Photovoltaic power station unmanned aerial vehicle automatic fire extinguishing method capable of achieving rapid identification and accurate positioning

Abstract

The invention relates to the technical field of image processing, and discloses a photovoltaic power station unmanned aerial vehicle automatic fire extinguishing method for rapid identification and accurate positioning, and the method comprises the steps: obtaining a real-time region grayscale image, and determining a standard region grayscale image; performing region division on the real-time region grayscale image to obtain a plurality of sub-real-time region grayscale images, constructing a real-time image pixel value set and constructing a standard image pixel value set; comparing the real-time image pixel value set with the standard image pixel value set, and calculating a first pixel value coefficient; calculating a second pixel value coefficient based on the first pixel value coefficient, and calculating a fire risk factor according to the first pixel value coefficient and the second pixel value coefficient; judging whether the photovoltaic power station has a fire risk according to the fire risk factor; the unmanned aerial vehicle is guided to fly to the fire area, fire extinguishing treatment is performed on the fire area, whether a fire occurs in the photovoltaic power station can be rapidly and accurately judged, the fire area is accurately positioned, and rapid response and efficient fire extinguishing of the fire of the photovoltaic power station are achieved.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a method for automatically extinguishing a fire using an unmanned aerial vehicle in a photovoltaic power station by rapidly identifying and accurately positioning the fire. Background Art

[0002] Photovoltaic power stations are facilities that use solar energy to produce electricity by directly converting sunlight into electrical energy. As a clean and efficient way to produce energy, photovoltaic power stations have been widely used and developed rapidly around the world. With the continuous advancement of technology and further reduction of costs, it is expected that photovoltaic power stations will play a more important role in the energy market in the future.

[0003] However, as the scale of photovoltaic power plants continues to expand, the risk of fire also increases. Traditional firefighting methods rely on manual inspections and ground firefighting equipment, which are difficult to quickly respond to fires in photovoltaic power plants, especially in large-scale, distributed photovoltaic panel areas. Therefore, it is particularly important to develop a method for automatic fire extinguishing of photovoltaic power plants by drones that can quickly identify and accurately locate. Summary of the invention

[0004] The embodiment of the present invention provides a method for automatic fire extinguishing of a photovoltaic power station by a drone with rapid identification and precise positioning. The present invention can quickly and accurately determine whether a fire has occurred in a photovoltaic power station, and accurately locate the fire area, thereby achieving rapid response and efficient fire extinguishing of a photovoltaic power station fire.

[0005] In order to achieve the above-mentioned purpose, the present invention provides a method for automatic fire extinguishing of a photovoltaic power station drone with rapid identification and accurate positioning, comprising: Acquire a real-time regional image of the photovoltaic power station, perform grayscale processing on the real-time regional image to obtain a corresponding real-time regional grayscale image, and determine a standard regional grayscale image corresponding to the real-time regional grayscale image; Divide the real-time regional grayscale image into regions to obtain a plurality of sub-real-time regional grayscale images, extract the real-time image pixel values ​​corresponding to the sub-real-time regional grayscale images, construct a real-time image pixel value set, and extract the corresponding standard image pixel values ​​from the standard regional grayscale image to construct a standard image pixel value set; Comparing the real-time image pixel value set with the standard image pixel value set, and calculating a first pixel value coefficient of the real-time image pixel value set based on the comparison result; Calculating a second pixel value coefficient of the real-time image pixel value set based on the first pixel value coefficient, and calculating a fire risk factor of the photovoltaic power station according to the first pixel value coefficient and the second pixel value coefficient; Determining whether the photovoltaic power station has a fire risk according to the fire risk factor, and if so, treating the photovoltaic power station as a fire area; Based on a preset flight control system, the drone is guided to fly to the fire area, and the fire area is extinguished according to the fire extinguishing device carried by the drone.

[0006] Further, when comparing the real-time image pixel value set with the standard image pixel value set and calculating the first pixel value coefficient of the real-time image pixel value set based on the comparison result, it includes: Calculating a first set mean corresponding to the real-time image pixel value set, and calculating a second set mean corresponding to the standard image pixel value set; Determine a mean difference between the first set mean and the second set mean, and use the difference as a first sub-pixel value coefficient of the real-time image pixel value set; Comparing the real-time image pixel values ​​in the real-time image pixel value set with the standard image pixel values ​​in the standard image pixel value set one by one to determine a plurality of image pixel value differences; Determine a maximum image pixel value difference and a minimum image pixel value difference from all image pixel value differences; Determine an extreme difference between the maximum image pixel value difference and the minimum image pixel value difference, and use the extreme difference as a second sub-pixel value coefficient of the real-time image pixel value set; A first pixel value coefficient of the real-time image pixel value set is calculated according to the first sub-pixel value coefficient, the second sub-pixel value coefficient, the maximum image pixel value difference and the minimum image pixel value difference.

[0007] Further, when calculating the first pixel value coefficient of the real-time image pixel value set according to the first sub-pixel value coefficient, the second sub-pixel value coefficient, the maximum image pixel value difference and the minimum image pixel value difference, it includes: The first pixel value coefficient of the real-time image pixel value set is calculated according to the following formula: ; Among them, a1 is the first pixel value coefficient of the real-time image pixel value set, c1 is the first sub-pixel value coefficient, c2 is the second sub-pixel value coefficient, d1 is the maximum image pixel value difference, and d2 is the minimum image pixel value difference.

[0008] Further, when calculating a second pixel value coefficient of the real-time image pixel value set based on the first pixel value coefficient, and calculating a fire risk factor of the photovoltaic power station according to the first pixel value coefficient and the second pixel value coefficient, it includes: The second pixel value coefficient corresponding to each real-time image pixel value set is calculated according to the following formula: ; Wherein, a2 is the second pixel value coefficient corresponding to the real-time image pixel value set, f is the first pixel value coefficient corresponding to the real-time image pixel value set, k i is the first pixel value coefficient corresponding to the i-th real-time image pixel value set; Constructing a pixel value coefficient chain, wherein the pixel value coefficient chain includes a plurality of pixel value coefficient nodes, and each pixel value coefficient node includes a first pixel value coefficient and a second pixel value coefficient; Randomly determine two pixel value coefficient nodes, and extract the corresponding first extracted pixel value coefficient, second extracted pixel value coefficient, third extracted pixel value coefficient, and fourth extracted pixel value coefficient; Calculating a first coefficient difference between the first extracted pixel value coefficient and the third extracted pixel value coefficient; Determine a maximum pixel value coefficient and a minimum pixel value coefficient from all first pixel value coefficients, and calculate a second coefficient difference between the maximum pixel value coefficient and the minimum pixel value coefficient; Calculating a first coefficient difference ratio of the first coefficient difference and the second coefficient difference; Calculating a third coefficient difference between the third extracted pixel value coefficient and the fourth extracted pixel value coefficient; Determine a maximum pixel value coefficient and a minimum pixel value coefficient from all second pixel value coefficients, and calculate a fourth coefficient difference between the maximum pixel value coefficient and the minimum pixel value coefficient; Calculating a second coefficient difference ratio of the third coefficient difference and the fourth coefficient difference; Determining a sum of the first coefficient difference ratio and the second coefficient difference ratio as a sub-fire risk factor of the photovoltaic power station; The fire risk factor of the photovoltaic power station is calculated according to all the sub-fire risk factors.

[0009] Furthermore, when calculating the fire risk factor of the photovoltaic power station according to all the sub-fire risk factors, it includes: All sub-fire risk factors are sorted by numerical value, and the largest sub-fire risk factor and the smallest sub-fire risk factor are combined in pairs; The sub-fire risk factors adjacent to the maximum sub-fire risk factor and the sub-fire risk factors adjacent to the minimum sub-fire risk factor are combined in pairs, and the remaining are iterated in sequence to obtain multiple sub-fire risk factor combinations; The fire risk factor of the photovoltaic power station is calculated based on all the sub-fire risk factors.

[0010] Furthermore, when calculating the fire risk factor of the photovoltaic power station according to all the sub-fire risk factors, it includes: The fire risk factor of the photovoltaic power station is calculated according to the following formula: ; Among them, q is the fire risk factor of the photovoltaic power station, m is the number of sub-fire risk factor combinations, g1 i is the larger sub-fire risk factor in the ith sub-fire risk factor combination, g2 is the smaller sub-fire risk factor in the ith sub-fire risk factor combination, For all The minimum value in For all The maximum value among them, h is the adjustment coefficient of the fire risk factor.

[0011] Furthermore, the adjustment coefficient of the fire risk factor is determined according to the following steps: Calculate all The variance p of Setting a first preset variance and a second preset variance; Setting a first preset adjustment coefficient, a second preset adjustment coefficient, and a third preset adjustment coefficient; When the variance p is less than the first preset variance, the first preset adjustment coefficient is used as the adjustment coefficient of the fire risk factor; When the variance p is greater than or equal to the first preset variance and less than the second preset variance, the second preset adjustment coefficient is used as the adjustment coefficient of the fire risk factor; When the variance p is greater than or equal to the second preset variance, the third preset adjustment coefficient is used as the adjustment coefficient of the fire risk factor.

[0012] Further, when judging whether the photovoltaic power station has a fire risk according to the fire risk factor, it includes: Determining whether there is a fire risk in the photovoltaic power station according to the relationship between the fire risk factor and a preset fire risk factor; When the fire risk factor is less than the preset fire risk factor, it is determined that there is no fire risk in the photovoltaic power station; When the fire risk factor is greater than or equal to the preset fire risk factor, it is determined that there is a fire risk in the photovoltaic power station.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. Improve fire extinguishing efficiency: The present invention can quickly locate the fire source and guide the UAV to quickly reach the fire scene, which significantly improves the fire extinguishing efficiency of photovoltaic power station fires.

[0014] 2. Reduce personnel risks: UAV firefighting operations reduce the risk of firefighters entering the fire scene and improve the safety of firefighting operations.

[0015] 3. High degree of intelligence: Combining deep learning algorithms and advanced flight control systems, autonomous identification and autonomous flight of drones are realized, improving the intelligence level of the system.

[0016] 4. Strong adaptability: The present invention is applicable to photovoltaic power stations of different scales and layouts and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A schematic diagram of the process of the automatic fire extinguishing method of a photovoltaic power station drone with rapid identification and precise positioning in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0018] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0019] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0020] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0021] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0022] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0023] like Figure 1 As shown, an embodiment of the present invention discloses a method for automatically extinguishing a fire by using a drone in a photovoltaic power station with rapid identification and accurate positioning, comprising: S110: acquiring a real-time regional image of the photovoltaic power station, performing grayscale processing on the real-time regional image to obtain a corresponding real-time regional grayscale image, and determining a standard regional grayscale image corresponding to the real-time regional grayscale image; In this embodiment, the standard area grayscale image refers to an image captured when no fire occurs.

[0024] S120: Divide the real-time regional grayscale image into regions to obtain a plurality of sub-real-time regional grayscale images, extract real-time image pixel values ​​corresponding to the sub-real-time regional grayscale images, construct a real-time image pixel value set, and extract corresponding standard image pixel values ​​from the standard regional grayscale image to construct a standard image pixel value set; In this embodiment, even regional division may be performed according to the area of ​​the real-time regional grayscale image.

[0025] In this embodiment, the standard regional grayscale image is divided into regions to obtain a plurality of sub-standard regional grayscale images, and the region division method of the real-time regional grayscale image is consistent with the region division method of the standard regional grayscale image.

[0026] S130: Compare the real-time image pixel value set with the standard image pixel value set, and calculate a first pixel value coefficient of the real-time image pixel value set based on the comparison result; In some embodiments of the present application, when comparing the real-time image pixel value set with the standard image pixel value set and calculating the first pixel value coefficient of the real-time image pixel value set based on the comparison result, the method includes: Calculating a first set mean corresponding to the real-time image pixel value set, and calculating a second set mean corresponding to the standard image pixel value set; Determine a mean difference between the first set mean and the second set mean, and use the difference as a first sub-pixel value coefficient of the real-time image pixel value set; Comparing the real-time image pixel values ​​in the real-time image pixel value set with the standard image pixel values ​​in the standard image pixel value set one by one to determine a plurality of image pixel value differences; Determine a maximum image pixel value difference and a minimum image pixel value difference from all image pixel value differences; Determine an extreme difference between the maximum image pixel value difference and the minimum image pixel value difference, and use the extreme difference as a second sub-pixel value coefficient of the real-time image pixel value set; A first pixel value coefficient of the real-time image pixel value set is calculated according to the first sub-pixel value coefficient, the second sub-pixel value coefficient, the maximum image pixel value difference and the minimum image pixel value difference.

[0027] The beneficial effect of the above technical solution is: the present invention calculates the first pixel value coefficient of the real-time image pixel value set according to the first sub-pixel value coefficient, the second sub-pixel value coefficient, the maximum image pixel value difference and the minimum image pixel value difference, thereby ensuring the calculation accuracy of the first pixel value coefficient and providing reliable data support for judging whether there is a fire in a photovoltaic power station.

[0028] In some embodiments of the present application, when calculating the first pixel value coefficient of the real-time image pixel value set according to the first sub-pixel value coefficient, the second sub-pixel value coefficient, the maximum image pixel value difference and the minimum image pixel value difference, it includes: The first pixel value coefficient of the real-time image pixel value set is calculated according to the following formula: ; Among them, a1 is the first pixel value coefficient of the real-time image pixel value set, c1 is the first sub-pixel value coefficient, c2 is the second sub-pixel value coefficient, d1 is the maximum image pixel value difference, and d2 is the minimum image pixel value difference.

[0029] S140: Calculating a second pixel value coefficient of the real-time image pixel value set based on the first pixel value coefficient, and calculating a fire risk factor of the photovoltaic power station according to the first pixel value coefficient and the second pixel value coefficient; In some embodiments of the present application, when calculating the second pixel value coefficient of the real-time image pixel value set based on the first pixel value coefficient, and calculating the fire risk factor of the photovoltaic power station according to the first pixel value coefficient and the second pixel value coefficient, it includes: The second pixel value coefficient corresponding to each real-time image pixel value set is calculated according to the following formula: ; Wherein, a2 is the second pixel value coefficient corresponding to the real-time image pixel value set, f is the first pixel value coefficient corresponding to the real-time image pixel value set, k i is the first pixel value coefficient corresponding to the i-th real-time image pixel value set; Constructing a pixel value coefficient chain, wherein the pixel value coefficient chain includes a plurality of pixel value coefficient nodes, and each pixel value coefficient node includes a first pixel value coefficient and a second pixel value coefficient; Randomly determine two pixel value coefficient nodes, and extract the corresponding first extracted pixel value coefficient, second extracted pixel value coefficient, third extracted pixel value coefficient, and fourth extracted pixel value coefficient; Calculating a first coefficient difference between the first extracted pixel value coefficient and the third extracted pixel value coefficient; Determine a maximum pixel value coefficient and a minimum pixel value coefficient from all first pixel value coefficients, and calculate a second coefficient difference between the maximum pixel value coefficient and the minimum pixel value coefficient; Calculating a first coefficient difference ratio of the first coefficient difference and the second coefficient difference; Calculating a third coefficient difference between the third extracted pixel value coefficient and the fourth extracted pixel value coefficient; Determine a maximum pixel value coefficient and a minimum pixel value coefficient from all second pixel value coefficients, and calculate a fourth coefficient difference between the maximum pixel value coefficient and the minimum pixel value coefficient; Calculating a second coefficient difference ratio of the third coefficient difference and the fourth coefficient difference; Determining a sum of the first coefficient difference ratio and the second coefficient difference ratio as a sub-fire risk factor of the photovoltaic power station; The fire risk factor of the photovoltaic power station is calculated according to all the sub-fire risk factors.

[0030] In this embodiment, the sum of all first pixel value coefficients is calculated, and then the ratio of the first pixel value coefficient and the sum is calculated to obtain the second pixel value coefficient.

[0031] In this embodiment, the number of pixel value coefficient nodes is consistent with the number of real-time image pixel value sets.

[0032] In this embodiment, two pixel value coefficient nodes are randomly determined. For the convenience of distinction, the first pixel value coefficient corresponding to one of the pixel value coefficient nodes is used as the first extracted pixel value coefficient, and the second pixel value coefficient is used as the second extracted pixel value coefficient. The first pixel value coefficient corresponding to the other pixel value coefficient node is used as the third extracted pixel value coefficient, and the second pixel value coefficient is used as the fourth extracted pixel value coefficient.

[0033] The beneficial effects of the above technical solution are as follows: The present invention determines the sum value of the first coefficient difference ratio and the second coefficient difference ratio, and uses it as the sub-fire risk factor of the photovoltaic power station, thereby laying a foundation for the calculation of the fire risk factor of the photovoltaic power station.

[0034] In some embodiments of the present application, when calculating the fire risk factor of the photovoltaic power station according to all the sub-fire risk factors, it includes: Sort all the sub-fire risk factors by numerical size, and combine the maximum sub-fire risk factor and the minimum sub-fire risk factor pairwise; Combine the sub-fire risk factors adjacent to the maximum sub-fire risk factor and the sub-fire risk factors adjacent to the minimum sub-fire risk factor pairwise, and perform successive iterations for the rest to obtain multiple combinations of sub-fire risk factors; Calculate the fire risk factor of the photovoltaic power station according to all the combinations of sub-fire risk factors.

[0035] The beneficial effects of the above technical solution are as follows: The present invention calculates the fire risk factor of the photovoltaic power station according to all the combinations of sub-fire risk factors, providing a reliable basis for judging whether there is a fire in the photovoltaic power station, ensuring the judgment efficiency and accuracy, and avoiding judgment errors.

[0036] In some embodiments of the present application, when calculating the fire risk factor of the photovoltaic power station according to all the combinations of sub-fire risk factors, it includes: Calculate the fire risk factor of the photovoltaic power station according to the following formula: ; where q is the fire risk factor of the photovoltaic power station, m is the number of combinations of sub-fire risk factors, g1 i is the larger sub-fire risk factor in the i-th combination of sub-fire risk factors, g2 is the smaller sub-fire risk factor in the i-th combination of sub-fire risk factors, is the minimum value among all , is the maximum value among all , and h is the adjustment coefficient of the fire risk factor.

[0037] In some embodiments of the present application, the adjustment coefficient of the fire risk factor is determined according to the following steps: Calculate the variance p of all ; Set a first preset variance and a second preset variance; Set a first preset adjustment coefficient, a second preset adjustment coefficient, and a third preset adjustment coefficient; When the variance p is less than the first preset variance, the first preset adjustment coefficient is used as the adjustment coefficient of the fire risk factor; When the variance p is greater than or equal to the first preset variance and less than the second preset variance, the second preset adjustment coefficient is used as the adjustment coefficient of the fire risk factor; When the variance p is greater than or equal to the second preset variance, the third preset adjustment coefficient is used as the adjustment coefficient of the fire risk factor.

[0038] In this embodiment, the first preset variance is smaller than the second preset variance.

[0039] In this embodiment, the first preset adjustment coefficient is smaller than the second preset adjustment coefficient and smaller than the third preset adjustment coefficient.

[0040] The beneficial effect of the above technical solution is that the present invention selects the corresponding preset adjustment coefficient according to the variance p, the first preset variance and the second preset variance, thereby realizing the dynamic adjustment of the fire risk factor and further ensuring the comprehensiveness and accuracy of the calculation.

[0041] S150: judging whether the photovoltaic power station has a fire risk according to the fire risk factor, and if so, treating the photovoltaic power station as a fire area; In some embodiments of the present application, when judging whether the photovoltaic power station has a fire risk according to the fire risk factor, it includes: Determining whether there is a fire risk in the photovoltaic power station according to the relationship between the fire risk factor and a preset fire risk factor; When the fire risk factor is less than the preset fire risk factor, it is determined that there is no fire risk in the photovoltaic power station; When the fire risk factor is greater than or equal to the preset fire risk factor, it is determined that there is a fire risk in the photovoltaic power station.

[0042] The beneficial effect of the above technical solution is that the present invention can quickly locate the fire source, guide the drone to quickly reach the fire scene, and significantly improve the fire extinguishing efficiency of photovoltaic power station fires.

[0043] S160: Guiding the drone to fly to the fire area based on a preset flight control system, and extinguishing the fire in the fire area using a fire extinguishing device carried by the drone.

[0044] In this embodiment, laser radar, GPS, Beidou and other satellite navigation systems are used in combination with the data processing algorithm of the ground control station to achieve accurate distance measurement and positioning between the UAV and the fire source. The system can update the location information of the UAV and the fire source in real time, providing accurate navigation for the flight control of the UAV.

[0045] In this embodiment, the drone is equipped with an advanced flight control system, which can autonomously plan the flight path according to the fire source information and ranging positioning data provided by the ground control station. The flight control system has an obstacle avoidance function, which can automatically avoid obstacles such as photovoltaic panels and wires to ensure safe flight. The drone also has flexible control capabilities such as hovering, pitching, and yaw to meet the fire extinguishing needs of different fire source locations. Fire extinguishing device and control system: The drone is equipped with a fire extinguishing device, such as dry powder fire extinguishing bombs, water-based fire extinguishing bombs, etc., and the appropriate fire extinguishing method is selected according to the type and scale of the fire source. The fire extinguishing device is controlled by a remote control system, and the ground control station can adjust the parameters and fire extinguishing strategies of the fire extinguishing device in real time.

[0046] The beneficial effects of the above technical solution are: the firefighting operation of the drone of the present invention reduces the risk of firefighters entering the fire scene and improves the safety of the firefighting operation. Combined with the deep learning algorithm and the advanced flight control system, the autonomous identification and autonomous flight of the drone are realized, and the intelligence level of the system is improved.

[0047] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0048] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0049] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for automatic fire extinguishing of a photovoltaic power station drone with rapid identification and accurate positioning, characterized in that: include: Acquire a real-time regional image of the photovoltaic power station, perform grayscale processing on the real-time regional image to obtain a corresponding real-time regional grayscale image, and determine a standard regional grayscale image corresponding to the real-time regional grayscale image; Divide the real-time regional grayscale image into regions to obtain a plurality of sub-real-time regional grayscale images, extract the real-time image pixel values ​​corresponding to the sub-real-time regional grayscale images, construct a real-time image pixel value set, and extract the corresponding standard image pixel values ​​from the standard regional grayscale image to construct a standard image pixel value set; Comparing the real-time image pixel value set with the standard image pixel value set, and calculating a first pixel value coefficient of the real-time image pixel value set based on the comparison result; Calculating a second pixel value coefficient of the real-time image pixel value set based on the first pixel value coefficient, and calculating a fire risk factor of the photovoltaic power station according to the first pixel value coefficient and the second pixel value coefficient; Determining whether the photovoltaic power station has a fire risk according to the fire risk factor, and if so, treating the photovoltaic power station as a fire area; Based on a preset flight control system, the drone is guided to fly to the fire area, and the fire area is extinguished according to the fire extinguishing device carried by the drone.

2. The photovoltaic power station drone automatic fire extinguishing method with rapid identification and accurate positioning according to claim 1 is characterized in that: When comparing the real-time image pixel value set with the standard image pixel value set, and calculating the first pixel value coefficient of the real-time image pixel value set based on the comparison result, the method includes: Calculating a first set mean corresponding to the real-time image pixel value set, and calculating a second set mean corresponding to the standard image pixel value set; Determine a mean difference between the first set mean and the second set mean, and use the difference as a first sub-pixel value coefficient of the real-time image pixel value set; Comparing the real-time image pixel values ​​in the real-time image pixel value set with the standard image pixel values ​​in the standard image pixel value set one by one to determine a plurality of image pixel value differences; Determine a maximum image pixel value difference and a minimum image pixel value difference from all image pixel value differences; Determine an extreme difference between the maximum image pixel value difference and the minimum image pixel value difference, and use the extreme difference as a second sub-pixel value coefficient of the real-time image pixel value set; A first pixel value coefficient of the real-time image pixel value set is calculated according to the first sub-pixel value coefficient, the second sub-pixel value coefficient, the maximum image pixel value difference and the minimum image pixel value difference.

3. The photovoltaic power station drone automatic fire extinguishing method with rapid identification and accurate positioning according to claim 2 is characterized in that: When calculating the first pixel value coefficient of the real-time image pixel value set according to the first sub-pixel value coefficient, the second sub-pixel value coefficient, the maximum image pixel value difference and the minimum image pixel value difference, it includes: The first pixel value coefficient of the real-time image pixel value set is calculated according to the following formula: ； Among them, a1 is the first pixel value coefficient of the real-time image pixel value set, c1 is the first sub-pixel value coefficient, c2 is the second sub-pixel value coefficient, d1 is the maximum image pixel value difference, and d2 is the minimum image pixel value difference.

4. The photovoltaic power station drone automatic fire extinguishing method with rapid identification and accurate positioning according to claim 1 is characterized in that: When calculating a second pixel value coefficient of the real-time image pixel value set based on the first pixel value coefficient, and calculating a fire risk factor of the photovoltaic power station according to the first pixel value coefficient and the second pixel value coefficient, the method includes: The second pixel value coefficient corresponding to each real-time image pixel value set is calculated according to the following formula: ； Wherein, a2 is the second pixel value coefficient corresponding to the real-time image pixel value set, f is the first pixel value coefficient corresponding to the real-time image pixel value set, k i is the first pixel value coefficient corresponding to the i-th real-time image pixel value set; Constructing a pixel value coefficient chain, wherein the pixel value coefficient chain includes a plurality of pixel value coefficient nodes, and each pixel value coefficient node includes a first pixel value coefficient and a second pixel value coefficient; Randomly determine two pixel value coefficient nodes, and extract the corresponding first extracted pixel value coefficient, second extracted pixel value coefficient, third extracted pixel value coefficient, and fourth extracted pixel value coefficient; Calculating a first coefficient difference between the first extracted pixel value coefficient and the third extracted pixel value coefficient; Determine a maximum pixel value coefficient and a minimum pixel value coefficient from all first pixel value coefficients, and calculate a second coefficient difference between the maximum pixel value coefficient and the minimum pixel value coefficient; Calculating a first coefficient difference ratio of the first coefficient difference and the second coefficient difference; Calculating a third coefficient difference between the third extracted pixel value coefficient and the fourth extracted pixel value coefficient; Determine a maximum pixel value coefficient and a minimum pixel value coefficient from all second pixel value coefficients, and calculate a fourth coefficient difference between the maximum pixel value coefficient and the minimum pixel value coefficient; Calculating a second coefficient difference ratio of the third coefficient difference and the fourth coefficient difference; Determining a sum of the first coefficient difference ratio and the second coefficient difference ratio as a sub-fire risk factor of the photovoltaic power station; The fire risk factor of the photovoltaic power station is calculated according to all the sub-fire risk factors.

5. The photovoltaic power station drone automatic fire extinguishing method with rapid identification and accurate positioning according to claim 4 is characterized in that: When calculating the fire risk factor of the photovoltaic power station according to all the sub-fire risk factors, it includes: All sub-fire risk factors are sorted by numerical value, and the largest sub-fire risk factor and the smallest sub-fire risk factor are combined in pairs; The sub-fire risk factors adjacent to the maximum sub-fire risk factor and the sub-fire risk factors adjacent to the minimum sub-fire risk factor are combined in pairs, and the remaining are iterated in sequence to obtain multiple sub-fire risk factor combinations; The fire risk factor of the photovoltaic power station is calculated based on all the sub-fire risk factors.

6. The photovoltaic power station drone automatic fire extinguishing method with rapid identification and accurate positioning according to claim 5 is characterized in that: When calculating the fire risk factor of the photovoltaic power station according to all the sub-fire risk factors, it includes: The fire risk factor of the photovoltaic power station is calculated according to the following formula: ； Among them, q is the fire risk factor of the photovoltaic power station, m is the number of sub-fire risk factor combinations, g1 i is the larger sub-fire risk factor in the ith sub-fire risk factor combination, g2 is the smaller sub-fire risk factor in the ith sub-fire risk factor combination, For all The minimum value in For all The maximum value among them, h is the adjustment coefficient of the fire risk factor.

7. The photovoltaic power station drone automatic fire extinguishing method with rapid identification and accurate positioning according to claim 6 is characterized in that: Determine the adjustment factor for the fire risk factor according to the following steps: Calculate all The variance p of Setting a first preset variance and a second preset variance; Setting a first preset adjustment coefficient, a second preset adjustment coefficient, and a third preset adjustment coefficient; When the variance p is less than the first preset variance, the first preset adjustment coefficient is used as the adjustment coefficient of the fire risk factor; When the variance p is greater than or equal to the first preset variance and less than the second preset variance, the second preset adjustment coefficient is used as the adjustment coefficient of the fire risk factor; When the variance p is greater than or equal to the second preset variance, the third preset adjustment coefficient is used as the adjustment coefficient of the fire risk factor.

8. The photovoltaic power station drone automatic fire extinguishing method with rapid identification and accurate positioning according to claim 1 is characterized in that: When judging whether the photovoltaic power station has a fire risk according to the fire risk factor, it includes: Determining whether there is a fire risk in the photovoltaic power station according to the relationship between the fire risk factor and a preset fire risk factor; When the fire risk factor is less than the preset fire risk factor, it is determined that there is no fire risk in the photovoltaic power station; When the fire risk factor is greater than or equal to the preset fire risk factor, it is determined that there is a fire risk in the photovoltaic power station.

Images