Power transmission line distributed acquisition method and system

Abstract

The invention provides a power transmission line distributed acquisition method and system, and relates to a data processing technology, and the method comprises the steps: determining an associated first equipment group in a high-altitude region based on the attribute information and position information of to-be-detected equipment; generating a first detection group corresponding to the first equipment group, and extracting first audio data in the first equipment group based on the first detection group; extracting a first audio waveform in the first audio data, and performing comparison processing on the first audio waveform based on the position information of the to-be-detected equipment in the first detection group to obtain a second audio waveform; according to the method, appropriate equipment can be selected for high-altitude line acquisition according to a complex line layout, so that the use efficiency of the equipment is improved, and the accuracy of acquired information is enhanced.

Description

Technical Field

[0001] The present invention relates to data processing technologies, and in particular, to a distributed acquisition method and system for transmission lines. Background Art

[0002] In modern power systems, transmission lines, as key infrastructure for power transmission, are widely distributed in various complex environments such as cities, rural areas, mountains, deserts, etc. They have a long span and a wide distribution. For example, a cross-regional transmission line may stretch for hundreds of kilometers, passing through different terrains and climate regions. With the continuous growth of power demand and the increasing requirement for power supply reliability, real-time monitoring of transmission lines has become particularly important. However, due to the complexity and diversity of transmission lines, traditional monitoring methods are difficult to meet the current monitoring needs, and there is an urgent need for an efficient and accurate monitoring method to ensure the safe and stable operation of transmission lines.

[0003] In the prior art, audio monitoring can be used to collect the sounds of electrical equipment and analyze the collected data to determine whether there is a fault. However, since there is a lot of noise in the picked-up sound data, such as wind noise, drone noise, etc., it is impossible to effectively filter them during subsequent processing of the sound data, resulting in low accuracy of fault judgment.

[0004] Therefore, how to automatically process the picked-up sound data in combination with environmental information to improve the accuracy of the processing results has become an urgent problem to be solved. Summary of the Invention

[0005] An embodiment of the present invention provides a distributed acquisition method and system for transmission lines, which can automatically process the picked-up sound data in combination with environmental information to improve the accuracy of the processing results.

[0006] In a first aspect of an embodiment of the present invention, a distributed acquisition method for transmission lines is provided, including: Determining a first device group associated in a high-altitude area based on the attribute information and location information of a device to be detected; Generating a first detection group corresponding to the first device group, and extracting first audio data in the first device group based on the first detection group, where the high-altitude detection devices include at least drones and audio collectors; Extracting a first audio waveform from the first audio data, and performing comparison processing on the first audio waveform based on the location information of the device to be detected in the first detection group to obtain a second audio waveform; Determining the fault content corresponding to the second audio waveform.

[0007] Optionally, in a possible implementation manner of the first aspect, the determining a first device group associated in a high-altitude area based on the attribute information and location information of the device to be detected includes: Determine the synchronous monitoring distance according to the attribute information of the device to be detected and generate a synchronous monitoring frame; Retrieve the line model corresponding to the transmission line to be detected, and segment the high-altitude area of the line model based on the synchronous monitoring frame to obtain the first device group associated within the synchronous monitoring frame.

[0008] Optionally, in a possible implementation manner of the first aspect, the segmenting the high-altitude area of the line model based on the synchronous monitoring frame to obtain the first device group associated within the synchronous monitoring frame includes: The synchronous monitoring frame is a three-dimensional frame; Determine the initial positioning point in the line model, and correspondingly set the center point of the three-dimensional frame with the initial positioning point; Obtain all devices within the three-dimensional frame in real time and perform adaptive position adjustment to obtain the first device group associated within the synchronous monitoring frame.

[0009] Optionally, in a possible implementation manner of the first aspect, the obtaining all devices within the three-dimensional frame in real time and performing adaptive position adjustment to obtain the first device group associated within the synchronous monitoring frame includes: Obtain the first quantity of the devices to be detected within the three-dimensional frame; If the first quantity is less than the preset value, move the three-dimensional frame until the first quantity is greater than or equal to the preset value; Count the devices to be detected within the three-dimensional frame to obtain the associated first device group.

[0010] Optionally, in a possible implementation manner of the first aspect, the moving the three-dimensional frame if the first quantity is less than the preset value includes: Obtain the device to be detected within the three-dimensional frame as the first device, and determine the distance between the other devices to be detected outside the three-dimensional frame and the first device; Determine the first device and the second device corresponding to the minimum distance, and form a ray from the center point of the first device to the center point of the second device; Move the three-dimensional frame a preset distance in the direction of the ray.

[0011] Optionally, in a possible implementation manner of the first aspect, the generating the first detection group corresponding to the first device group and extracting the first audio data in the first device group based on the first detection group includes: Determine the device model corresponding to each device in the first device group within the line model; Based on the device model and the minimum interval module of the high-altitude detection device, determine all possible moving methods; Based on all possible moving methods, determine the moving directions and moving distances of all first detection groups.

[0012] Optionally, in a possible implementation manner of the first aspect, the determination of all possible movement modes based on the device model and the minimum interval module of the high-altitude detection device includes: Generate a minimum interval module corresponding to the high-altitude detection device, and correspondingly set the center point of the minimum interval module and the center point of the device model; Move the minimum interval module at a preset angle until the minimum interval module does not intersect any device in the line model to obtain all possible movement distances.

[0013] Optionally, in a possible implementation manner of the first aspect, the determination of the movement direction and movement distance of all first detection groups based on all possible movement modes includes: Statistically obtain the maximum first movement distance of all high-altitude detection devices at the same preset angle, statistically obtain the minimum second movement distance among all first movement distances as the determined movement distance, and use the preset angle corresponding to the second movement distance as the movement direction.

[0014] Optionally, in a possible implementation manner of the first aspect, the extraction of the first audio waveform in the first audio data and the comparison and processing of the first audio waveform based on the position information of the device to be detected in the first detection group to obtain the second audio waveform includes: The audio collector is a microphone array, the microphone array includes sub-microphones at different positions, and each sub-microphone has a preset microphone number; Compare the first audio waveform of each device in the first device group with the first audio waveforms of other devices to obtain a different second audio waveform; The comparison of the first audio waveform of each device in the first device group with the first audio waveforms of other devices to obtain a different second audio waveform includes: Compare the sub-waveforms corresponding to the microphone numbers in the first audio waveforms of every two devices to obtain a comparison waveform; Fusion-process the comparison waveforms of all microphone numbers to obtain the second audio waveform.

[0015] Optionally, in a possible implementation manner of the first aspect, the determination of the fault content corresponding to the second audio waveform includes: Statistically obtain the absolute values of all wave points in the second audio waveform to obtain the first wave value, and statistically obtain the sum of the first wave values to obtain the total wave value; If the total wave value is less than or equal to the threshold wave value, the fault content is empty; If the total wave value is greater than the threshold wave value, segment the second audio waveform to obtain the fault content; The segmenting the second audio waveform to obtain the fault content if the total wave value is greater than the threshold wave value includes: If it is determined that the total wave value of a device and other devices is greater than the threshold wave value, then calculate the average wave value of the corresponding device compared to other devices to obtain the average wave value; After segmenting the average wave value, extract the features of each segment to obtain the first segment wave value features; Calculate the similarity between the first segment wave value features and the preset segment wave value features, and determine the fault content corresponding to the segment wave value features with the highest similarity.

[0016] In a second aspect, the present invention provides a distributed acquisition system for a transmission line, which is applied to a distributed server and includes: An association module for determining a first device group associated within a high-altitude area based on the attribute information and location information of the device to be detected; A detection module for generating a first detection group corresponding to the first device group, and extracting first audio data in the first device group based on the first detection group. The high-altitude detection device at least includes a drone and an audio collector; A processing module for extracting the first audio waveform from the first audio data, and performing comparison processing on the first audio waveform based on the location information of the device to be detected in the first detection group to obtain a second audio waveform; A determination module for determining the fault content corresponding to the second audio waveform.

[0017] Technical effects: Based on the attribute information and location information of the device to be detected, the present invention can determine the first device group associated within the high-altitude area, thereby realizing the block division of the transmission line. By determining the synchronous monitoring distance according to the device attributes and generating a synchronous monitoring frame, the line model is segmented and adaptively adjusted in position, so that the acquisition environment in each divided block is similar. In a certain divided block, environmental factors such as the wind sound size and the distribution of surrounding devices are basically the same. This precise block division method provides a more stable and consistent environmental basis for subsequent audio data acquisition and processing, effectively reducing the interference of environmental factors on the processing of acquired data, and improving the reliability and effectiveness of the data.

[0018] When the present invention collects audio data, this solution uses high-altitude detection devices such as drones and audio collectors. Based on the device model and the minimum interval module, a reasonable moving method and distance are determined to ensure that the device can safely and effectively collect data. At the same time, since the collection environment of each block is similar, when performing waveform comparison, environmental sounds (such as noise like wind sounds and drone sounds) can be filtered out more effectively. Audio data is collected through a microphone array, and the audio waveforms of different devices are carefully compared and fused. Combining the similar environmental information within the block, the sound characteristics emitted by the device itself can be extracted more accurately. This method improves the accuracy of waveform judgment, reduces misjudgment cases caused by environmental noise, and makes fault diagnosis more accurate and reliable.

[0019] The present invention realizes the automated processing of the picked-up sound data. From determining the first device group, generating the first detection group and collecting audio data, to extracting the audio waveform, comparing and processing to obtain the second audio waveform, and then to determining the fault content, the entire process is automatically controlled and processed by a distributed server. By statistically calculating the total wave value of the second audio waveform and comparing it with the threshold wave value, as well as performing operations such as mean wave value calculation, segmented feature extraction, and similarity calculation on the devices that may have faults, it is possible to determine whether there is a fault in the device and the specific type of the fault. Compared with traditional methods, this solution improves the accuracy of the processing results. Brief Description of the Drawings

[0020] Figure 1 It is a flowchart of a transmission line distributed collection method provided by the present invention; Figure 2 It is a structural diagram of a transmission line distributed collection system provided by the present invention. Detailed Embodiments

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.

[0023] It should be understood that in various embodiments of the present invention, the sequence numbers of the various processes do not imply the order of execution, and the order of execution of the various processes should be determined by their functions and internal logics, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0024] It should be understood that in the present invention, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0025] It should be understood that in the present invention, "a plurality of" means two or more. "And / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "Including A, B, and C" and "including A, B, C" mean that all of A, B, and C are included. "Including A, B, or C" means including any one of A, B, and C. "Including A, B, and / or C" means including any one or any two or all three of A, B, and C.

[0026] It should be understood that in the present invention, "B corresponding to A", "B corresponding to A relatively", "A corresponding to B relatively", or "B corresponding to A relatively" means that B is associated with A, and B can be determined according to A. Determining B according to A does not mean determining B only according to A, but also B can be determined according to A and / or other information. The matching of A and B means that the similarity between A and B is greater than or equal to a preset threshold.

[0027] Depending on the context, as used herein, "if" can be interpreted as "when", "while", "in response to determining", or "in response to detecting".

[0028] The technical solution of the present invention will be described in detail below with specific embodiments. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0029] As Figure 1 shown, the present invention provides a transmission line distributed acquisition method, which is applied to a distributed server and includes: S1, determining a first device group associated in the high-altitude area based on the attribute information and location information of the device to be detected.

[0030] Among them, the distributed server represents multiple servers. Due to the long span of the power transmission line, the distributed server can quickly respond to and process the data within its coverage area. In the scenario of monitoring the power transmission line of the power system, in order to accurately detect the equipment in a specific area, it is first necessary to determine the set of equipment that needs to be focused on, that is, the first equipment group. By combining the attribute information (such as the type of equipment, etc.) and location information of the equipment to be detected, the monitoring range can be delimited more pertinently.

[0031] In some embodiments, the specific implementation manner of step S1 (determining the first equipment group associated in the high-altitude area based on the attribute information and location information of the equipment to be detected) includes: S11, determine the synchronous monitoring distance according to the attribute information of the equipment to be detected and generate a synchronous monitoring frame.

[0032] Due to the different physical characteristics, operating principles, and the influence ranges of possible faults of different types of equipment to be detected, it is necessary to set an appropriate synchronous monitoring distance. For example, as a key component to ensure the insulation performance of the power transmission line, the failure of the insulator may affect the line operation within a relatively small range, and its synchronous monitoring distance can be, for example, 2 meters; while for large equipment such as transformers, the influence range of its failure is wider, and its synchronous monitoring distance can be set to, for example, 3 meters. Based on these synchronous monitoring distances determined according to the equipment attribute information, the corresponding synchronous monitoring frame is generated. This synchronous monitoring frame is an important tool for subsequent segmentation of the line model and determination of the equipment group. It can be a three-dimensional frame, which defines the monitoring range in an intuitive geometric form.

[0033] S12, retrieve the line model corresponding to the power transmission line to be detected, and based on the synchronous monitoring frame, segment the high-altitude area of the line model to obtain the first equipment group associated within the synchronous monitoring frame.

[0034] In some embodiments, the specific implementation manner of step S12 (segmenting the high-altitude area of the line model based on the synchronous monitoring frame to obtain the first equipment group associated within the synchronous monitoring frame) includes: S121, the synchronous monitoring frame is a three-dimensional frame.

[0035] Considering that the power transmission line has a three-dimensional structure in space and the equipment is distributed at different heights and positions, using a three-dimensional frame as the synchronous monitoring frame can cover the target area more comprehensively and accurately. The three-dimensional frame can define the high-altitude area of the line model in three dimensions of length, width, and height, avoiding equipment omission caused by two-dimensional monitoring methods and ensuring the all-round monitoring of the power transmission line.

[0036] S122. Determine the initial positioning point in the line model, and correspondingly set the center point of the three-dimensional frame with the initial positioning point.

[0037] In the line model of the transmission line, determining a representative initial positioning point is the key to accurately positioning the synchronous monitoring frame. For example, select the tip of the transmission tower as the initial positioning point (for different line models, appropriate positioning points can be preset according to the actual situation. For some line models, it may be more suitable to select the middle position of the transmission tower). Correspondingly setting the center point of the three-dimensional frame with this initial positioning point can use this positioning point as a reference to make the synchronous monitoring frame cover the target area and provide a position reference for obtaining the equipment inside the frame subsequently.

[0038] S123. Real-time obtain all the equipment inside the three-dimensional frame and perform adaptive position adjustment to obtain the first equipment group associated inside the synchronous monitoring frame.

[0039] This solution can dynamically optimize the monitoring range according to the actual equipment distribution, ensure that the first equipment group covers a sufficient number of representative equipment, and improve the comprehensiveness of monitoring.

[0040] In some embodiments, the specific implementation manner in step S123 (real-time obtain all the equipment inside the three-dimensional frame and perform adaptive position adjustment to obtain the first equipment group associated inside the synchronous monitoring frame) includes: S1231. Obtain the first quantity of the equipment to be detected inside the three-dimensional frame.

[0041] During the monitoring of the transmission line, it is necessary to understand the quantity of the equipment to be detected inside the three-dimensional frame in real time. For example, in a three-dimensional line model of a transmission line, identify and count the equipment such as insulators and transformers inside the three-dimensional frame to obtain the first quantity.

[0042] S1232. If the first quantity is less than the preset value, move the three-dimensional frame until the first quantity is greater than or equal to the preset value.

[0043] The preset value is a standard quantity preset according to the monitoring requirements and actual situation, and its purpose is to ensure that there are sufficient quantities of the equipment to be detected inside the three-dimensional frame to ensure the comprehensiveness and effectiveness of monitoring. It can be set manually according to the requirements. If the first quantity is less than the preset value, it means that the quantity of the equipment covered by the current three-dimensional frame is insufficient and may not fully reflect the operating conditions of the transmission line in this area. At this time, it is necessary to adjust the position of the three-dimensional frame. For example, if the preset value is set to 3 equipment and the current first quantity is only 1, then it is necessary to perform a moving operation on the three-dimensional frame.

[0044] In some embodiments, the specific implementation of step S1232 (wherein if the first quantity is less than a preset value, the three-dimensional frame is moved) includes: S12321, Obtain the device to be detected within the three-dimensional frame as the first device, and determine the distances between the first device and other devices to be detected outside the three-dimensional frame.

[0045] When it is determined that the three-dimensional frame needs to be moved, first take the device to be detected within the three-dimensional frame as the first device. Then, calculate the distances between the first device and other devices to be detected outside the three-dimensional frame. For example, use the GPS positioning technology to obtain the coordinate information of each device, and then calculate the distance between two points through the coordinates. This can clearly understand the spatial position relationship between the devices outside the frame and the devices inside the frame, providing a basis for determining the moving direction subsequently.

[0046] S12322, Determine the first device and the second device corresponding to the minimum distance, and form a ray from the center point of the first device to the center point of the second device.

[0047] After obtaining the distances between all the devices outside the frame and the first device, find the first device (inside the frame) and the second device (outside the frame) corresponding to the minimum distance. Take the center points of the first device and the second device as endpoints to form a ray. This ray represents the most likely moving direction of the three-dimensional frame because moving in this direction can include more devices to be detected into the frame most quickly. For example, after distance calculation, it is found that the distance between an insulator outside the frame and an insulator inside the frame is the minimum, then a ray is formed from the center point of the insulator inside the frame to the center point of the insulator outside the frame.

[0048] S12323, Move the three-dimensional frame a preset distance in the direction of the ray.

[0049] After determining the moving direction (the direction of the ray), move the three-dimensional frame a preset distance according to the preset distance. The preset distance is a suitable moving length according to the actual situation. For example, if the preset distance is set to 1 meter, then move the three-dimensional frame 1 meter along the ray direction. After moving, count the first quantity again. If it is still less than the preset value, repeat the above steps until the first quantity is greater than or equal to the preset value.

[0050] S1233, Count the devices to be detected within the three-dimensional frame to obtain the associated first device group.

[0051] When the three-dimensional frame moves to make the first quantity greater than or equal to the preset value, it indicates that there are already a sufficient number of devices to be detected within the frame. By statistically analyzing and sorting the devices to be detected within the frame again, these devices are determined as the associated first device group. This first device group will be the object of subsequent data collection and analysis, used to further detect whether there are faults in the transmission line and evaluate the operating status of the line.

[0052] S2. Generate a first detection group corresponding to the first device group, and extract first audio data of the first device group based on the first detection group. The aerial detection device at least includes a drone and an audio collector.

[0053] After determining the first device group, it is necessary to generate a corresponding first detection group, and use an aerial detection device (at least including a drone and an audio collector) to extract the first audio data of the first device group. Because specific audio signals are generated during the operation of transmission line equipment, analyzing these audio data can detect whether there are faults in the equipment. The role of the first detection group is to be responsible for collecting audio data of the first device group, providing a data basis for subsequent fault diagnosis.

[0054] In some embodiments, the specific implementation of step S2 (generating a first detection group corresponding to the first device group and extracting first audio data of the first device group based on the first detection group) includes: S21. Determine the device models corresponding to each device in the first device group within the line model.

[0055] In the line model, it is necessary to accurately find the device models corresponding to each device in the first device group. For example, if there are insulators and transformers in the first device group, then the insulator model and the transformer model should be determined separately in the line model.

[0056] S22. Determine all possible movement methods based on the device model and the minimum interval module of the aerial detection device.

[0057] This solution needs to find all possible movement methods and move during the subsequent data collection process.

[0058] In some embodiments, the specific implementation of step S22 (determining all possible movement methods based on the device model and the minimum interval module of the aerial detection device) includes: S221. Generate a minimum interval module corresponding to the aerial detection device, and set the center point of the minimum interval module corresponding to the center point of the device model.

[0059] To ensure that the high-altitude detection device does not collide with other devices in the transmission line when collecting audio data, it is necessary to generate a minimum interval module for it. This module is a three-dimensional spatial range, representing the minimum safe space required for the high-altitude detection device when collecting data. The center point of the minimum interval module is correspondingly set with the center point of the device model, so that the detection device can make a reasonable movement plan centered on the device model. For example, for drones and audio collectors, their corresponding minimum interval modules are generated respectively, and the center points of them are coincided with the center point of the device model to be detected, so as to more accurately determine the initial position of the detection device.

[0060] S222. Move the minimum interval module at a preset angle until the minimum interval module does not intersect with any device within the line model to obtain all possible moving distances.

[0061] Perform a moving operation on the minimum interval module at a preset angle. The preset angle can be set according to the actual situation and experience. For example, a moving attempt is made every 30 degrees. During each movement, check whether the minimum interval module intersects with other devices within the line model. If it intersects, it means there is interference and this moving direction is not feasible; if it does not intersect, record the moving distance of this time. Continuously repeat this process until all preset angle movements are tried, so as to obtain all possible moving distances. This step can find out all the effective distances that the high-altitude detection device can move without colliding with other devices, providing a comprehensive selection for determining the movement plan of the detection group.

[0062] S23. Determine the moving directions and moving distances of all first detection groups based on all possible moving methods.

[0063] In some embodiments, the specific implementation manner in step S23 (determining the moving directions and moving distances of all first detection groups based on all possible moving methods) includes: S231. Statistically obtain the maximum first moving distance of all high-altitude detection devices at the same preset angle, and statistically obtain the minimum second moving distance among all the first moving distances as the determined moving distance, and use the preset angle corresponding to the second moving distance as the moving direction.

[0064] After obtaining all possible moving distances, for each preset angle, count the maximum distance that all high-altitude detection devices can move at this angle, and record these maximum distances as the first moving distance. Then, find the smallest one among all the first moving distances and record it as the second moving distance. The reason for choosing the smallest moving distance is to ensure that all high-altitude detection devices can get as close as possible to the device to be detected to obtain data. Take the preset angle corresponding to the second moving distance as the moving direction of the first detection group. The moving direction and moving distance determined in this way not only ensure the safety of the detection devices but also enable them to cover the first device group as much as possible, thus efficiently completing the audio data acquisition task.

[0065] S3. Extract the first audio waveform from the first audio data, and perform comparison processing on the first audio waveform based on the position information of the device to be detected in the first detection group to obtain the second audio waveform.

[0066] After completing the acquisition of the first audio data, it is necessary to further extract the first audio waveform from these data and perform comparison processing on it in combination with the position information of the device to be detected in the first detection group to obtain the second audio waveform. Since different devices generate audio waveforms with different characteristics during normal operation and when a fault occurs, the differences in the audio waveforms between devices can be found through comparison and analysis, and these differences may imply that the device has a fault. Therefore, this step can provide data to be analyzed for subsequent detection of faults in transmission line devices.

[0067] In some embodiments, the specific implementation manner of step S3 (extracting the first audio waveform from the first audio data and performing comparison processing on the first audio waveform based on the position information of the device to be detected in the first detection group) includes: S31. The audio collector is a microphone array, and the microphone array includes sub-microphones at different positions, and each sub-microphone has a preset microphone number.

[0068] Since the audio collector is a microphone array, it is composed of multiple sub-microphones located at different positions, and each sub-microphone has a preset microphone number. Sub-microphones at different positions can collect audio data from multiple angles, thus more comprehensively capturing the sound information emitted by the device. The preset microphone number facilitates subsequent differentiation and processing of the audio data collected by each sub-microphone. For example, in a microphone array containing 4 sub-microphones, the sub-microphone numbers can be 1, 2, 3, and 4 respectively, and the data collected by each sub-microphone can be classified and analyzed according to the number.

[0069] S32. Compare the first audio waveform of each device in the first device group with the first audio waveforms of other devices to obtain the different second audio waveforms.

[0070] In some embodiments, the specific implementation of step S32 (wherein the first audio waveforms of each device in the first device group are compared with the first audio waveforms of other devices to obtain the differential second audio waveforms) includes: S321, comparing the sub-waveforms corresponding to the microphone numbers in the first audio waveforms of every two devices to obtain comparison waveforms; For every two devices in the first device group, compare the sub-waveforms with the same microphone number in their first audio waveforms. Since the audio signals collected by each sub-microphone from different positions reflect the sound characteristics of the device at that position, by comparing the corresponding sub-waveforms, the differences in audio characteristics between the two devices can be found more precisely. For example, for device A and device B, compare the sub-waveforms collected by their sub-microphone numbered 1, calculate the difference value between the two, and thus obtain a comparison waveform. Perform such comparison operations on the sub-waveforms corresponding to all sub-microphone numbers, and a series of comparison waveforms can be obtained.

[0071] S322, performing fusion processing on the comparison waveforms of all microphone numbers to obtain the second audio waveform.

[0072] After obtaining the comparison waveforms corresponding to all microphone numbers, it is necessary to perform fusion processing on these comparison waveforms. The purpose of the fusion processing is to comprehensively consider the information collected by each sub-microphone and obtain a second audio waveform that can comprehensively reflect the audio differences between the devices. Fusion can be performed by addition or other means. For example, add the amplitudes of all comparison waveforms at the same time point to obtain a new waveform, and this new waveform is the second audio waveform. By analyzing the second audio waveform, it can be determined whether the device has a fault.

[0073] S4, determining the fault content corresponding to the second audio waveform.

[0074] After obtaining the second audio waveform, it is necessary to further analyze this waveform to determine the corresponding fault content. The second audio waveform contains key information about the operating state of the power transmission line equipment. Through reasonable processing and analysis, it can be determined whether the device has a fault and the specific type of the fault.

[0075] In some embodiments, the determining the fault content corresponding to the second audio waveform includes: S41, statistically calculating the absolute values of all wave points in the second audio waveform to obtain the first wave value, and statistically calculating the sum of the first wave values to obtain the total wave value.

[0076] To perform quantitative analysis on the second audio waveform, first, count the absolute values of all wave points (points on the waveform) in the second audio waveform, and record these absolute values as the first wave values. Then, add up all the first wave values to obtain the total wave value. The total wave value reflects the overall fluctuation degree of the second audio waveform and is an important indicator for judging whether the device has a fault. For example, if there are 1000 wave points in the second audio waveform, calculate the absolute value of each wave point, and then add up these 100 absolute values to obtain the total wave value.

[0077] S42. If the total wave value is less than or equal to the threshold wave value, the fault content is empty.

[0078] In this solution, a threshold wave value can be preset to distinguish the audio waveform characteristics when the device is operating normally and when a fault occurs. If the calculated total wave value is less than or equal to the threshold wave value, it indicates that the fluctuation degree of the second audio waveform is within the normal range, and the device is probably operating normally. At this time, the fault content is empty.

[0079] S43. If the total wave value is greater than the threshold wave value, the second audio waveform is segmented and processed to obtain the fault content.

[0080] When the total wave value is greater than the threshold wave value, it indicates that the device may have a fault, and it is necessary to further process the second audio waveform to determine the specific fault content.

[0081] The step of, if the total wave value is greater than the threshold wave value, segmenting and processing the second audio waveform to obtain the fault content includes: S431. If it is determined that the total wave values of a device and other devices are all greater than the threshold wave value, then calculate the average wave value by statistically calculating the average of the total wave values of the corresponding device compared to other devices.

[0082] For each device in the first device group, judge whether the total wave value obtained by comparing it with other devices is greater than the threshold wave value. If so, it indicates that the device may have a fault. Next, statistically calculate the average of the total wave values of this device compared to other devices to obtain the average wave value. For example, device A obtains total wave values when compared with devices B, C, and D respectively. Add up these total wave values and divide by the number of devices (here it is 3) to obtain the average wave value of device A.

[0083] S432. After segmenting and processing the average wave value, extract the characteristics of each segment to obtain the first segment wave value characteristics.

[0084] The average wave value is segmented according to a preset duration, which can be set according to the actual situation and analysis requirements. For example, the average wave value is divided into segments of 2 seconds each. Then, the features of each segment are extracted, and these features can include information such as wave peaks, wave valleys, and frequencies. The extracted features are recorded as the first segment of wave value features. Different fault types may cause the audio waveform to exhibit different features in different time periods. By extracting features in segments, the fault situation can be analyzed more carefully.

[0085] S433. Calculate the similarity between the first segment of wave value features and the preset segment wave value features, and determine the fault content corresponding to the segment wave value feature with the highest similarity.

[0086] The server has pre-stored a series of preset segment wave value features, and each feature corresponds to a specific fault content. Calculate the similarity between the first segment of wave value features and these preset segment wave value features. Among them, the segment wave value features can be the maximum value and the minimum value. The calculation method of similarity can be to calculate the difference between the maximum values and the difference between the minimum values, and then add the two differences. If the difference is smaller, it means the similarity is higher. Find the segment wave value feature with the highest similarity, and its corresponding fault content is the possible fault of the current device. For example, if it is found through calculation that the similarity between the first segment of wave value features and the preset segment wave value features representing insulator damage is the highest, then the fault content can be determined as insulator damage.

[0087] See Figure 2 , An embodiment of the present invention provides a distributed acquisition system for transmission lines, which is applied to a distributed server and is characterized by including: An association module for determining a first device group associated in the high-altitude area based on the attribute information and location information of the device to be detected; A detection module for generating a first detection group corresponding to the first device group, and extracting first audio data in the first device group based on the first detection group. The high-altitude detection device at least includes a drone and an audio collector; A processing module for extracting the first audio waveform in the first audio data and obtaining a second audio waveform by comparing and processing the first audio waveform based on the location information of the device to be detected in the first detection group; A determination module for determining the fault content corresponding to the second audio waveform.

[0088] The present invention also provides a storage medium in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the above various embodiments.

[0089] Among them, the storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium accessible by a general or special-purpose computer. For example, the storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Additionally, the ASIC can be located in a user device. Of course, the processor and the storage medium can also exist as discrete components in a communication device. The storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0090] The present invention also provides a program product, which includes execution instructions stored in a storage medium. At least one processor of the device can read the execution instructions from the storage medium, and the execution of the execution instructions by at least one processor enables the device to implement the methods provided by the above various embodiments.

[0091] In the above embodiments of the terminal or the server, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the present invention can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A distributed data collection method for power transmission lines, applied to distributed servers, characterized in that: include: Determine a first device group associated in the high-altitude area based on the attribute information and location information of the device to be detected; Generate a first detection group corresponding to the first device group, and extract first audio data in the first device group based on the first detection group, wherein the high-altitude detection device includes at least a drone and an audio collector; Extracting a first audio waveform from the first audio data, and obtaining a second audio waveform by comparing and processing the first audio waveform based on the position information of the device to be detected in the first detection group; Determine the fault content corresponding to the second audio waveform.

2. The method according to claim 1, characterized in that The method of determining the first device group associated in the high-altitude area based on the attribute information and location information of the device to be detected includes: Determine the synchronous monitoring distance and generate a synchronous monitoring frame according to the attribute information of the device to be detected; The line model corresponding to the power transmission line to be detected is retrieved, and the high-altitude area of ​​the line model is segmented based on the synchronous monitoring frame to obtain a first device group associated in the synchronous monitoring frame.

3. The method according to claim 2, characterized in that The step of segmenting the high-altitude area of ​​the line model based on the synchronous monitoring frame to obtain a first device group associated in the synchronous monitoring frame includes: The synchronous monitoring frame is a three-dimensional frame; Determine an initial positioning point in the line model, and set the center point of the three-dimensional frame corresponding to the initial positioning point; All devices in the three-dimensional frame are acquired in real time and their positions are adjusted adaptively to obtain a first device group associated in the synchronous monitoring frame.

4. The method according to claim 3, characterized in that The real-time acquisition of all devices in the three-dimensional frame and adaptive position adjustment to obtain a first device group associated in the synchronous monitoring frame includes: Acquire a first number of devices to be detected in the three-dimensional frame; If the first number is less than a preset value, the three-dimensional frame is moved until the first number is greater than or equal to the preset value; The devices to be detected in the three-dimensional frame are counted to obtain an associated first device group.

5. The method according to claim 4, characterized in that If the first number is less than a preset value, moving the three-dimensional frame includes: Acquire a device to be detected within the three-dimensional frame as a first device, and determine a distance between other devices to be detected outside the three-dimensional frame and the first device; Determine the first device and the second device corresponding to the minimum distance, and form a ray from the center point of the first device to the center point of the second device; The three-dimensional frame is moved a preset distance according to the ray direction.

6. The method according to claim 1, characterized in that The generating a first detection group corresponding to the first device group, and extracting first audio data in the first device group based on the first detection group, comprises: Determining, within the line model, a device model corresponding to each device in the first device group; Determine all possible movement modes based on the equipment model and the minimum interval module of the high-altitude detection equipment; The moving directions and moving distances of all first detection groups are determined based on all possible moving modes.

7. The method according to claim 6, characterized in that The minimum interval module based on the equipment model and the high-altitude detection equipment determines all possible movement modes, including: Generate a minimum interval module corresponding to the high-altitude detection equipment, and set the center point of the minimum interval module to correspond to the center point of the equipment model; The minimum interval module is moved at a preset angle until the minimum interval module does not intersect with any device in the line model to obtain all possible moving distances.

8. The method according to claim 6, characterized in that The determining the moving directions and moving distances of all first detection groups based on all possible moving modes includes: The maximum first moving distance of all high-altitude detection devices under the same preset angle is obtained by counting, the smallest second moving distance among all first moving distances is counted as the determined moving distance, and the preset angle corresponding to the second moving distance is used as the moving direction.

9. The method according to claim 1, characterized in that: The extracting the first audio waveform from the first audio data, and comparing and processing the first audio waveform based on the position information of the device to be detected in the first detection group to obtain the second audio waveform, includes: The audio collector is a microphone array, which includes sub-microphones at different positions, and each sub-microphone has a preset microphone number; Compare the first audio waveform of each device in the first device group with the first audio waveforms of other devices to obtain a different second audio waveform; The step of comparing the first audio waveform of each device in the first device group with the first audio waveforms of other devices to obtain a different second audio waveform includes: Compare the sub-waveforms corresponding to the microphone numbers in the first audio waveforms of every two devices to obtain a comparison waveform; The compared waveforms of all microphone numbers are fused to process the second audio waveform.

10. The method according to claim 9, characterized in that The determining of the fault content corresponding to the second audio waveform includes: Counting the absolute values ​​of all wave points in the second audio waveform to obtain the first wave value, and counting the sum of the first wave values ​​to obtain the total wave value; If the total wave value is less than or equal to the threshold wave value, the fault content is empty; If the total wave value is greater than the threshold wave value, the second audio waveform is processed in sections to obtain the fault content; If the total wave value is greater than the threshold wave value, the second audio waveform is processed in sections to obtain fault content, including: If it is determined that the total wave value of a device and other devices are greater than the threshold wave value, the average wave value of the total wave value of the corresponding device compared with other devices is calculated to obtain the average wave value; After segmenting the mean wave value (according to the preset time length), the characteristics of each segment are extracted to obtain the first segment wave value characteristics; The similarity between the first segment wave value feature and the preset segment wave value feature is calculated, and the fault content corresponding to the segment wave value feature with the highest similarity is determined.

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