CN115512506A - Terminal cloud linkage fire-fighting diagram detection method and system based on two buffer pools - Google Patents
Terminal cloud linkage fire-fighting diagram detection method and system based on two buffer pools Download PDFInfo
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Abstract
The invention provides a method and a system for detecting a terminal cloud linkage fire-fighting diagram based on two buffer pools, wherein the system comprises a front-end sensing device, video monitoring equipment and a cloud center server; the cloud center server is provided with: the buffer module comprises a long-time buffer pool and a short-time buffer pool and is used for respectively storing and updating the acquired long-time image data and the acquired short-time image data; the image detection module is used for calculating the difference of historical images of the long-time buffer pool and the short-time buffer pool in a window period acquired by the real-time video stream; after the real-time video stream is obtained, calculating the real-time image difference between the long-time buffer pool and the real-time video; when the cloud center server receives an alarm signal of the front-end sensing device, auxiliary analysis is provided for manual image detection through historical image difference and/or real-time image difference calculation. The invention adopts two buffer pool technologies, and effectively improves the timeliness and the accuracy of the fire alarm report through double analysis of the detection difference of the historical image and the detection difference of the real-time image.
Description
Technical Field
The invention relates to the technical field of fire prevention and control automation platforms, in particular to a method and a system for detecting a terminal cloud linkage fire-fighting diagram based on two buffer pools.
Background
Fire safety is an important component of urban public safety, relates to thousands of households and every industry, and is directly related to urban municipal safety, traffic safety, production operation safety, sanitation safety and people life and property safety. In the aspect of conflagration intelligence prevention and control, current traditional fire extinguishing system generally only stops at business management's information-based layer, has mainly realized the collection of workplace response data, but the data of gathering are not timely enough, comprehensive, and is also not practical enough, and the analytical value of data is not exerted, still relies on manual analysis, and such business application mode is to the conflagration take precautions against and the rescue help of putting out a fire is limited. The development of the machine vision technology provides powerful support for the integration of image information processing and the working depth of fire service and the creation of a modern fire service mechanism which meets the actual combat requirements.
The modern fire prevention and control automation platform takes an electrical fire detector, a combustible gas detector, a fire door controller and the like as main front-end sensing devices, and takes video imaging equipment and coding, decoding and forwarding equipment as visual equipment of a fire alarm field. The cloud platform of video picture passback is carried out at first moment of fire alarm, and the work of confirming is listened to the manual work picture that carries out the fire source in the high in the clouds, gains valuable time for putting out early stage conflagration and the golden 3 minutes of saving oneself. The fire alarm prevention and control which must be struggled in minutes and seconds needs to analyze and judge the trend of fire and the development of disaster as fast as possible. However, in practical applications, due to data packet loss caused by network bandwidth limitation or communication abnormality and distribution switching of video streams, there is a short delay or jam in video picture transmission, and confirmation work of manual image detection is delayed. Meanwhile, the worker needs to keep a working state of being absorbed by the worker for a long time based on manual picture fire alarm investigation, and certain searching and judging time is also needed for fire source target positioning.
Disclosure of Invention
According to the problems in the prior art, the invention provides the terminal cloud linkage fire-fighting image detection method and system based on the two buffer pools, so that a blank window with image detection image transmission loss is effectively made up, and the timeliness and the accuracy of a fire alarm report are improved.
The technical scheme of the invention is as follows:
a terminal cloud linkage fire-fighting image detection system based on two buffer pools is characterized by comprising a front-end sensing device, video monitoring equipment and a cloud center server; the front end sensing device and the video monitoring equipment are respectively in communication connection with the cloud center server; the cloud center server is provided with:
the buffer module comprises a long-time buffer pool and a short-time buffer pool and is used for respectively storing and updating the acquired long-time image data and short-time image data of the video monitoring equipment;
the image detection module is used for calculating the difference of historical images of the long-time buffer pool and the short-time buffer pool in a window period acquired by the real-time video stream; after the real-time video stream is obtained, calculating the real-time image difference between the long-time buffer pool and the real-time video;
when the cloud center server receives an alarm signal of the front-end sensing device, auxiliary analysis is provided for manual image detection through historical image difference and/or real-time image difference calculation.
Furthermore, the cloud center server is further provided with an address matching module which is used for setting a geographic coordinate-IP address matching table of the front-end sensing device and the video monitoring equipment and providing a communication address of the video monitoring equipment corresponding to the front-end sensing device.
Further, the cloud center server is in communication connection with the front-end sensing device and the video monitoring equipment through the communication module.
A terminal cloud linkage fire-fighting diagram detection method based on two buffer pools is characterized by comprising the following steps:
s1, setting a geographic coordinate-IP address matching table of a front-end sensing device and video monitoring equipment;
s2, setting two buffer pools, wherein the first buffer pool is a long-time buffer pool, and the second buffer pool is a short-time buffer pool, and respectively storing and updating the acquired long-time image data and short-time image data of the video monitoring equipment;
s3, when the cloud center server receives an alarm signal of the front-end sensing device, the cloud center server inquires a communication address of corresponding video monitoring equipment through a matching table to remotely acquire a video stream;
s4, performing historical image difference calculation through image data of the long-time buffer pool and the short-time buffer pool in a window period acquired by the real-time video stream; after the real-time video stream is obtained, performing real-time image difference calculation through the long-time buffer pool and the image data of the real-time video stream;
and S5, providing auxiliary analysis for the artificial graph detection by using the difference calculation result of the historical images and/or the difference calculation result of the real-time images.
Wherein, step S1 specifically includes:
s11, front end sensing device x i Deploying defense in a gridding mode and storing a front-end sensing device x i (ii) geographic coordinates of;
s12, video monitoring equipment y j Covering 1 to N front end sensing devices by the field of view, and storing the video monitoring equipment y j The IP address of (2);
s13, front end sensing device x i And video monitoring equipment y j The matching table of the geographic coordinates and the IP addresses is an N:1 linear table, namely N front-end sensing devices correspond to one video monitoring device.
The step S2 specifically includes:
s21, setting long-term buffer pool data updating time t1, receiving remote images of the video monitoring equipment at intervals of t1, and updating image data of the long-term buffer pool;
s22, setting short-time buffer pool data updating time t2, receiving remote images of the video monitoring equipment at intervals of t2, and updating image data of the short-time buffer pool; wherein t2 < t1.
Further, in step S21, the long-term buffer pool image data update method is a gaussian background modeling method, which specifically includes:
let the mapping p (x) of each pixel point (x, y) to the Gaussian background model satisfy for the background image B
the long-term buffer pool updates each frame of image by the following method:
wherein p is an update threshold, which is a constant and is used to reflect the background update rate, and the larger p is, the slower the background update is.
In step S22, the updating method of the image data in the short-time buffer pool is a replacement method, which specifically includes:
after the image is acquired by the short-time buffer pool, the newly acquired image is processedAll image pixels M t (x, y) completely covers the previous pixel M t-1 （x，y）：
In step S4, the calculation method of the difference between the historical images and the difference between the real-time images is a structural similarity difference calculation method, and the specific steps include:
inputting image data of a long-term buffer pool and a short-term buffer pool or image data of a long-term buffer pool and a real-time video, and setting each two pictures as x and y respectively;
taking the average gray mu as brightness measurement, and respectively calculating average gray ux and uy;
taking the gray standard deviation sigma as contrast measurement, and respectively calculating gray standard deviations sigma x and sigma y;
and calculating structural difference D (x, y) among the pictures according to the calculated ux, uy, sigma x and sigma y:
wherein C is 1 ，C 2 The constant value is adopted, so that the instability caused when the denominator is close to 0 is avoided;
the larger D is, the smaller the difference of the representative picture is, and the smaller D is, the larger the difference of the representative picture is.
In step S5, providing the auxiliary analysis for the artificial image frame includes: after the front-end sensing device gives an alarm, before the cloud center server does not acquire a live real-time video stream, if the difference D1 of the historical images is greater than a certain threshold value, the fact that the probability is not that the front-end sensing device gives a false alarm is proved, and a worker can obtain the possible place and time of a fire according to the IP address and the video time, send out a fire early warning and prepare for fire-fighting operation in advance; after the live real-time video stream reaches the cloud center server, if the difference D2 of the real-time images is larger than a certain threshold value, the fire disaster at the position with high probability is judged, and the staff can determine the fire disaster place and time according to the IP address and the video time and send out a fire disaster alarm.
The invention has the technical effects that:
according to the method and the system for detecting the end cloud linkage fire fighting diagram based on the two buffer pools, the short-time buffer pool and the long-time buffer pool are arranged, and the obtained long-time image data and the obtained short-time image data are stored and updated respectively; when waiting for real-time picture transmission, the information of the short-time buffer pool and the long-time buffer pool is utilized to calculate the detection difference of the historical map, thereby effectively making up a blank window lacking in picture transmission and finding out a potential fire point in time; after receiving the real-time picture, calculating the real-time picture detection difference by using the information of the real-time video and the long-time buffer pool; according to the dual analysis of the detection difference of the historical images and the detection difference of the real-time images, a powerful technical guarantee is provided for the staff to finally determine the alarm result, the timeliness and the accuracy of the fire alarm report are effectively improved, and the method is worthy of popularization and application.
Drawings
Fig. 1 is a schematic structural view of a terminal cloud linkage fire-fighting image detection system based on two buffer pools.
Fig. 2 is a schematic flow chart of an embodiment of a terminal cloud-linked fire-fighting diagram detection method based on two buffer pools according to the present invention.
Detailed Description
For a further understanding of the invention, reference is made to the following description of the preferred embodiments of the invention taken in conjunction with the accompanying drawings, but it is understood that the description is intended to illustrate further features and advantages of the invention, and not to limit the scope of the appended claims.
Fig. 1 is a schematic structural view of a terminal cloud linkage fire-fighting image detection system based on two buffer pools.
A terminal cloud linkage fire-fighting image detection system based on two buffer pools comprises a front-end sensing device, video monitoring equipment and a cloud center server; the front-end sensing device and the video monitoring equipment are respectively in communication connection with the cloud center server; in this embodiment, the front-end sensing device and the video monitoring device are respectively connected to the cloud center server through the communication module. The cloud center server is provided with: the buffer module and the image detection module are also provided with an address matching module; the buffer module comprises a long-term buffer pool and a short-term buffer pool, and is used for respectively storing and updating the acquired long-term image data and short-term image data of the video monitoring equipment; the image detection module is used for calculating the difference of historical images of the long-time buffer pool and the short-time buffer pool in a window period acquired by the real-time video stream; after the real-time video stream is obtained, calculating the real-time image difference between the long-time buffer pool and the real-time video; when the cloud center server receives an alarm signal of the front-end sensing device, auxiliary analysis is provided for artificial image detection through historical image difference and/or real-time image difference calculation. The cloud center server is also provided with an address matching module which is used for setting a geographic coordinate-IP address matching table of the front-end sensing device and the video monitoring equipment and providing a communication address of the video monitoring equipment corresponding to the front-end sensing device.
Fig. 2 is a schematic flow chart of an embodiment of a terminal cloud linked fire fighting diagram detection method based on two buffer pools according to the present invention.
A terminal cloud linkage fire-fighting diagram detection method based on two buffer pools comprises the following steps:
s1, setting a geographic coordinate-IP address matching table of a front-end sensing device and video monitoring equipment;
s2, setting two buffer pools, wherein the first buffer pool is a long-time buffer pool, and the second buffer pool is a short-time buffer pool, and respectively storing and updating the acquired long-time image data and short-time image data of the video monitoring equipment;
s3, when the cloud center server receives an alarm signal of the front-end sensing device, the cloud center server inquires a communication address of corresponding video monitoring equipment through a matching table to remotely acquire a video stream;
s4, performing historical image difference calculation through image data of the long-time buffer pool and the short-time buffer pool in a window period acquired by the real-time video stream; after the real-time video stream is obtained, performing real-time image difference calculation through the long-time buffer pool and the image data of the real-time video stream;
and S5, providing auxiliary analysis for the artificial image detection by using the difference calculation results of the historical images and/or the difference calculation results of the real-time images.
Wherein, step S1 specifically includes:
s11, front end sensing device x i Deploying defense in a gridding manner and storing front-end sensing device x i (ii) geographic coordinates of;
s12, video monitoring equipment y j Covering 1 to N front end sensing devices by the field of view, and storing the video monitoring equipment y j The IP address of (2);
s13, front end sensing device x i And video monitoring equipment y j The matching table of the geographic coordinates and the IP addresses is an N:1 linear table, namely N front-end sensing devices correspond to one video monitoring device. In this embodiment, every 3-5 front-end sensing devices correspond to a video monitoring apparatus.
The step S2 specifically includes:
s21, setting long-term buffer pool data updating time t1, receiving a remote image of the video monitoring equipment at intervals of t1 (in a 'no alarm' or 'alarm eliminated' state), and updating the long-term buffer pool image data; in this embodiment, t1 is set to 12h, that is, the long-term buffer pool image data is updated every 12 hours;
s22, setting short-time buffer pool data updating time t2, receiving a remote image of the video monitoring equipment at the time of every t2 (in a 'non-alarm' or 'alarm eliminated' state), and updating the short-time buffer pool image data; wherein t2 < t1; in this embodiment, t2 is set to 15s, that is, the image data of the buffer pool is updated every 15 seconds in a short time;
further, the long-term buffer pool image data updating method is a gaussian background modeling method, and specifically includes:
let the mapping p (x) of each pixel point (x, y) to the Gaussian background model satisfy for the background image B
The long-term buffer pool updates each frame of image by the following method:
wherein p is an update threshold, which is a constant and used to reflect the background update rate, and the larger p, the slower the background update. In the present embodiment, p is set to 0.2.
The image data updating method of the short-time buffer pool is a replacement method, and specifically comprises the following steps:
after the image is acquired in the short-time buffer pool, all the image pixels M acquired newly are processed t (x, y) completely covers the previous pixel M t-1 （x，y）：
In step S3, when the cloud center server receives the alarm signal of the front-end sensing device xi, the IP address of the corresponding video monitoring equipment yj is inquired through the matching table, the real-time video stream remote acquisition request is carried out,
the video stream remote acquisition request is carried out in two modes, one is a static image mode for acquiring picture data at intervals, and the other is a video stream mode for continuously acquiring real-time streaming signals.
In step S4, the calculation method of the difference between the historical images and the difference between the real-time images is a structural similarity difference calculation method, and the specific steps include:
inputting image data of a long-term buffer pool and a short-term buffer pool or image data of a long-term buffer pool and a real-time video, and setting each two pictures as x and y respectively;
taking the average gray mu as brightness measurement, and respectively calculating average gray ux and uy;
taking the gray standard deviation sigma as contrast measurement, and respectively calculating gray standard deviations sigma x and sigma y;
and calculating structural difference D (x, y) among the pictures according to the calculated ux, uy, sigma x and sigma y:
wherein C is 1 ，C 2 The constant value is adopted, so that the instability caused when the denominator is close to 0 is avoided; in this embodiment, C1 is set to 0.0001 and C2 is set to 0.0009, and errors are avoided as much as possible while preventing the denominator from being close to 0.
The larger the calculated D is, the smaller the difference of the representative picture is, and the smaller the D is, the larger the difference of the representative picture is.
In this embodiment, first, in a period of a remote acquisition request window of a video stream, difference calculation is performed on image data of a long-term buffer pool and image data of a short-term buffer pool to obtain D1, where D1 is a historical map detection difference; and then, after the acquisition request is successful, the long-time buffer pool and the real-time video are subjected to difference calculation to obtain D2, wherein the D2 is the real-time image detection difference.
In step S5, providing the auxiliary analysis for the artificial image frame includes: after the front-end sensing device gives an alarm, before the cloud center server does not acquire the live real-time video stream, if the difference D1 of the historical images is greater than a certain threshold value, in the embodiment, if the difference D1 of the historical image frames is greater than 0.75, it is shown that the probability is not the false alarm of the front-end sensing device, a worker can inquire the IP address and the video time to obtain the possible place and time of the fire, send out fire early warning, and prepare for fire control operation in advance; after the live real-time video stream reaches the cloud center server, if the difference D2 of the real-time image is greater than a certain threshold, in this embodiment, if the difference D2 of the real-time image frame is greater than 0.5, it is determined that a fire disaster occurs at this point with a high probability, and the worker can determine the location and time of the fire disaster according to the IP address and the video time, and send out a fire alarm.
It should be noted that the above-mentioned embodiments enable a person skilled in the art to more fully understand the invention, without restricting it in any way. All technical solutions and modifications thereof without departing from the spirit and scope of the present invention are covered by the protection scope of the present invention.
Claims (10)
1. A terminal cloud linkage fire-fighting image detection system based on two buffer pools is characterized by comprising a front-end sensing device, video monitoring equipment and a cloud center server; the front end sensing device and the video monitoring equipment are respectively in communication connection with the cloud center server; the cloud center server is provided with:
the buffer module comprises a long-time buffer pool and a short-time buffer pool and is used for respectively storing and updating the acquired long-time image data and short-time image data of the video monitoring equipment;
the image detection module is used for calculating the difference of historical images of the long-time buffer pool and the short-time buffer pool in a window period acquired by the real-time video stream; after the real-time video stream is obtained, calculating the real-time image difference between the long-time buffer pool and the real-time video;
when the cloud center server receives an alarm signal of the front-end sensing device, auxiliary analysis is provided for manual image detection through historical image difference and/or real-time image difference calculation.
2. The system according to claim 1, wherein the cloud center server is further provided with an address matching module for setting a geographic coordinate-IP address matching table of the front-end sensing device and the video monitoring equipment and providing a communication address of the video monitoring equipment corresponding to the front-end sensing device.
3. The two-buffer-pool-based end cloud-linked fire fighting image detection system according to claim 1, wherein the cloud center server is in communication connection with the front end sensing device and the video monitoring device through communication modules respectively.
4. A terminal cloud linkage fire-fighting diagram detection method based on two buffer pools is characterized by comprising the following steps:
s1, setting a geographic coordinate-IP address matching table of a front-end sensing device and video monitoring equipment;
s2, setting two buffer pools, wherein the first buffer pool is a long-term buffer pool, and the second buffer pool is a short-term buffer pool, and respectively storing and updating the obtained long-term image data and short-term image data of the video monitoring equipment;
s3, when the cloud center server receives an alarm signal of the front-end sensing device, the cloud center server inquires a communication address of corresponding video monitoring equipment through a matching table to remotely acquire a video stream;
s4, performing historical image difference calculation through image data of the long-time buffer pool and the short-time buffer pool in a window period acquired by the real-time video stream; after the real-time video stream is obtained, performing real-time image difference calculation through the long-time buffer pool and the image data of the real-time video stream;
and S5, providing auxiliary analysis for the artificial image detection by using the difference calculation results of the historical images and/or the difference calculation results of the real-time images.
5. The method for detecting the fire fighting image based on the end cloud linkage of the two buffer pools according to claim 4, wherein the step S1 specifically comprises:
s11, front end sensing device x i Deploying defense in a gridding manner and storing front-end sensing device x i The geographic coordinates of (a);
s12, video monitoring equipment y j Covering 1 to N front end sensing devices by the field of view, and storing the video monitoring equipment y j The IP address of (2);
s13, front end sensing device x i With video monitoring equipment y j The matching table of the geographic coordinates and the IP addresses is an N:1 linear table, namely N front-end sensing devices correspond to one video monitoring device.
6. The method for detecting the fire fighting image based on the end cloud linkage of the two buffer pools according to claim 4, wherein the step S2 specifically comprises:
s21, setting long-term buffer pool data updating time t1, receiving remote images of the video monitoring equipment at intervals of t1, and updating image data of the long-term buffer pool;
s22, setting short-time buffer pool data updating time t2, receiving remote images of the video monitoring equipment at intervals of t2, and updating image data of the short-time buffer pool; wherein t2 < t1.
7. The method for detecting the fire fighting image based on the end cloud linkage of the two buffer pools as claimed in claim 6, wherein in the step S21, the image data updating mode of the long-term buffer pool is a Gaussian background modeling method, which specifically comprises:
let the mapping p (x) of each pixel point (x, y) to the Gaussian background model satisfy for the background image B
the long-term buffer pool updates each frame of image by the following method:
wherein p is an update threshold, which is a constant and used to reflect the background update rate, and the larger p, the slower the background update.
8. The method for detecting the fire fighting image based on the end cloud linkage of the two buffer pools as claimed in claim 6, wherein in the step S22, the image data updating mode of the short-time buffer pool is a replacement method, specifically:
after the image is acquired in the short-time buffer pool, all the image pixels M acquired newly are processed t (x, y) completely covers the previous pixel M t-1 （x，y）：
9. The method for detecting the fire fighting image based on the end cloud linkage of the two buffer pools according to claim 4, wherein in the step S4, the calculation method of the difference of the historical images and the difference of the real-time images is a structural similarity difference calculation method, and the specific steps include:
inputting image data of a long-term buffer pool and a short-term buffer pool or image data of a long-term buffer pool and a real-time video, and setting each two pictures as x and y respectively;
taking the average gray mu as brightness measurement, and respectively calculating average gray ux and uy;
taking the gray standard deviation sigma as contrast measurement, and respectively calculating gray standard deviations sigma x and sigma y;
and calculating the structural difference D (x, y) among the pictures according to the calculated ux, uy, sigma x and sigma y:
wherein C is 1 ，C 2 The constant value is adopted, so that the instability caused when the denominator is close to 0 is avoided;
the larger D is, the smaller the difference of the representative picture is, and the smaller D is, the larger the difference of the representative picture is.
10. The method for end-cloud linked fire fighting image detection based on two buffer pools according to claim 9, wherein in the step S5, providing auxiliary analysis for the artificial image frame includes: after the front-end sensing device gives an alarm, before the cloud center server does not acquire the field real-time video stream, if the difference D1 of the historical images is larger than a certain threshold value, the probability is that the front-end sensing device gives a false alarm, and the worker can obtain the possible place and time of the fire according to the IP address and the video time, send out fire early warning and prepare fire-fighting operation in advance; after the live real-time video stream reaches the cloud center server, if the difference D2 of the real-time images is larger than a certain threshold value, the fire disaster at the position with high probability is judged, and the staff can determine the fire disaster place and time according to the IP address and the video time and send out a fire disaster alarm.
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