging monitoring point is set every interval distance N, and an infrared thermal imaging camera is used to shoot infrared images of the monitoring road section in the tunnel of the infrared thermal imaging monitoring point every interval time T, and each collected infrared image is divided into z regions.

[0023] In the present embodiment, the values of N, T and z are 20m, 0.5s and 225 respectively, the values of N, T and z are all preset by human, and the implementer can adjust them according to the actual shooting range of the infrared thermal imaging camera and the actual length of the tunnel to be detected, which is not specially limited in the present application.

[0024] Step 2, each infrared image is divided into regions, and each high-temperature region in each infrared image at each time is obtained through the distribution of temperature values of all regions in each infrared image at each time; the temperature change sequence of each high-temperature region is obtained through the position distribution and temperature value of all high-temperature regions in each infrared image at each time; the trend change coefficient of each high-temperature region is obtained through the length and trend change intensity of the temperature change sequence, combined with the temperature value of each high-temperature region; and the temperature drift coefficient of each high-temperature region is obtained by combining the temperature value change of each high-temperature region and its preset each adjacent region in each infrared image at each time within a previous preset time period.

[0025] For infrared images in a highway tunnel, when no vehicle passes, the overall temperature change is weak and only changes with the change of external temperature. For example, in summer, the temperature is higher during the day and lower at night, but the change is slow. When a fire point appears, the fire point rapidly heats up, and the temperature of the region near the fire point also rises. Specifically, in the infrared image, the gray value of the pixel points in the local region sharply increases and shows a diffusion trend. However, due to the temperature drift, there may be low-temperature pixel points in the high-temperature region, or high-temperature pixel points in the originally low-temperature region, making it difficult to identify the real fire location, so it is necessary to distinguish.

[0026] Specifically, for a real fire point, the actual fire location is fixed, and the relative change of the temperature at the location of the fire point is small when a fire occurs because the speed of the fire is fast. In contrast, the fire will cause the temperature of the region around the fire point to sharply increase. When temperature drift occurs, a high-temperature point may appear in a region other than the fire point, but the stability of the high-temperature point is low due to the temperature drift, the temperature change is large, and the temperature of the region around the high-temperature point will not change due to the influence of the high-temperature point, so the overall temperature change is small. Specifically, the gray value of the real fire point in the infrared image changes little, but the gray value of the pixel points in the local region around the real fire point changes rapidly. In addition, due to the continuous operation of the ventilation system in the highway tunnel, the smoke produced when a fire occurs will move with the wind direction in the tunnel. The smoke of a fire usually has a high temperature, so the temperature of the location where the smoke is located will also increase. However, as the smoke moves, the temperature of the smoke will also decrease to some extent. Because of the operation of the ventilation system, the region affected by the temperature of the smoke will be consistent with the direction of the smoke, that is, in the infrared image, the high-temperature region has a high continuity and is distributed more concentratedly, and the temperature gradually decreases from the fire point to the direction of the ventilation wind direction.

[0027] In order to characterize the temperature change in a single region, the mean value of the gray value of all pixels in each region is taken as the temperature value of each region. Taking the s-th moment as an example, the segmentation threshold of the temperature value of all regions in all infrared images at the s-th moment is obtained. The region with a temperature value greater than the segmentation threshold is taken as a high-temperature region, and the remaining region is taken as a low-temperature region. The difference value between the temperature values of any two adjacent moments within a preset period before the s-th moment is calculated for each region. The mean value of the difference value between all arbitrary adjacent moments within the preset period before the s-th moment is taken as the temperature change speed of each region at the s-th moment. The high-temperature region with the smallest temperature value in each infrared image at the s-th moment is recorded as the lowest high-temperature region in each infrared image at the s-th moment. The center points of each high-temperature region and the center point of the lowest high-temperature region in each infrared image at the s-th moment are connected. The temperature values of all high-temperature regions passed by the connecting line are arranged in order from each high-temperature region to the lowest high-temperature region to form a temperature change sequence of each high-temperature region in each infrared image at the s-th moment. It should be noted that if there are multiple high-temperature regions with the same minimum temperature value, then each high-temperature region is connected with the multiple high-temperature regions with the minimum temperature value, and the temperature change sequence is constructed. Then the longest temperature change sequence is selected as the temperature change sequence of each high-temperature region.

[0028] In this embodiment, the temperature values of all regions in all infrared images at the s-th moment are taken as input, and the cross-validation method is used to output the segmentation threshold of the temperature values of all regions in all infrared images at the s-th moment. The method for obtaining the segmentation threshold by cross-validation is a known technology, and will not be described herein. As other embodiments, on the basis of being able to obtain the segmentation threshold of the temperature values of all regions in all infrared images at the s-th moment, the implementer can use other existing technologies such as Otsu threshold segmentation algorithm and global threshold segmentation, and the present application does not make special limitations.

[0029] In this embodiment, the difference value between the temperature values is the absolute value of the difference. As other embodiments, on the basis of being able to measure the difference between the temperature values, the implementer can use other calculation methods such as the square of the difference, and the present application does not make special limitations.

[0030] Further, the trend change coefficient of each high-temperature region in each infrared image at each moment is obtained by the length and trend change intensity of the temperature change sequence of each high-temperature region in each infrared image at each moment, combined with the temperature value of each high-temperature region in each infrared image at each moment. The expression is: ; in the formula, represents the trend change coefficient of the i-th high-temperature region in the p-th infrared image at the s-th moment; a length of a temperature change sequence of the i-th high-temperature region in the p-th infrared image at the s-th moment; a trend intensity of a temperature change sequence of the i-th high-temperature region in the p-th infrared image at the s-th moment; a temperature value of the i-th high-temperature region in the p-th infrared image at the s-th moment. The trend intensity of the smoke change sequence can be obtained by a trend intensity calculation formula in an STL (Seasonal and Trend decomposition using Loess) decomposition algorithm, which is a known technology and will not be described herein.

[0031] It should be noted that the longer the temperature change sequence of the i-th high-temperature region, the more likely the temperature change sequence of the i-th high-temperature region is formed by smoke moving when the i-th high-temperature region is a fire starting point; the greater the trend intensity of the temperature change sequence of the i-th high-temperature region, the more the path of the temperature change sequence of the i-th high-temperature region conforms to the rule that the temperature gradually decreases from the fire starting point to the direction of the ventilation wind direction; at the same time, the high temperature itself is still a necessary condition for judging fire; therefore, the greater the trend change coefficient, the more likely the i-th high-temperature region is a fire starting point.

[0032] Further, by the trend change coefficients of the high-temperature regions in the infrared images at each moment, in combination with the change of the temperature values of the high-temperature regions and their preset adjacent regions in the infrared images at each moment within a preset time period before each moment, a temperature drift coefficient of each high-temperature region in each infrared image at each moment is obtained, and the expression is: ; in the formula, a temperature drift coefficient of the i-th high-temperature region in the p-th infrared image at the s-th moment; a total number of moments within a preset time period before the s-th moment and adjacent to the s-th moment; , respectively represent the temperature values of the i-th high-temperature region in the p-th infrared image at the s-th moment at the v-th moment and the v-1-th moment within the preset time period; a trend change coefficient of the i-th high-temperature region in the p-th infrared image at the s-th moment; J represents a total number of preset adjacent regions of the i-th high-temperature region in the p-th infrared image at the s-th moment; a temperature change speed of the j-th preset adjacent region of the i-th high-temperature region in the p-th infrared image at the s-th moment; represents an absolute value operation; α represents a preset positive number, which is used to avoid a denominator of 0, and the value of α is preset by a person, which can be set by the implementer, and in the embodiment, the value of α is 0.01.

[0033] In this embodiment, the length of the preset time period is 10s, and the length of the preset time period is preset by a person, and the implementer can set it according to the actual situation, and the present application does not make special limitation.

[0034] In this embodiment, the preset adjacent region of the i-th high-temperature region is the 8-neighborhood of the i-th high-temperature region, and the implementer can set the adjacent region of the i-th high-temperature region according to the actual situation.

[0035] It should be noted that: the smaller the overall temperature change rate of the neighborhood region of the i-th high-temperature region, the more obvious the temperature change of the i-th high-temperature region in the short term, and the lower the trend change coefficient of the i-th high-temperature region, the more likely the position of the i-th high-temperature region is caused by temperature drift, and the more likely it represents the position of the fire point itself.

[0036] Step 3, obtaining the space-time feature value of each infrared image at each time point by the time point at which each high-temperature region in each infrared image at each time point has the maximum mutation of the previous appearing temperature value, in combination with the distance from each high-temperature region to the edge of the infrared image to which it belongs.

[0037] Because there are many ventilation systems in the tunnel, and the wind speed in the tunnel is fast, when the fire occurs, the smoke generated by the fire will move quickly due to the wind speed in the tunnel, and thus the remaining sections in the tunnel may also appear smoke. Because the smoke itself has a high temperature, when the smoke moves away from the fire point and enters the monitoring range of another infrared thermal imaging camera, it will also cause a high-temperature region in the infrared image, and the distribution rule is similar to the region where the fire point is located. Therefore, when calculating by the method of step 2, it is easy to mistake the high-temperature region caused by the smoke as the region where the fire point is located, thereby causing errors in the judgment of the position of the fire point and delaying the fire. Therefore, further analysis is needed.

[0038] Specifically, for the fire position in the tunnel, fire may occur in each region in the tunnel, so when the fire occurs, the position of the fire point is usually far away from the boundary of the monitoring range of a single monitoring point. On the contrary, when a high-temperature region caused by smoke appears in the monitoring range of a monitoring point, the smoke comes from a position outside the monitoring point, so when the smoke first enters the monitoring range of the monitoring point, it is close to the boundary of the monitoring region. In addition, for the entire tunnel, when the fire occurs, the temperature in the local tunnel region will suddenly change, and the temperature change in the region where the fire point is located occurs earliest, and as the smoke spreads, the temperature in the remaining tunnel regions gradually changes, so the temperature change in the region other than the fire point occurs later than the fire point.

[0039] To characterize the above characteristics, the temperature values ​​of each high-temperature area in each infrared image at the sth moment before the sth moment are arranged in time sequence to obtain the temperature sequence of each high-temperature area in each infrared image at the sth moment, and the maximum mutation point in each temperature sequence is obtained. Furthermore, the spatiotemporal characteristic value of each infrared image at each moment is obtained by combining the time when the maximum temperature mutation occurred in each high-temperature area in each infrared image at each moment and the distance from each high-temperature area in each infrared image at each moment to the edge of the infrared image to which it belongs. The expression is: Where, represents the spatiotemporal feature value of the pth infrared image at the sth moment; represents the number of high-temperature areas in the p-th infrared image at the s-th moment; represents the average value of the distance from the center point of the i-th high-temperature area in the p-th infrared image to the four edges of the infrared image at the s-th moment; min() represents the minimum value operation; It represents the sequence number of the maximum mutation point in the temperature sequence of the i-th high-temperature area in the p-th infrared image at the s-th time, where all moments are numbered in time sequence.

[0040] In this embodiment, the Pettitt mutation point detection algorithm is used to obtain the maximum mutation point in each temperature sequence. Specifically, the statistics of each mutation point in each temperature sequence are obtained by the Pettitt mutation point detection algorithm. , the statistics in each temperature series The mutation point with the largest absolute value is taken as the maximum mutation point in each temperature sequence. The Pettitt mutation point detection algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, on the basis of being able to obtain the maximum mutation point in each temperature sequence, the implementer may adopt other existing technologies, such as the Mann-Kendall mutation point detection algorithm, etc., and this application does not impose any special restrictions.

[0041] In this embodiment, the distance from the center point of the i-th high-temperature area to the edge of the infrared image is the Euclidean distance.

[0042] It should be noted that: if the temperature mutation occurs earlier in the monitoring range of the monitoring point where the p-th infrared image is taken, and the location of the high-temperature area in the monitoring range is farther away from the edge of the infrared image, it means that there is a greater possibility that a fire point exists within the monitoring range of the monitoring point where the p-th infrared image is taken.

[0043] Step 4, obtaining the fire possibility coefficient of each high-temperature region in each infrared image at each time point through the temperature drift coefficient of each high-temperature region in each infrared image at each time point and the space-time characteristic value of each infrared image at each time point, and using the fire possibility coefficient to screen each suspected fire region from the high-temperature region in each infrared image at each time point; and mobilizing the mobile detection unmanned vehicle to shoot a video through the distance between each mobile detection unmanned vehicle in the tunnel to be detected and the monitoring point corresponding to each suspected fire region, and using the video to evaluate the fire situation in the tunnel to be detected.

[0044] Further, the fire possibility coefficient of each high-temperature region in each infrared image at each time point is obtained through the temperature drift coefficient of each high-temperature region in each infrared image at each time point and the space-time characteristic value of each infrared image at each time point, and the expression is: ; in the formula, represents the fire possibility coefficient of the i-th high-temperature region in the p-th infrared image at the s-th time point; represents the space-time characteristic value of the p-th infrared image at the s-th time point; represents the temperature drift coefficient of the i-th high-temperature region in the p-th infrared image at the s-th time point; and β represents a preset positive number, which is used to avoid the denominator being 0, and the value of β is artificially preset, which can be set by the implementer, and in the embodiment, the value of β is 0.01.

[0045] It should be noted that when the temperature change of the i-th high-temperature region in the p-th infrared image at the s-th time point is less likely to be caused by temperature drift phenomenon, and the p-th infrared image has a greater possibility of having a fire region, it is more likely that there is a fire position in the highway tunnel within the monitoring range of the monitoring point that shoots the p-th infrared image.

[0046] Further, a segmentation threshold of the fire possibility coefficient of all high-temperature regions in all infrared images at the s-th time point is obtained, and a high-temperature region with a fire possibility coefficient greater than or equal to the segmentation threshold is regarded as a suspected fire region. The screening process of the suspected fire region is shown in Figure 2 .

[0047] In the embodiment, the fire possibility coefficient of all high-temperature regions in all infrared images at the s-th time point is taken as input, and a segmentation threshold of the fire possibility coefficient of all high-temperature regions in all infrared images at the s-th time point is output by using cross-validation. The method of obtaining the segmentation threshold by using cross-validation is a known technology, and will not be described herein. As other embodiments, on the basis of being able to obtain the segmentation threshold of the fire possibility coefficient of all high-temperature regions in all infrared images at the s-th time point, the implementer can use other existing technologies, such as Otsu threshold segmentation algorithm, global threshold segmentation, etc., which are not specially limited in the present application.

[0048] Further, if there is a suspected fire area at the s-th moment, the positions of the monitoring points corresponding to each suspected fire area are obtained and recorded as the monitoring points of each suspected fire area. Meanwhile, the tunnel fire monitoring system obtains the positions of each mobile detection unmanned vehicle in the tunnel to be detected, sends a control signal to control the mobile detection unmanned vehicle closest to the monitoring points of each suspected fire area to go to the positions of the monitoring points of each suspected fire area, and then shoot the video of the scene to return to the tunnel fire monitoring system to assist the staff to determine whether a fire occurs in the tunnel to be detected and whether to start the tunnel fire emergency plan.

[0049] Based on the same inventive concept as the above method, the embodiments of the present application also provide a fixed and mobile cooperative detection system for highway tunnel fire, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the methods in the above fixed and mobile cooperative detection method for highway tunnel fire when executing the computer program.

[0050] In summary, the present application can exclude non-fire point areas by extracting high temperature areas, focus on high temperature areas that may have fire hazards, and improve the pertinence of fire monitoring. The length of the smoke diffusion path and the temperature change trend on the smoke diffusion path are used to obtain the trend change coefficient of the high temperature area by superimposing the temperature value, reflecting the possibility of each high temperature area being a fire point, and then combining the temperature change of the high temperature area and the temperature change of the neighboring area of the high temperature area to obtain the temperature drift coefficient, effectively measuring the possibility of each high temperature area caused by temperature drift. Considering that smoke diffusion to non-fire areas may cause high temperature areas caused by smoke to be misjudged as fire point areas, the spatiotemporal feature value is obtained by the characteristics of smoke diffusion in the tunnel and the position characteristics of the fire point, which can effectively avoid misjudgment caused by smoke movement and further improve the accuracy of the position judgment of the fire point. The fire possibility coefficient is obtained by comprehensively considering the temperature drift coefficient and the spatiotemporal feature value, which can comprehensively and accurately evaluate the possibility of fire in each high temperature area, provide a scientific and reliable quantitative index for screening suspected fire areas, and help to accurately select the area most likely to have a fire from numerous high temperature areas, providing a clear target for subsequent accurate monitoring and emergency handling. The distance from the mobile detection unmanned vehicle to the position of the monitoring point corresponding to each suspected fire area is obtained to flexibly schedule the mobile detection unmanned vehicle, which can shoot the video of the suspected fire area in time to provide intuitive and accurate fire information for the staff, help to quickly and accurately determine whether a fire occurs, and timely start the tunnel fire emergency plan to minimize fire loss.

[0051] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flow diagrams and / or block diagrams.

[0052] It is apparent that a person skilled in the art can make a variety of modifications to the application described herein without departing from the spirit and scope of the application. Therefore, the described embodiments are to be considered in all respects as illustrative and not restrictive.

Claims

1. A method for the coordinated detection of fixed and mobile fires in highway tunnels, characterized in that: The method comprises the following steps: Preset monitoring points in the tunnel to be detected and take real-time infrared images of the monitored sections of the tunnel at each monitoring point; Each infrared image is divided into regions, and each high-temperature region in each infrared image at each moment is obtained by the distribution of temperature values ​​of all regions in each infrared image at each moment; a temperature change sequence of each high-temperature region is obtained by the position distribution and temperature values ​​of all high-temperature regions in each infrared image at each moment; a trend change coefficient of each high-temperature region is obtained by combining the length and trend change strength of the temperature change sequence with the temperature value of each high-temperature region; a temperature drift coefficient of each high-temperature region is obtained by combining the temperature value changes of each high-temperature region and its preset neighboring regions in each infrared image at each moment within a previously preset time period; and a spatiotemporal characteristic value of each infrared image at each moment is obtained by combining the time when the maximum temperature mutation occurred in each high-temperature region in each infrared image at each moment and the distance from each high-temperature region to the edge of the infrared image to which it belongs, thereby obtaining a fire possibility coefficient of each high-temperature region, which is used to screen suspected fire regions from the high-temperature regions in each infrared image at each moment; By measuring the distance between each mobile detection unmanned vehicle in the tunnel to be detected and the corresponding monitoring point of each suspected fire area, the mobile detection unmanned vehicle is mobilized to shoot video to assess the fire situation in the tunnel to be detected.

2. The fixed and mobile coordinated detection method for highway tunnel fires according to claim 1, characterized in that: The acquisition process of the high temperature area is as follows: The segmentation threshold of the temperature values ​​of all regions in all infrared images at each moment is obtained, and the regions with temperature values ​​greater than or equal to the segmentation threshold are regarded as high-temperature regions.

3. The fixed and mobile coordinated detection method for highway tunnel fires according to claim 1, characterized in that: The process of obtaining the temperature change sequence is as follows: The high-temperature area with the smallest temperature value in each infrared image at each moment is recorded as the lowest high-temperature area in each infrared image at each moment; the center point of each high-temperature area in each infrared image at each moment is connected with the center point of the lowest high-temperature area, and the temperature values ​​of all high-temperature areas passed by the connecting line are arranged in the order from each high-temperature area to the lowest high-temperature area to form a temperature change sequence of each high-temperature area in each infrared image at each moment.

4. The fixed and mobile coordinated detection method for highway tunnel fires according to claim 1, characterized in that: The process of obtaining the trend change coefficient is as follows: Calculating the product of the length of the temperature change sequence of each high temperature area and the trend intensity; The trend change coefficient is the sum of the temperature values ​​of the high-temperature areas and the product.

5. The fixed and mobile coordinated detection method for highway tunnel fires according to claim 1, characterized in that: The process of obtaining the temperature drift coefficient is as follows: Calculating the difference between the temperature values ​​of each high-temperature region between any two adjacent moments in the preset time period; and calculating the cumulative value of the difference between all the temperature values ​​of each high-temperature region between any two adjacent moments in the preset time period; Calculate the average of the difference values ​​between any two moments in the preset neighboring regions of each high-temperature region within the preset time period; Calculate the cumulative sum of the mean values ​​of all preset neighboring areas of each high-temperature area within the preset time period; The product value of the trend change coefficient and the accumulated sum is calculated, and the product value is mapped to a first positive number; the temperature drift coefficient is a ratio of the accumulated value to the first positive number.

6. The fixed and mobile coordinated detection method for highway tunnel fires according to claim 1, characterized in that: The process of obtaining the spatiotemporal eigenvalues ​​is as follows: Arrange the temperature values ​​of each high-temperature area in each infrared image at each moment before each moment in time sequence to obtain the temperature sequence of each high-temperature area at each moment, and obtain the maximum mutation point in each temperature sequence; All moments are numbered in time sequence; the minimum value among the sequence numbers of the maximum mutation points corresponding to all high-temperature areas in each infrared image at each moment is counted; Calculate the average value of the distances from the center point of each high-temperature area to the four edges of the infrared image to which it belongs; calculate the cumulative value of the average values ​​corresponding to all high-temperature areas in each infrared image at each moment; The spatiotemporal characteristic value is the ratio of the cumulative value to the minimum value.

7. The fixed and mobile coordinated detection method for highway tunnel fires according to claim 1, characterized in that: The process of obtaining the fire probability coefficient is as follows: mapping the temperature drift coefficient to a second positive number, and the fire probability coefficient is a ratio of the spatiotemporal characteristic value to the second positive number.

8. The fixed and mobile coordinated detection method for highway tunnel fires according to claim 1, characterized in that: The process of obtaining the suspected fire area is as follows: The segmentation threshold of the fire probability coefficient of all high-temperature areas in all infrared images at each moment is obtained, and the high-temperature areas with fire probability coefficients greater than or equal to the segmentation threshold are regarded as suspected fire areas.

9. The fixed and mobile coordinated detection method for highway tunnel fires according to claim 1, characterized in that: The mobile detection unmanned vehicle is mobilized to capture videos of suspected fire areas for use in assessing the fire situation in the tunnel to be detected, including: Obtain the position of each mobile detection unmanned vehicle in the tunnel to be detected in real time; obtain the position of the monitoring point corresponding to each suspected fire area and record it as the monitoring point of each suspected fire area; Control the mobile detection unmanned vehicle closest to the monitoring point of each suspected fire area to go to the location of the monitoring point of each suspected fire area, and then shoot video of the scene to assist staff in determining whether there is a fire in the tunnel to be detected and whether to activate the tunnel fire emergency plan.

10. A fixed and mobile coordinated detection system for highway tunnel fires, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the fixed and mobile collaborative detection method for highway tunnel fires as described in any one of claims 1 to 9 are implemented.

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