Distribution line defect positioning method based on beam imaging

Abstract

The invention discloses a distribution line defect positioning method based on beam imaging, and the method comprises the following steps: 1, enabling an unmanned plane to carry a wireless acoustic imager and a camera, and enabling the wireless acoustic imager to be provided with a microphone array; step 2, collecting array sound wave signals of the distribution line to be detected through a microphone array; the camera collects video pictures in real time; step 3, performing time delay compensation on the array sound wave signals based on the prediction model; step 4, analyzing sound source distribution data to carry out defect positioning; and 5, fusing the sound source distribution data and the video picture to generate a sound image cloud picture containing the sound source distribution data and the video picture. The structure is simple, the design is reasonable, in the process of outputting the time delay predicted value of the array sound wave signal based on the prediction model and constructing the spatial spectrum according to the covariance matrix, firstly, singular value decomposition is performed on the covariance matrix, the calculation process is simplified, the calculation efficiency is improved, the high calculation requirement in real-time processing is further met, and the use effect is good.

Description

Technical Field

[0001] The invention belongs to the technical field of testing objects using sound waves, and in particular relates to a distribution line defect locating method based on beam imaging. Background Art

[0002] In beam imaging, an array structure consisting of multiple array elements is usually used to receive and transmit sound waves or radio waves. Through beamforming technology, the array can form directivity in a predetermined direction, so that the energy of the transmitted sound waves is concentrated in a certain direction and the echo in the spatial pointing direction is received. For the received echo signal, the beamformer will process it, including delay compensation, signal weighting and summation operations, to form a beam with spatial directivity. These beams can be used to construct two-dimensional or three-dimensional acoustic images, thereby realizing the detection and imaging of underwater environments, internal structures of objects, or radio signal propagation paths.

[0003] Defects in distribution lines include the following categories:

[0004] Wire defects: wire breakage, outer sheath wear and cracking, inconsistent sag of each phase, etc.

[0005] Tower defects: The tower tilt exceeds the allowable range, the tower components are bent, cracked or damaged, the tower foundation sinks, moves downward or freezes.

[0006] Crossarm and hardware defects: Crossarms are severely corroded, crooked, deformed, etc.; Hardware are corroded, deformed, bolts are loose, caps are missing; Cotter pins are corroded, broken, and fallen off.

[0007] Insulator defects: Cracks and flashover marks appear on the insulator surface, surface scratches exceed the specified value, insulator is skewed, fastening bolts are loose; iron feet and iron caps are rusted and bent; synthetic insulator sheds are broken and burned, and other insulator structural defects.

[0008] When beam imaging is used to locate defects in distribution lines, the transmission and reception timing of multiple sensors, such as ultrasonic probes, is controlled to focus or scan sound waves in space. When sound waves encounter defects, reflection, scattering, or diffraction will occur, and the echo signals generated by these phenomena are received by the sensor and converted into electrical signals. When the beam is focused on the defect position, the defect will generate a strong echo signal. By receiving and processing these echo signals, relevant information about the defect, such as location, size, and shape, can be extracted, and an acoustic image of the inside of the object can be constructed, thereby locating and detecting the defect.

[0009] Although the defect localization method based on beam imaging has many advantages, in practical applications, the acoustic wave signal needs to be processed in real time, and the high computing requirements in real-time processing are also an important challenge. Summary of the invention

[0010] The technical problem to be solved by the present invention is to provide a distribution line defect location method based on beam imaging in view of the deficiencies in the above-mentioned prior art. The method has a simple structure and a reasonable design. The method outputs the time delay prediction value of the array acoustic wave signal based on the prediction model. In the process of constructing the spatial spectrum according to the covariance matrix, the covariance matrix is ​​firstly subjected to singular value decomposition, which simplifies the calculation process, improves the calculation efficiency, further solves the high calculation requirements in real-time processing, and has a good use effect.

[0011] In order to solve the above technical problems, the technical solution adopted by the present invention is: a distribution line defect location method based on beam imaging, characterized in that it includes the following steps:

[0012] Step 1: The drone is equipped with a wireless acoustic imager and a camera, and the wireless acoustic imager has a microphone array; the microphone array at least includes a first microphone array and a second microphone array arranged in a circular shape from inside to outside;

[0013] Step 2: Data collection: The array acoustic wave signal of the power distribution line to be tested is collected through the microphone array; the camera collects the video picture in real time;

[0014] Step 3: Signal compensation processing: Perform time delay compensation on the array acoustic wave signal based on the prediction model to obtain the acoustic wave intensity information at each position in the target area; convert the acoustic wave intensity information into sound source distribution data;

[0015] Step 4: Analyze the sound source distribution data to locate the defect;

[0016] Step 5: Fuse the sound source distribution data and the video image to generate an audio-visual cloud map including the sound source distribution data and the video image.

[0017] The above-mentioned distribution line defect location method based on beam imaging is characterized in that: the specific method of "compensating the array acoustic wave signal for delay based on the prediction model" in step 3 includes:

[0018] Step 301: Collect historical data about microphone array transmission; construct a three-dimensional tensor based on the historical data;

[0019] Step 302: The label of the three-dimensional tensor is the delay value;

[0020] Step 303: construct a CNN neural network structure, wherein the dimension of the input layer of the CNN neural network structure matches the dimension of the multidimensional time series;

[0021] Step 304: Use the data set to train the CNN neural network structure, minimize the prediction error by adjusting the parameters of the CNN neural network structure; use the mean square error loss function to calculate the prediction error; use the stochastic gradient descent algorithm to update the network weights; and obtain a trained CNN neural network structure;

[0022] Step 305: Obtain a three-dimensional tensor to be predicted, input the three-dimensional tensor to be predicted into a trained CNN neural network structure, and the trained CNN neural network structure outputs a delay prediction value;

[0023] Step 306: Perform delay compensation on the array acoustic wave signal according to the delay prediction value.

[0024] The above-mentioned distribution line defect location method based on beam imaging is characterized in that the three-dimensional tensor includes an environmental correction term, a transmission distance and an array element spacing.

[0025] The above-mentioned distribution line defect location method based on beam imaging is characterized in that it also includes using the GIS system to collect the geographical location information of the distribution line, importing the distribution line data from the existing power system, performing coordinate conversion on the defect position of the distribution line, importing the defect position of the distribution line into the GIS system, and displaying the defect position of the distribution line on a map.

[0026] The above-mentioned distribution line defect location method based on beam imaging is characterized in that: the specific method of "analyzing sound source distribution data for defect location" in step 4 includes:

[0027] Step 401, constructing a sound wave matrix based on sound source distribution data;

[0028] Step 402: determining a covariance matrix of the microphone array based on the sound wave matrix;

[0029] Step 403: construct a spatial spectrum according to the covariance matrix, and search for a peak value on the spatial spectrum;

[0030] Step 404, determining the defect target direction of the microphone array according to the peak value;

[0031] Step 405: Cross-locate at least two defect target directions to obtain the defect location of the distribution line.

[0032] The above-mentioned distribution line defect location method based on beam imaging is characterized in that: the specific method of "constructing a spatial spectrum according to the covariance matrix and searching for peaks on the spatial spectrum" in step 403 is:

[0033] Step 4031: Perform singular value decomposition on the covariance matrix Cov, and decompose the covariance matrix Cov into Cov = U·S·V T, where U represents the spatial singular vector matrix, S represents the diagonal matrix, the elements on the diagonal are singular values, and V T represents the time singular vector matrix, V T Represents the transposed matrix of V, both U and V are orthogonal matrices;

[0034] Step 4032: Arrange the singular values ​​in the diagonal matrix S in descending order, select the first k singular values, set the other singular values ​​in the diagonal matrix S to 0, and obtain a reconstructed diagonal matrix S';

[0035] Step 4033: The sub-matrix after singular value decomposition is Cov'=U·S'·V T ;

[0036] Step 4034: Calculate the projection matrix of the sub-matrix;

[0037] Step 4035: Calculate the spatial spectrum based on the projection matrix;

[0038] Step 4036: Search for a spectrum peak on the spatial spectrum, and determine the defect target direction based on the spectrum peak.

[0039] The above-mentioned distribution line defect location method based on beam imaging is characterized in that: the specific method of "determining the covariance matrix of the microphone array based on the acoustic wave matrix" in step 402 is: Among them, Cov represents the covariance moment, L represents the sampling frequency, D represents the sound wave matrix, and D T Represents the transposed matrix of the sound wave matrix.

[0040] The present invention provides a distribution line defect location device based on beam imaging, which is characterized by:

[0041] including drones, designed to carry wireless acoustic imagers and cameras,

[0042] A wireless acoustic imager, carried on a UAV, is used to transmit high-frequency sound waves to a target area of ​​a power distribution line. The wireless acoustic imager has a microphone array, and the microphone array is used to receive array sound wave signals from different positions in space;

[0043] A camera, mounted on a drone, is used to collect video footage of a target area of ​​a distribution line in real time;

[0044] A drone farm, which is wirelessly connected to the drones and receives, processes and stores data from the drones;

[0045] The ground terminal is wirelessly connected to the wireless acoustic imager to compensate the array acoustic wave signal to obtain acoustic wave intensity information, convert the acoustic wave intensity information into sound source distribution data, analyze the sound source distribution data to locate defects; and fuse the sound source distribution data and video images to generate an acoustic cloud map.

[0046] The above-mentioned distribution line defect location method based on beam imaging is characterized in that: a connecting rod is provided on the back of the wireless acoustic imager, a two-way pipe is sleeved on the connecting rod, and cavities are opened at both ends of the two-way pipe along the length direction; a clamp is detachably connected to the bottom of the drone, and the clamp has two clamping parts arranged opposite to each other, and the clamping part includes a clamping rod movable along the length direction of the two-way pipe, and the diameter of the clamping rod is adapted to the cavity.

[0047] The above-mentioned distribution line defect location method based on beam imaging is characterized in that: the microphone array includes at least a first circular ring area for arranging a first microphone array and a second circular ring area for arranging a second microphone array, which are arranged from the inside to the outside; the surface of the first circular ring area has a first arc-shaped groove for installing the first microphone, and the surface of the second circular ring area has a second arc-shaped groove for installing the second microphone, and the first arc-shaped groove and the second arc-shaped groove are connected to form an arc radiating from the center to the surrounding areas.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] 1. The invention has a simple structure, reasonable design, and is easy to implement and operate.

[0050] 2. In the present invention, the UAV is equipped with a wireless acoustic imager and a camera, which generates an acoustic-visual cloud map including the sound source distribution data and the video images by fusing the sound source distribution data and the video images, and displays the defects or fault points on the distribution line in an intuitive manner, which is convenient for the staff to quickly understand the fault situation and reduces the dependence on the staff's professional knowledge.

[0051] 3. The present invention realizes delay compensation by outputting the delay prediction value of the array acoustic wave signal based on the prediction model. The neural network model has a concise and intuitive structure, which makes the algorithm easier to understand and implement. At the same time, the complexity of the overall algorithm can be reduced by simplifying the calculation process and reducing parameter adjustments.

[0052] 4. In the process of constructing the spatial spectrum according to the covariance matrix, the present invention first performs singular value decomposition on the covariance matrix for data dimensionality reduction and feature extraction, thereby avoiding the time-consuming and memory-intensive problems caused by direct calculation, further solving the high computing requirements in real-time processing, improving computing efficiency, saving storage space, and achieving good use effect.

[0053] In summary, the present invention has a simple structure and a reasonable design. It outputs the time delay prediction value of the array acoustic wave signal based on the prediction model. In the process of constructing the spatial spectrum according to the covariance matrix, the covariance matrix is ​​firstly subjected to singular value decomposition, which simplifies the calculation process, improves the calculation efficiency, further solves the high calculation requirements in real-time processing, and has a good use effect.

[0054] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The figure is a flow chart of the method of the present invention.

[0056] Figure 2 The present invention is a flow chart of a method for performing time delay compensation on array acoustic wave signals in a prediction model.

[0057] Figure 3 It is a structural schematic diagram of the present invention.

[0058] Figure 4 The figure is a schematic diagram of the structure of the UAV equipped with a wireless acoustic imager and a camera according to the present invention.

[0059] Figure 5 The figure is a schematic diagram of the structure of the wireless acoustic imager of the present invention.

[0060] Figure 6 It is a schematic structural diagram of the clamp of the present invention.

[0061] Description of reference numerals:

[0062] 1—UAV; 2—UAV airport; 3—Ground terminal;

[0063] 4—wireless acoustic imager; 5—fixture; 6—camera;

[0064] 41—two-way pipe; 42—connecting rod; 51—connecting plate;

[0065] 52—rotating handle; 53—housing; 54—clamping rod. DETAILED DESCRIPTION

[0066] The method of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments of the present invention.

[0067] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0068] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0069] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0070] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used here to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0071] like Figure 1 As shown, the distribution line defect location method based on beam imaging included in the present invention includes the following steps:

[0072] Step 1: The drone 1 is equipped with a wireless acoustic imager 4 and a camera 6, and the wireless acoustic imager 4 has a microphone array; the microphone array at least includes a first microphone array and a second microphone array arranged in a circular ring from the inside to the outside;

[0073] Step 2: Data collection: The array acoustic wave signal of the power distribution line to be detected is collected through the microphone array; the camera 6 collects the video picture in real time;

[0074] Step 3: Signal compensation processing: Perform time delay compensation on the array acoustic wave signal based on the prediction model to obtain the acoustic wave intensity information at each position in the target area; convert the acoustic wave intensity information into sound source distribution data;

[0075] Step 4: Analyze the sound source distribution data to locate the defect;

[0076] Step 5: Fuse the sound source distribution data and the video image to generate an audio-visual cloud map including the sound source distribution data and the video image.

[0077] In actual use, the drone 1 is equipped with a wireless acoustic imager 4 and a camera 6, and flies along a preset trajectory. The drone 1 is suitable for different types of distribution lines and defect types, such as cables, overhead lines, etc., and has wide applicability.

[0078] The wireless acoustic imager 4 transmits high-frequency sound waves to the target area of ​​the distribution line. When these sound waves encounter media or interfaces of different densities during propagation, reflection, scattering and other phenomena will occur. The wireless acoustic imager 4 has a microphone array that can simultaneously receive sound wave signals from different positions in space.

[0079] The ground terminal 3 is wirelessly connected to the wireless acoustic imager 4. The ground terminal 3 receives the sound wave signal. The sound wave signal is first converted into an electrical signal, and then subjected to signal compensation processing to eliminate the time delay of the sound wave reaching each microphone due to the different distances between the sound source and each microphone, thereby obtaining the sound wave intensity information. The sound wave intensity information is converted into sound source distribution data, which can be used to locate the defect position of the distribution line, and at the same time, an image can be generated to reflect the surface or internal structure of the defect target of the distribution line. The image can be three-dimensional or two-dimensional. In the image, different colors, brightness or patterns are usually used to represent the intensity and distribution of the sound. The image is displayed on the ground terminal 3.

[0080] The video images captured by camera 1 are transmitted to the ground terminal 3 through a wireless image transmission module, wherein the wireless image transmission module includes module U2 of model INTQ6600 and module U3 of model INTQ6600. Module 2 is mounted on drone 1 and is communicatively connected to camera 1, and module 3 is communicatively connected to ground terminal 3.

[0081] The ground terminal 3 integrates the sound source distribution data and the video images to generate an audio-visual cloud map containing the sound source distribution data and the video images, which displays the defects or fault points on the distribution line in an intuitive way, making it easier for the staff to quickly understand the fault situation and reducing the dependence on the staff's professional knowledge.

[0082] In this embodiment, the specific method of "compensating the array acoustic wave signal for delay based on the prediction model" in step 3 includes:

[0083] Step 301: Collect historical data about microphone array transmission; construct a three-dimensional tensor based on the historical data.

[0084] In one possible embodiment, the three-dimensional tensor selects three quantities that are closely related to the calculation of the delay compensation value, including the environmental correction term, the transmission distance, and the array element spacing. In actual use, the three-dimensional tensor is represented by w, w = (V, D, d), V represents the environmental correction term, D represents the transmission distance from the array element to a point in space, and d represents the array element spacing in the microphone array.

[0085] Under standard atmospheric pressure, the speed of sound wave propagation V is mainly affected by temperature T. In the normal humidity range, the effect of humidity on the speed of sound is relatively small. However, under extreme humidity conditions, such as the rainy days in the south, or dense fog and rain and snow, changes in humidity will have a significant impact on the speed of sound. Therefore, the environmental correction term V is expressed as: V = V set +(α·T)+β·(HH set ), where T represents the actual temperature, α represents the temperature coefficient, α = 0.606, and H represents the actual humidity, H set represents the humidity threshold, and β represents the humidity coefficient.

[0086] Step 302: The label of the three-dimensional tensor is the delay value; the label array is a separate one-dimensional array.

[0087] Step 303: Construct a CNN neural network structure, where the dimension of the input layer of the CNN neural network structure matches the dimension of the multidimensional time series.

[0088] It should be noted that the three dimensions of a three-dimensional tensor are independent of each other, so a three-dimensional tensor can be regarded as a three-dimensional image. The CNN neural network structure uses a three-dimensional convolutional layer.

[0089] Step 304: Use the data set to train the CNN neural network structure, minimize the prediction error by adjusting the parameters of the CNN neural network structure; use the mean square error loss function to calculate the prediction error; and use the stochastic gradient descent algorithm to update the network weights.

[0090] The dataset is composed of three-dimensional tensors and delay value labels, and the dataset is input into the CNN neural network structure to train the model. The trained CNN neural network structure is obtained.

[0091] Step 305: Obtain the three-dimensional tensor to be predicted, input the three-dimensional tensor to be predicted into the trained CNN neural network structure, and the trained CNN neural network structure outputs the delay prediction value.

[0092] The three-dimensional tensor to be predicted is obtained through the sensor carried by the drone, and the transmission distance is measured by the distance sensor. The transmission distance refers to the distance between the drone and the target area of ​​the distribution line. The signal frequency and signal amplitude are obtained by the microphone array.

[0093] Step 306: Perform delay compensation on the array acoustic wave signal according to the delay prediction value.

[0094] In actual use, the array acoustic wave signal is delayed and compensated based on the prediction model. The neural network model has a concise and intuitive structure, which makes the algorithm easier to understand and implement. At the same time, it can reduce the complexity of the overall algorithm by simplifying the calculation process and reducing parameter adjustments.

[0095] In this embodiment, the geographical location information of the distribution line is collected by using the GIS system, and the distribution line data is exported from the existing power system. The distribution line data includes the direction of the distribution line, the location of the pole tower, the cable laying path, the cable length, etc., and then the distribution line data is imported into the GIS system. The coordinate conversion of the defective position of the distribution line is performed. It should be noted that the coordinate conversion tool provided by the GIS system or third-party software is used to perform accurate coordinate conversion. After the conversion is completed, the defective position of the distribution line is imported into the GIS system, and the defective position of the distribution line is integrated with the distribution line data. The map making function of the GIS system is used to display the defective position of the distribution line on the map.

[0096] In this embodiment, the specific method of "analyzing the sound source distribution data to locate defects" in step 4 includes:

[0097] Step 401: Construct an acoustic wave matrix based on the sound source distribution data. In one possible embodiment, the acoustic wave matrix is ​​a two-dimensional matrix, each element of the matrix represents the intensity and phase of the acoustic wave at a certain position in space, and the acoustic wave matrix is ​​obtained by mapping the sound source parameters of the measurement point to the corresponding position of the acoustic wave matrix.

[0098] Step 402: determining a covariance matrix of the microphone array based on the sound wave matrix;

[0099] The specific method of "determining the covariance matrix of the microphone array based on the sound wave matrix" in step 402 is: Among them, Cov represents the covariance moment, L represents the sampling frequency, D represents the sound wave matrix, and D T Represents the transposed matrix of the sound wave matrix.

[0100] Step 403: construct a spatial spectrum according to the covariance matrix, and search for a peak value on the spatial spectrum;

[0101] In this embodiment, the specific method of "constructing a spatial spectrum according to the covariance matrix and searching for peaks on the spatial spectrum" in step 403 is:

[0102] Step 4031: Perform singular value decomposition on the covariance matrix Cov, and decompose the covariance matrix Cov into Cov = U·S·V T, where U represents the spatial singular vector matrix, S represents the diagonal matrix, the elements on the diagonal are singular values, and V T represents the time singular vector matrix, V T Represents the transposed matrix of V, both U and V are orthogonal matrices;

[0103] Step 4032: Arrange the singular values ​​in the diagonal matrix S in descending order, select the first k singular values, set the other singular values ​​in the diagonal matrix S to 0, and obtain a reconstructed diagonal matrix S';

[0104] Step 4033: The sub-matrix after singular value decomposition is Cov'=U·S'·V T ;

[0105] Step 4034: Calculate the projection matrix of the sub-matrix;

[0106] Step 4035: Calculate the spatial spectrum based on the projection matrix;

[0107] Step 4036: Search for a spectrum peak on the spatial spectrum, and determine the defect target direction based on the spectrum peak.

[0108] Step 404, determining the defect target direction of the microphone array according to the peak value;

[0109] Step 405: Cross-locate at least two defect target directions to obtain the defect location of the distribution line.

[0110] In the process of constructing the spatial spectrum according to the covariance matrix, the covariance matrix Cov is firstly subjected to singular value decomposition for data dimensionality reduction and feature extraction, so as to avoid the time-consuming and memory-intensive problems caused by direct calculation, further solve the high computing requirements in real-time processing, improve computing efficiency, save storage space, and achieve good use effect.

[0111] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0112] Embodiment 2

[0113] The distribution line defect location device based on beam imaging of the present invention comprises a drone 1, which is used to carry a wireless acoustic imager 4 and a camera 6.

[0114] The wireless acoustic imager 4 is carried on the drone 1 and is used to transmit high-frequency sound waves to the target area of ​​the power distribution line. The wireless acoustic imager 4 has a microphone array, and the microphone array is used to receive array sound wave signals from different positions in space;

[0115] A camera, mounted on the drone 1, for collecting video images of a target area of ​​the power distribution line in real time;

[0116] UAV airport 2, wirelessly connected to UAV 1, receives, processes and stores data from UAV 1;

[0117] The ground terminal 3 is wirelessly connected to the wireless acoustic imager 4 for compensating the array acoustic wave signal to obtain acoustic wave intensity information, converting the acoustic wave intensity information into sound source distribution data, analyzing the sound source distribution data for defect location, and fusing the sound source distribution data and the video image to generate an acoustic-image cloud map.

[0118] In actual use, the drone 1 is equipped with a wireless acoustic imager 4 and a camera 6, and flies along a preset trajectory. The drone 1 is suitable for different types of distribution lines and defect types, such as cables, overhead lines, etc., and has wide applicability.

[0119] The wireless acoustic imager 4 transmits high-frequency sound waves to the target area of ​​the distribution line. When these sound waves encounter media or interfaces of different densities during propagation, reflection, scattering and other phenomena will occur. The wireless acoustic imager 4 has a microphone array that can simultaneously receive sound wave signals from different positions in space.

[0120] The ground terminal 3 is wirelessly connected to the wireless acoustic imager 4. The ground terminal 3 receives the sound wave signal. The sound wave signal is first converted into an electrical signal, and then subjected to signal compensation processing to eliminate the time delay of the sound wave reaching each microphone due to the different distances between the sound source and each microphone, thereby obtaining the sound wave intensity information. The sound wave intensity information is converted into sound source distribution data, which can be used to locate the defect position of the distribution line, and at the same time, an image can be generated to reflect the surface or internal structure of the defect target of the distribution line. The image can be three-dimensional or two-dimensional. In the image, different colors, brightness or patterns are usually used to represent the intensity and distribution of the sound. The image is displayed on the ground terminal 3.

[0121] The video images captured by camera 1 are transmitted to the ground terminal 3 through a wireless image transmission module, wherein the wireless image transmission module includes module U2 of model INTQ6600 and module U3 of model INTQ6600. Module 2 is mounted on drone 1 and is communicatively connected to camera 1, and module 3 is communicatively connected to ground terminal 3.

[0122] The ground terminal 3 integrates the sound source distribution data and the video images to generate an audio-visual cloud map containing the sound source distribution data and the video images, which displays the defects or fault points on the distribution line in an intuitive way, making it easier for the staff to quickly understand the fault situation and reducing the dependence on the staff's professional knowledge.

[0123] In this embodiment, a connecting rod 42 is provided on the back of the wireless acoustic imager 4, and a two-way pipe 41 is sleeved on the connecting rod 42, and cavities are respectively opened at both ends of the two-way pipe 41 along the length direction; a clamp 5 is detachably connected to the bottom of the drone 1, and the clamp 5 has two clamping parts arranged opposite to each other, and the clamping part includes a clamping rod 54 that can move along the length direction of the two-way pipe 41, and the diameter of the clamping rod 54 is adapted to the cavity.

[0124] In a possible embodiment, the clamping portion includes a housing 53, a lead screw nut pair disposed in the housing 53, a lead screw exposed at one end of the housing 53, the exposed lead screw connected to the rotating handle 52, a clamping rod 54 disposed on the nut, and the clamping rod 54 exposed at the other side of the housing 53. When the rotating handle is turned and the lead screw rotates relative to the nut, the ball will both rotate in the raceway and circulate along the raceway, and this rolling motion causes the nut to generate axial displacement relative to the lead screw, so that the clamping rod 54 can move along the length direction of the two-way tube 41.

[0125] In this embodiment, the first microphone array is composed of acoustic microphones; and the second microphone array is composed of optical microphones.

[0126] In this embodiment, the microphone array includes at least a first circular ring area for arranging a first microphone array and a second circular ring area for arranging a second microphone array, which are arranged from the inside to the outside; the surface of the first circular ring area has a first arc-shaped groove for installing the first microphone, and the surface of the second circular ring area has a second arc-shaped groove for installing the second microphone, and the first arc-shaped groove and the second arc-shaped groove are connected to form an arc radiating from the center to the surrounding areas.

[0127] In a possible embodiment, the first microphone array consists of acoustic microphones; and the second microphone array consists of optical microphones.

[0128] It should be noted that the arc-shaped microphone array forms a beam in the desired direction through beamforming technology, thereby suppressing noise and interference from other directions. In addition, the arc-shaped microphone array can radiate in all directions to achieve omnidirectional sound source coverage, allowing the microphone array to capture sound signals from different directions, thereby improving the accuracy of sound source positioning.

[0129] In a possible embodiment, a third microphone array is further included, and the third microphone array is composed of bone conduction microphones. The microphone array includes a first circular ring area for arranging the first microphone array, a second circular ring area for arranging the second microphone array, and a third circular ring area for arranging the third microphone array, which are arranged from the inside to the outside. The surface of the first microphone array has a first arc-shaped groove for installing the first microphone, the surface of the second microphone array has a second arc-shaped groove for installing the second microphone, and the surface of the third microphone array has a third arc-shaped groove for installing the third microphone. The first arc-shaped groove, the second arc-shaped groove, and the third arc-shaped groove are connected to form an arc radiating from the center to the surroundings.

[0130] In strong noise environments, optical microphones and bone conduction microphones are more effective; in normal environments, acoustic microphones are sufficient to meet the needs. A microphone array composed of three different microphones can reduce the errors and interference that may be caused by a single type of microphone, improve the accuracy of sound source collection, and make the sound clearer and easier to identify.

[0131] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] The above description is only an embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A distribution line defect location method based on beam imaging, characterized in that: The following steps are involved: Step 1: The drone is equipped with a wireless acoustic imager and a camera, and the wireless acoustic imager has a microphone array; the microphone array at least includes a first microphone array and a second microphone array arranged in a circular shape from inside to outside; Step 2: Data collection: The array acoustic wave signal of the power distribution line to be tested is collected through the microphone array; the camera collects the video picture in real time; Step 3: Signal compensation processing: Perform time delay compensation on the array acoustic wave signal based on the prediction model to obtain the acoustic wave intensity information at each position in the target area; convert the acoustic wave intensity information into sound source distribution data; Step 4: Analyze the sound source distribution data to locate the defect; Step 5: Fuse the sound source distribution data and the video image to generate an audio-visual cloud map including the sound source distribution data and the video image.

2. A distribution line defect location method based on beam imaging according to claim 1, characterized in that: The specific method of "compensating the array acoustic wave signal for delay based on the prediction model" in step 3 includes: Step 301: Collect historical data about microphone array transmission; construct a three-dimensional tensor based on the historical data; Step 302: The label of the three-dimensional tensor is the delay value; Step 303: construct a CNN neural network structure, wherein the dimension of the input layer of the CNN neural network structure matches the dimension of the multidimensional time series; Step 304: Use the data set to train the CNN neural network structure, minimize the prediction error by adjusting the parameters of the CNN neural network structure; use the mean square error loss function to calculate the prediction error; use the stochastic gradient descent algorithm to update the network weights; and obtain a trained CNN neural network structure; Step 305: Obtain a three-dimensional tensor to be predicted, input the three-dimensional tensor to be predicted into a trained CNN neural network structure, and the trained CNN neural network structure outputs a delay prediction value; Step 306: Perform delay compensation on the array acoustic wave signal according to the delay prediction value.

3. A distribution line defect location method based on beam imaging according to claim 2, characterized in that: The three-dimensional tensor includes environmental correction terms, transmission distance, and array element spacing.

4. A distribution line defect location method based on beam imaging according to claim 1, characterized in that: It also includes using the GIS system to collect the geographical location information of the distribution lines, importing the distribution line data from the existing power system, performing coordinate conversion on the defective locations of the distribution lines, importing the defective locations of the distribution lines into the GIS system, and displaying the defective locations of the distribution lines on a map.

5. A distribution line defect location method based on beam imaging according to claim 1, characterized in that: The specific methods of "analyzing sound source distribution data for defect location" in step 4 include: Step 401, constructing a sound wave matrix based on sound source distribution data; Step 402: determining a covariance matrix of the microphone array based on the sound wave matrix; Step 403: construct a spatial spectrum according to the covariance matrix, and search for a peak value on the spatial spectrum; Step 404, determining the defect target direction of the microphone array according to the peak value; Step 405: Cross-locate at least two defect target directions to obtain the defect location of the distribution line.

6. A distribution line defect location method based on beam imaging according to claim 5, characterized in that: The specific method of "constructing a spatial spectrum according to the covariance matrix and searching for peaks on the spatial spectrum" in step 403 is: Step 4031: Perform singular value decomposition on the covariance matrix Cov, and decompose the covariance matrix Cov into Cov = U·S·V T , where U represents the spatial singular vector matrix, S represents the diagonal matrix, the elements on the diagonal are singular values, and V T represents the time singular vector matrix, V T Represents the transposed matrix of V, both U and V are orthogonal matrices; Step 4032: Arrange the singular values ​​in the diagonal matrix S in descending order, select the first k singular values, set the other singular values ​​in the diagonal matrix S to 0, and obtain a reconstructed diagonal matrix S'; Step 4033: The sub-matrix after singular value decomposition is Cov'=U·S'·V T ; Step 4034: Calculate the projection matrix of the sub-matrix; Step 4035: Calculate the spatial spectrum based on the projection matrix; Step 4036: Search for a spectrum peak on the spatial spectrum, and determine the defect target direction based on the spectrum peak.

7. A distribution line defect location method based on beam imaging according to claim 5, characterized in that: The specific method of "determining the covariance matrix of the microphone array based on the sound wave matrix" in step 402 is: Among them, Cov represents the covariance moment, L represents the sampling frequency, D represents the sound wave matrix, and D T Represents the transposed matrix of the sound wave matrix.

8. A distribution line defect location device based on beam imaging, characterized in that: The invention comprises a drone (1) for carrying a wireless acoustic imager (4) and a camera (6), A wireless acoustic imager (4) is carried on the drone (1) and is used to transmit high-frequency sound waves to a target area of ​​the power distribution line. The wireless acoustic imager 4 has a microphone array, and the microphone array is used to receive array sound wave signals from different positions in space. A camera, mounted on the drone (1), for collecting video images of a target area of ​​the power distribution line in real time; A drone airport (2) is wirelessly connected to the drone (1) to receive, process and store data from the drone (1); The ground terminal (3) is wirelessly connected to the wireless acoustic imager (4) and is used to compensate the array acoustic wave signal to obtain acoustic wave intensity information, convert the acoustic wave intensity information into sound source distribution data, analyze the sound source distribution data to locate defects, and fuse the sound source distribution data with the video image to generate an acoustic image cloud map.

9. A distribution line defect location method based on beam imaging according to claim 8, characterized in that: The back of the wireless acoustic imager (4) is provided with a connecting rod (42), a two-way pipe (41) is sleeved on the connecting rod (42), and cavities are respectively opened at both ends of the two-way pipe (41) along the length direction; the bottom of the drone (1) is detachably connected with a clamp (5), the clamp (5) has two clamping parts arranged opposite to each other, the clamping parts include a clamping rod (54) that can move along the length direction of the two-way pipe (41), and the diameter of the clamping rod (54) is adapted to the cavity.

10. A distribution line defect location method based on beam imaging according to claim 8, characterized in that: The microphone array at least includes a first annular area for arranging a first microphone array and a second annular area for arranging a second microphone array, which are arranged from inside to outside; The surface of the first annular area has a first arc-shaped groove for installing a first microphone, and the surface of the second annular area has a second arc-shaped groove for installing a second microphone. The first arc-shaped groove and the second arc-shaped groove are connected to form an arc radiating from the center to the surroundings.

Images