An array acoustic imaging method carried by an unmanned aerial vehicle

Abstract

The present application relates to the technical field of acoustic imaging, and discloses an array acoustic imaging method carried by a drone, including: step 1, collecting environmental data before the drone is operated, analyzing the environmental data, and judging whether the conditions for acoustic imaging are met; step 2, when the conditions for acoustic imaging are met, controlling the drone to be in a hovering state, and obtaining the noise spectrum of the drone in the hovering state; step 3, identifying the noise spectrum of the drone in the hovering state, obtaining the spectrum key points of each cycle, adjusting the array acoustic imaging acquisition frequency interval according to each spectrum key point, and performing a comprehensive analysis based on the array acoustic imaging obtained under all spectrum key points to obtain the target array acoustic imaging. By selecting a smaller acquisition frequency interval according to the frequency corresponding to each spectrum key point, the influence of the drone wing rotation on the accuracy of the array acoustic imaging is reduced on the basis of ensuring that the abnormal noise is imaged by the array acoustic imaging.

Description

Technical Field

[0001] The present application relates to the technical field of acoustic imaging, and in particular to an array acoustic imaging method carried by an unmanned aerial vehicle. Background Art

[0002] In the process of power facility maintenance, the detection of abnormal noise is a key item. By detecting abnormal noise of power equipment, it is often possible to determine the fault problems or fault risks of power equipment during operation. Among them, detecting abnormal noise of circuit facilities through acoustic imagers is an efficient and non-contact fault diagnosis method, which is especially suitable for local discharge, mechanical vibration or poor contact of power equipment (such as transformers, switch cabinets, cable joints) and other problems. Acoustic imagers are composed of dozens to hundreds of high-sensitivity microphones to capture the spatial sound field distribution. They are also equipped with cameras, infrared cameras, signal processing units, etc. The sound wave signals emitted by the equipment are received through the microphone array, the sound source position is calculated through the beamforming algorithm, and the sound image is generated. Finally, the frequency characteristics of the abnormal noise are extracted to assist in the fault type judgment.

[0003] In the existing technical solutions for detecting abnormal noise of circuit facilities through acoustic imagers, it is usually completed by personnel operating the acoustic imager to aim at the detection position. This method can quickly complete the detection process for circuit facilities that are easy to detect, but it is difficult to achieve this process for circuit facilities that are inconvenient to detect. By using the acoustic imager on a drone, the detection process of abnormal noise in circuit facilities in complex situations can be completed with high efficiency.

[0004] However, although the method of using an acoustic imager on a drone has many advantages, during the specific operation process, the noise generated by the operation of the drone will cause certain interference to the detection process of the acoustic imager. Although the prior art reduces the noise of the motor and transmission machinery by setting a sound insulation device inside the drone, the sound of the drone rotor will also have a certain impact on the detection process. Therefore, how to reduce the impact of the sound of the drone rotor on the abnormal noise of the acoustic imager detection circuit facility is the fundamental problem to be solved by the present invention. Summary of the invention

[0005] In order to reduce the impact of drone rotor sound on abnormal noise in acoustic imager detection circuit facilities, the present application provides an array acoustic imaging method carried by a drone.

[0006] In a first aspect, the present application provides an array acoustic imaging method carried by a drone, which adopts the following technical solution:

[0007] An array acoustic imaging method carried by an unmanned aerial vehicle, the method comprising:

[0008] Step 1: Collect environmental data before the UAV is operated, analyze the environmental data, and determine whether the conditions for acoustic imaging are met;

[0009] Step 2: When acoustic imaging conditions are met, the drone is controlled to be in a hovering state, and the noise spectrum of the drone in the hovering state is obtained;

[0010] Step 3: Identify the noise spectrum of the drone in the hovering state, obtain the spectrum key points of each cycle, adjust the array acoustic imaging acquisition frequency range according to each spectrum key point, perform comprehensive analysis based on the array acoustic imaging obtained at all spectrum key points, and obtain the target array acoustic imaging.

[0011] By adopting the above technical solution, environmental data is collected and analyzed, and whether the conditions for acoustic imaging are currently met is judged to determine whether to perform drone detection operations, thereby ensuring that the detection process can proceed normally and that the obtained array acoustic imaging results are relatively accurate; the drone is controlled to be in a hovering state, and the rotor speed of the drone is stable in the hovering state, and the spectrum shows clear fundamental frequency and harmonic peaks. Therefore, by obtaining the noise spectrum of the drone in the hovering state, the acquisition frequency can be adjusted in the subsequent acquisition and detection process, so that the obtained results are more accurate; the noise spectrum of the drone in the hovering state is identified to obtain the spectrum key points of each cycle, and then the array acoustic imaging acquisition is adjusted according to each spectrum key point. The collection frequency interval, since the abnormal noise of the circuit facilities is in a larger frequency range, in order to reduce the impact of the UAV wing rotation on the collection, a smaller collection frequency interval is selected according to the frequency corresponding to each spectrum key point. The collection frequency interval is within the abnormal noise frequency range and there is a certain difference between the collection frequency interval and the frequency corresponding to the spectrum key point. Therefore, interference can be reduced in the array acoustic imaging process. At the same time, since there are multiple spectrum key points and they are all different, the array acoustic imaging obtained under all spectrum key points is comprehensively analyzed to obtain the target array acoustic imaging, thereby ensuring that the abnormal noise is imaged by the array acoustic imaging and reducing the impact of the UAV wing rotation on the accuracy of the array acoustic imaging.

[0012] Optionally, the process of adjusting the array acoustic imaging acquisition frequency interval according to each spectrum key point includes:

[0013] Divide the preset acquisition frequency band into N segments;

[0014] By formula Calculate the distance between the spectrum key point and the i-th segment ;

[0015] The segment corresponding to the maximum value in the distance value is selected as the acquisition frequency interval of array acoustic imaging;

[0016] in, is the frequency corresponding to the key point of the spectrum; is the median frequency value of the i-th segment.

[0017] By adopting the above technical solution, a distance value is obtained, which reflects the absolute value of the difference between the frequency corresponding to each spectral key point and the middle value of each segment. Obviously, when the distance value is larger, it means that the array acoustic imaging process is less affected by the UAV wing rotation noise. Therefore, through the above process, the optimal array acoustic imaging acquisition frequency range can be selected for each spectral key point, thereby reducing the influence of the UAV wing rotation on the accuracy of array acoustic imaging.

[0018] Optionally, the comprehensive analysis process includes:

[0019] The array acoustic imaging obtained at all spectral key points at the same position is numerically superimposed and averaged to form the target array acoustic imaging.

[0020] By adopting the above technical solution, since the acoustic imager will mark the target position according to the color during the display process, and the color is more obvious in the central position, the numerical values ​​corresponding to the colors of different position points are superimposed and averaged, the array acoustic imaging collected at multiple spectral key points can be obtained, which reduces the impact of a single array acoustic imaging on the overall result and improves the accuracy of the inspection result.

[0021] Optionally, the environmental data includes the wind force value of the area where the drone is operating and the maximum value of the environmental noise in a preset frequency range during the collection period;

[0022] The process of analyzing environmental data includes:

[0023] Compare the wind speed value and the maximum ambient noise value with the corresponding preset thresholds respectively:

[0024] When either the wind force value or the maximum ambient noise value is higher than the corresponding preset threshold, it is determined that the acoustic imaging condition is not met;

[0025] Otherwise, a combined analysis of the wind force value and the maximum ambient noise value is performed to determine whether the conditions for acoustic imaging are met based on the analysis results.

[0026] By adopting the above technical solution, the interference in the acoustic imaging process of the UAV airborne array is greatly reduced by collecting and analyzing the maximum wind force value and the maximum ambient noise. The acoustic imaging conditions are judged based on the analysis results, and the overall impact of the two on the acoustic imaging of the acoustic imager array is judged to ensure the normal operation of the acoustic imager.

[0027] Optionally, the process of combining the analysis includes:

[0028] By formula Calculate and obtain the environmental impact value E;

[0029] Where W is the wind force value, is the wind speed reference value, B is the maximum value of the ambient noise, is the ambient noise baseline value, is the adjustment factor;

[0030] Compare the environmental impact value E with the environmental threshold Et:

[0031] When E<Et, it is determined that the acoustic imaging conditions are met.

[0032] By adopting the above technical solution, the overall influence of the wind force value and the maximum environmental noise on the acoustic imaging of the acoustic imager array can be obtained through the environmental impact value E. By comparing the environmental impact value E with the environmental threshold Et, the environmental threshold Et is set after fitting the critical values ​​in multiple test data. Therefore, when E<Et, it means that the current environment will have a greater impact on the array acoustic imaging of the acoustic imager, and therefore it is judged that the conditions for acoustic imaging are met.

[0033] Optionally, the method further comprises:

[0034] Step 4: Collect infrared images of the operation area through the UAV, conduct collaborative analysis based on the infrared images and target array acoustic images of the same location, obtain display results and output them.

[0035] By adopting the above technical solution, the infrared imaging of the working area is collected by the drone, and the infrared imaging and the target array acoustic imaging of the same position are collaboratively analyzed to obtain and output the display results. The results obtained by both can be combined to facilitate circuit facility maintenance personnel to quickly determine the location of abnormal noise.

[0036] Optionally, the collaborative analysis process includes:

[0037] aligning infrared imaging with target array acoustic imaging;

[0038] Marking the regional contour of the target noise in the target array acoustic imaging according to a preset strategy, and hiding the target array acoustic imaging;

[0039] The area outline is displayed in the infrared imaging to obtain the display result.

[0040] By adopting the above technical solution, the risk location point can be obtained by extracting the regional contour in the array acoustic imaging, and it can be displayed in the infrared imaging at the same time, which means that it can be directly viewed and compared when viewing the infrared imaging. Obviously, the above process can facilitate the detection process of circuit facility maintenance personnel.

[0041] Optionally, the preset strategy includes:

[0042] By formula Calculate the sound intensity value of the edge of the area contour ;

[0043] in, is a preset fixed strength value, is the maximum sound intensity within the area contour, is the tuning coefficient.

[0044] By adopting the above technical solution, through the calculation process of the sound intensity value of the edge of the regional contour, the sound intensity value of the edge of the regional contour can be dynamically adjusted according to different abnormal noise levels, thereby avoiding the obtained regional contour being too small or too large to be of reference significance, thereby ensuring the rationality of the regional contour size, and then when combined with infrared imaging, it can be more convenient for circuit facility maintenance personnel to detect.

[0045] In summary, the present application includes at least one of the following beneficial technical effects:

[0046] 1. By collecting and analyzing environmental data, it is determined whether the conditions for acoustic imaging are currently met to decide whether to perform drone detection operations, thereby ensuring that the detection process can proceed normally and that the array acoustic imaging results obtained are relatively accurate; by controlling the drone to be in a hovering state, the rotor speed of the drone is stable in the hovering state, and the spectrum shows clear fundamental frequency and harmonic peaks. Therefore, by obtaining the noise spectrum of the drone in the hovering state, the acquisition frequency can be adjusted in the subsequent acquisition and detection process to make the obtained results more accurate; by identifying the noise spectrum of the drone in the hovering state, the spectrum key points of each cycle are obtained, and then the array acoustic imaging acquisition frequency interval is adjusted according to each spectrum key point ,Since the abnormal noise of the circuit facilities is in a large frequency range, in order to reduce the impact of the UAV wing rotation on the acquisition, a smaller acquisition frequency interval is selected according to the frequency corresponding to each spectrum key point. The acquisition frequency interval is within the abnormal noise frequency range and there is a certain difference between the acquisition frequency interval and the frequency corresponding to the spectrum key point. Therefore, interference can be reduced in the array acoustic imaging process. At the same time, since there are multiple spectrum key points and they are all different, the array acoustic imaging obtained under all spectrum key points is comprehensively analyzed to obtain the target array acoustic imaging, thereby ensuring that the abnormal noise is imaged by the array acoustic imaging. The impact of the UAV wing rotation on the accuracy of the array acoustic imaging is reduced. By obtaining the distance value to reflect the absolute value of the difference between the corresponding frequency of each spectrum key point and the middle value of each segment, it is obvious that when the distance value is larger, it means that the array acoustic imaging process is less affected by the UAV wing rotation noise. Therefore, through the above process, the best array acoustic imaging acquisition frequency interval can be selected for each spectrum key point, thereby reducing the impact of the UAV wing rotation on the accuracy of the array acoustic imaging.

[0047] 2. The present invention combines the wind force value and the maximum value of the ambient noise for analysis, determines whether the acoustic imaging conditions are met based on the analysis results, and determines the degree of influence of the two on the overall acoustic imaging of the acoustic imaging array to ensure the normal operation of the acoustic imaging device.

[0048] 3. The present invention collects infrared imaging of the operating area through a drone, performs collaborative analysis based on the infrared imaging of the same position and the acoustic imaging of the target array, obtains and outputs display results, and can combine the results obtained by both to facilitate circuit facility maintenance personnel to more quickly determine the location of abnormal noise. Through the calculation process of the sound intensity value of the edge of the regional contour, the sound intensity value of the edge of the regional contour can be dynamically adjusted according to different levels of abnormal noise, thereby avoiding the acquisition of a regional contour that is too small or too large to be of reference significance, ensuring the rationality of the regional contour size, and then when combined with infrared imaging, it can be more convenient for circuit facility maintenance personnel to detect. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flowchart of steps of an array acoustic imaging method carried by a UAV;

[0050] Figure 2 is the target array acoustic imaging map;

[0051] Figure 3 This is the display result diagram after collaborative analysis. DETAILED DESCRIPTION

[0052] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0053] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0054] The present application embodiment discloses an array acoustic imaging method carried by a drone, referring to the attached Figure 1, including: step 1, collecting environmental data before the UAV is operated, analyzing the environmental data, and judging whether the acoustic imaging conditions are met; this process mainly judges whether the current environmental conditions meet the detection requirements. When the external environment does not meet the acoustic imaging conditions, on the one hand, the obtained results are inaccurate, resulting in misjudgment and misjudgment, thereby affecting the subsequent maintenance and debugging process. On the other hand, since the present embodiment uses the UAV airborne acoustic imager, the environmental conditions will also affect the stable operation of the UAV. Therefore, by collecting and analyzing the environmental data, it is determined whether the UAV detection operation is performed by judging whether the acoustic imaging conditions are currently met, thereby ensuring that the detection process can be carried out normally, and at the same time ensuring that the obtained array acoustic imaging results are relatively accurate; step 2, when there are When the acoustic imaging conditions are met, the drone is controlled to be in a hovering state, and the noise spectrum of the drone in the hovering state is obtained. Since there are various noise influences during the operation of the drone, including the motor and transmission mechanical noise inside the drone and the rotation noise of the drone wings, the present embodiment blocks the motor and transmission mechanical noise through the noise reduction element inside the drone, and therefore only considers the rotation noise of the drone wings. When the drone moves, the noise generated by its wings is irregular. Therefore, the present embodiment controls the drone to be in a hovering state. The rotor speed of the drone in the hovering state is stable, and the spectrum shows clear fundamental frequency and harmonic peaks. Therefore, by obtaining the noise spectrum of the drone in the hovering state, the acquisition frequency can be adjusted in the subsequent acquisition and detection process, so that the acquired result is more accurate. Refer to the attached Figure 2, step three, identify the noise spectrum of the drone in the hovering state, obtain the spectrum key points of each cycle, adjust the array acoustic imaging acquisition frequency range according to each spectrum key point, and perform comprehensive analysis based on the array acoustic imaging obtained at all spectrum key points to obtain the target array acoustic imaging; by identifying the noise spectrum of the drone in the hovering state, the spectrum key points of each cycle are obtained. It should be noted that the identification process is based on the noise spectrum data fitting setting of the drone under stable hovering, so it conforms to the characteristics of the drone wing at a stable rotation speed. Then, the array acoustic imaging acquisition frequency range is adjusted according to each spectrum key point. It should be noted that the acoustic imager can adjust its frequency acquisition area during the detection process. Since the abnormal noise of the circuit facilities is in a larger frequency range, in order to reduce the impact of the UAV wing rotation on the acquisition, a smaller acquisition frequency interval is selected according to the frequency corresponding to each spectrum key point. The acquisition frequency interval is within the abnormal noise frequency range and there is a certain difference between the acquisition frequency interval and the frequency corresponding to the spectrum key point. Therefore, interference can be reduced in the array acoustic imaging process. At the same time, since there are multiple spectrum key points and they are all different, the array acoustic imaging obtained under all spectrum key points is comprehensively analyzed to obtain the target array acoustic imaging, thereby reducing the impact of the UAV wing rotation on the accuracy of the array acoustic imaging while ensuring that the abnormal noise is imaged by the array acoustic imaging.

[0055] In one embodiment, the process of adjusting the acquisition frequency interval of array acoustic imaging according to each spectrum key point includes: dividing the preset acquisition frequency band into N segments; using the formula Calculate the distance between the spectrum key point and the i-th segment ; Select the segment corresponding to the maximum value in the distance value as the acquisition frequency interval of array acoustic imaging; where, is the frequency corresponding to the key point of the spectrum; is the frequency median value of the ith segment. This embodiment provides a method for adjusting the acquisition frequency interval of array acoustic imaging according to each spectrum key point, by equally dividing the preset acquisition frequency band into N segments, wherein the preset acquisition frequency band is set according to the common abnormal noise frequency interval of the circuit facility, and the size of N is selected and set according to the size of the preset acquisition frequency band and the accuracy requirement. The larger N is, the higher the accuracy requirement is. Then, by formula Calculate the distance between the spectrum key point and the i-th segment , the distance value reflects the absolute value of the difference between the frequency corresponding to each spectral key point and the middle value of each segment. Obviously, when the distance value is larger, it means that the array acoustic imaging process is less affected by the UAV wing rotation noise. Therefore, through the above process, the optimal array acoustic imaging acquisition frequency range can be selected for each spectral key point, thereby reducing the impact of the UAV wing rotation on the accuracy of array acoustic imaging.

[0056] In one embodiment, a comprehensive analysis process is provided, including: numerically superimposing and averaging array acoustic imaging obtained at all spectral key points at the same position point to form a target array acoustic imaging. After obtaining array acoustic imaging corresponding to multiple spectral key points, in order to better display the array acoustic imaging, this embodiment numerically superimposes and averages the array acoustic imaging obtained at all spectral key points at the same position point. Since the acoustic imager will mark the target position according to color during the display process, and the more central the position, the more obvious the color, this embodiment numerically superimposes and averages the values ​​corresponding to the colors of different position points, thereby being able to obtain the array acoustic imaging collected at multiple spectral key points, thereby reducing the impact of a single array acoustic imaging on the overall result and improving the accuracy of the inspection result.

[0057] In one embodiment, the environmental data includes the wind force value of the area where the drone is operating and the maximum value of the environmental noise in a preset frequency interval during the collection period, wherein the wind force value of the area where the drone is operating will affect the normal and stable operation of the drone on the one hand, and on the other hand, under a relatively strong wind condition, the noise generated by the operation of the drone will have a relatively large impact on the imaging process of the acoustic imager, so it is necessary to judge the wind force value of the area where the drone is operating; and the maximum value of the environmental noise in the preset frequency interval during the collection period will also have an impact on the imaging process of the acoustic imager, so by collecting and analyzing the above parameters, the interference of the acoustic imaging process of the drone's airborne array is greatly reduced; wherein the process of analyzing the environmental data includes: respectively analyzing the wind force value and the environmental noise The maximum value is compared with the corresponding preset threshold value. It should be noted that the corresponding preset threshold value is set according to the test data fitting of different wind force values ​​and maximum environmental noise values ​​acting on the UAV and the acoustic imager in the empirical data. Therefore, when any one of the wind force value and the maximum environmental noise value is higher than the corresponding preset threshold value, it is judged that the acoustic imaging condition is not met; it should also be noted that when the environmental noise is too large, its position can be judged relatively simply; when the wind force value and the maximum environmental noise value are both lower than the corresponding preset threshold value, the wind force value and the maximum environmental noise value are combined for analysis, and whether the acoustic imaging condition is met is judged according to the analysis result. The normal operation of the acoustic imager can be guaranteed according to the judgment of the overall influence of the two on the acoustic imaging of the acoustic imager array.

[0058] In one embodiment, a combined analysis process is provided, including: The environmental impact value E is calculated; where W is the wind force value, is the wind speed reference value, B is the maximum value of the ambient noise, is the ambient noise baseline value, is the adjustment factor, where the wind force reference value and noise level Both are set based on empirical data, and are used to determine the magnitude of wind force and maximum ambient noise values ​​that exceed normal levels, and to adjust the coefficients The setting is fitted according to the influence of the wind force value and the maximum environmental noise value in the test data. Therefore, the environmental influence value E can be used to obtain the overall influence of the wind force value and the maximum environmental noise value on the acoustic imaging of the acoustic imager array. The environmental influence value E is compared with the environmental threshold Et, and the environmental threshold Et is set after fitting according to the critical values ​​in multiple test data. Therefore, when E<Et, it means that the current environment will have a greater impact on the array acoustic imaging of the acoustic imager, and therefore it is judged that the acoustic imaging conditions are met.

[0059] In one embodiment, the array acoustic imaging method carried by the drone is supplemented, and the method also includes: step 4, collecting infrared imaging of the operating area by the drone, and performing collaborative analysis based on the infrared imaging of the same position and the target array acoustic imaging, obtaining display results and outputting them. Since the acoustic imager also has an infrared imaging function, the combination of the two can facilitate circuit facility maintenance personnel to more quickly determine the location of abnormal noise. The existing acoustic imager will set up a method of displaying the two on the same screen to assist management personnel in comparison. However, this method is not intuitive enough. Therefore, in this embodiment, the infrared imaging of the operating area is collected by the drone, and the infrared imaging of the same position and the target array acoustic imaging are collaboratively analyzed to obtain display results and output them. Refer to the attached Figure 3 , which is a display result diagram after collaborative analysis. With the assistance of the display result diagram, the results obtained by both can be combined to facilitate circuit facility maintenance personnel to quickly determine the location of abnormal noise.

[0060] Among them, the collaborative analysis process includes: aligning the infrared imaging with the target array acoustic imaging, this process ensures the accuracy of the displayed results after combination, marking the regional contour of the target noise in the target array acoustic imaging according to the preset strategy, hiding the target array acoustic imaging, and displaying the regional contour in the infrared imaging to obtain the display result. Through the above process, the risk location point can be obtained by extracting the regional contour in the array acoustic imaging, and it can be displayed in the infrared imaging at the same time, which means that the comparison can be directly viewed when viewing the infrared imaging. Obviously, the above process can be more convenient for the detection process of circuit facility maintenance personnel.

[0061] In one embodiment, a preset strategy is provided, including: Calculate the sound intensity value of the area contour edge ;in, is a preset fixed strength value, which is set according to empirical data. is the maximum sound intensity within the area contour, is the parameter adjustment coefficient, which is obtained after fitting the test data, and the sound intensity value of the edge of the above-mentioned area contour is The calculation process can dynamically adjust the sound intensity value of the edge of the regional contour according to different abnormal noise levels, thereby avoiding the acquisition of an area contour that is too small or too large to be of reference significance, ensuring the rationality of the area contour size. When combined with infrared imaging, it can be more convenient for circuit facility maintenance personnel to detect.

[0062] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. An array acoustic imaging method carried by an unmanned aerial vehicle, characterized in that: The method comprises: Step 1: Collect environmental data before the UAV is operated, analyze the environmental data, and determine whether the conditions for acoustic imaging are met; Step 2: When acoustic imaging conditions are met, the drone is controlled to be in a hovering state, and the noise spectrum of the drone in the hovering state is obtained; Step 3: Identify the noise spectrum of the drone in the hovering state, obtain the spectrum key points of each cycle, adjust the array acoustic imaging acquisition frequency interval according to each spectrum key point, perform comprehensive analysis based on the array acoustic imaging obtained at all spectrum key points, and obtain the target array acoustic imaging; The process of adjusting the acquisition frequency interval of array acoustic imaging according to each spectral key point includes: Divide the preset acquisition frequency band into N segments; By formula Calculate the distance between the spectrum key point and the i-th segment ; The segment corresponding to the maximum value in the distance value is selected as the acquisition frequency interval of array acoustic imaging; in, is the frequency corresponding to the key point of the spectrum; is the median frequency value of the i-th segment; The comprehensive analysis process includes: The array acoustic imaging obtained at all spectral key points at the same position is numerically superimposed and averaged to form the target array acoustic imaging.

2. The array acoustic imaging method for drones according to claim 1, characterized in that: The environmental data includes the wind force value of the area where the drone is operating and the maximum value of the environmental noise in a preset frequency range during the collection period; The process of analyzing environmental data includes: Compare the wind speed value and the maximum ambient noise value with the corresponding preset thresholds respectively: When either the wind force value or the maximum ambient noise value is higher than the corresponding preset threshold, it is determined that the acoustic imaging condition is not met; Otherwise, a combined analysis of the wind force value and the maximum ambient noise value is performed to determine whether the conditions for acoustic imaging are met based on the analysis results.

3. The array acoustic imaging method for drones according to claim 2, characterized in that: The process of the combined analysis includes: By formula Calculate and obtain the environmental impact value E; Where W is the wind force value, is the wind speed reference value, B is the maximum value of the ambient noise, is the ambient noise baseline value, is the adjustment factor; Compare the environmental impact value E with the environmental threshold Et: When E<Et, it is determined that the acoustic imaging conditions are met.

4. The array acoustic imaging method carried by a drone according to claim 1, characterized in that: The method further comprises: Step 4: Collect infrared images of the operation area through the UAV, conduct collaborative analysis based on the infrared images and target array acoustic images of the same location, obtain display results and output them.

5. The array acoustic imaging method carried by a drone according to claim 4, characterized in that: The collaborative analysis process includes: aligning infrared imaging with target array acoustic imaging; Marking the regional contour of the target noise in the target array acoustic imaging according to a preset strategy, and hiding the target array acoustic imaging; The area outline is displayed in the infrared imaging to obtain the display result.

6. The array acoustic imaging method carried by a drone according to claim 5, characterized in that: The preset strategies include: By formula Calculate the sound intensity value of the edge of the area contour ; in, is a preset fixed strength value, is the maximum sound intensity within the area contour, is the tuning coefficient.

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