The paper considers the possibility of providing early detection of res in oil and gas wells through the use of the RGB palette, as well as intelligent decision-making to eliminate the consequences of re using the possibility of remote dynamic monitoring based on robotic platforms. The result was a simulated prototype of a hardware-software complex (APC), which allows monitoring of the territory based on the distributed processing of large stream of graphical information in real time (including the exact coordinates of the re, its size, direction of re, proximity to settlements). For the most e ective detection and prevention of growth of natural and man-made res. The proposed agroindustrial complex will be implemented on the basis of modern technologies: robotics, GPS-technologies, GIS-technologies, client-server Internet technologies, video surveillance, intelligent information technologies. Accurate automated determination of coordinates with the help of GPS- re signal makes it possible to timely proceed to the localization and elimination of the re, thereby preventing and reducing the negative impact on people, nature, wildlife, reducing the damage caused by the re. The created software and hardware complex will allow to quickly develop and make the most optimal decisions on the direction of re brigades and re equipment to the places of re, especially in particularly remote areas.
Fires at gas and oil facilities are a disaster that causes incalculable material, moral and environmental losses.
Analysis of re statistics shows that every fourth re is accompanied by the explosion and the subsequent development of the combustion in the area of 5000 m2. If a re occurs without an explosion, the re area in most cases is 500 m2, and the maximum area reaches 3000 m2, so its temporary detection of natural and man-made res is an important factor in ensuring their rapid extinguishing. Currently, this function is performed by drones and unmanned aerial vehicles. In recent years they have become increasingly popular in the detection of res in forests and elds. They are especially necessary in hard-to-reach places where the de nition of re without the use of aviation is impossible. This task will e ectively manage the unmanned aerial vehicle of the type "copter". The main tasks of using quadrocopters in emergency situations are to determine the localization of areas a ected by natural disasters, conduct cartographic surveys, Search for missing people.
Centralized control of the unmanned aerial vehicle is carried out by a human operator (one to one). The structure of the control system: - human operator; - remote terminal; - aircraft with on-Board intelligent system of information processing.
The entire process of controlling the aircraft is carried out by the operator through the terminal, the on-Board information processing system displays the image on the operator's screen, and he, based on his experience, makes the appropriate decision.
The paper proposes to develop a prototype of an automated hardware and software complex, which in addition to performing the function of dynamic monitoring of the territory in real time provides a continuous analysis of the data coming from the sensors, and gives the operator an operational emergency alert when the parameters deviate from the speci ed norm, predicts possible emergencies, is able to calculate and form the optimal route to the source of the re, i.e. is an expert intelligent system.
The advantages of using a quadcopter for the monitoring of gas and oil wells: - ability to continuously monitor even the most remote and wild areas, including in any windy weather;
- objectivity of the obtained data (absence of human factor-fatigue, falling asleep during visual observation of res from specialized towers); - reducing the cost of one hour ight; - speed of data collection; - accuracy of data collection; - optimization of frames.
The analysis of modern applications and publications in the eld of re monitoring in Russia and abroad has shown that the use of unmanned aerial vehicles (UAVs) for these purposes is in demand. However, this trend has not yet become widespread due to imperfect implementation approaches and the complexity of processing large amounts of data, as well as the following disadvantages: - not all systems have exible integration to adapt the system to di erent subject areas; - high technical requirements for equipment; - have a closed program code.
The relevance of this scienti c problem is con rmed by theoretical and practical works of foreign and Russian researchers. The General direction studying the problems of data mining is de ned as "Monitoring Wild re UAVs".
The authors, led by Sharon Rabinovich [
Scientists from America [
The researchers of the University of Toronto [
Scientists Zhongjie Lin and Hugh H. T. Liu proposed a model for distributed
topology-based optimization for joint monitoring of res through the use of
multiple UAVs. The problem of interaction of several UAVs in a distributed network
is formulated. A method is proposed that allows cooperation between UAVs at
the group level [
The actual research is presented by the authors under the leadership of A. S.
Vasilyev [
In work [
Researchers Connie Phan and Hugh H. T. Liu describe a technique for
controlling UAVs through mobile devices. The authors propose a methodology for
the interaction of several aircraft through the use of a hierarchical platform [
Scientists from China presented the work on re identi cation based on deep
learning. The authors in their study took into account unmanned aerial vehicles
equipped with global positioning systems (GPS), through which it is possible
to provide direct georeferencing images, mapping the area with high resolution.
The paper presents a 15-level self-learning architecture DCNN called "F ireN et".
The disadvantage of such a system is the speed of processing a large amount of
data [
In his scienti c work V. Vipin proposed a method of classi cation of re
pixels using rule-based models in the RGB and YCbCr color space [
Researchers K. Angayarkkani and N. Radhakrishnan developed fuzzy rules
for the use Of YCbCr color space for segmentation and re image detection [
Scientist C. Yuan developed a set of algorithms for tracking res, including
median ltering, color space transformation [
Statistical and machine learning methods are not actively used in this
direction. The Gaussian mixture model (GMM) is used for ame detection [
Satellite imagery is a common method of re detection [
To get the coordinates of the quadcopter you need to have the coordinates of the UAV, the initial values of which are obtained by GPS. Next, we have a pixelby-pixel shift of the x and y values. the shift Equation is shown in formulas (1) and (2):
X = Y = x (y
2 ;
2 ; where x and y are the coordinates of the transmitted images, the X and Y coordinates of the received image, Camera image produced on the camera quadracopter.
To detect the source of re, we use color analysis based on the RGB model. This model describes each color with a set of the following colors: red, green and blue.
We describe the possibility of ame pixel detection for RGB color model by the following system:
R(x; y) > Rred Rred = K1 PiK=1 R(x; y)) R(x; y) > G(x; y) > B(x; y); 9 = where values Rx;y, Bx;y, Gx;y { value of red, blue and green colors in pixel on coordinates x, y, K is the total number of pixels, Rred is the average intensity of the red color.
r, g and b are normalized components of RGB space de ned by the following formulas: (1) (2) (3) (4) (5) (6) r = R=(R + G + B); g = G=(R + G + B); b = B=(R + G + B); The algorithm for determining the ignition source is shown in g. 1.
According to the presented algorithm, the quadcopter transmits the image to a personal computer by scanning the marked GPS point. By using the RGB palette, the color that is closest to the ignition source (red) is determined. If the system detects a re, it automatically noti es the operator, who decides to take the necessary measures.
The results of the re detection simulation are shown in g. 2.
Thus, the process of modeling the ignition of oil wells is carried out and the algorithm for early re detection is described. The developed methods and approaches for analyzing oil well ignition are planned to be used in forecasting and intellectual veri cation of upcoming changes in the heterogeneous infrastructure of the complex model, based on spatial and temporal distribution and proactive forecasting of the use of computing resources.
The paper proposes an algorithm for color processing of RGB palette, which allows to control the territory on the basis of distributed processing of a large ow of graphic images in automatic real-time mode (including accurate determination of the coordinates of the re, its area, the direction of the ame, the proximity of settlements, etc.).
The socio-economic e ect of the implementation of the proposed intelligent automated software and hardware complex is as follows:
- remote access via the Internet to an interactive map of operational monitoring, which allows you to obtain information about the state of the controlled territory;
- the use of agriculture will signi cantly reduce the cost of special services to monitor res, through the use of drones;
- collection and analysis of operational data on the state of the re situation.