Method and device for determining unsteady state detection boundary of high-altitude unmanned detection equipment

Abstract

The invention discloses a method and device for determining an unsteady state detection boundary of high-altitude unmanned detection equipment, and relates to the technical field of unmanned detection, and the method comprises the steps: obtaining a detection index of the detection equipment; the detection equipment comprises an overall deformation optical detection module, an abnormal heating and sound positioning module, a non-contact stress detection module and a paint layer surface crack detection module. A detection index is a performance limit which can be reached by detection equipment according to a 100% success rate under a static condition, namely, steady-state detection; the detection indexes comprise detection spacing, deformation detection resolution, minimum sound intensity, minimum heat source resolution, positioning precision, minimum stress resolution, paint layer thickness, minimum crack depth and minimum crack width; and determining an unsteady state detection boundary of the detection equipment based on the detection index of the detection equipment and the corresponding target detection accuracy. According to the invention, the motion index that the unmanned aerial vehicle should have when the specific detection equipment can reach a relatively high accuracy rate can be given.

Description

Technical Field

[0001] This application relates to the field of unmanned detection technology, and particularly to a method and device for determining the non-steady state detection boundary of high-altitude unmanned detection equipment. Background Art

[0002] Large-scale amusement facilities are carriers for creating joy, and their safe operation is crucial for ensuring the safety of tourists' lives and promoting the healthy development of the tourism economy. Currently, large-scale amusement facilities are developing towards larger and more complex directions (Ferris wheels over 120m, other facilities over 50m), bringing new requirements and challenges to inspection and testing as well as safe operation. Existing inspections of super-large amusement facilities mostly involve traditional manual climbing to high altitudes with conventional inspection equipment to conduct close-range operations on the structure, facing difficulties such as hard-to-reach high altitudes and high risks.

[0003] The inspection requirements for the metal structures of super-large amusement facilities mainly include: surface wear and corrosion of metal structures, overall structural deformation or offset caused by foundation settlement and large loads, abnormal noise and heat generation caused by damage to moving and electrical components, and stress concentration, abnormalities, and cracks in key load-bearing parts. Visual or electromagnetic non-destructive testing technologies can be used for surface wear and corrosion. For example, UAV vision inspection has been widely used in the detection of wear and cracking, but the commonly used magnetic flux leakage detection method is limited by its own weight and is difficult to transport to high altitudes for close-range inspection. Moving and electrical components are usually located in non-visible parts such as inside the housing, and indirect detection needs to be achieved by relying on the characteristics brought by faults. For example, infrared cameras and acoustic fingerprint recognition can detect abnormal heat generation and noise associated with faults. Local stress detection usually uses local stress detection devices such as strain gauges, but there are problems with cable connection and signal acquisition. If wireless sensors are used, device installation and maintenance need to be considered. The difficulty in surface crack detection lies in that the metal structures of amusement facilities and the like are usually covered with paint layers and cannot be observed by optical cameras. In addition, it is difficult to detect early cracks with a size of less than 1mm through close-range non-contact detection methods such as ultrasonic, so it is necessary to consider lightweight electromagnetic detection technologies.

[0004] With the popularization of intelligent detection technology and UAV technology, existing high-altitude unmanned detection has begun to use UAVs as the carrier platform for advanced detection equipment to achieve high-altitude unmanned intelligent detection of super-large metal structures under high-altitude operation conditions. High-altitude unmanned detection technology can generally be divided into two categories: remote detection and close-range detection. The former usually uses computer geometric optics methods to measure typical visible defects such as cracks, wear, and corrosion on the surface of metal structures; the latter uses non-destructive testing technologies such as ultrasonic and electromagnetic at the end of the UAV robotic arm to detect invisible defects such as thinning of metal structures or cracks under paint layers. In addition, with the development of high-altitude unmanned detection technology, there have also emerged detection UAVs that integrate remote and close-range detection methods, improving the coverage of detection objects and defects.

[0005] Drones cannot remain completely still or move in a straight line at a constant speed when hovering or inspecting. There are always non-steady-state movements such as jitter, shaking, and dynamic displacement of hovering. The position deviation, speed, and acceleration of the detection equipment will affect the detection results, such as image blur caused by shaking of the captured image, and lift-off effect caused by position change of the approaching probe. Therefore, the detection equipment needs to have the ability to achieve high-accuracy detection under non-steady-state conditions. Existing research results mainly involve methods for detection equipment to resist non-steady-state motion, such as an anti-shake device suitable for camera modules and its signal processing method, and a detection device and signal processing algorithm for compensating for the lift-off change between eddy current probes and tools. However, the former does not give the speed and amplitude of the jitter when the detection system maintains a high accuracy rate; the latter does not give the scope of use of signal compensation.

[0006] Therefore, how to give the motion indicators that a drone should have when a specific detection device (including its signal processing method) can achieve a high accuracy rate has become a technical problem that urgently needs to be solved in this field. Summary of the invention

[0007] The purpose of this application is to provide a method and device for determining the non-steady-state detection boundary of high-altitude unmanned detection equipment, which can be used to specify the motion performance indicators that the drone should have when the detection equipment can achieve a high detection accuracy under non-steady-state motion conditions.

[0008] To achieve the above objectives, this application provides the following solutions:

[0009] In a first aspect, the present application provides a method for determining a non-steady-state detection boundary of high-altitude unmanned detection equipment, the method for determining a non-steady-state detection boundary of high-altitude unmanned detection equipment comprising:

[0010] The detection index of the detection equipment is obtained; the detection equipment includes: an overall deformation optical detection module, an abnormal heating and sound positioning module, a non-contact stress detection module and a paint layer surface crack detection module; the overall deformation optical detection module measures the overall deformation of the structure through a binocular optical camera; the abnormal heating and sound positioning module locates the abnormal sound and heat source emitted by the operating components under fault conditions through an acoustic array sensor and an infrared camera; the non-contact stress detection module measures the target local strain through a passive wireless stress sensing system carried by an unmanned aerial vehicle and a passive wireless sensor installed on the surface of the structure under test; the paint layer surface crack detection module scans the surface cracks of the structure under test through an eddy current crack detection system; the detection index is the performance limit that the detection equipment can achieve with a 100% success rate under static conditions, that is, steady-state detection; the detection index includes: detection spacing, deformation detection resolution, minimum sound intensity, minimum heat source resolution, positioning accuracy, minimum stress resolution, paint layer thickness, minimum crack depth and minimum crack width.

[0011] Determine the non-steady-state detection boundary of the detection equipment based on the detection indexes of the detection equipment and the corresponding target detection accuracy rate.

[0012] In a second aspect, the present application provides a device for determining the non-steady-state detection boundary of an unmanned aerial detection equipment, where the device for determining the non-steady-state detection boundary of the unmanned aerial detection equipment includes:

[0013] A detection index acquisition module, configured to acquire the detection indexes of the detection equipment; the detection equipment includes: an overall deformation optical detection module, an abnormal heat generation and sound localization module, a non-contact stress detection module, and a surface crack detection module with a paint layer; the overall deformation optical detection module measures the overall deformation of the structure through a binocular optical camera; the abnormal heat generation and sound localization module locates the abnormal sound and heat source emitted by the operating components under fault conditions through an acoustic array sensor and an infrared camera; the non-contact stress detection module measures the target local strain through a passive wireless stress sensing system carried by a drone and passive wireless sensors installed on the surface of the structure to be measured; the surface crack detection module with a paint layer scans the surface crack of the structure to be measured through an eddy current crack detection system; the detection index is the performance limit that the detection equipment can achieve with a 100% success rate under static conditions, that is, steady-state detection; the detection indexes include: detection spacing, deformation detection resolution, minimum sound intensity, minimum heat source resolution, positioning accuracy, minimum stress resolution, paint layer thickness, minimum crack depth, and minimum crack width.

[0014] A non-steady-state detection boundary determination module, configured to determine the non-steady-state detection boundary of the detection equipment based on the detection indexes of the detection equipment and the corresponding target detection accuracy rate.

[0015] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for determining the non-steady-state detection boundary of the unmanned aerial detection equipment described in any one of the above.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for determining the non-steady-state detection boundary of the unmanned aerial detection equipment described in any one of the above.

[0017] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method for determining the non-steady-state detection boundary of the unmanned aerial detection equipment described in any one of the above.

[0018] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application:

[0019] The present application provides a method and device for determining the non-steady state detection boundary of high-altitude unmanned detection equipment. The method includes: obtaining the detection indexes of the detection equipment; the detection equipment includes: an overall deformation optical detection module, an abnormal heat and sound positioning module, a non-contact stress detection module, and a surface crack detection module with a paint layer; the overall deformation optical detection module measures the overall deformation of the structure through a binocular optical camera; the abnormal heat and sound positioning module locates the abnormal sound and heat source emitted by the operating components under fault conditions through an acoustic array sensor and an infrared camera; the non-contact stress detection module measures the local strain of the target through a passive wireless stress sensing system carried by a drone and a passive wireless sensor installed on the surface of the structure to be measured; the surface crack detection module with a paint layer scans the surface cracks of the structure to be measured through an eddy current crack detection system; the detection indexes are the performance limits that the detection equipment can reach with a 100% success rate under static conditions, that is, steady state detection; the detection indexes include: detection distance, deformation detection resolution, minimum sound intensity, minimum heat source resolution, positioning accuracy, minimum stress resolution, paint layer thickness, minimum crack depth, and minimum crack width; based on the detection indexes of the detection equipment and the corresponding target detection accuracy rate, the non-steady state detection boundary of the detection equipment is determined. The present application provides a method for determining the implementation boundary of high-altitude unmanned detection for the errors caused by the shaking of the drone during high-altitude unmanned detection, and can give the motion indexes that the drone should have when a specific detection device (including its signal processing method) can achieve a high accuracy rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a schematic flow chart of a method for determining the non-steady state detection boundary of high-altitude unmanned detection equipment provided by an embodiment of the present application.

[0022] Figure 2 It is a schematic diagram of a method for determining the non-steady state detection boundary of the overall deformation optical detection module provided by an embodiment of the present application.

[0023] Figure 3 It is a schematic signal processing flow chart of the overall deformation optical detection module provided by an embodiment of the present application.

[0024] Figure 4 It is a schematic diagram of a method for determining the non-steady state detection boundary of the abnormal heat and sound positioning module provided by an embodiment of the present application.

[0025] Figure 5 Schematic diagram of the signal processing flow of the abnormal heat generation and sound localization module provided by an embodiment of the present application.

[0026] Figure 6 Schematic diagram of the method for determining the non-steady state detection boundary of the non-contact stress detection module provided by an embodiment of the present application.

[0027] Figure 7 Schematic diagram of the signal processing flow of the non-contact stress detection module provided by an embodiment of the present application.

[0028] Figure 8 Schematic diagram of the method for determining the non-steady state detection boundary of the surface crack detection module with a paint layer provided by an embodiment of the present application.

[0029] Figure 9 Schematic diagram of the signal processing flow of the surface crack detection module with a paint layer provided by an embodiment of the present application.

[0030] Figure 10 Schematic diagram of the functional modules of a device for determining the non-steady state detection boundary of an unmanned aerial detection equipment provided by an embodiment of the present application.

[0031] Figure 11 Schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0032] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0033] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0034] As an emerging technology, high-altitude unmanned detection lacks dedicated high-altitude unmanned detection equipment, and there is no method for determining the non-steady-state detection performance indicators of drones and detection equipment. On the one hand, the detection technology needs to improve the electromechanical system design and optimize the signal processing method to enhance the ability to resist non-steady-state effects; on the other hand, the motion performance and stability of the drone should also be constrained and improved so that the detection equipment has a high accuracy rate. The existing technology only starts from the detection equipment and gives methods to improve the equipment's resistance to non-steady-state motion, but lacks the analysis of the change in the accuracy rate of the detection results caused by the drone's motion, and does not limit the motion indicators of the drone according to the requirements of the detection equipment.

[0035] In view of the errors caused by the shaking of the drone during high-altitude unmanned detection, this application provides a method for determining the implementation boundary of high-altitude unmanned detection, which can give the motion indicators that the drone should have when a specific detection equipment (including its signal processing method) can achieve a high accuracy rate.

[0036] In this application, a high-altitude unmanned intelligent detection equipment for super-large metal structures can be divided into the following four modules according to its functions:

[0037] The overall deformation optical detection module measures the overall deformation of the super-large structure through binocular optical cameras.

[0038] The abnormal heat generation and sound localization module quickly locates the abnormal sounds and heat sources emitted by the operating components under fault conditions through an acoustic array sensor and an infrared camera.

[0039] The non-contact stress detection module measures the local strain of the target through a passive wireless stress sensing system carried by the drone and passive wireless sensors installed on the surface of the structure to be measured.

[0040] The surface crack detection module with a paint layer scans the surface cracks of the structure to be measured through an eddy current crack detection system.

[0041] Among them, the detection equipment should have dynamic detection energy so as to achieve a high detection accuracy rate under the condition of non-steady-state motion of the drone.

[0042] Step 1: First, analyze the influence of the non-steady-state motion of the drone on the detection equipment and list the methods to eliminate errors. The significance of this step is to clarify the influencing factors of the non-steady-state motion on the detection equipment, so as to design targeted experiments to determine the non-steady-state boundary of specific dynamic indicators. The results obtained in this step are dynamic indicators, such as positioning error, speed, and acceleration. For example, the overall deformation measurement of the super-large structure allows a relatively high positioning error of the binocular optical camera during detection, but too fast a motion speed will cause blurring, so the non-steady-state detection boundary of this detection equipment is speed. When the detection module shakes randomly with the drone, the influence on each detection module is as follows:

[0043] When a binocular optical camera measures the overall deformation of an ultra-large metal structure, it is required to remain stationary. If there is jitter, it will cause the position of the edge or feature points of the target object to shift in the image, thereby introducing deformation error. When measuring the continuous deformation of an object, misjudgment of the structure's mode will also occur due to the error introduced by jitter. Methods to reduce the error of binocular optical cameras include hardware means such as adding an anti-jitter gimbal between the detection equipment and the drone, or using dynamic deblurring algorithms such as deep learning algorithms or optical flow methods. The non-steady detection boundary of binocular optical cameras is speed.

[0044] When an infrared camera locates the position of an abnormal heat source, it is required to remain stationary. If there is jitter, it will also cause image blurring and heat source position shift. When a sound array sensor jitters during the location of an abnormal sound source, it will cause a phase difference in the sound wave signals received by the microphone array, thereby causing positioning error. Methods to reduce the positioning error of abnormal heat sources and sound sources include hardware methods such as anti-jitter gimbals, and signal processing methods such as multi-frame image averaging and multi-source signal fusion positioning. The non-steady detection boundary of infrared cameras and sound array sensors is speed.

[0045] When a passive wireless stress sensing system measures the stress of a measured structure, it is required to be stationary. If there is jitter, it will cause the distance between the receiving coil and the detection coil to change, making the measured reflection coefficient change over time, thereby causing random jitter in the frequency-domain signal. This noise will cause an error in the resonant frequency when identifying the resonant frequency of the passive wireless stress sensor. Methods to reduce the stress detection error include increasing the single-frequency sampling speed of the network analyzer. At the same time, signal processing algorithms such as averaging multiple frequency-domain signals and fitting the characteristic function of the frequency-domain signal can also improve the accuracy of the resonant frequency. The non-steady detection boundaries of passive wireless stress sensing systems are distance and speed.

[0046] When an eddy current crack detection system scans, it is required that the eddy current array probe closely adheres to the surface of the measured object and slowly translates. If there is jitter perpendicular to the surface of the measured structure, it will cause the distance between the eddy current array probe and the surface of the measured structure to increase, change the impedance of the detection coil, and cause signal jumps. If the probe translation speed is relatively fast, it will also cause the loss of micro-crack signals. Methods to reduce the signal noise caused by the change in distance include hardware methods such as increasing the suction force of the permanent magnet at the end of the approaching probe, control methods such as force feedback control of the drone robotic arm, and signal processing methods such as the impedance lift-off compensation algorithm for eddy current signals. Methods to avoid signal loss caused by relatively fast movement speed include increasing the signal processing speed and detection sensitivity of the eddy current detection circuit. The non-steady detection boundaries of eddy current crack detection systems are lift-off (distance) and scan speed.

[0047] Step 2: To ensure relatively reliable detection results for high-altitude UAV detection, it is necessary to determine the non-steady-state detection implementation boundary of a detection module under current hardware (e.g., mechanical and circuit design), software (e.g., signal processing and control algorithms), and parameter configuration conditions. The definition of the non-steady-state detection implementation boundary is as follows: Under the extreme conditions of the detection equipment's technical indicators, the motion indicators that the UAV should have when carrying this detection module for detection to achieve a specific detection accuracy rate, including the hovering jitter speed and position deviation of the UAV, the jitter and moving speed of the end of the robotic arm, etc.

[0048] The detection accuracy rate k can be taken as 0.9, that is, there is a 90% probability of detecting defects; the uncertainty of detection is determined by the randomness of the signal and the environmental signal-to-noise ratio. When the UAV motion indicators exceed the implementation boundary, the detection accuracy rate of the detection module under the extreme conditions of the detection indicators will decrease. This patent presents a method for determining the non-steady-state boundaries of four detection modules in a super-large metal structure high-altitude unmanned intelligent detection equipment, and all use the conditions of good lighting, low electromagnetic and noise interference, and constant temperature and humidity as the experimental environment.

[0049] In an exemplary embodiment, as Figure 1 shown, a method for determining the non-steady-state detection boundary of a high-altitude unmanned detection equipment is provided, and the method includes the following steps:

[0050] S1: Obtain the detection indicators of the detection equipment; the detection equipment includes: an overall deformation optical detection module, an abnormal heat generation and sound localization module, a non-contact stress detection module, and a surface crack detection module with a paint layer; the overall deformation optical detection module measures the overall deformation of the structure through a binocular optical camera; the abnormal heat generation and sound localization module locates the abnormal sounds and heat sources emitted by the operating components under fault conditions through an acoustic array sensor and an infrared camera; the non-contact stress detection module measures the local strain of the target through a passive wireless stress sensing system carried by the UAV and passive wireless sensors installed on the surface of the structure to be measured; the surface crack detection module with a paint layer scans the surface cracks of the structure to be measured through an eddy current crack detection system; the detection indicators are the performance limits that the detection equipment can reach with a 100% success rate under static conditions, that is, steady-state detection; the detection indicators include: detection spacing, deformation detection resolution, minimum sound intensity, minimum heat source resolution, positioning accuracy, minimum stress resolution, paint layer thickness, minimum crack depth, and minimum crack width.

[0051] S2: Based on the detection indicators of the detection equipment and the corresponding target detection accuracy rate, determine the non-steady-state detection boundary of the detection equipment.

[0052] Implementing the above steps S1 to S2 can be used to specify the motion performance indicators that the UAV should have when the detection module can achieve a relatively high detection accuracy rate under non-steady-state motion conditions.

[0053] As an alternative implementation, when the detection equipment is an overall deformation optical detection module, based on the detection indexes of the detection equipment and the corresponding target detection accuracy rate, determine the non-steady state detection boundary of the detection equipment, specifically including:

[0054] A1: Determine the target cantilever beam deflection based on the detection distance from the binocular optical camera lens to the structure to be measured, the length of the cantilever beam, and the overall deformation detection resolution.

[0055] A2: Determine the moving speed and translation range of the sliding platform, and generate a first reciprocating motion instruction.

[0056] A3: Based on the first reciprocating motion instruction, calculate a number of deflections of the cantilever beam to be measured according to the operation process and signal processing algorithm of the binocular optical camera.

[0057] A4: Based on a number of deflections of the cantilever beam to be measured and the target cantilever beam deflection, calculate the first measured detection accuracy rate.

[0058] A5: When the first measured detection accuracy rate is greater than the target detection accuracy rate of the overall deformation optical detection module, generate an instruction to increase the moving speed of the sliding platform until the first measured detection accuracy rate is equal to the target detection accuracy rate of the overall deformation optical detection module.

[0059] A6: Take the moving speed of the sliding platform at this time as the upper bound of the non-steady state boundary of the overall deformation optical detection module, and take 0 as the lower bound of the non-steady state boundary of the overall deformation optical detection module.

[0060] For detection modules such as binocular optical cameras, infrared cameras, acoustic array sensors, and passive wireless stress detection modules that require fixed-point hovering, a high-speed sliding guide rail 501 is needed to simulate the acceleration, speed, and distance generated when the drone jitters.

[0061] The detection indexes of the overall deformation optical detection module are the detection distance and the deformation detection resolution, and the non-steady state detection index is the camera jitter speed. (The detection indexes are experimental conditions and are all constants. The non-steady state detection index is an unknown quantity to be determined in the experiment. For this module, the detection distance: the distance L from the binocular optical camera lens to the structure to be measured 1 ; the camera jitter speed: the moving speed v of the sliding platform) The method for determining the non-steady state boundary when the target detection accuracy rate is k is as follows, see Figure 2 .

[0062] ① Experimental setup. Fix the cantilever beam experimental device 502 with a length of l 0 and place the high-speed sliding guide rail horizontally. Fix the binocular optical camera above the sliding platform, and the distance from the binocular camera lens to the structure to be measured is L 1If the camera is equipped with a pan-tilt head, fix the pan-tilt head together with the camera.

[0063] ② Detection object setting. According to the overall deformation detection resolution α, which is the minimum resolvable resolution in the stationary state, calculate the deflection d of the cantilever beam 0 = l 0 α, and apply an appropriate weight to cause such an offset at the end of the cantilever beam.

[0064] ③ Unsteady state motion setting. Set the motion speed of the sliding platform to v and the translation range to s, and start reciprocating motion.

[0065] ④ Signal acquisition. According to the operation process of the binocular optical camera and the signal processing algorithm, calculate the deflection of the measured cantilever beam. For example Figure 3 , the method is as follows: a. Use a checkerboard calibration board to calibrate the internal parameters of the camera to obtain the internal parameters of the camera (such as focal length, principal point position, distortion coefficient) and external parameters (the pose of the camera relative to the calibration board); b. Calculate the distance between the spatial coordinates of the target pixel and the visual center point of the binocular camera by using the parallax method through the matching of the same feature points on the left and right images; c. Extract the optical flow between two frames of images based on the Farnerback method, calculate the overall pixel displacement of the image frame, so as to eliminate the image displacement caused by camera jitter and reduce the deformation measurement error caused by jitter; d. Based on the otsu segmentation algorithm, automatically segment the target object in the area to be analyzed for continuous frames to generate a binary mask image; e. Extract the coordinates of the center point of the target binary mask image, track the displacement between different frames relative to the original center point, and convert the scale measured in the image coordinate system to the three-dimensional camera coordinate system based on the calibrated parameters of the camera to calculate the deformation value. If in N consecutive detections, the number of times the offset can be accurately detected is M, then the measured detection accuracy rate is k' = M / N.

[0066] ⑤ Boundary determination. If the measured detection accuracy rate is greater than the target detection accuracy rate, that is, k' > k, then increase the motion speed of the sliding platform until k' = k. The motion speed at this time is the upper bound of the unsteady state boundary of the binocular optical camera, and the lower bound is 0.

[0067] As an alternative implementation, when the detection equipment is an abnormal heat and sound localization module, based on the detection indicators of the detection equipment and the corresponding target detection accuracy rate, determine the unsteady state detection boundary of the detection equipment, specifically including:

[0068] B1: Based on the detection distance from the surface of the acoustic array sensor to the circular ring positioning target, the laboratory ambient noise, the ambient temperature, the minimum sound intensity, and the minimum heat source resolution, determine the sound intensity of the artificial sound source and the temperature of the artificial heat source.

[0069] B2: Determine the motion speed and translation range of the sliding platform, and generate a second reciprocating motion command.

[0070] B3: Based on the second reciprocating motion instruction, calculate the positions of several abnormal heat sources and sound sources according to the operation process and signal processing algorithm of the multi-source positioning fusion system composed of an infrared camera and an acoustic array sensor.

[0071] B4: Calculate the second measured detection accuracy rate based on the positions of several abnormal heat sources and sound sources, the sound intensity of the artificial sound source, and the temperature of the artificial heat source.

[0072] B5: When the second measured detection accuracy rate is greater than the target detection accuracy rate of the abnormal heat generation and sound localization module, generate an instruction to increase the movement speed of the sliding platform until the second measured detection accuracy rate is equal to the target detection accuracy rate of the abnormal heat generation and sound localization module.

[0073] B6: Take the movement speed of the sliding platform at this time as the upper bound of the non-steady state boundary of the abnormal heat generation and sound localization module, and take 0 as the lower bound of the non-steady state boundary of the abnormal heat generation and sound localization module.

[0074] The detection indexes of the abnormal heat generation and sound localization module are detection distance, minimum sound intensity, minimum heat source resolution, and positioning accuracy. The non-steady state detection index is the jitter speed of the device. (Detection distance: The distance between the surface of the acoustic array sensor and the circular ring positioning target is L 2 . Minimum sound intensity: According to the minimum sound intensity A given by the detection module min Set the sound intensity of the artificial sound source to A 0 +A min ; Minimum heat source resolution: Set the temperature of the artificial heat source to T according to the minimum heat source resolution ΔT 0 +ΔT; Positioning accuracy: The inner ring radius is equal to the positioning accuracy of the detection module. If the position of the positioning point is within the inner ring of the circular ring positioning target, it is considered accurate). The method for determining the non-steady state boundary with a target detection accuracy rate of k is as follows, see Figure 4 .

[0075] ① Experimental setup. Coincide the artificial sound source 503 and the artificial heat source 504 and place them in the inner ring of the circular ring positioning target 505. The inner ring radius is equal to the positioning accuracy of the detection module. Stack the infrared camera and the acoustic array sensor vertically and fix them above the sliding platform. The distance between the surface of the acoustic array sensor and the circular ring positioning target is L 2 . If the device is equipped with a pan-tilt, fix the pan-tilt and the detection device together.

[0076] ② Detection object setup. The ambient noise in the laboratory is A 0 , the ambient temperature is T 0 , and according to the minimum sound intensity A given by the detection module min Set the sound intensity of the artificial sound source to A0 +A min , set the temperature of the artificial heat source as T according to the minimum heat source resolution ΔT 0 +ΔT.

[0077] ③ Unsteady motion setting. Set the motion speed of the sliding platform as v, the translation range as s, and start reciprocating motion.

[0078] ④ Signal acquisition. Calculate the positions of the abnormal heat source and the sound source according to the operation process and signal processing algorithm of the multi-source positioning fusion system composed of the infrared camera and the acoustic array sensor. For example Figure 5 , the method is as follows: a. Take the infrared camera as the reference coordinate system, and the infrared camera and the acoustic array sensor respectively measure the elevation angle and azimuth angle of the heat source and the sound source; during the signal processing, the infrared camera extracts the abnormal source area as the analysis area, and the acoustic array sensor extracts the sound source frequency through short-time Fourier transform and filters the remaining frequency bands; b. According to the relative position between the infrared camera lens and the center of the acoustic array sensor, transform the elevation angle and azimuth angle measured by the acoustic array sensor to the reference coordinate system; c. Perform infrared and sound field fusion positioning, and the position of the abnormal source is, where are the angle coordinates given by the infrared camera and the acoustic array sensor, and are the weights and ; the weight is inversely proportional to the noise of the positioning point, that is, the more stable the measurement method of the given coordinates, the more credible it is, and 0 if it cannot be positioned. If in N consecutive detections, the number of times the position of the positioning point is in the inner ring of the circular positioning target is M, then the measured detection accuracy rate is k' = M / N.

[0079] ⑤ Boundary determination. If the measured detection accuracy rate is greater than the target detection accuracy rate, that is, k' > k, then increase the motion speed of the sliding platform until k' = k. The motion speed at this time is the upper bound of the unsteady boundary of the abnormal heat source and sound source positioning module, and the lower bound is 0.

[0080] As an optional implementation manner, when the detection equipment is a non-contact stress detection module, based on the detection indexes of the detection equipment and the corresponding target detection accuracy rate, determine the unsteady detection boundary of the detection equipment, specifically including:

[0081] C1: Determine the minimum stress resolution based on the detection distance between the detection coil and the receiving coil and the tensile stress on the metal surface.

[0082] C2: Determine the motion speed and translation range of the sliding platform, and generate the third reciprocating motion instruction.

[0083] C3: Based on the third reciprocating motion instruction, calculate a number of resonant frequencies according to the frequency domain signal processing algorithm and anti-jitter algorithm of the passive wireless sensor.

[0084] C4: Calculate the third measured detection accuracy rate based on a number of the resonant frequencies and the minimum stress resolution.

[0085] C5: Establish a first two-dimensional two-factor table; the rows and columns of the first two-dimensional two-factor table are the translation range and the moving speed of the sliding platform respectively.

[0086] C6: Generate instructions to alternately change the translation range and the moving speed of the sliding platform, and determine the points in the first two-dimensional two-factor table where the third measured detection accuracy rate is greater than the target detection accuracy rate of the non-contact stress detection module.

[0087] C7: Generate instructions to alternately change the translation range and the moving speed of the sliding platform again until all the points in the first two-dimensional two-factor table that satisfy the third measured detection accuracy rate being equal to the target detection accuracy rate of the non-contact stress detection module are determined.

[0088] C8: Take the curve obtained by fitting all the points in the first two-dimensional two-factor table that satisfy the third measured detection accuracy rate being equal to the target detection accuracy rate of the non-contact stress detection module as the upper bound of the non-steady state boundary of the non-contact stress detection module, and take 0 as the lower bound of the non-steady state boundary of the non-contact stress detection module.

[0089] The detection indexes of the non-contact stress detection module are the detection distance and the minimum stress resolution, and the non-steady state detection indexes are the range of distance change and the speed of distance change. (Detection distance: The distance between the detection coil and the receiving coil is the detection distance index L of the sensor 3 . Minimum stress resolution: Determine the weight of the heavy object applied to the end of the cantilever beam. After applying the heavy object, the tensile stress on the metal surface at the sensor installation position is numerically equal to the minimum stress resolution of the stress detection module; Range of distance change: The range of distance change s in the two-factor experiment, the moving distance of the sliding platform, which determines the distance between the coils; Speed of distance change: The speed of distance change v in the two-factor experiment, the moving speed of the sliding platform, which determines the moving speed of the detection coil) The method for determining the non-steady state boundary with a target detection accuracy rate of k is as follows, see Figure 6 .

[0090] ① Experimental setup. The sensor composed of the receiving coil and the passive wireless stress sensor is adhered to the upper surface of the cantilever beam experimental device. The high-speed sliding navigation is placed vertically. The device composed of the compensation circuit, the network analyzer, and the detection coil is fixed above the sliding platform. Adjust the position of the guide rail so that the detection coil and the receiving coil are coaxial, and the distance between them is the detection distance index L of the sensor 3 .

[0091] ② Detection object setup. There are two states of the stress on the measured surface: no load and load. In the load state, a heavy object is applied to the end of the cantilever beam so that the tensile stress on the metal surface at the sensor installation position is numerically equal to the minimum stress resolution of the non-contact stress detection module.

[0092] ③ Unsteady motion setting. Set the moving speed of the sliding platform to v, the translation range to s, and start reciprocating motion.

[0093] ④ Signal acquisition. According to the frequency-domain signal processing algorithm and anti-jitter algorithm of the passive wireless sensor, extract the peak points of the frequency-domain characteristic signal curve, that is, the resonant frequency. As Figure 7 , the method is as follows: a. Search for the approximate frequency of the resonant peak signal in a wide frequency band, narrow the bandwidth and change to a narrow frequency band to search and extract high-resolution frequency-domain signals; b. Use the average value method to eliminate the noise caused by the change in spacing, and take the average value of continuous K measurement data as the output signal; c. Use the full characteristic signal extraction algorithm, use the second-order rational function as the fitting function to fit the output signal, and extract the minimum value of the sensor characteristic peak reflection coefficient in the fitting curve as the resonant frequency.

[0094] During the experiment, repeat the process of loading and unloading to make the stress at the sensor installation position change periodically. If in continuous N repeated experiments, the number of times the stress change can be correctly identified is M, then the measured detection accuracy rate is k' = M / N.

[0095] ⑤ Boundary determination. Conduct a two-factor experiment. The rows and columns of the established two-dimensional two-factor table are the translation range s and the moving speed v of the sliding platform respectively. Since the signal error of the non-contact stress detection module is the smallest when it is stationary, the lower bounds of the unsteady state boundaries of both the moving speed and the spacing change speed are 0, and the upper bounds are to be determined. By alternately changing the two-factor variables s and v, find the points in the table where the measured detection accuracy rate is greater than the target detection accuracy rate, that is, k' > k, until all the points satisfying the condition k' = k in the table are found. At this time, the upper bounds of the unsteady state boundaries of the moving speed and the spacing change speed are the curve f(s, v) = 0 obtained by fitting the points satisfying the condition k' = k.

[0096] As an optional implementation manner, when the detection equipment is a surface crack detection module with a paint layer, based on the detection indexes of the detection equipment and the corresponding target detection accuracy rate, determine the unsteady state detection boundary of the detection equipment, specifically including:

[0097] D1: Based on the paint layer thickness, determine the minimum crack depth and the minimum crack width.

[0098] D2: Determine the scanning speed, the moving speed of the sliding platform, and the translation range, and generate a fourth reciprocating motion instruction.

[0099] D3: Based on the fourth reciprocating motion instruction, process the crack detection signal according to the lift-off compensation algorithm of the eddy current array sensor, and calculate the depths and widths of several cracks.

[0100] D4: Calculate a fourth measured detection accuracy rate based on the depths and widths of several of the cracks, as well as the minimum crack depth and minimum crack width.

[0101] D5: Establish a second two-dimensional two-factor table; the rows and columns of the second two-dimensional two-factor table are the scanning speed and the liftoff change, respectively.

[0102] D6: Generate instructions to alternately change the scanning speed and the liftoff change, and determine the points in the second two-dimensional two-factor table where the fourth measured detection accuracy rate is greater than the target detection accuracy rate of the painted surface crack detection module.

[0103] D7: Generate instructions to alternately change the scanning speed and the liftoff change again until it is determined that all points in the second two-dimensional two-factor table satisfy that the fourth measured detection accuracy rate is equal to the target detection accuracy rate of the painted surface crack detection module.

[0104] D8: Use the curve obtained by fitting all the points in the second two-dimensional two-factor table that satisfy that the fourth measured detection accuracy rate is equal to the target detection accuracy rate of the painted surface crack detection module as the non-steady state boundary of the painted surface crack detection module.

[0105] The detection indexes of the painted surface crack detection module are the paint layer thickness, the minimum crack depth and width, and the non-steady state detection indexes are the liftoff change and the scanning speed. (Paint layer thickness: a characteristic of the crack standard part, which determines the detection liftoff and affects the amplitude of the signal base value. The higher the liftoff, the smaller the amplitude and the more difficult it is to be detected; minimum crack depth: a characteristic of the crack standard part, the deeper the depth, the more obvious the signal and the easier it is to be detected; minimum crack width: a characteristic of the crack standard part, the wider the width, the faster the processing speed required by the detection equipment and the more difficult it is to completely measure the crack size. If the probe translation speed is too fast, only part of the eddy current coil will sweep over the crack, and the detected crack size will be smaller than the actual crack size, which does not belong to correct identification) The method for determining the non-steady state boundary to achieve a target detection accuracy rate of k is as follows, see Figure 8 。

[0106] ① Experimental setup. Place the high-speed sliding navigation horizontally, fix the robotic arm above the sliding platform, and mount the proximity probe at the end of the robotic arm.

[0107] ② Detection object setup. Vertically place the crack standard part with several standard cracks on its surface, with a depth of h and a width of w.

[0108] ③ Non-steady state motion setup. Control the joints of the robotic arm to make the proximity probe slide up and down along the surface of the crack standard part, maintaining the scanning speed v s. To control the lift-off change Δh, it is necessary to set the moving speed of the sliding platform to v, the translation range to s, and start reciprocating motion. By controlling the robotic arm, keep the proximity probe in contact with the crack standard as much as possible.

[0109] ④ Signal acquisition. Process the crack detection signals according to the lift-off compensation algorithm of the eddy current array sensor. As Figure 9 , the method is as follows: a. First, establish a signal feature library. For different lift-off heights, record the signals when the probe sweeps over the defect-free area as the base values, and record the signal amplitudes of different depth defects as the characteristic values of this depth to form a two-dimensional mapping matrix; b. During detection, use wavelet transform to identify the defect characteristic signals, and take the signal amplitude adjacent to the defect signal as the corresponding base value; c. Find the lift-off height corresponding to the base value of the defect signal, and find the defect depth of the amplitude of the defect signal from the signal feature library. During the experiment, control the robotic arm to make the proximity probe slide repeatedly in the crack area. If the probe sweeps over the crack N times during the experiment, and the number of times the crack can be correctly identified is M, then the measured detection accuracy rate is k’ = M / N.

[0110] ⑤ Boundary determination. Conduct a two-factor experiment. The rows and columns of the established two-dimensional two-factor table are the scanning speed v s and the lift-off change Δh. Measure the lift-off change at the end of the proximity probe under different sliding platform speeds and translation ranges, and indirectly obtain Δh; to accurately measure the lift-off, a spring-type piezoresistive displacement sensor can be added to the front end of the proximity probe. Since the eddy current array sensor is a dynamic detection device, too slow a scanning speed will result in the inability to identify crack signals. Therefore, there are non-zero non-steady state detection boundary lower and upper bounds for the scanning speed v s at the same time, and the lower bound of the non-steady state boundary of the lift-off change is 0. By alternately changing the two-factor variables v s and Δh, find the points in the table where the measured detection accuracy rate is greater than the target detection accuracy rate, that is, k’ > k, until all points satisfying the condition k’ = k in the table are found. At this time, the non-steady state boundaries of the scanning speed v s and the lift-off change Δh are the curves f(v s , Δh) = 0 obtained by fitting the points satisfying the condition k’ = k.

[0111] This application proposes a method for determining the non-steady state boundary of an unmanned high-altitude detection equipment, which can be used to determine the upper and lower limits of the motion parameters of the unmanned aerial vehicle when different detection devices are carried by the unmanned aerial vehicle.

[0112] This application first proposed the concept and experimental determination method of the unsteady detection boundary, while the existing methods have not involved the research on the accuracy rate of the detection module due to the unsteady movement of drones and the corresponding methods. The unsteady boundary determination method proposed in this application can also be applied to other types of high-altitude unmanned detection modules, such as ultrasonic and magnetic flux leakage detection. This application gives the determination method of the upper and lower bounds of the unsteady boundary. For static detection equipment, the lower bound of all motion indicators is 0, and the upper bound can be determined by gradually increasing the speed and motion range of the sliding platform equipped with the detection module. For dynamic detection equipment, such as eddy current and magnetic flux leakage detection equipment, there are non-zero upper and lower bounds, and gradually increasing the dynamic parameters of the experimental device can obtain the detection accuracy rate that first increases and then decreases.

[0113] Based on the same inventive concept, an embodiment of this application also provides a device for determining the unsteady detection boundary of a high-altitude unmanned detection equipment for implementing the method for determining the unsteady detection boundary of the high-altitude unmanned detection equipment involved above. The implementation solution provided by this device to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for determining the unsteady detection boundary of the high-altitude unmanned detection equipment provided below can refer to the limitations on the method for determining the unsteady detection boundary of the high-altitude unmanned detection equipment in the above text, and will not be repeated here.

[0114] In an exemplary embodiment, as Figure 10 shown, a device for determining the unsteady detection boundary of a high-altitude unmanned detection equipment is provided. The device for determining the unsteady detection boundary of the high-altitude unmanned detection equipment includes:

[0115] A detection index acquisition module M1, configured to acquire the detection indexes of the detection equipment; the detection equipment includes: an overall deformation optical detection module, an abnormal heat generation and sound localization module, a non-contact stress detection module, and a surface crack detection module with a paint layer; the overall deformation optical detection module measures the overall deformation of the structure through a binocular optical camera; the abnormal heat generation and sound localization module locates the abnormal sound and heat source emitted by the operating components under fault conditions through an acoustic array sensor and an infrared camera; the non-contact stress detection module measures the local strain of the target through a passive wireless stress sensing system carried by the drone and passive wireless sensors installed on the surface of the measured structure; the surface crack detection module with a paint layer scans the surface cracks of the measured structure through an eddy current crack detection system; the detection index is the performance limit that the detection equipment can reach with a 100% success rate under static conditions, that is, steady-state detection; the detection indexes include: detection spacing, deformation detection resolution, minimum sound intensity, minimum heat source resolution, positioning accuracy, minimum stress resolution, paint layer thickness, minimum crack depth, and minimum crack width.

[0116] The non-steady state detection boundary determination module M2 is used to determine the non-steady state detection boundary of the detection equipment based on the detection indexes of the detection equipment and the corresponding target detection accuracy rate.

[0117] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 11 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store detection indexes. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for determining the non-steady state detection boundary of an unmanned aerial detection equipment.

[0118] Those skilled in the art can understand that Figure 11 the structure shown in

[0119] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0120] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above-mentioned method embodiments are implemented.

[0121] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the above-mentioned method embodiments are implemented.

[0122] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0123] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0124] The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., and are not limited thereto.

[0125] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0126] In this article, specific examples are used to elaborate on the principles and implementation manners of this application. The descriptions of the above embodiments are only used to help understand the method and its core idea of this application. At the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for determining the non-steady-state detection boundary of high-altitude unmanned detection equipment, characterized in that: The method for determining the non-steady-state detection boundary of the high-altitude unmanned detection equipment includes: Obtain the detection index of the detection equipment; the detection equipment includes: an overall deformation optical detection module, an abnormal heating and sound positioning module, a non-contact stress detection module and a paint layer surface crack detection module; the overall deformation optical detection module measures the overall deformation of the structure through a binocular optical camera; the abnormal heating and sound positioning module locates the abnormal sound and heat source emitted by the operating components under fault conditions through an acoustic array sensor and an infrared camera; the non-contact stress detection module measures the target local strain through a passive wireless stress sensing system carried by an unmanned aerial vehicle and a passive wireless sensor installed on the surface of the structure under test; the paint layer surface crack detection module scans the surface cracks of the structure under test through an eddy current crack detection system; the detection index is the performance limit that the detection equipment can achieve with a 100% success rate under static conditions, that is, steady-state detection; the detection index includes: detection spacing, deformation detection resolution, minimum sound intensity, minimum heat source resolution, positioning accuracy, minimum stress resolution, paint layer thickness, minimum crack depth and minimum crack width; Based on the detection index of the detection equipment and the corresponding target detection accuracy, a non-steady-state detection boundary of the detection equipment is determined.

2. The method for determining the non-steady-state detection boundary of high-altitude unmanned detection equipment according to claim 1 is characterized in that: When the detection equipment is an overall deformation optical detection module, determining the non-steady-state detection boundary of the detection equipment based on the detection index of the detection equipment and the corresponding target detection accuracy rate specifically includes: Determine the target cantilever beam deflection based on the detection distance from the binocular optical camera lens to the structure under test, the cantilever beam length and the overall deformation detection resolution; Determine the movement speed and translation range of the sliding platform, and generate a first reciprocating motion instruction; Based on the first reciprocating motion instruction, according to the operation process and signal processing algorithm of the binocular optical camera, several deflections of the cantilever beam to be measured are calculated; Based on the plurality of measured cantilever beam deflections and the target cantilever beam deflections, a first measured detection accuracy rate is calculated; When the first measured detection accuracy is greater than the target detection accuracy of the overall deformation optical detection module, an instruction to increase the movement speed of the sliding platform is generated until the first measured detection accuracy is equal to the target detection accuracy of the overall deformation optical detection module; The movement speed of the sliding platform at this time is taken as the upper limit of the non-steady-state boundary of the overall deformation optical detection module, and 0 is taken as the lower limit of the non-steady-state boundary of the overall deformation optical detection module.

3. The method for determining the non-steady-state detection boundary of high-altitude unmanned detection equipment according to claim 2 is characterized in that: The calculation formula of the target cantilever beam deflection is: d0＝l0α； Among them, d0 is the target cantilever beam deflection, l0 is the length of the cantilever beam experimental device, and α is the overall deformation detection resolution.

4. The method for determining the non-steady-state detection boundary of high-altitude unmanned detection equipment according to claim 1 is characterized in that: When the detection equipment is an abnormal heating and sound localization module, determining the non-steady-state detection boundary of the detection equipment based on the detection index of the detection equipment and the corresponding target detection accuracy rate specifically includes: Based on the detection distance from the surface of the acoustic array sensor to the circular positioning target, the laboratory environmental noise, the ambient temperature, the minimum sound intensity and the minimum heat source resolution, the sound intensity of the artificial sound source and the temperature of the artificial heat source are determined; Determine the movement speed and translation range of the sliding platform, and generate a second reciprocating motion instruction; Based on the second reciprocating motion instruction, the positions of several abnormal heat sources and sound sources are calculated according to the operation flow and signal processing algorithm of the multi-source positioning fusion system composed of the infrared camera and the acoustic array sensor; Based on the positions of the plurality of abnormal heat sources and sound sources, the sound intensity of the artificial sound source, and the temperature of the artificial heat source, a second measured detection accuracy rate is calculated; When the second measured detection accuracy is greater than the target detection accuracy of the abnormal heating and sound localization module, an instruction to increase the movement speed of the sliding platform is generated until the second measured detection accuracy is equal to the target detection accuracy of the abnormal heating and sound localization module; The movement speed of the sliding platform at this time is taken as the upper limit of the non-steady-state boundary of the abnormal heating and sound positioning module, and 0 is taken as the lower limit of the non-steady-state boundary of the abnormal heating and sound positioning module.

5. The method for determining the non-steady-state detection boundary of high-altitude unmanned detection equipment according to claim 1 is characterized in that: When the detection equipment is a non-contact stress detection module, determining the non-steady-state detection boundary of the detection equipment based on the detection index of the detection equipment and the corresponding target detection accuracy rate specifically includes: Determine the minimum stress resolution based on the detection distance between the detection coil and the receiving coil and the tensile stress on the metal surface; Determine the movement speed and translation range of the sliding platform, and generate a third reciprocating motion instruction; Based on the third reciprocating motion instruction, several resonant frequencies are calculated according to the frequency domain signal processing algorithm and anti-jitter algorithm of the passive wireless sensor; Based on the plurality of the resonant frequencies and the minimum stress resolution, a third measured detection accuracy rate is calculated; Establishing a first two-dimensional two-factor table; the rows and columns of the first two-dimensional two-factor table are respectively the translation range and the sliding platform movement speed; generating instructions for changing the translation range and the movement speed of the sliding platform in turn, and determining a point in the first two-dimensional two-factor table where the third measured detection accuracy is greater than the target detection accuracy of the non-contact stress detection module; Generate instructions for changing the translation range and the movement speed of the sliding platform in turn again until all points in the first two-dimensional dual-factor table are determined to satisfy the third measured detection accuracy equal to the target detection accuracy of the non-contact stress detection module; The curve obtained by fitting all the points in the first two-dimensional two-factor table that satisfy the third measured detection accuracy equal to the target detection accuracy of the non-contact stress detection module is used as the upper limit of the non-steady-state boundary of the non-contact stress detection module, and 0 is used as the lower limit of the non-steady-state boundary of the non-contact stress detection module.

6. The method for determining the non-steady-state detection boundary of high-altitude unmanned detection equipment according to claim 1, characterized in that: When the detection equipment is a paint layer surface crack detection module, based on the detection index of the detection equipment and the corresponding target detection accuracy, determining the non-steady-state detection boundary of the detection equipment specifically includes: Based on the paint layer thickness, determine the minimum crack depth and minimum crack width; Determine the scanning speed, the sliding platform movement speed and the translation range, and generate a fourth reciprocating motion instruction; Based on the fourth reciprocating motion instruction, and in accordance with a lift-off compensation algorithm of an eddy current array sensor, the crack detection signal is processed to calculate the depths and widths of a plurality of cracks; Based on the depths and widths of the plurality of cracks and the minimum crack depth and the minimum crack width, a fourth measured detection accuracy rate is calculated; A second two-dimensional two-factor table is established; the rows and columns of the second two-dimensional two-factor table are scanning speed and lift-off change, respectively; generating instructions for alternating the scanning speed and lift-off variation, and determining a point in the second two-dimensional two-factor table at which the fourth measured detection accuracy is greater than the target detection accuracy of the paint layer surface crack detection module; Generate instructions for changing the scanning speed and lift-off change alternately again until all points in the second two-dimensional two-factor table are determined to satisfy the fourth measured detection accuracy equal to the target detection accuracy of the paint layer surface crack detection module; The curve obtained by fitting all the points in the second two-dimensional two-factor table that satisfy the fourth measured detection accuracy equal to the target detection accuracy of the paint layer surface crack detection module is used as the non-steady-state boundary of the paint layer surface crack detection module.

7. A device for determining the non-steady-state detection boundary of high-altitude unmanned detection equipment, characterized in that: The device for determining the non-steady-state detection boundary of the high-altitude unmanned detection equipment comprises: A detection index acquisition module is used to acquire the detection index of the detection equipment; the detection equipment includes: an overall deformation optical detection module, an abnormal heating and sound positioning module, a non-contact stress detection module and a paint layer surface crack detection module; the overall deformation optical detection module measures the overall deformation of the structure through a binocular optical camera; the abnormal heating and sound positioning module locates the abnormal sound and heat source emitted by the operating components under fault conditions through an acoustic array sensor and an infrared camera; the non-contact stress detection module measures the target local strain through a passive wireless stress sensing system carried by an unmanned aerial vehicle and a passive wireless sensor installed on the surface of the structure under test; the paint layer surface crack detection module scans the surface cracks of the structure under test through an eddy current crack detection system; the detection index is the performance limit that the detection equipment can achieve with a 100% success rate under static conditions, that is, steady-state detection; the detection index includes: detection spacing, deformation detection resolution, minimum sound intensity, minimum heat source resolution, positioning accuracy, minimum stress resolution, paint layer thickness, minimum crack depth and minimum crack width; The non-steady-state detection boundary determination module is used to determine the non-steady-state detection boundary of the detection equipment based on the detection index of the detection equipment and the corresponding target detection accuracy.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for determining the non-steady-state detection boundary of high-altitude unmanned detection equipment described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining the non-steady-state detection boundary of the high-altitude unmanned detection equipment described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for determining the non-steady-state detection boundary of the high-altitude unmanned detection equipment described in any one of claims 1 to 6 is implemented.

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