Intelligent unmanned station safety monitoring method, device and equipment and storage medium

Abstract

The invention discloses an intelligent unmanned station safety monitoring method, device and equipment and a storage medium, and relates to the technical field of unmanned polling, and the method comprises the steps: obtaining current array information according to photovoltaic panel array information and current information of a photovoltaic panel current output unit, obtaining current monitoring information based on the current array information, and carrying out the monitoring of the current array information according to the current monitoring information; the method comprises the steps of determining an audio time difference according to an abnormal audio signal acquired by an acoustic sensor, positioning acoustic abnormal positioning information according to the audio time difference, determining a reflective feature of an image according to unmanned aerial vehicle inspection image information, and determining image monitoring information based on the reflective feature, and generating a safety monitoring result based on the current monitoring information, the image monitoring information or the acoustic anomaly positioning information. Through the above mode, the unmanned station is monitored from multiple angles of current output, image monitoring and sound monitoring, omnibearing safety detection can be realized, and the fault response speed of the unmanned station is improved.

Description

Technical Field

[0001] The present application relates to the field of unmanned inspection technology, and in particular to intelligent unmanned station safety monitoring methods, devices, equipment and storage media. Background Art

[0002] With the vigorous promotion of clean energy, more and more clean energy power generation stations are also rapidly emerging. The safety inspection of the station is one of the most important links in production activities. This often requires a lot of labor costs to realize the safety inspection of the station, and the proportion of labor costs also increases accordingly. At the same time, there are also large false detections and missed detections. At present, in order to solve the problem of labor costs and improve the inspection accuracy of the station, unmanned stations have been created, that is, unmanned stations are used to replace manual safety inspections, which can solve the manual-based safety inspection behavior. Taking unmanned photovoltaic stations as an example, the current drone inspection is to inspect the photovoltaic panels by drones, obtain the surface images of the photovoltaic panels, and then determine whether there are abnormal conditions such as occlusion on the photovoltaic panels. However, this method cannot fully realize the safety detection of unmanned stations, has great limitations, and cannot directly and accurately determine the fault type and fault location. Therefore, the fault determination rate of the current unmanned inspection method is still high.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide an intelligent unmanned station safety monitoring method, device, equipment and storage medium, aiming to solve the technical problem of low fault detection accuracy in unmanned stations in the prior art.

[0005] To achieve the above objectives, the present application provides a smart unmanned station safety monitoring method, the method comprising:

[0006] Obtaining current array information according to the photovoltaic panel array information and the current information of the photovoltaic panel current output unit, and obtaining current monitoring information based on the current array information;

[0007] Determine an audio time difference according to the abnormal audio signal acquired by the acoustic sensor, and locate acoustic abnormality positioning information according to the audio time difference;

[0008] Determine the reflective features of the image according to the inspection image information of the drone, and determine the image monitoring information based on the reflective features;

[0009] A safety monitoring result is generated based on the current monitoring information, the image monitoring information or the acoustic anomaly positioning information.

[0010] In one embodiment, the step of obtaining the current array information according to the photovoltaic panel array information and the current information of the photovoltaic panel current output unit, and obtaining the current monitoring information based on the current array information comprises:

[0011] Determine identification information of a photovoltaic panel current output unit, and collect current output values ​​of the photovoltaic panel current output unit;

[0012] Acquire photovoltaic panel array information, map the current output value to the photovoltaic panel array information based on the identification information, and obtain current array information;

[0013] Performing current value gradient detection on the current array information to determine the current gradient value;

[0014] A current partition is determined according to the current gradient value to obtain a low current partition, and current monitoring information is generated with the low current partition.

[0015] In one embodiment, the step of determining the audio time difference according to the abnormal audio signal acquired by the acoustic sensor, and locating the acoustic abnormality positioning information according to the audio time difference includes:

[0016] Perform high-pass filtering on the audio information acquired by the acoustic sensor to obtain high-frequency audio information;

[0017] The high-frequency audio information is regarded as abnormal audio information, and an audio mark is performed on the abnormal audio information;

[0018] Determine a first acquisition time of the abnormal audio information, and determine a second acquisition time of the abnormal audio information corresponding to the audio tag in other acoustic sensors;

[0019] determining an audio time difference according to the first acquisition time and the second acquisition time;

[0020] A relative distance deviation is determined according to the audio time difference, and isometric growth is performed according to the relative distance deviation to determine acoustic anomaly positioning information.

[0021] In one embodiment, after the steps of determining the relative distance deviation according to the audio time difference, performing isometric growth according to the relative distance deviation, and determining the acoustic anomaly positioning information, the method includes:

[0022] generating an image monitoring request according to the acoustic abnormality positioning information, and receiving an abnormal re-examination image fed back based on the image monitoring request;

[0023] Performing edge detection on the abnormal re-inspection image to obtain texture features;

[0024] Comparing the texture features with the photovoltaic panel splicing texture to determine a texture increment set;

[0025] When the texture increment set is not an empty set, a texture image corresponding to the texture increment set is output, and the texture image is output as image monitoring information.

[0026] In one embodiment, the step of determining the reflective features of the image according to the drone inspection image information and determining the image monitoring information based on the reflective features includes:

[0027] Acquire UAV inspection image information, and perform image denoising on the UAV inspection image information to obtain a denoised image;

[0028] Extract the brightness channel of the denoised image to obtain brightness information;

[0029] Perform edge detection based on the brightness information to obtain edge information, perform Fourier transform on the edge information to extract high-frequency components in the edge information, and determine the boundary of the reflective area;

[0030] Determine the reflective features on both sides of the reflective area boundary, and determine the image monitoring information according to the reflective features.

[0031] In one embodiment, the step of determining the reflective features on both sides of the boundary of the reflective area and determining the image monitoring information according to the reflective features includes:

[0032] Determine a first brightness value and a second brightness value of areas on both sides of the boundary of the reflective area;

[0033] Performing histogram analysis on the first brightness value and the second brightness value respectively to obtain a first brightness distribution and a second brightness distribution, wherein the first brightness distribution corresponds to the first brightness value, and the second brightness distribution corresponds to the second brightness value;

[0034] Determine a first brightness peak value and a second brightness peak value of the first brightness distribution and the second brightness distribution respectively;

[0035] Comparing the first brightness peak value and the second brightness peak value with a brightness threshold value respectively, determining a brightness peak value greater than the brightness threshold value as a strong reflection area, and determining a brightness peak value less than the brightness threshold value as a weak reflection area;

[0036] A reflection feature is obtained according to the strong reflection area and the weak reflection area, and image monitoring information is determined according to the reflection feature.

[0037] In one embodiment, the step of generating a safety monitoring result based on the current monitoring information, the image monitoring information or the acoustic anomaly positioning information includes:

[0038] Analyzing the current monitoring information to obtain a current monitoring information analysis result;

[0039] Analyzing the image monitoring information to obtain an image monitoring information analysis result;

[0040] Compare the current monitoring information analysis result and the image monitoring information analysis result with the target safety threshold respectively to obtain a comparison result;

[0041] When the comparison result is an abnormal result, a safety monitoring result is generated according to the abnormal result or the acoustic abnormality positioning information.

[0042] In addition, to achieve the above purpose, the present application also proposes an intelligent unmanned station safety monitoring device, which includes:

[0043] A current monitoring module, used to obtain current array information according to the photovoltaic panel array information and the current information of the photovoltaic panel current output unit, and obtain current monitoring information based on the current array information;

[0044] An acoustic monitoring module, used to determine an audio time difference according to an abnormal audio signal acquired by an acoustic sensor, and locate acoustic abnormality positioning information according to the audio time difference;

[0045] An image monitoring module, used to determine the reflective features of the image according to the inspection image information of the drone, and determine the image monitoring information based on the reflective features;

[0046] A safety monitoring module is used to generate a safety monitoring result based on the current monitoring information, the image monitoring information or the acoustic anomaly positioning information.

[0047] In addition, to achieve the above-mentioned purpose, the present application also proposes an intelligent unmanned station safety monitoring device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the intelligent unmanned station safety monitoring method as described above.

[0048] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the intelligent unmanned station safety monitoring method as described above are implemented.

[0049] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the intelligent unmanned station safety monitoring method as described above.

[0050] The present application provides a smart unmanned station safety monitoring method, which obtains current array information based on photovoltaic panel array information and current information of photovoltaic panel current output units, obtains current monitoring information based on the current array information, determines an audio time difference based on an abnormal audio signal acquired by an acoustic sensor, locates acoustic anomaly positioning information based on the audio time difference, determines the reflective features of an image based on drone inspection image information, determines image monitoring information based on the reflective features, generates safety monitoring results based on the current monitoring information, the image monitoring information or the acoustic anomaly positioning information, and can comprehensively monitor the work and production of unmanned stations from multiple angles, thereby improving the safety detection efficiency of unmanned stations, solving the technical problems of the current overly single inspection method for unmanned stations and the low fault recognition rate, and improving the fault recognition accuracy of unmanned stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0053] Figure 1 This is a flow chart of the first embodiment of the intelligent unmanned station safety monitoring method of this application;

[0054] Figure 2 This is a schematic diagram of photovoltaic panel power generation according to an embodiment of the intelligent unmanned station safety monitoring method of this application;

[0055] Figure 3 A schematic diagram of low current partitioning of an embodiment of the intelligent unmanned station safety monitoring method of the present application;

[0056] Figure 4 This is a schematic diagram of acoustic anomaly positioning information positioning of an embodiment of the intelligent unmanned station safety monitoring method of this application;

[0057] Figure 5 This is a schematic diagram of the module structure of the intelligent unmanned station safety monitoring device according to the embodiment of the present application;

[0058] Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the intelligent unmanned station safety monitoring method in the embodiment of the present application.

[0059] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0060] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0061] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0062] The main solution of the embodiment of the present application is: obtaining current array information based on photovoltaic panel array information and current information of the photovoltaic panel current output unit, and obtaining current monitoring information based on the current array information; determining the audio time difference based on the abnormal audio signal acquired by the acoustic sensor, and locating the acoustic abnormality positioning information based on the audio time difference; determining the reflective characteristics of the image based on the drone inspection image information, and determining the image monitoring information based on the reflective characteristics; generating a safety monitoring result based on the current monitoring information, the image monitoring information or the acoustic abnormality positioning information.

[0063] At present, drones are the main method in the process of unmanned photovoltaic stations. Drones are used to collect visible light images of the surface of photovoltaic panels, and perform image recognition on the visible light images to achieve surface detection of photovoltaic panels. This is used to determine whether there are obstructions blocking the photovoltaic panels, thereby reducing the photoelectric conversion efficiency. It is impossible to achieve comprehensive detection of photovoltaic stations, and the current inspection method has the problem of low accuracy of the monitoring results.

[0064] The present application provides a solution that can conduct safety monitoring of unmanned photovoltaic stations from multiple angles, and obtain multi-dimensional inspection data by combining the current output of photovoltaic panels, visible light inspection images, and abnormal sound tracking, which can further improve the fault identification accuracy of photovoltaic stations.

[0065] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device that can realize the above functions, an intelligent unmanned station safety monitoring device, etc., and this embodiment does not specifically limit this. The following takes the intelligent unmanned station safety monitoring device as an example to illustrate this embodiment and the following embodiments.

[0066] The present application embodiment provides a smart unmanned station safety monitoring method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the intelligent unmanned station safety monitoring method of the present application.

[0067] In this embodiment, the intelligent unmanned station safety monitoring method includes steps S10 to S40:

[0068] Step S10, obtaining current array information according to the photovoltaic panel array information and the current information of the photovoltaic panel current output unit, and obtaining current monitoring information based on the current array information.

[0069] It should be noted that the photovoltaic panel array information refers to a photovoltaic array assembled from multiple photovoltaic modules, reflecting the composition information of the photovoltaic panel, including size specifications, the number of photovoltaic modules, the azimuth and tilt angle of the photovoltaic panel and other information. The photovoltaic panel current output unit refers to the unit that generates current through the photoelectric effect of the photovoltaic module and outputs it to the outside.

[0070] It should be understood that the current information reflects the current output value of the current output unit of the current panel, and the current monitoring information refers to the current summary information after the current output information of the photovoltaic panel is monitored and abnormal current values ​​are marked.

[0071] It is understood that when the photovoltaic panel is in working state, each photovoltaic component can produce a photoelectric effect and generate a certain value of current. For the photovoltaic panel, its output current value is the sum of the current values ​​output by the electronic controllers in each photovoltaic component that constitutes the photovoltaic panel. Figure 2 , Figure 2 This is a schematic diagram of photovoltaic panel power generation. When the reflective concentrator collects light, no matter what type of solar panel it is, the output current will pass through the electronic controller, that is, the photovoltaic panel current output unit, to provide it to the load or store it in the energy storage device. Normally, the light received by each photovoltaic module is the same, and the current value generated is theoretically the same. However, in actual applications, due to environmental factors and daily losses, the degree of wear of different photovoltaic modules will gradually differ, which in turn affects the photoelectric conversion efficiency, causing the output current value to change, and there will be a certain degree of downward trend compared to normal conditions. Therefore, according to the array information of the photovoltaic panel, the arrangement information of the photovoltaic panel current output unit can be determined, which can be used to generate a matrix of the same specifications to record the current information of the photovoltaic panel. The current information and the position of the photovoltaic module are in a one-to-one correspondence, and the matrix obtained to record the current information is the current array information.

[0072] During use, photovoltaic panels will define a state to distinguish between normal and abnormal states, which can be intuitively distinguished by the output current value. Therefore, a current threshold can be set to determine whether the working state of the current output unit of the photovoltaic panel is abnormal. If the output current value is lower than the set current threshold, the current current value can be marked as abnormal, so that the corresponding specific photovoltaic component can be located according to the position of the abnormal mark. For example, under certain working conditions, when the set current threshold of a single photovoltaic component is 0.8I, I is the standard output current under the current working conditions. The current value can be judged according to this standard, and the current value lower than 0.8I is determined as an abnormal current value, and an abnormal table mark is added to obtain current monitoring information.

[0073] In a feasible implementation manner, the step of obtaining the current array information according to the photovoltaic panel array information and the current information of the photovoltaic panel current output unit, and obtaining the current monitoring information based on the current array information includes:

[0074] Determine identification information of a photovoltaic panel current output unit, and collect current output values ​​of the photovoltaic panel current output unit;

[0075] Acquire photovoltaic panel array information, map the current output value to the photovoltaic panel array information based on the identification information, and obtain current array information;

[0076] Performing current value gradient detection on the current array information to determine the current gradient value;

[0077] A current partition is determined according to the current gradient value to obtain a low current partition, and current monitoring information is generated with the low current partition.

[0078] It should be noted that the identification information can be considered as the unique identification information of the photovoltaic panel current output unit, which is used to accurately locate the photovoltaic panel current output unit. The current gradient value reflects the difference in current values ​​generated by adjacent photovoltaic modules. The larger the current gradient value, the greater the difference between the current values. The low current partition refers to the area with relatively low current output.

[0079] In a specific implementation, when the photovoltaic panel is installed and deployed, the unique identification information label of the photovoltaic panel current output unit can be extracted, and the deployment location site of the photovoltaic panel current output unit can be recorded to form array information for representing any photovoltaic panel current output unit.<label,site> , and use the array information of the unit to form the photovoltaic panel array information T = [<label,site> ] i×j When determining the identification information of the photovoltaic panel current output unit, the current output value I of the photovoltaic panel current output unit can be collected at the same time. site , the current output value I siteBased on the mapping of the identification information label to the photovoltaic panel array information T=[<label,site> ] i×j , get the current array information TI = [<label,site> , I site ] i×j Then, in the current array information, the current current value information is compared with the current value of the adjacent area, referring to Figure 3 , Figure 3 The current difference is determined by comparing the current value with the current value of other adjacent photovoltaic modules, and the current gradient value is obtained by comparing the current difference with the current value of the adjacent photovoltaic modules. Figure 3 Reflects the current output of the local area. When determining the current gradient, the current difference can be determined by taking the current output unit of the current photovoltaic panel as the center and the difference between the current output values ​​of the surrounding 3 to 8 photovoltaic panel current output units, and all the current differences are accumulated to obtain the current gradient value of the current photovoltaic panel current output unit. Specifically, it can be expressed as: S = ∑ (I site -I neighbour ), where I neighbour = {I i-1,j-1 ,I i-1,j ,I i-1,j+1 ,I i,j-1 ,I i,j+1 ,I i+1,j+1 ,I i+1,j ,I i+1,j+1}. When any position is null, the current value of the position is assigned as the current output unit value of the photovoltaic panel. Taking the current output value of the position marked in red as an example, the current gradient value is S = -1.3. Based on the same processing method, the current value of each position is calculated with the current value of the adjacent position, and the area with S < 0 can be marked as a low current partition.

[0080] Step S20: determining an audio time difference according to the abnormal audio signal acquired by the acoustic sensor, and locating acoustic abnormality positioning information according to the audio time difference.

[0081] It should be noted that the acoustic sensor can collect sound information in the area where the photovoltaic panels are located. Abnormal audio signals refer to sudden sounds such as photovoltaic panels breaking. Such sounds are usually high-frequency audio. It takes a certain amount of time for sound to propagate in the air. Therefore, there is a time difference from the generation of the sound to the acquisition of the sound by the acoustic sensor to generate an audio signal. This time difference is the audio time difference, which can reflect the relative position of the sound occurrence location, and thus determine the acoustic anomaly positioning information.

[0082] It is understandable that the acoustic sensor can be set on the periphery of the photovoltaic panel, which can reduce the interference with the power generation efficiency of the photovoltaic panel. In order to locate the location where the sound occurs, at least three acoustic sensors are required to work together. The more acoustic sensors there are, the more accurate the location will be. For ease of explanation, this embodiment is described using three acoustic sensors as an example. When the acoustic sensor collects sounds in the environment, it can process the audio, exclude background sounds such as wind, retain high-frequency audio, and record the audio features of the audio, and record the duration of the audio to identify whether it is normal high-frequency audio in the environment, such as audio information such as birds singing in the airspace. If one of the acoustic sensors can separate the audio features of V1 when receiving a certain high-frequency audio signal V1, and the same audio features are also collected in other acoustic sensors, it can determine the timestamp information of the audio information, and obtain three timestamp information, namely T1, T2 and T3. And determine the audio time difference T2-T1 and T3-T1, and the offset position can be determined based on the audio time difference. Refer to Figure 4 , Figure 4 The following is a schematic diagram of acoustic anomaly positioning information. The position of the acoustic sensor is taken as the center of the circle, the offset distance is the initial radius, and the initial radii are r1 = 0 and r2 = (T2-T1)·V. s and r3=(T3-T1)·V s The position where the three circles intersect is determined with the same radius growth, and the position is determined as the acoustic anomaly positioning information, such as the red point mark area in the image.

[0083] In a feasible implementation manner, the step of determining the audio time difference according to the abnormal audio signal acquired by the acoustic sensor, and locating the acoustic abnormality positioning information according to the audio time difference includes:

[0084] Perform high-pass filtering on the audio information acquired by the acoustic sensor to obtain high-frequency audio information;

[0085] The high-frequency audio information is regarded as abnormal audio information, and an audio mark is performed on the abnormal audio information;

[0086] Determine a first acquisition time of the abnormal audio information, and determine a second acquisition time of the abnormal audio information corresponding to the audio tag in other acoustic sensors;

[0087] determining an audio time difference according to the first acquisition time and the second acquisition time;

[0088] A relative distance deviation is determined according to the audio time difference, and isometric growth is performed according to the relative distance deviation to determine acoustic anomaly positioning information.

[0089] It should be noted that the audio tag refers to a tag used to distinguish audio files, which is used to distinguish different audio signals. The first acquisition time and the second acquisition time refer to the time information when the acoustic sensor collects the corresponding audio. The first acquisition time is the time when a certain audio is first acquired, and the second acquisition time is the time when other acoustic sensors acquire the audio signal.

[0090] In the specific implementation, the audio information obtained by the acoustic sensor is high-pass filtered to obtain high-frequency audio information. By high-pass filtering the audio information, according to the formula:

[0091] y[n]=x[n]-x[n-1]+a·y[n-1]

[0092] Where y[n] is the nth sample of the output sequence, x[n] is the nth sample of the input sequence, and a is a coefficient between 0 and 1.

[0093] The transfer function of a first-order high-pass filter is expressed as:

[0094]

[0095] Determine the cutoff frequency f c , calculate the a value according to the cutoff frequency, specifically:

[0096]

[0097] After obtaining the high-frequency audio information, the high-frequency audio information can be determined as abnormal audio information, and the abnormal audio information can be marked. The first acquisition time of the abnormal audio information and the second acquisition time of the abnormal audio information corresponding to the audio mark in other acoustic sensors are determined. The audio time difference is determined according to the first acquisition time and the second acquisition time. Then, the relative distance deviation is determined according to the audio time difference. Then, when all the circular areas generated by the relative distance deviations do not intersect at one point, the relative distance deviation is used for equal distance growth, the acoustic abnormality positioning information is determined, and the position information is output.

[0098] This embodiment can high-pass filter the audio signal to filter out low-frequency noise, including high-frequency signals including abnormal audio, and mark the audio based on audio features, and determine the abnormal location information by the time difference between the audio tag and the audio corresponding to the audio obtained by each acoustic sensor, thereby achieving the purpose of determining the location of the abnormal audio through sound information.

[0099] In a feasible implementation manner, after the steps of determining the relative distance deviation according to the audio time difference, performing isometric growth according to the relative distance deviation, and determining the acoustic anomaly positioning information, the method includes:

[0100] generating an image monitoring request according to the acoustic abnormality positioning information, and receiving an abnormal re-examination image fed back based on the image monitoring request;

[0101] Performing edge detection on the abnormal re-inspection image to obtain texture features;

[0102] Comparing the texture features with the photovoltaic panel splicing texture to determine a texture increment set;

[0103] When the texture increment set is not an empty set, a texture image corresponding to the texture increment set is output, and the texture image is output as image monitoring information.

[0104] It should be noted that the image monitoring request is generated based on the acoustic anomaly positioning information, and is a request command used to assist in sound anomaly detection and assist in determining the cause of the anomaly. The anomaly re-inspection image is the image information of the area where the acoustic anomaly positioning information is located, and the texture features are the crack information of the photovoltaic panel in the image, etc.

[0105] In the specific implementation, after obtaining the acoustic anomaly positioning information, an image monitoring request can be generated based on the acoustic anomaly positioning information, and the image monitoring request is sent to the re-inspection drone to obtain the abnormal re-inspection image at the position corresponding to the acoustic anomaly positioning information. The abnormal re-inspection image is pre-processed, the image is grayed and denoised, and the pre-processed image is edge detected based on the Sobel operator. Two 3×3 convolution kernels are used to calculate the gradient of the image in the X-axis and Y-axis directions respectively. The two convolution kernels are:

[0106] Gradient Gx in the X direction:

[0107]

[0108] Gradient Gy in the Y direction:

[0109]

[0110] Among them, I is the input image, * is the convolution operation, and through these two convolution kernels, we can get the gradient values ​​Gx and Gy of each pixel in the X and Y directions.

[0111] After obtaining Gx and Gy, the gradient size of each pixel can be calculated:

[0112] |G|=|Gx|+|Gy|

[0113] Then calculate the edge direction of each pixel In order to refine the edge, the non-maximum value of the gradient size can be suppressed, and only the maximum value along the edge direction is retained. By setting two high and low thresholds, the final edge map is obtained by connecting strong edges and weak edges to obtain texture features. The texture features are compared with the spliced ​​texture of the photovoltaic panel to determine the texture increment set. When the texture increment set is not an empty set, the texture image corresponding to the texture increment set is output, and the texture image is output as image monitoring information. It indicates that crack features appear on the current photovoltaic panel.

[0114] When the sound is determined to be abnormal, the present embodiment can enable the drone to perform image detection on the abnormal area to determine whether the target texture features exist in the abnormal area, so as to detect whether cracks occur. When the photovoltaic panel is broken, the photoelectric conversion efficiency will be seriously affected. By performing edge detection on the re-inspected image, the cracks of the broken parts can be highlighted, and the fault type can be accurately identified.

[0115] Step S30, determining the reflective features of the image according to the drone inspection image information, and determining the image monitoring information based on the reflective features.

[0116] It should be noted that the image monitoring information refers to the image information obtained by collecting visible light images of the photovoltaic panel area through a drone, and the reflective feature is the light reflected when the light is irradiated to the focusing area of ​​the photovoltaic panel.

[0117] In the specific implementation, when conducting inspection and safety monitoring of the photovoltaic panel area, the drone can be enabled to obtain the reflective features on the photovoltaic panel to detect whether there is dust or other objects on the photovoltaic panel blocking the photovoltaic components. In this way, the reflective features in the drone inspection image information can be obtained. It can be known that the focusing area of ​​the photovoltaic panel is usually mirror reflection, so the brightness corresponding to the reflective feature will be higher, and when there is dust or other objects blocking it, diffuse reflection will occur, and the brightness of the corresponding reflective feature will be lower. Therefore, the brightness information of the reflective feature can be judged, and the area with low brightness can be determined as the target abnormal area.

[0118] In a feasible implementation manner, the step of determining the reflective features of the image according to the drone inspection image information and determining the image monitoring information based on the reflective features includes:

[0119] Acquire UAV inspection image information, and perform image denoising on the UAV inspection image information to obtain a denoised image;

[0120] Extract the brightness channel of the denoised image to obtain brightness information;

[0121] Perform edge detection based on the brightness information to obtain edge information, perform Fourier transform on the edge information to extract high-frequency components in the edge information, and determine the boundary of the reflective area;

[0122] Determine the reflective features on both sides of the reflective area boundary, and determine the image monitoring information according to the reflective features.

[0123] It should be noted that the boundary of the reflective area is a dividing line used to distinguish between high-brightness and low-brightness areas. The high-brightness area is a normal area, and the low-brightness area is an abnormal area.

[0124] In a specific implementation, the drone inspection image may be affected by various noises. In order to avoid the influence of noise, this embodiment performs filtering by Gaussian filtering. According to the formula:

[0125]

[0126] Among them, σ is the standard deviation, which determines the width of the Gaussian function. The size of the Gaussian filter is (2k+1)×(2k+1), where k is an integer.

[0127] After Gaussian filtering, the brightness information of the V channel in the HSV color space is extracted from the denoised image, and the edge information in the denoised image is determined by the edge detection mentioned in the above embodiment, and the edge information is Fourier transformed to extract the high-frequency components in the edge information, and the boundary of the reflective area is determined. The boundary of the reflective area is used to determine the reflective features on both sides of the boundary, and the high-brightness area and the low-brightness area are determined to obtain image monitoring information.

[0128] This embodiment can determine the regional distribution of abnormal areas and normal areas in image information by brightness information, can determine the corresponding floating dust areas and obstruction areas by brightness distribution, and can determine the working status of the photovoltaic panel by the characteristics of image reflection.

[0129] In a feasible implementation manner, the step of determining the reflective features on both sides of the boundary of the reflective area and determining the image monitoring information according to the reflective features includes:

[0130] Determine a first brightness value and a second brightness value of areas on both sides of the boundary of the reflective area;

[0131] Performing histogram analysis on the first brightness value and the second brightness value respectively to obtain a first brightness distribution and a second brightness distribution, wherein the first brightness distribution corresponds to the first brightness value, and the second brightness distribution corresponds to the second brightness value;

[0132] Determine a first brightness peak value and a second brightness peak value of the first brightness distribution and the second brightness distribution respectively;

[0133] Comparing the first brightness peak value and the second brightness peak value with a brightness threshold value respectively, determining a brightness peak value greater than the brightness threshold value as a strong reflection area, and determining a brightness peak value less than the brightness threshold value as a weak reflection area;

[0134] A reflection feature is obtained according to the strong reflection area and the weak reflection area, and image monitoring information is determined according to the reflection feature.

[0135] It should be noted that the first brightness value and the second brightness value are brightness values ​​corresponding to areas on both sides of the boundary of the reflective area.

[0136] In a specific implementation, the areas on both sides of the reflective area boundary are set as area A and area B, so that the brightness of area A and area B can be detected to obtain the brightness value L of area A. A (x, y) and the brightness value L of area B B (x, y), where (x, y) is the pixel coordinate. Histogram analysis is performed on the first brightness value and the second brightness value. Histogram analysis can be used to count the distribution of brightness values. Assuming that the brightness value range is [0, 255], two histograms can be constructed, namely, the brightness histogram H of area A and the brightness histogram H of area A. A (b) and the brightness histogram H of area A B (b), where b is the brightness value, H A (b) and H B (b) respectively represent the number of pixels at brightness value b. Determine the first brightness peak value and the second brightness peak value of the first brightness distribution and the second brightness distribution. The brightness peak value of area A is P A = argmax b H A (b) and the brightness peak of area B is P B = argmax b H B (b). Therefore, after obtaining the brightness peak, it can be compared with the brightness threshold T. If it is greater than the brightness threshold, it is a strong reflection area, or less than the brightness threshold, it is a weak reflection area, and then the reflection characteristics of the strong reflection area and the weak reflection area are obtained, and the image monitoring information is determined according to the reflection characteristics.

[0137] Step S40: generating a safety monitoring result based on the current monitoring information, the image monitoring information or the acoustic anomaly positioning information.

[0138] It should be noted that the safety monitoring results refer to the final judgment results of the photovoltaic panels, which are used to describe whether there are abnormalities and the location of the abnormal area.

[0139] It is understandable that when the current monitoring information, image monitoring information or the acoustic abnormality positioning information is obtained, the current monitoring information, image monitoring information or the acoustic abnormality positioning information can be comprehensively judged, the current monitoring information can be analyzed to obtain the current monitoring information analysis result; the image monitoring information can be analyzed to obtain the image monitoring information analysis result; the current monitoring information analysis result and the image monitoring information analysis result are respectively compared with the target safety threshold to obtain the comparison result; when the comparison result is an abnormal result, a safety monitoring result is generated according to the abnormal result or the acoustic abnormality positioning information. Specifically: when the current monitoring information is abnormal, the position of the photovoltaic component with abnormal current can be recorded and output to obtain an abnormal result; when the image monitoring information shows the presence of floating dust or obstructions, the area of ​​floating dust and obstructions can be determined and output to obtain an abnormal result; or according to the acoustic abnormality positioning information, the safety monitoring result can be output to reflect the fault type and fault location.

[0140] This embodiment provides a smart unmanned station safety monitoring method, which obtains current array information based on photovoltaic panel array information and current information of photovoltaic panel current output unit, obtains current monitoring information based on the current array information, determines audio time difference based on abnormal audio signal acquired by acoustic sensor, locates acoustic abnormality positioning information based on the audio time difference, determines image reflective features based on drone inspection image information, determines image monitoring information based on the reflective features, and generates safety monitoring results based on the current monitoring information, the image monitoring information or the acoustic abnormality positioning information. Through the above methods, unmanned stations can be monitored from multiple angles such as current output, image monitoring and sound monitoring, which can achieve all-round safety detection and improve the fault response speed of unmanned stations.

[0141] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the intelligent unmanned station safety monitoring method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0142] This application also provides a smart unmanned station safety monitoring device, please refer to Figure 5 , the intelligent unmanned station safety monitoring device includes:

[0143] A current monitoring module 10, used to obtain current array information according to the photovoltaic panel array information and the current information of the photovoltaic panel current output unit, and obtain current monitoring information based on the current array information;

[0144] The acoustic monitoring module 20 is used to determine the audio time difference according to the abnormal audio signal acquired by the acoustic sensor, and locate the acoustic abnormality positioning information according to the audio time difference;

[0145] An image monitoring module 30, configured to determine the reflective features of an image according to the inspection image information of the drone, and determine the image monitoring information based on the reflective features;

[0146] The safety monitoring module 40 is used to generate a safety monitoring result based on the current monitoring information, the image monitoring information or the acoustic anomaly positioning information.

[0147] In a feasible implementation, the current monitoring module 10 is also used to determine the identification information of the photovoltaic panel current output unit, and collect the current output value of the photovoltaic panel current output unit; obtain the photovoltaic panel array information, map the current output value to the photovoltaic panel array information based on the identification information, and obtain the current array information; perform current value gradient detection on the current array information to determine the current gradient value; determine the current partition according to the current gradient value to obtain the low current partition, and generate current monitoring information with the low current partition.

[0148] In a feasible implementation, the acoustic monitoring module 20 is also used to perform high-pass filtering on the audio information acquired by the acoustic sensor to obtain high-frequency audio information; use the high-frequency audio information as abnormal audio information, and perform audio marking on the abnormal audio information; determine a first acquisition time of the abnormal audio information, and determine a second acquisition time of the abnormal audio information corresponding to the audio mark in other acoustic sensors; determine an audio time difference based on the first acquisition time and the second acquisition time; determine a relative distance deviation based on the audio time difference, perform equidistant growth based on the relative distance deviation, and determine acoustic anomaly positioning information.

[0149] In a feasible implementation, the acoustic monitoring module 20 is also used to generate an image monitoring request based on the acoustic anomaly positioning information, and receive an abnormal re-inspection image based on the image monitoring request feedback; perform edge detection on the abnormal re-inspection image to obtain texture features; compare the texture features with the photovoltaic panel splicing texture to determine a texture increment set; when the texture increment set is not an empty set, output the texture image corresponding to the texture increment set, and output the texture image as image monitoring information.

[0150] In a feasible implementation, the image monitoring module 30 is also used to obtain drone inspection image information, perform image denoising on the drone inspection image information to obtain a denoised image; extract the brightness channel of the denoised image to obtain brightness information; perform edge detection based on the brightness information to obtain edge information, perform Fourier transform on the edge information to extract high-frequency components in the edge information, and determine the boundary of the reflective area; determine the reflective features on both sides of the boundary of the reflective area, and determine the image monitoring information based on the reflective features.

[0151] In a feasible embodiment, the image monitoring module 30 is also used to determine the first brightness value and the second brightness value of the areas on both sides of the boundary of the reflective area; perform histogram analysis on the first brightness value and the second brightness value respectively to obtain a first brightness distribution and a second brightness distribution, the first brightness distribution corresponds to the first brightness value, and the second brightness distribution corresponds to the second brightness value; determine the first brightness peak value and the second brightness peak value of the first brightness distribution and the second brightness distribution respectively; compare the first brightness peak value and the second brightness peak value with the brightness threshold respectively, determine the brightness peak value greater than the brightness threshold as a strong reflection area, and determine the brightness peak value less than the brightness threshold as a weak reflection area; obtain reflection characteristics based on the strong reflection area and the weak reflection area, and determine image monitoring information based on the reflection characteristics.

[0152] In a feasible implementation, the safety monitoring module 40 is also used to parse the current monitoring information to obtain a current monitoring information analysis result; to parse the image monitoring information to obtain an image monitoring information analysis result; to compare the current monitoring information analysis result and the image monitoring information analysis result with a target safety threshold value respectively to obtain a comparison result; when the comparison result is an abnormal result, a safety monitoring result is generated according to the abnormal result or the acoustic abnormality positioning information.

[0153] The intelligent unmanned station safety monitoring device provided by the present application adopts the intelligent unmanned station safety monitoring method in the above embodiment, which can solve the technical problem of low fault detection accuracy in unmanned stations. Compared with the prior art, the beneficial effects of the intelligent unmanned station safety monitoring device provided by the present application are the same as the beneficial effects of the intelligent unmanned station safety monitoring method provided by the above embodiment, and other technical features in the intelligent unmanned station safety monitoring device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0154] The present application provides an intelligent unmanned station safety monitoring device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intelligent unmanned station safety monitoring method in the above-mentioned embodiment one.

[0155] Reference below Figure 6, which shows a schematic diagram of the structure of an intelligent unmanned station safety monitoring device suitable for implementing the embodiment of the present application. The intelligent unmanned station safety monitoring device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The intelligent unmanned station safety monitoring equipment shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0156] like Figure 6 As shown, the intelligent unmanned station safety monitoring equipment may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the intelligent unmanned station safety monitoring equipment are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the intelligent unmanned field station safety monitoring device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an intelligent unmanned field station safety monitoring device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0157] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0158] The intelligent unmanned station safety monitoring equipment provided by this application adopts the intelligent unmanned station safety monitoring method in the above embodiment, which can solve the technical problems of intelligent unmanned station safety monitoring. Compared with the prior art, the beneficial effects of the intelligent unmanned station safety monitoring equipment provided by this application are the same as the beneficial effects of the intelligent unmanned station safety monitoring method provided by the above embodiment, and the other technical features in the intelligent unmanned station safety monitoring equipment are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0159] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0160] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0161] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the intelligent unmanned station safety monitoring method in the above-mentioned embodiment.

[0162] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0163] The above-mentioned computer-readable storage medium may be included in the intelligent unmanned station safety monitoring device; or it may exist independently without being assembled into the intelligent unmanned station safety monitoring device.

[0164] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the intelligent unmanned station safety monitoring equipment, the intelligent unmanned station safety monitoring equipment: obtains current array information according to the photovoltaic panel array information and the current information of the photovoltaic panel current output unit, and obtains current monitoring information based on the current array information; determines the audio time difference according to the abnormal audio signal acquired by the acoustic sensor, and locates the acoustic abnormality positioning information according to the audio time difference; determines the reflective characteristics of the image according to the drone inspection image information, and determines the image monitoring information based on the reflective characteristics; generates a safety monitoring result based on the current monitoring information, the image monitoring information or the acoustic abnormality positioning information.

[0165] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0166] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0167] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0168] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned intelligent unmanned station safety monitoring method, and can solve the technical problems of intelligent unmanned station safety monitoring. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the intelligent unmanned station safety monitoring method provided by the above-mentioned embodiment, and will not be repeated here.

[0169] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned intelligent unmanned station safety monitoring method.

[0170] The computer program product provided by this application can solve the technical problem of intelligent unmanned station safety monitoring. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the intelligent unmanned station safety monitoring method provided by the above embodiment, which will not be repeated here.

[0171] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A smart unmanned station safety monitoring method, characterized in that: The intelligent unmanned station safety monitoring method comprises: Obtaining current array information according to the photovoltaic panel array information and the current information of the photovoltaic panel current output unit, and obtaining current monitoring information based on the current array information; Determine an audio time difference according to the abnormal audio signal acquired by the acoustic sensor, and locate acoustic abnormality positioning information according to the audio time difference; Determine the reflective features of the image according to the inspection image information of the drone, and determine the image monitoring information based on the reflective features; A safety monitoring result is generated based on the current monitoring information, the image monitoring information or the acoustic anomaly positioning information.

2. The method according to claim 1, characterized in that The step of obtaining the current array information according to the photovoltaic panel array information and the current information of the photovoltaic panel current output unit, and obtaining the current monitoring information based on the current array information comprises: Determine identification information of a photovoltaic panel current output unit, and collect current output values ​​of the photovoltaic panel current output unit; Acquire photovoltaic panel array information, map the current output value to the photovoltaic panel array information based on the identification information, and obtain current array information; Performing current value gradient detection on the current array information to determine the current gradient value; A current partition is determined according to the current gradient value to obtain a low current partition, and current monitoring information is generated with the low current partition.

3. The method according to claim 1, characterized in that The step of determining the audio time difference according to the abnormal audio signal acquired by the acoustic sensor, and locating the acoustic abnormality positioning information according to the audio time difference comprises: Perform high-pass filtering on the audio information acquired by the acoustic sensor to obtain high-frequency audio information; The high-frequency audio information is regarded as abnormal audio information, and an audio mark is performed on the abnormal audio information; Determine a first acquisition time of the abnormal audio information, and determine a second acquisition time of the abnormal audio information corresponding to the audio tag in other acoustic sensors; determining an audio time difference according to the first acquisition time and the second acquisition time; A relative distance deviation is determined according to the audio time difference, and isometric growth is performed according to the relative distance deviation to determine acoustic anomaly positioning information.

4. The method according to claim 3, characterized in that After the steps of determining the relative distance deviation according to the audio time difference, performing isometric growth according to the relative distance deviation, and determining the acoustic anomaly positioning information, the method further comprises: generating an image monitoring request according to the acoustic abnormality positioning information, and receiving an abnormal re-examination image fed back based on the image monitoring request; Performing edge detection on the abnormal re-inspection image to obtain texture features; Comparing the texture features with the photovoltaic panel splicing texture to determine a texture increment set; When the texture increment set is not an empty set, a texture image corresponding to the texture increment set is output, and the texture image is output as image monitoring information.

5. The method according to claim 1, characterized in that The step of determining the reflective features of the image according to the drone inspection image information and determining the image monitoring information based on the reflective features includes: Acquire UAV inspection image information, and perform image denoising on the UAV inspection image information to obtain a denoised image; Extract the brightness channel of the denoised image to obtain brightness information; Perform edge detection based on the brightness information to obtain edge information, perform Fourier transform on the edge information to extract high-frequency components in the edge information, and determine the boundary of the reflective area; Determine the reflective features on both sides of the reflective area boundary, and determine the image monitoring information according to the reflective features.

6. The method according to claim 5, characterized in that The step of determining the reflective features on both sides of the boundary of the reflective area and determining the image monitoring information according to the reflective features comprises: Determine a first brightness value and a second brightness value of areas on both sides of the boundary of the reflective area; Performing histogram analysis on the first brightness value and the second brightness value respectively to obtain a first brightness distribution and a second brightness distribution, wherein the first brightness distribution corresponds to the first brightness value, and the second brightness distribution corresponds to the second brightness value; Determine a first brightness peak value and a second brightness peak value of the first brightness distribution and the second brightness distribution respectively; Comparing the first brightness peak value and the second brightness peak value with a brightness threshold value respectively, determining a brightness peak value greater than the brightness threshold value as a strong reflection area, and determining a brightness peak value less than the brightness threshold value as a weak reflection area; A reflection feature is obtained according to the strong reflection area and the weak reflection area, and image monitoring information is determined according to the reflection feature.

7. The method according to claim 1, characterized in that The step of generating a safety monitoring result based on the current monitoring information, the image monitoring information or the acoustic abnormality positioning information comprises: Analyzing the current monitoring information to obtain a current monitoring information analysis result; Analyzing the image monitoring information to obtain an image monitoring information analysis result; Compare the current monitoring information analysis result and the image monitoring information analysis result with the target safety threshold respectively to obtain a comparison result; When the comparison result is an abnormal result, a safety monitoring result is generated according to the abnormal result or the acoustic abnormality positioning information.

8. An intelligent unmanned station safety monitoring device, characterized in that: The intelligent unmanned station safety monitoring device includes: A current monitoring module, used to obtain current array information according to the photovoltaic panel array information and the current information of the photovoltaic panel current output unit, and obtain current monitoring information based on the current array information; An acoustic monitoring module, used to determine an audio time difference according to an abnormal audio signal acquired by an acoustic sensor, and locate acoustic abnormality positioning information according to the audio time difference; An image monitoring module, used to determine the reflective features of the image according to the inspection image information of the drone, and determine the image monitoring information based on the reflective features; A safety monitoring module is used to generate a safety monitoring result based on the current monitoring information, the image monitoring information or the acoustic anomaly positioning information.

9. An intelligent unmanned station safety monitoring device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the intelligent unmanned station safety monitoring method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the intelligent unmanned station safety monitoring method as described in any one of claims 1 to 7 are implemented.

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