Methods, devices, equipment, media, and procedures for monitoring transmission lines

Abstract

This application relates to a method, apparatus, equipment, medium, and program product for monitoring transmission lines, belonging to the field of transmission line monitoring technology. The method for monitoring transmission lines includes: acquiring sound signals within the monitoring range of the transmission line; determining characteristic data of the sound signals when the signal strength is greater than a signal strength threshold; determining the vehicle weight of a target vehicle operating within the monitoring range based on the characteristic data; and acquiring monitoring video of the monitoring range when the vehicle weight is greater than a preset weight threshold, in order to monitor the target vehicle. Through these steps, monitoring of the transmission line can be achieved without the continuous operation of the monitoring system, reducing monitoring energy consumption. Furthermore, there is no need to analyze the real-time captured monitoring video or images, which helps improve monitoring efficiency.

Description

Technical Field

[0001] This application relates to the field of line monitoring technology, and in particular to a method, apparatus, equipment, medium, and program product for monitoring transmission lines. Background Technology

[0002] As electricity demand continues to grow, power systems are becoming increasingly complex, requiring more transmission lines to carry electrical energy. These transmission lines need to be properly protected and managed to ensure the reliability and security of the power system.

[0003] Traditionally, power transmission lines are continuously monitored through surveillance systems to prevent damage from construction equipment such as excavators and cranes. However, this method requires continuous operation of the monitoring system, resulting in high energy consumption. Furthermore, the real-time captured video or images need to be analyzed to identify vehicles such as excavators and cranes, leading to a large data processing volume and low monitoring efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, equipment, medium, and program product for monitoring transmission lines that can reduce monitoring energy consumption and improve monitoring efficiency in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for monitoring transmission lines, comprising:

[0006] Acquire sound signals from the area to be monitored where the transmission line is located;

[0007] When the signal strength of an audio signal is greater than a signal strength threshold, the characteristic data of the audio signal are determined.

[0008] Based on the feature data, determine the vehicle weight of the target vehicles operating within the monitoring range;

[0009] If the vehicle weight exceeds a preset weight threshold, acquire surveillance video of the area to be monitored in order to monitor the target vehicle.

[0010] In one embodiment, determining the vehicle weight of a target vehicle operating within the monitoring range based on feature data includes: inputting the feature data into a vehicle weight recognition model to obtain the vehicle weight of the target vehicle; wherein the vehicle weight recognition model is obtained through the following steps: acquiring different training sample data and the sample vehicle weights corresponding to the training sample data; the training sample data includes sample sound signals of the sample vehicle in operation; using different training sample data as input data for the vehicle weight recognition model, and using the corresponding sample vehicle weights as training labels to train the vehicle weight recognition model.

[0011] In one embodiment, training the vehicle weight recognition model includes: for each iteration, obtaining the target population under the current iteration, the target population including at least one population individual, the population individual being used to characterize the model parameters of the vehicle weight recognition model; for each population individual, determining the predicted vehicle weight obtained by the vehicle weight recognition model from different training sample data under the model parameters corresponding to the population individual; determining the mean error between each predicted vehicle weight and the corresponding sample vehicle weight; if the iteration termination condition is not met, selecting at least one target population individual from the target population based on each mean error, and performing crossover and mutation on the target population individual to obtain the target population under the next iteration; if the iteration termination condition is met, using the population individual corresponding to the minimum mean error among the mean error values ​​as the model parameters of the vehicle weight recognition model.

[0012] In one embodiment, the method further includes: determining a target difference between the actual weight of the target vehicle and the vehicle weight; and adjusting the model parameters of the vehicle weight recognition model based on the actual weight, the vehicle weight, and the loss function of the vehicle weight recognition model if the target difference is greater than a preset difference.

[0013] In one embodiment, determining the feature data of the sound signal includes: reconstructing the sound signal based on the low-frequency and high-frequency components of the sound signal to obtain at least one frequency band signal; determining the feature vector of the frequency band signal for each frequency band signal; and using the feature vectors of different frequency band signals as the feature data of the sound signal.

[0014] In one embodiment, acquiring the sound signal of the area to be monitored where the transmission line is located includes: acquiring the initial sound signal of the area to be monitored where the transmission line is located; converting the initial sound signal into an initial frequency domain signal; and segmenting the initial frequency domain signal to obtain at least one sound signal.

[0015] Secondly, this application also provides a monitoring device for transmission lines, comprising:

[0016] The first acquisition module is used to acquire sound signals from the monitoring range where the transmission line is located;

[0017] The first determining module is used to determine the characteristic data of the sound signal when the signal strength of the sound signal is greater than the signal strength threshold.

[0018] The second determining module is used to determine the vehicle weight of the target vehicle operating within the monitoring range based on the feature data.

[0019] The second acquisition module is used to acquire monitoring video of the area to be monitored when the vehicle weight is greater than a preset weight threshold, so as to monitor the target vehicle.

[0020] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0021] Acquire sound signals from the area to be monitored where the transmission line is located;

[0022] When the signal strength of an audio signal is greater than a signal strength threshold, the characteristic data of the audio signal are determined.

[0023] Based on the feature data, determine the vehicle weight of the target vehicles operating within the monitoring range;

[0024] If the vehicle weight exceeds a preset weight threshold, acquire surveillance video of the area to be monitored in order to monitor the target vehicle.

[0025] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0026] Acquire sound signals from the area to be monitored where the transmission line is located;

[0027] When the signal strength of an audio signal is greater than a signal strength threshold, the characteristic data of the audio signal are determined.

[0028] Based on the feature data, determine the vehicle weight of the target vehicles operating within the monitoring range;

[0029] If the vehicle weight exceeds a preset weight threshold, acquire surveillance video of the area to be monitored in order to monitor the target vehicle.

[0030] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0031] Acquire sound signals from the area to be monitored where the transmission line is located;

[0032] When the signal strength of an audio signal is greater than a signal strength threshold, the characteristic data of the audio signal are determined.

[0033] Based on the feature data, determine the vehicle weight of the target vehicles operating within the monitoring range;

[0034] If the vehicle weight exceeds a preset weight threshold, acquire surveillance video of the area to be monitored in order to monitor the target vehicle.

[0035] The aforementioned monitoring methods, devices, equipment, media, and program products for transmission lines provide a raw data foundation for monitoring transmission lines by acquiring sound signals within the monitoring range of the transmission line. By comparing the signal strength of the sound signal with a signal strength threshold, invalid data is filtered out, preventing it from participating in subsequent processing calculations. When the sound signal strength exceeds the threshold, characteristic data of the sound signal is determined, and based on this characteristic data, the weight of the target vehicle operating within the monitoring range is determined. If the vehicle weight exceeds a preset weight threshold, it indicates the presence of a large vehicle near the transmission line. In this case, monitoring video of the monitoring range is acquired to monitor the target vehicle, preventing it from causing external damage to the transmission line. This method allows for transmission line monitoring without requiring continuous operation of the monitoring system, reducing monitoring energy consumption. It also eliminates the need for continuous capture and analysis of monitoring video or images, improving monitoring efficiency. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating a method for monitoring transmission lines in one embodiment;

[0038] Figure 2 This is a flowchart illustrating the training steps of a vehicle weight recognition model in one embodiment.

[0039] Figure 3 This is a flowchart illustrating the steps for determining feature data in one embodiment;

[0040] Figure 4 This is a flowchart illustrating a method for monitoring transmission lines in another embodiment;

[0041] Figure 5 This is a structural block diagram of a monitoring device for a transmission line in one embodiment;

[0042] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] In one embodiment, such as Figure 1 As shown, a method for monitoring transmission lines is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0045] S110. Obtain the sound signal of the area to be monitored where the transmission line is located.

[0046] The sound signal can be understood as the ambient sound signal within the monitoring area where the power transmission line is located. It's easy to understand that the ambient sound signal will change as different vehicles or objects pass through the monitoring area. By acquiring the sound signal within the monitoring area of ​​the power transmission line, the raw data foundation for monitoring the power transmission line is provided.

[0047] In an optional embodiment, after acquiring the sound signal of the area to be monitored where the transmission line is located, the sound signal can be preprocessed. Preprocessing includes at least one of filtering, noise reduction, and normalization of the sound signal.

[0048] In an optional embodiment, an initial sound signal of the area to be monitored within the transmission line can be acquired; the initial sound signal can be converted into an initial frequency domain signal; and the initial frequency domain signal can be segmented to obtain at least one sound signal. For example, the initial sound signal can be converted into an initial frequency domain signal using a Fast Fourier Transform.

[0049] S120. When the signal strength of the sound signal is greater than the signal strength threshold, determine the characteristic data of the sound signal.

[0050] In an alternative embodiment, the signal strength of the sound signal can be determined by determining the amplitude spectrum of the sound signal.

[0051] In one optional embodiment, the signal strength threshold can be set to a preset multiple of the ambient noise. In another optional embodiment, the signal strength threshold can also be determined by the mean, standard deviation, and sensitivity coefficient of the ambient noise. It should be noted that the signal strength threshold can also be set by a technician according to needs or experience, or determined through a large number of experiments, and this application does not impose any limitations on this.

[0052] For example, the signal strength threshold T kIt can be determined using the following formula:

[0053] ;

[0054] Among them, T k Indicates the signal strength threshold; This represents the mean value of environmental noise. The standard deviation of the ambient noise is represented by α; α represents the sensitivity coefficient. The sensitivity coefficient α can be set by a technician based on needs or experience, or determined through extensive experimentation; this application does not impose any limitations on this. For example, the sensitivity coefficient α can be set to 2 or 3.

[0055] For example, the mean of ambient noise It can be determined using the following formula:

[0056]

[0057] in, This represents the mean value of environmental noise. S represents a reference sound signal used to estimate ambient noise. i N represents the signal strength of the reference sound signal. ref This indicates the number of reference sound signals.

[0058] The reference sound signal can be understood as the sound signal used to estimate environmental noise within the sliding window. The reference sound signal can include a left reference sound signal and a right reference sound signal. The left reference sound signal can be understood as the reference sound signal before the target object passes through the monitoring range, and the right reference sound signal can be understood as the reference sound signal after the target object passes through the monitoring range.

[0059] For example, the standard deviation of environmental noise It can be determined using the following formula:

[0060]

[0061] in, This represents the standard deviation of environmental noise; S represents a reference sound signal used to estimate ambient noise. i N represents the signal strength of the reference sound signal. ref Indicates the number of reference sound signals; This represents the mean value of environmental noise.

[0062] S130. Based on the feature data, determine the vehicle weight of the target vehicles operating within the monitoring range.

[0063] In an optional embodiment, feature data can be input into a vehicle weight recognition model to obtain the vehicle weight of the target vehicle. The vehicle weight recognition model is obtained through the following steps: acquiring different training sample data and the corresponding sample vehicle weights; the training sample data includes sample sound signals of the sample vehicle in operation; using different training sample data as input data to the vehicle weight recognition model, and using the corresponding sample vehicle weights as training labels, the vehicle weight recognition model is trained.

[0064] The vehicle weight recognition model can be a neural network model. For example, a genetic algorithm can be used to train the vehicle weight recognition model, and the trained model can be obtained through selection, crossover, and mutation. For example, the termination condition for training the vehicle weight recognition model can be that the number of training sample data reaches a preset number, the model tends to converge, or the model accuracy reaches a preset accuracy threshold, etc., and this application does not impose any limitations on this.

[0065] S140. If the vehicle weight exceeds a preset weight threshold, acquire monitoring video of the area to be monitored in order to monitor the target vehicle.

[0066] The preset weight threshold can be set by technicians based on needs or experience, or determined through extensive experimentation; this application does not impose any limitations on this. It is easy to understand that if the vehicle's weight exceeds the preset weight threshold, it indicates the presence of a large target vehicle within the monitored area of ​​the transmission line, whose construction work could easily damage the transmission line. In this case, monitoring video of the monitored area is used to monitor the target vehicle.

[0067] In an optional embodiment, if the vehicle weight exceeds a preset weight threshold, a wake-up request can be sent to the control terminal. The control terminal will then respond to the wake-up request, activating the corresponding monitoring equipment based on the location information in the request and acquiring monitoring video of the area to be monitored. Furthermore, the target vehicle can be photographed and tracked in real time.

[0068] In an optional embodiment, the target distance between the target vehicle and the monitoring location within the monitoring range can be determined. If the target distance is greater than a preset distance, a stop message is sent to the monitoring equipment to terminate the photographing and tracking of the target vehicle. The preset distance can be set by technicians as needed or based on experience; this application does not impose any limitations on it. For example, the preset distance can be 100m.

[0069] For example, the control terminal can determine the target distance between the target vehicle and the monitoring location within the monitoring range through monitoring video or images; correspondingly, the control terminal can send a stop message to the monitoring equipment.

[0070] For example, the real-time location of a target vehicle can be determined using smart road beacons, and based on this location, the target distance between the target vehicle and a monitoring location within the monitoring range can be determined. Correspondingly, the smart road beacon can send a stop message to the monitoring equipment.

[0071] In an optional embodiment, if the vehicle weight is not greater than a preset weight threshold, a sleep request can be sent to the control terminal so that the control terminal responds to the sleep request and switches the corresponding monitoring device to sleep mode according to the location information in the wake-up request.

[0072] In an optional embodiment, YOLOv5 (You Only Look Once version 5, a deep learning-based object detection algorithm) can be used to predict the current position of the target vehicle in real time based on the detection results in each frame of the monitoring image and the position information of the target vehicle in the previous frame, and using a Kalman filter. This combines the advantages of object detection and tracking algorithms, improving the accuracy and robustness of target vehicle tracking.

[0073] In this embodiment, sound signals within the monitoring range of the transmission line are acquired, providing a raw data foundation for transmission line monitoring. By comparing the sound signal strength with a signal strength threshold, invalid data is filtered out, preventing it from participating in subsequent processing calculations. When the sound signal strength exceeds the threshold, characteristic data of the sound signal is determined, and based on this characteristic data, the weight of the target vehicle operating within the monitoring range is determined. If the vehicle weight exceeds a preset weight threshold, it indicates the presence of a large vehicle near the transmission line. In this case, monitoring video of the monitoring range is acquired to monitor the target vehicle, preventing it from causing external damage to the transmission line. This method allows for transmission line monitoring without continuous operation of the monitoring system, reducing monitoring energy consumption. It also eliminates the need for continuous capture and analysis of monitoring video or images, improving monitoring efficiency.

[0074] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment in which the training steps of the vehicle weight recognition model are refined.

[0075] See Figure 2 The training steps for the vehicle weight recognition model shown include:

[0076] S210. For each iteration, obtain the target population under the current iteration. The target population includes at least one population individual, which is used to characterize the model parameters of the vehicle weight recognition model.

[0077] In one optional embodiment, the initial model parameters of the vehicle weight recognition model can be used as the initial population individuals. Mutation and crossover are then performed on these initial population individuals to obtain the target population for the initial iteration process. In another optional embodiment, multiple initial model parameters can be pre-selected and used as the target population for the initial iteration process.

[0078] S220. For each individual in the population, determine the predicted vehicle weight obtained by the vehicle weight recognition model from different training sample data under the model parameters corresponding to the individual in the population.

[0079] S230. Determine the mean error between each predicted vehicle weight and the corresponding sample vehicle weight.

[0080] For ease of understanding, let's take individual x in the population as an example, F i This represents the i-th training sample data. This represents the weight of the vehicle corresponding to the i-th training sample data. This represents the predicted vehicle weight corresponding to the i-th training sample data under the model parameters for individual x in the population.

[0081] For example, the predicted vehicle weight can be determined using the following formula. With the corresponding sample vehicle weight The sum of squared errors E between them:

[0082]

[0083] Where E represents the sum of squared errors; N represents the number of training sample data.

[0084] For example, the sum of squared errors E can be divided by the number of training sample data N to obtain the mean error MSE corresponding to individual x in the population.

[0085] S240. If the iteration termination condition is not met, select at least one target population individual from the target population based on the mean of each error, and perform crossover and mutation on the target population individual to obtain the target population in the next iteration.

[0086] In an optional embodiment, selecting at least one target population individual from the target population may include: determining the fitness of different population individuals; determining the selection probability of different population individuals based on the fitness of the population individuals; and selecting at least one target population individual from the target population based on the selection probability of different population individuals.

[0087] Alternatively, the fitness f of an individual in the population can be determined using the following formula:

[0088] f = -MSE;

[0089] Where MSE represents the mean error for each individual in the population.

[0090] Optionally, the fitness f of each individual in the population can be summed to obtain the total fitness; for each individual in the population, the proportion of the individual's fitness in the total fitness can be determined to obtain the probability of being selected.

[0091] It should be further explained that crossover of individuals in the target population can be understood as randomly selecting two or more parent individuals from the target population for crossover operation, that is, generating new offspring individuals by exchanging some genes or parameters of the parent individuals.

[0092] It should be further explained that mutating individuals in the target population can be understood as performing mutation operations on newly generated offspring individuals, that is, randomly changing some gene or parameter values ​​of offspring individuals according to a preset probability, in order to increase the diversity of the population and prevent the algorithm from getting stuck in a local optimum.

[0093] S250. If the iteration termination condition is met, the population individual corresponding to the minimum mean error among the mean error values ​​is used as the model parameter of the vehicle weight recognition model.

[0094] In an optional embodiment, the iteration termination condition may be that the number of iterations reaches a preset number, the model tends to converge, or the model accuracy reaches a preset accuracy threshold, etc., and this application does not impose any limitations on this.

[0095] It is understandable that after training, the vehicle weight recognition model can be used to identify the weight of a target vehicle through sound signals. The relationship between the vehicle weight label output by the vehicle weight recognition model and the output layer can be represented as follows:

[0096]

[0097] in, This represents the activation value of the p-th node in the output layer; This represents the vehicle weight label that the vehicle weight recognition model ultimately outputs.

[0098] In an optional embodiment, the method for monitoring transmission lines further includes: determining a target difference between the actual weight of the target vehicle and the vehicle weight; and, if the target difference is greater than a preset difference, adjusting the model parameters of the vehicle weight identification model based on the actual weight, the vehicle weight, and the loss function of the vehicle weight identification model.

[0099] Optionally, the parameters of each layer of the neural network in the vehicle weight recognition model can be adjusted sequentially through backpropagation.

[0100] Optionally, the model parameters of the vehicle weight recognition model can be adjusted using the following gradient update formula:

[0101]

[0102] in, This indicates the adjusted model parameters; This indicates the model parameters before adjustment; Indicates the learning rate; Represents the loss function; y final y represents the actual weight of the target vehicle; y represents the vehicle weight identified by the vehicle weight recognition model.

[0103] In this embodiment, the target population can be effectively optimized in each iteration. By determining the mean error between the predicted vehicle weight and the sample vehicle weight under the corresponding model parameters for different training sample data, the accuracy of the model prediction can be quantified. When the iteration termination condition is not met, high-quality target population individuals are selected based on the mean error, and crossover and mutation operations are used to promote population evolution and improve the optimization efficiency of model parameters. When the iteration termination condition is met, the population individual with the minimum mean error can be directly obtained as the optimal model parameter, thereby improving the recognition accuracy and generalization ability of the vehicle weight recognition model.

[0104] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment in which the steps for determining feature data are refined.

[0105] See Figure 3 The steps for determining the feature data shown include:

[0106] S310. Based on the low-frequency and high-frequency components of the sound signal, reconstruct the sound signal to obtain at least one frequency band signal.

[0107] For example, a sound signal can be decomposed into low-frequency and high-frequency components using wavelet packet transform, which can be specifically represented as:

[0108]

[0109] in, For wavelet packet components; j represents the decomposition level; n represents the number of nodes; t represents the time or signal sampling point index; h m Represents the coefficients of the low-pass filter; g m This represents the coefficients of the high-pass filter; m is the filter index, i.e., the window width.

[0110] For example, by weighting the low-frequency and high-frequency components, the original signal can be reconstructed as:

[0111]

[0112] The frequency range corresponding to each frequency band is:

[0113]

[0114] Among them, f s The signal sampling frequency is represented by j; the number of decomposition layers is represented by j; and the number of nodes is represented by n. This indicates the width of each frequency band.

[0115] S320. For each frequency band signal, determine the characteristic vector of the frequency band signal.

[0116] The characteristic vector of the frequency band signal may include at least one of the following: frequency band signal energy, frequency band signal mean, frequency band signal standard deviation, and frequency band signal skewness.

[0117] For ease of understanding, the following section focuses on frequency band signals. Further explanation is provided. Here, j represents the number of decomposition layers; n represents the number of nodes; t represents the time or signal sampling point index; and T represents the time length of the frequency band signal.

[0118] For example, the frequency band signal energy can be extracted using the following formula. :

[0119]

[0120] For example, the mean of the frequency band signal can be extracted using the following formula. :

[0121]

[0122] For example, the standard deviation of the frequency band signal can be extracted using the following formula. :

[0123]

[0124] For example, the frequency band signal skewness can be extracted using the following formula. :

[0125]

[0126] For example, the frequency band signal can be determined using the following formula. eigenvector F:

[0127] .

[0128] S330. Use the feature vectors of signals from different frequency bands as feature data of the sound signal.

[0129] In this embodiment, the sound signal is reconstructed based on its low-frequency and high-frequency components to facilitate the analysis of its characteristics across different frequency ranges and improve computational efficiency. For each frequency band, its feature vector is determined, thereby capturing the key attributes of the sound signal within that band. Integrating these feature vectors from different frequency bands and using them as feature data for the sound signal enhances the richness of feature representation and reduces the impact of interference noise on the overall characteristics.

[0130] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment in which the monitoring method for transmission lines is described in detail.

[0131] See Figure 4 The monitoring methods for the transmission lines shown include:

[0132] S401. Obtain the initial sound signal of the area to be monitored where the transmission line is located.

[0133] S402. Convert the initial sound signal into an initial frequency domain signal.

[0134] S403. Segment the initial frequency domain signal to obtain at least one sound signal.

[0135] S404. When the signal strength of the sound signal is greater than the signal strength threshold, the sound signal is reconstructed based on the low-frequency and high-frequency components of the sound signal to obtain at least one frequency band signal.

[0136] S405. For each frequency band signal, determine the characteristic vector of the frequency band signal.

[0137] S406. Use the feature vectors of signals from different frequency bands as feature data of the sound signal.

[0138] S407. Input the feature data into the vehicle weight recognition model to obtain the vehicle weight of the target vehicle.

[0139] S408. When the vehicle weight exceeds a preset weight threshold, acquire monitoring video of the area to be monitored in order to monitor the target vehicle.

[0140] S409. Determine the target difference between the actual weight of the target vehicle and the vehicle weight.

[0141] S410. If the target difference is greater than the preset difference, adjust the model parameters of the vehicle weight recognition model according to the actual weight, vehicle weight and loss function of the vehicle weight recognition model.

[0142] The detailed technical content of steps S401-S410 above has been recorded in the above examples and will not be repeated here.

[0143] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0144] Based on the same inventive concept, this application also provides a transmission line monitoring device for implementing the transmission line monitoring method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more transmission line monitoring device embodiments provided below can be found in the limitations of the transmission line monitoring method described above, and will not be repeated here.

[0145] In one exemplary embodiment, such as Figure 5 As shown, a monitoring device for transmission lines is provided, comprising: a first acquisition module 510, a first determination module 520, a second determination module 530, and a second acquisition module 540, wherein:

[0146] The first acquisition module 510 is used to acquire the sound signal of the area to be monitored where the transmission line is located.

[0147] The first determining module 520 is used to determine the characteristic data of the sound signal when the signal strength of the sound signal is greater than the signal strength threshold.

[0148] The second determining module 530 is used to determine the vehicle weight of the target vehicle operating within the monitoring range based on the feature data.

[0149] The second acquisition module 540 is used to acquire monitoring video of the range to be monitored when the vehicle weight is greater than a preset weight threshold, so as to monitor the target vehicle.

[0150] In one embodiment, the second determining module 530 includes an input unit for inputting feature data into a vehicle weight recognition model to obtain the vehicle weight of the target vehicle. The vehicle weight recognition model is obtained through the following steps: acquiring different training sample data and the corresponding sample vehicle weights; the training sample data includes sample sound signals of the sample vehicle in operation; using different training sample data as input data for the vehicle weight recognition model, and using the corresponding sample vehicle weights as training labels, the vehicle weight recognition model is trained.

[0151] In one embodiment, the monitoring device for transmission lines further includes a training module, comprising: a first acquisition unit, configured to acquire, for each iteration, a target population under the current iteration, the target population including at least one population individual, the population individual being used to characterize the model parameters of the vehicle weight recognition model; a first determination unit, configured to, for each population individual, determine the predicted vehicle weight obtained by the vehicle weight recognition model from different training sample data under the model parameters corresponding to the population individual; a second determination unit, configured to determine the mean error between each predicted vehicle weight and the corresponding sample vehicle weight; a first processing unit, configured to, if the iteration termination condition is not met, select at least one target population individual from the target population based on each mean error, and perform crossover and mutation on the target population individual to obtain the target population under the next iteration; and a second processing unit, configured to, if the iteration termination condition is met, use the population individual corresponding to the minimum mean error among the mean error values ​​as the model parameters of the vehicle weight recognition model.

[0152] In one embodiment, the monitoring device for the transmission line further includes: a third determining module, used to determine a target difference between the actual weight of the target vehicle and the vehicle weight; and an adjusting module, used to adjust the model parameters of the vehicle weight recognition model according to the actual weight, the vehicle weight, and the loss function of the vehicle weight recognition model when the target difference is greater than a preset difference.

[0153] In one embodiment, the first determining module 520 includes: a reconstruction unit, configured to reconstruct the sound signal based on the low-frequency and high-frequency components of the sound signal to obtain at least one frequency band signal; a third determining unit, configured to determine the feature vector of the frequency band signal for each frequency band signal; and an integration unit, configured to use the feature vectors of different frequency band signals as feature data of the sound signal.

[0154] In one embodiment, the first acquisition module 510 includes: a second acquisition unit for acquiring an initial sound signal of the monitoring range where the transmission line is located; a conversion unit for converting the initial sound signal into an initial frequency domain signal; and a segmentation unit for segmenting the initial frequency domain signal to obtain at least one sound signal.

[0155] Each module in the aforementioned power transmission line monitoring device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0156] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for monitoring power transmission lines. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0157] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0158] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0159] Acquire sound signals from the area to be monitored where the transmission line is located;

[0160] When the signal strength of an audio signal is greater than a signal strength threshold, the characteristic data of the audio signal are determined.

[0161] Based on the feature data, determine the vehicle weight of the target vehicles operating within the monitoring range;

[0162] If the vehicle weight exceeds a preset weight threshold, acquire surveillance video of the area to be monitored in order to monitor the target vehicle.

[0163] In one embodiment, when the processor executes the computer program, it further implements the following steps: inputting feature data into the vehicle weight recognition model to obtain the vehicle weight of the target vehicle; wherein the vehicle weight recognition model is obtained through the following steps: acquiring different training sample data and the sample vehicle weights corresponding to the training sample data; the training sample data includes sample sound signals of the sample vehicle in operation; using different training sample data as input data for the vehicle weight recognition model, and using the corresponding sample vehicle weights as training labels to train the vehicle weight recognition model.

[0164] In one embodiment, when the processor executes the computer program, it further implements the following steps: for each iteration, obtaining the target population under the current iteration, the target population including at least one population individual, the population individual being used to characterize the model parameters of the vehicle weight recognition model; for each population individual, determining the predicted vehicle weight obtained by the vehicle weight recognition model from different training sample data under the model parameters corresponding to the population individual; determining the mean error between each predicted vehicle weight and the corresponding sample vehicle weight; if the iteration termination condition is not met, selecting at least one target population individual from the target population based on each mean error, and performing crossover and mutation on the target population individual to obtain the target population under the next iteration; if the iteration termination condition is met, using the population individual corresponding to the minimum mean error among the mean error values ​​as the model parameters of the vehicle weight recognition model.

[0165] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining a target difference between the actual weight of the target vehicle and the vehicle weight; and, if the target difference is greater than a preset difference, adjusting the model parameters of the vehicle weight recognition model based on the actual weight, the vehicle weight, and the loss function of the vehicle weight recognition model.

[0166] In one embodiment, when the processor executes the computer program, it further performs the following steps: reconstructing the sound signal based on the low-frequency and high-frequency components of the sound signal to obtain at least one frequency band signal; determining the feature vector of the frequency band signal for each frequency band signal; and using the feature vectors of different frequency band signals as feature data of the sound signal.

[0167] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring an initial sound signal within the monitoring range where the transmission line is located; converting the initial sound signal into an initial frequency domain signal; and segmenting the initial frequency domain signal to obtain at least one sound signal.

[0168] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0169] Acquire sound signals from the area to be monitored where the transmission line is located;

[0170] When the signal strength of an audio signal is greater than a signal strength threshold, the characteristic data of the audio signal are determined.

[0171] Based on the feature data, determine the vehicle weight of the target vehicles operating within the monitoring range;

[0172] If the vehicle weight exceeds a preset weight threshold, acquire surveillance video of the area to be monitored in order to monitor the target vehicle.

[0173] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: inputting feature data into the vehicle weight recognition model to obtain the vehicle weight of the target vehicle; wherein the vehicle weight recognition model is obtained through the following steps: acquiring different training sample data and the sample vehicle weights corresponding to the training sample data; the training sample data includes sample sound signals of the sample vehicle in operation; using different training sample data as input data for the vehicle weight recognition model, and using the corresponding sample vehicle weights as training labels to train the vehicle weight recognition model.

[0174] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: for each iteration, obtaining the target population under the current iteration, the target population including at least one population individual, the population individual being used to characterize the model parameters of the vehicle weight recognition model; for each population individual, determining the predicted vehicle weight obtained by the vehicle weight recognition model from different training sample data under the model parameters corresponding to the population individual; determining the mean error between each predicted vehicle weight and the corresponding sample vehicle weight; if the iteration termination condition is not met, selecting at least one target population individual from the target population based on each mean error, and performing crossover and mutation on the target population individual to obtain the target population under the next iteration; if the iteration termination condition is met, using the population individual corresponding to the minimum mean error among the mean error values ​​as the model parameters of the vehicle weight recognition model.

[0175] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining a target difference between the actual weight of the target vehicle and the vehicle weight; and, if the target difference is greater than a preset difference, adjusting the model parameters of the vehicle weight recognition model based on the actual weight, the vehicle weight, and the loss function of the vehicle weight recognition model.

[0176] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: reconstructing the sound signal based on the low-frequency and high-frequency components of the sound signal to obtain at least one frequency band signal; determining the feature vector of the frequency band signal for each frequency band signal; and using the feature vectors of different frequency band signals as feature data of the sound signal.

[0177] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring an initial sound signal within the monitoring range where the transmission line is located; converting the initial sound signal into an initial frequency domain signal; and segmenting the initial frequency domain signal to obtain at least one sound signal.

[0178] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0179] Acquire sound signals from the area to be monitored where the transmission line is located;

[0180] When the signal strength of an audio signal is greater than a signal strength threshold, the characteristic data of the audio signal are determined.

[0181] Based on the feature data, determine the vehicle weight of the target vehicles operating within the monitoring range;

[0182] If the vehicle weight exceeds a preset weight threshold, acquire surveillance video of the area to be monitored in order to monitor the target vehicle.

[0183] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: inputting feature data into the vehicle weight recognition model to obtain the vehicle weight of the target vehicle; wherein the vehicle weight recognition model is obtained through the following steps: acquiring different training sample data and the sample vehicle weights corresponding to the training sample data; the training sample data includes sample sound signals of the sample vehicle in operation; using different training sample data as input data for the vehicle weight recognition model, and using the corresponding sample vehicle weights as training labels to train the vehicle weight recognition model.

[0184] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: for each iteration, obtaining the target population under the current iteration, the target population including at least one population individual, the population individual being used to characterize the model parameters of the vehicle weight recognition model; for each population individual, determining the predicted vehicle weight obtained by the vehicle weight recognition model from different training sample data under the model parameters corresponding to the population individual; determining the mean error between each predicted vehicle weight and the corresponding sample vehicle weight; if the iteration termination condition is not met, selecting at least one target population individual from the target population based on each mean error, and performing crossover and mutation on the target population individual to obtain the target population under the next iteration; if the iteration termination condition is met, using the population individual corresponding to the minimum mean error among the mean error values ​​as the model parameters of the vehicle weight recognition model.

[0185] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining a target difference between the actual weight of the target vehicle and the vehicle weight; and, if the target difference is greater than a preset difference, adjusting the model parameters of the vehicle weight recognition model based on the actual weight, the vehicle weight, and the loss function of the vehicle weight recognition model.

[0186] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: reconstructing the sound signal based on the low-frequency and high-frequency components of the sound signal to obtain at least one frequency band signal; determining the feature vector of the frequency band signal for each frequency band signal; and using the feature vectors of different frequency band signals as feature data of the sound signal.

[0187] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring an initial sound signal within the monitoring range where the transmission line is located; converting the initial sound signal into an initial frequency domain signal; and segmenting the initial frequency domain signal to obtain at least one sound signal.

[0188] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0189] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0190] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for monitoring transmission lines, characterized in that, The method comprises: Acquire sound signals from the area to be monitored where the transmission line is located; When the signal strength of the sound signal is greater than a signal strength threshold, the characteristic data of the sound signal are determined. The feature data is input into the vehicle weight identification model to obtain the vehicle weight of the target vehicle operating within the monitoring range; If the vehicle weight exceeds a preset weight threshold, acquire surveillance video of the monitored area to monitor the target vehicle. The vehicle weight recognition model is obtained through the following steps: Acquire different training sample data and the corresponding sample vehicle weights; the training sample data includes sample sound signals of the sample vehicles in operation. Different training sample data are used as input data for the vehicle weight recognition model, and the corresponding sample vehicle weight is used as the training label to train the vehicle weight recognition model. The training of the vehicle weight recognition model includes: For each iteration, the target population under the current iteration is obtained. The target population includes at least one population individual, which is used to characterize the model parameters of the vehicle weight recognition model. For each individual in the population, the predicted vehicle weight obtained by the vehicle weight recognition model under the model parameters corresponding to the individual in the population is determined by different training sample data. Determine the mean error between each predicted vehicle weight and the corresponding sample vehicle weight; If the iteration termination condition is not met, at least one target population individual is selected from the target population based on the mean error of each individual, and crossover and mutation are performed on the target population individual to obtain the target population in the next iteration. If the iteration termination condition is met, the individual in the population corresponding to the minimum mean error among the mean error values ​​is used as the model parameter of the vehicle weight recognition model.

2. The method according to claim 1, characterized in that, The method further includes: Determine the target difference between the actual weight of the target vehicle and the vehicle weight; If the target difference is greater than the preset difference, the model parameters of the vehicle weight recognition model are adjusted according to the actual weight, vehicle weight, and the loss function of the vehicle weight recognition model.

3. The method according to any one of claims 1-2, characterized in that, The determination of the feature data of the sound signal includes: Based on the low-frequency and high-frequency components of the sound signal, the sound signal is reconstructed to obtain at least one frequency band signal; For each frequency band signal, determine the feature vector of the frequency band signal; The feature vectors of signals from different frequency bands are used as the feature data of the sound signal.

4. The method according to any one of claims 1-2, characterized in that, The acquisition of the sound signal within the monitoring range of the transmission line includes: Acquire the initial sound signal of the area to be monitored where the transmission line is located; Convert the initial sound signal into an initial frequency domain signal; The initial frequency domain signal is segmented to obtain at least one sound signal.

5. A monitoring device for a power transmission line, characterized in that, The device comprises: The first acquisition module is used to acquire sound signals from the monitoring range where the transmission line is located; The first determining module is used to determine the feature data of the sound signal when the signal strength of the sound signal is greater than the signal strength threshold. The second determining module is used to input the feature data into the vehicle weight recognition model to obtain the vehicle weight of the target vehicle operating within the monitoring range; The second acquisition module is used to acquire monitoring video of the monitoring range when the vehicle weight is greater than a preset weight threshold, so as to monitor the target vehicle. The vehicle weight recognition model is obtained through the following steps: acquiring different training sample data and the corresponding sample vehicle weights; the training sample data includes sample sound signals of the sample vehicles in operation; using different training sample data as input data for the vehicle weight recognition model, and using the corresponding sample vehicle weights as training labels, the vehicle weight recognition model is trained. The training of the vehicle weight recognition model includes: For each iteration, the target population under the current iteration is obtained. The target population includes at least one population individual, which is used to characterize the model parameters of the vehicle weight recognition model. For each individual in the population, the predicted vehicle weight obtained by the vehicle weight recognition model under the model parameters corresponding to the individual in the population is determined by different training sample data. Determine the mean error between each predicted vehicle weight and the corresponding sample vehicle weight; If the iteration termination condition is not met, at least one target population individual is selected from the target population based on the mean error of each individual, and crossover and mutation are performed on the target population individual to obtain the target population in the next iteration. If the iteration termination condition is met, the individual in the population corresponding to the minimum mean error among the mean error values ​​is used as the model parameter of the vehicle weight recognition model.

6. The apparatus according to claim 5, characterized in that, Also includes: The third determining module is used to determine the target difference between the actual weight of the target vehicle and the vehicle weight; The adjustment module is used to adjust the model parameters of the vehicle weight recognition model based on the actual weight, vehicle weight, and the loss function of the vehicle weight recognition model when the target difference is greater than a preset difference.

7. The apparatus according to any one of claims 5-6, characterized in that, The first determining module includes: The reconstruction unit is used to reconstruct the sound signal based on the low-frequency and high-frequency components of the sound signal to obtain at least one frequency band signal. The third determining unit is used to determine the feature vector of the frequency band signal for each frequency band signal; An integration unit is used to take the feature vectors of signals from different frequency bands as feature data of the sound signal.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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