An active early warning method for preventing external damage to power transmission lines

Abstract

The present invention relates to the field of active early warning technology, and specifically, to an active early warning method for preventing external damage to power transmission lines. It includes the following steps: installing an ultrasonic sensor and a laser rangefinder at the same position of a tower of a power transmission line, converting the ultrasonic pulse signal received by the ultrasonic sensor and the laser rangefinder into an electrical signal, and further converting it into a digital signal; a central processing unit analyzes the digital signal in real time, compares the digital signal with a safety distance threshold, and determines whether there is an abnormality. The method can dynamically adjust nonlinear transformation parameters, adapt to changes in different signal strengths, ensure that the electrical signal can better adapt to the input range of the ADC when converted to a digital signal, and can achieve multi-level management and response to potential risks, ensuring that appropriate measures can be taken in different situations, thereby improving the safety and reliability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of active early warning, and in particular to an active early warning method for preventing external damage to a power transmission line. Background Art

[0002] Transmission lines are an important part of the power system, responsible for transmitting the electricity generated by power stations to various users. However, during operation, transmission lines face the risk of multiple external damages (referred to as "external damage"), such as contact with power construction equipment, which may lead to line failures, power outages, and even fires. In order to effectively deal with these risks, existing transmission line monitoring systems have adopted some technical means, such as sensor monitoring and data acquisition. However, these systems still have some limitations in practical applications. For example, the signal strength around the transmission line may fluctuate due to environmental changes, and the fixed nonlinear transformation parameters cannot adapt to these changes, which may cause the signal to exceed the input range of the ADC and affect the accuracy of signal acquisition. In addition, the existing early warning methods are mostly simple data superposition or logical judgment and fixed threshold settings, which often do not work well in complex and changing environments. Therefore, an active early warning method for preventing external damage to transmission lines is designed. Summary of the invention

[0003] The purpose of the present invention is to provide an active early warning method for preventing external damage to a power transmission line, so as to solve the problems mentioned in the above background technology that the signal strength around the transmission line may fluctuate due to environmental changes, the fixed nonlinear transformation parameters cannot adapt to these changes, which may cause the signal to exceed the input range of the ADC, and the early warning methods are mostly simple data superposition or logical judgment and fixed threshold setting.

[0004] To achieve the above object, the present invention provides an active early warning method for preventing external damage to a power transmission line, comprising the following steps:

[0005] S1. Install ultrasonic sensors and laser rangefinders at the same position of the transmission line towers. Use Beidou positioning technology to locate the precise position of the transmission equipment through the locator installed on the power construction equipment. When the power construction equipment approaches the transmission line and the tower, it is sent to the camera installed on the tower. The camera identifies the type of object. If the type of the object is power construction equipment, it sends a signal to the ultrasonic sensor and the laser rangefinder. The ultrasonic sensor and the laser rangefinder emit and receive ultrasonic pulse signals to measure the distance between the power construction equipment and the transmission line. At the same time, install environmental sensors on the transmission line towers to collect environmental data.

[0006] S2, converting the ultrasonic pulse signals received by the ultrasonic sensor and the laser rangefinder into electrical signals, and further converting them into digital signals, and then transmitting the digital signals to the central processing unit, and at the same time, transmitting the environmental data to the central processing unit;

[0007] S3, the central processing unit pre-processes the received environmental data, and adjusts the parameters of the ultrasonic sensor and the laser rangefinder in real time according to the pre-processed environmental data; at the same time, the central processing unit collects historical data of the ultrasonic sensor, the laser rangefinder and the environment, analyzes the error distribution of ultrasonic and laser ranging under different environmental conditions, and dynamically adjusts the safety distance threshold of the power construction equipment and the transmission line according to the historical data and the current environmental data;

[0008] S4. The central processing unit analyzes the digital signal in real time, compares the digital signal with the safety distance threshold, determines whether there is an abnormality, and generates an alarm signal if an abnormality occurs and sends the alarm signal to the sound and light alarm.

[0009] As a further improvement of the technical solution, the ultrasonic sensor and the laser distance measurement collect data at the same time point, and the data of the ultrasonic distance measurement can be corrected using the data of the laser distance measurement.

[0010] As a further improvement of the technical solution, in S2, the ultrasonic pulse signal received by the ultrasonic sensor and the laser rangefinder is converted into an electrical signal, and further converted into a digital signal, and then the digital signal is transmitted to the central processing unit. The specific steps are as follows:

[0011] S21, in the pulse signal receiving stage, using an adaptive gain control circuit to dynamically adjust the amplification factor according to the received pulse signal strength;

[0012] S22. In the process of converting the electrical signal into a digital signal, the electrical signal with a large dynamic range is compressed into the range of the ADC input through nonlinear transformation, and then the electrical signal after the dynamic range is expanded is sent to the ADC for digital-to-analog conversion, and the influence factors of noise and meteorology are introduced in the process of converting the electrical signal into a digital signal for optimization;

[0013] S23. After being converted into a digital signal, the high-dimensional digital signal is compressed into a low-dimensional digital signal through sparse representation and random measurement matrix, and then transmitted to the central processing unit.

[0014] As a further improvement of the technical solution, the S22 is specifically as follows:

[0015] Nonlinear transformation function:

[0016] ;

[0017] in, is the signal amplitude after nonlinear transformation; is the scale factor; is the gain factor; is the amplitude of the original electrical signal; is the bias factor;

[0018] Real-time signal strength:

[0019] ;

[0020] ;

[0021] in, For The maximum value of the signal at time; For The signal minimum value at time; For The amplitude of the original signal in time;

[0022] The input range of the ADC is known to be ,according to and Adjustment , and :

[0023] ;

[0024] ;

[0025] .

[0026] As a further improvement of the technical solution, in S22, noise and meteorological factors are introduced to optimize the process of converting the electrical signal into a digital signal, and the optimization is as follows:

[0027] ;

[0028] ;

[0029] in, is the meteorological compensation factor; is the meteorological compensation coefficient; is a comprehensive meteorological index; is the reference meteorological index; is the noise suppression factor; is the noise suppression coefficient; is the noise level;

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] in, is the optimized gain factor; is the optimized scaling factor; is the signal amplitude after optimization and nonlinear transformation.

[0035] As a further improvement of the technical solution, the specific steps of S23 are as follows:

[0036] S231, selecting a dilute basis and estimating the sparsity of the digital signal, projecting the digital signal onto the selected sparse basis, and obtaining a sparse representation vector;

[0037] S232, designing a measurement matrix according to the characteristics of the digital signal and the result of the sparse representation, and multiplying the sparse representation vector by the measurement matrix to obtain a measurement value of the low-dimensional digital signal;

[0038] S233, quantizing, encoding and compressing the measured value of the low-dimensional digital signal, and finally transmitting the compressed digital signal to the central processing unit through the network.

[0039] As a further improvement of the technical solution, the S231 is specifically as follows:

[0040]

[0041] in, is a sparse basis; A model for predicting the optimal sparse basis; Parameters of the model for predicting the optimal sparse basis;

[0042]

[0043] in, is the estimated sparsity; Estimation model for sparsity; is the input digital signal; are the parameters of the model;

[0044]

[0045] in, is a sparse representation vector; is the transpose operation.

[0046] As a further improvement of the technical solution, the S232 is specifically as follows:

[0047]

[0048] in, is the measurement matrix, which is used to convert the high-dimensional sparse representation vector Convert to low-dimensional measurements ; is a neural network model; are the parameters of the neural network model;

[0049]

[0050] in, is the measurement value of the low-dimensional digital signal.

[0051] As a further improvement of the present technical solution, the safety distance threshold between the power construction equipment and the transmission line in S3 includes a primary threshold and an advanced threshold.

[0052] As a further improvement of the technical solution, the digital signal is compared with the safety distance threshold in S4 to determine whether there is an abnormal situation, as follows:

[0053] If the distance measured by either ultrasonic ranging or laser ranging is less than the preset primary threshold, a primary alarm is triggered; if both the ultrasonic ranging and laser ranging are less than the preset advanced threshold, an advanced alarm is triggered; among them, the primary alarm measures include issuing an audible and visual alarm and initiating preliminary response measures; the advanced alarm measures include issuing a strong audible and visual alarm and stopping the operation of the power construction equipment.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. In this active early warning method for preventing external damage to power transmission lines, by introducing a meteorological compensation factor, it is possible to adapt to signal characteristics under different meteorological conditions and improve the robustness of the system; by introducing a noise suppression factor, it is possible to reduce the impact of noise on signal processing and improve signal quality; by calculating the maximum and minimum signal values ​​in real time, it is possible to dynamically adjust nonlinear transformation parameters to adapt to changes in different signal strengths; it is possible to significantly improve the effect of nonlinear transformation and ensure that electrical signals can better adapt to the input range of ADC when converted into digital signals.

[0056] 2. In this active early warning method for preventing external damage to power transmission lines, primary and advanced alarms are set to facilitate hierarchical management, which can achieve multi-level management and response to potential risks, ensuring that appropriate measures can be taken in different situations, thereby improving the safety and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 As shown, an active early warning method for preventing external damage to a power transmission line is provided, comprising the following steps:

[0060] S1. Install ultrasonic sensors and laser rangefinders at the same position of the transmission line towers. Use Beidou positioning technology to locate the precise position of the transmission equipment through the locator installed on the power construction equipment. When the power construction equipment approaches the transmission line and the tower, it is sent to the camera installed on the tower. The camera identifies the type of object. If the type of the object is power construction equipment, it sends a signal to the ultrasonic sensor and the laser rangefinder. The ultrasonic sensor and the laser rangefinder emit and receive ultrasonic pulse signals to measure the distance between the power construction equipment and the transmission line. At the same time, install environmental sensors on the transmission line towers to collect environmental data.

[0061] The images captured by the camera are transmitted to the central processing unit, which processes and analyzes the images, identifies the type of objects, extracts the features of the objects using image processing technology, and pre-trains a deep learning model using a large number of labeled data sets to enable it to accurately identify different types of objects. The captured images are input into the trained model, and the model outputs the type of object.

[0062] The ultrasonic sensor and the laser rangefinder collect data at the same time point, and the data of the laser rangefinder can be used to correct the data of the ultrasonic rangefinder; for example, if the data of the ultrasonic rangefinder differs greatly from the data of the laser rangefinder, the data of the laser rangefinder can be used as a reference to correct the error of the ultrasonic rangefinder;

[0063] Specifically, during the implementation process, the steps are as follows:

[0064] Locator data acquisition: The locator on the power construction equipment obtains the equipment location in real time and sends it to the central processing unit via wireless communication.

[0065] Distance monitoring: When the device enters the preset safety range, the central processing unit triggers the camera on the tower to start working.

[0066] Image Capture: The camera captures images of objects close to the tower.

[0067] Image preprocessing: grayscale, normalize, resize, etc.

[0068] Feature extraction: Use convolutional neural network (CNN) to extract image features.

[0069] Model structure:

[0070] Input layer: preprocessed image data.

[0071] Convolutional layer: extracts image features.

[0072] Convolution operation:

[0073] ;

[0074] in, is the input image, is the convolution kernel, represents the pixel coordinates of the output feature map, is the index coordinate of the convolution kernel.

[0075] Activation function: Introduce nonlinear characteristics and use the ReLU function: ;

[0076] Pooling layer: Reduce feature dimensions and retain main features.

[0077] Fully connected layer: maps the features extracted by the convolutional layer to the classification space.

[0078] Output layer: Use Softmax function for multi-classification:

[0079]

[0080] in, For the The scores of the categories.

[0081] Training process:

[0082] Loss function: The cross entropy loss function is used to measure the difference between the predicted result and the true label:

[0083] ;

[0084] in, is the true label, is the predicted probability.

[0085] Optimization algorithm: Update model parameters using gradient descent or Adam optimizer.

[0086] Back propagation: Calculate the partial derivative of the loss function with respect to each parameter and update the parameters.

[0087] Object recognition: Input features into a pre-trained deep learning model to identify the object type.

[0088] Judgment conditions for object type judgment: If the object type output by the model is power construction equipment, continue to perform the distance measurement operation.

[0089] Synchronous distance measurement: Ultrasonic sensor and laser rangefinder measure distance simultaneously.

[0090] Ultrasonic ranging formula: ;

[0091] in, is the speed of sound, is the ultrasonic round trip time.

[0092] Laser ranging formula: ;

[0093] in, is the speed of light, is the laser round trip time.

[0094] Data correction:

[0095] Calculate the difference in measurements:

[0096] if Exceeding the threshold , then correct the ultrasonic ranging data:

[0097] Sound velocity correction: Correct the sound velocity based on the temperature data provided by the environmental sensor :

[0098]

[0099] in, is the temperature (℃).

[0100] Recalculate the ultrasonic distance:

[0101]

[0102] Data fusion: Combine the corrected ultrasonic distance and laser ranging distance to get the final measurement result.

[0103] Result output: Send distance information to the safety control system to trigger corresponding early warning or control measures.

[0104] S2, converting the ultrasonic pulse signals received by the ultrasonic sensor and the laser rangefinder into electrical signals, and further converting them into digital signals, and then transmitting the digital signals to the central processing unit, and at the same time, transmitting the environmental data to the central processing unit;

[0105] In S2, the ultrasonic pulse signals received by the ultrasonic sensor and the laser rangefinder are converted into electrical signals, and further converted into digital signals, and then the digital signals are transmitted to the central processing unit. The specific steps are as follows:

[0106] S21. In the pulse signal receiving stage, an adaptive gain control circuit is used to dynamically adjust the amplification factor according to the strength of the received pulse signal to ensure that the amplitude of the output signal is stable within a suitable range;

[0107] S22. In the process of converting the electrical signal into a digital signal, the electrical signal with a large dynamic range is compressed into the range of the ADC input through nonlinear transformation, and then the electrical signal after the dynamic range is expanded is sent to the ADC for digital-to-analog conversion, and the influence factors of noise and meteorology are introduced in the process of converting the electrical signal into a digital signal for optimization;

[0108] Dynamic range extension technology is usually used in the process of converting electrical signals into digital signals. Specifically, this step is to process the electrical signal before converting it into a digital signal to ensure that its amplitude is suitable for the input range of the ADC. S22 is as follows:

[0109] Nonlinear transformation function:

[0110] ;

[0111] in, is the signal amplitude after nonlinear transformation; is the proportional factor, which is used to adjust the amplitude of the output signal; is the gain factor, which is used to adjust the amplitude of the output signal; is the amplitude of the original electrical signal; is the bias factor, which is used to prevent the logarithmic function from diverging near zero;

[0112] Real-time signal strength:

[0113] ;

[0114] ;

[0115] in, For The maximum value of the signal at time; For The signal minimum value at time; For The amplitude of the original signal in time;

[0116] The input range of the ADC is known to be ,according to and Adjustment , and :

[0117] To prevent the logarithmic function from diverging near zero, choose an appropriate bias factor :

[0118] ;

[0119] To ensure that the signal is within the appropriate output range after logarithmic transformation, select B so that Map to :

[0120] ;

[0121] To ensure that the signal is within the appropriate output range after logarithmic transformation, select A so that Map to :

[0122] ;

[0123] In the above formula , and It is dynamically adjusted according to the real-time signal strength to ensure the best signal compression effect under different environmental conditions. It uses logarithmic transformation as the basic function and combines it with adaptive parameter adjustment to enable the system to maintain high accuracy and high reliability in signals with a large dynamic range. Traditional logarithmic transformation parameters are usually fixed, and the adaptive adjustment here improves the flexibility and adaptability of the system.

[0124] In S22, noise and meteorological factors are introduced to optimize the process of converting electrical signals into digital signals. The optimization results are as follows:

[0125] ;

[0126] ;

[0127] in, is the meteorological compensation factor; is the meteorological compensation coefficient; is a comprehensive meteorological index; is the reference meteorological index; is the noise suppression factor; is the noise suppression coefficient; is the noise level;

[0128] ;

[0129] ;

[0130] ;

[0131] ;

[0132] in, is the optimized gain factor; is the optimized scaling factor; is the signal amplitude after optimization and nonlinear transformation;

[0133] By introducing a meteorological compensation factor, we can adapt to signal characteristics under different meteorological conditions and improve the robustness of the system; by introducing a noise suppression factor, we can reduce the impact of noise on signal processing and improve signal quality; by calculating the maximum and minimum signal values ​​in real time, we can dynamically adjust the nonlinear transformation parameters to adapt to changes in different signal strengths; this can significantly improve the effect of nonlinear transformation and ensure that the electrical signal can better adapt to the input range of the ADC when converted into a digital signal.

[0134] The specific implementation method is as follows:

[0135] Normalize each meteorological parameter (temperature T, humidity H, air pressure P, wind speed W, rainfall R, light intensity L) so that its value is between [0,1];

[0136] Assign weights to each meteorological parameter based on the actual degree of impact of each meteorological factor on the signal , , , , , ,The determination of weights is based on historical data analysis;

[0137] Calculate the comprehensive meteorological index:

[0138]

[0139] in, , , , , , Corresponding to the weight of each meteorological parameter, , , , , , They correspond to the normalized meteorological parameters respectively; the normalized meteorological parameters are formed into a matrix, the main components are extracted, and the dimension is reduced.

[0140] Temperature: T = 35°C, normalized temperature: ;

[0141] Humidity: H=80%, normalized humidity: ;

[0142] Air pressure: P=1000hPa, normalized air pressure: ;

[0143] Wind speed: W=5m / s, normalized wind speed: ;

[0144] Rainfall: R = 30 mm, normalized rainfall: R

[0145] Light intensity: L=6001x, normalized light intensity:

[0146] Temperature Weight: =0.25;

[0147] Moisture weight: =0.2w;

[0148] Air Pressure Weight: =0.2;

[0149] Wind speed weight: =0.15;

[0150] Rainfall weight: =0.1;

[0151] Light intensity weight: =0.1.

[0152] Calculate the comprehensive weather index :

[0153]

[0154] Reference weather index:

[0155] Meteorological compensation coefficient: α=0.1

[0156]

[0157] S23, after being converted into a digital signal, the high-dimensional digital signal is compressed into a low-dimensional digital signal through sparse representation and random measurement matrix, and then transmitted to the central processing unit;

[0158] Compressing high-dimensional digital signals into low-dimensional digital signals and transmitting them to the central processing unit can make use of compressed sensing technology, which is a signal processing technology that can recover the signal with a small number of measurements at sampling rates far below the Nyquist rate;

[0159] The specific steps of S23 are as follows:

[0160] S231, selecting a dilute basis and estimating the sparsity of the digital signal, projecting the digital signal onto the selected sparse basis, and obtaining a sparse representation vector;

[0161] The purpose of choosing a suitable sparse basis is to represent the original signal with as few non-zero coefficients as possible. The choice of sparse basis depends on the characteristics of the signal itself. At the same time, estimating the sparsity of the signal is to determine the degree to which the signal can be compressed. Using a sparse basis can significantly reduce the number of parameters required for signal representation, which is very beneficial for subsequent compression processing. In addition, accurately estimating the sparsity of the signal helps to design an effective measurement matrix and improve compression efficiency.

[0162] S232, designing a measurement matrix according to the characteristics of the digital signal and the result of the sparse representation, and multiplying the sparse representation vector by the measurement matrix to obtain a measurement value of the low-dimensional digital signal;

[0163] The purpose of designing a measurement matrix is ​​to ensure that the low-dimensional measurements obtained from the original high-dimensional signal can retain enough information to accurately reconstruct the original signal at the receiving end. A good measurement matrix can ensure that the signal reconstruction quality is good even when there are fewer measurements. This not only reduces the bandwidth requirements for data transmission, but also reduces storage requirements, and may also speed up signal processing.

[0164] S233, quantize, encode and compress the measured value of the low-dimensional digital signal, and finally transmit the compressed digital signal to the central processing unit through the network; quantization refers to the process of converting continuous values ​​into discrete values, and encoding is the encoding process of quantized data in order to improve data transmission efficiency. Compression can reduce the amount of data, thereby reducing transmission costs and storage requirements; finally, the compressed digital signal y~y~ will be sent to the central processing unit, where the original high-dimensional signal can be restored from the low-dimensional measurement value using compressed sensing reconstruction algorithms (such as basis pursuit, matching pursuit, etc.).

[0165] S231 is as follows:

[0166] The choice of sparse basis depends on the characteristics of the signal. Common sparse bases include wavelet transform, discrete cosine transform (DCT), Fourier transform, etc.

[0167]

[0168] in, is a sparse basis, which is a set of basis functions under which the signal can be sparsely represented; A model for predicting the optimal sparse basis; Parameters of the model for predicting the optimal sparse basis;

[0169] By selecting the most appropriate sparse basis, the representation of the signal under the basis can be made as sparse as possible, that is, most coefficients are close to zero, and there are only a few non-zero coefficients; The sparse basis of this embodiment adopts a wavelet basis, because the wavelet basis is suitable for signals with transient characteristics;

[0170]

[0171] in, is the estimated sparsity, i.e., the number of non-zero coefficients when the signal is represented in the selected sparse basis; Estimation model for sparsity; is the input digital signal; are the parameters of the model;

[0172]

[0173] in, is a sparse representation vector; is the transpose operation;

[0174] S232 is as follows:

[0175] Suppose we use a neural network to learn the optimal measurement matrix, and the neural network model is , then the measurement matrix It can be expressed as:

[0176]

[0177] in, is the measurement matrix, which is used to convert the high-dimensional sparse representation vector Convert to low-dimensional measurements ; is a neural network model; are the parameters of the neural network model;

[0178]

[0179] in, is the measurement value of the low-dimensional digital signal;

[0180] The continuous measurement value The quantized data is converted into a discrete digital signal, and the quantized data is encoded and compressed. In this embodiment, Huffman encoding is used to send the encoded data to the central processing unit through the network, and the received data is decoded and dequantized in the central processing unit to obtain the measured value. , reconstruct the signal using the compressed sensing reconstruction algorithm,

[0181]

[0182]

[0183] represents the reconstructed signal.

[0184] By using a neural network to learn the optimal measurement matrix and projecting the high-dimensional sparse representation vector into a low-dimensional space, efficient signal compression can be achieved. This method not only improves the efficiency of signal transmission and storage, but also ensures the high quality of signal reconstruction.

[0185] S3, the central processing unit pre-processes the received environmental data, and adjusts the parameters of the ultrasonic sensor and the laser rangefinder in real time according to the pre-processed environmental data; at the same time, the central processing unit collects historical data of the ultrasonic sensor, the laser rangefinder and the environment, analyzes the error distribution of ultrasonic and laser ranging under different environmental conditions, and dynamically adjusts the safety distance threshold of the power construction equipment and the transmission line according to the historical data and the current environmental data;

[0186] The safety distance thresholds between power construction equipment and transmission lines in S3 include primary thresholds and advanced thresholds;

[0187] S4, the central processing unit analyzes the digital signal in real time, compares the digital signal with the safety distance threshold, determines whether there is an abnormality, and generates an alarm signal if an abnormality occurs and sends the alarm signal to the sound and light alarm;

[0188] S4 compares the digital signal with the safety distance threshold to determine whether there is an abnormality, as follows:

[0189] If the distance measured by either ultrasonic ranging or laser ranging is less than the preset primary threshold, a primary alarm is triggered; if both the ultrasonic ranging and laser ranging are less than the preset advanced threshold, an advanced alarm is triggered;

[0190] Among them, the primary alarm measures include sound and light alarms to alert operators and initiate preliminary response measures; the advanced alarm measures include strong sound and light alarms (including sirens and flashing lights) and stopping the operation of the crane to avoid potential dangers;

[0191] The primary alarm is to issue a warning when the power construction equipment and the transmission line have not yet reached a critical distance, reminding the operator to pay attention; by slowing down the speed of the power construction equipment or increasing the monitoring frequency, the risk of accidents is reduced, and the alarm events are recorded to facilitate subsequent analysis and improvement of safety measures;

[0192] When a high-level alarm occurs, the operation of the power construction equipment will be stopped to avoid potential dangers, and higher-level managers or security teams will be notified to ensure that there are sufficient resources and authority to handle the emergency. Detailed alarm events will be recorded and reports will be generated to facilitate subsequent accident investigations and accountability.

[0193] Setting primary and advanced alarms facilitates hierarchical management. Primary alarms are used to deal with general risks and avoid further deterioration of risks through early warning and preventive measures. Advanced alarms are used to deal with serious risks and ensure the safety of personnel and equipment by taking immediate emergency measures. This can achieve multi-level management and response to potential risks, ensuring that appropriate measures can be taken in different situations, thereby improving the safety and reliability of the system.

[0194] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An active early warning method for preventing external damage to power transmission lines. Features: The steps include: S1. Install ultrasonic sensors and laser rangefinders at the same position of the transmission line towers. Use Beidou positioning technology to locate the precise position of the transmission equipment through the locator installed on the power construction equipment. When the power construction equipment approaches the transmission line and the tower, it is sent to the camera installed on the tower. The camera identifies the type of object. If the type of the object is power construction equipment, it sends a signal to the ultrasonic sensor and the laser rangefinder. The ultrasonic sensor and the laser rangefinder emit and receive ultrasonic pulse signals to measure the distance between the power construction equipment and the transmission line. At the same time, install environmental sensors on the transmission line towers to collect environmental data. S2, converting the ultrasonic pulse signals received by the ultrasonic sensor and the laser rangefinder into electrical signals, and further converting them into digital signals, and then transmitting the digital signals to the central processing unit, and at the same time, transmitting the environmental data to the central processing unit; In S2, the ultrasonic pulse signals received by the ultrasonic sensor and the laser rangefinder are converted into electrical signals, and further converted into digital signals, and then the digital signals are transmitted to the central processing unit. The specific steps are as follows: S21, in the pulse signal receiving stage, using an adaptive gain control circuit to dynamically adjust the amplification factor according to the received pulse signal strength; S22. In the process of converting the electrical signal into a digital signal, the electrical signal with a large dynamic range is compressed into the range of the ADC input through nonlinear transformation, and then the electrical signal after the dynamic range is expanded is sent to the ADC for digital-to-analog conversion, and the influence factors of noise and meteorology are introduced in the process of converting the electrical signal into a digital signal for optimization; The S22 is specifically as follows: Nonlinear transformation function: ； in, is the signal amplitude after nonlinear transformation; is the scale factor; is the gain factor; is the amplitude of the original electrical signal; is the bias factor; Real-time signal strength: ； ； in, For The maximum value of the signal at time; For The signal minimum value at time; For The amplitude of the original signal in time; The input range of the ADC is known to be ,according to and Adjustment , and : ； ； ； In S22, noise and weather factors are introduced to optimize the process of converting the electrical signal into a digital signal. The optimization is as follows: ； ； in, is the meteorological compensation factor; is the meteorological compensation coefficient; is a comprehensive meteorological index; is the reference meteorological index; is the noise suppression factor; is the noise suppression coefficient; is the noise level; ； ； ； ； in, is the optimized gain factor; is the optimized scaling factor; is the signal amplitude after optimization and nonlinear transformation; S23, after being converted into a digital signal, the high-dimensional digital signal is compressed into a low-dimensional digital signal through sparse representation and random measurement matrix, and then transmitted to the central processing unit; S3, the central processing unit pre-processes the received environmental data, and adjusts the parameters of the ultrasonic sensor and the laser rangefinder in real time according to the pre-processed environmental data; at the same time, the central processing unit collects historical data of the ultrasonic sensor, the laser rangefinder and the environment, analyzes the error distribution of ultrasonic and laser ranging under different environmental conditions, and dynamically adjusts the safety distance threshold of the power construction equipment and the transmission line according to the historical data and the current environmental data; S4, the central processing unit analyzes the digital signal in real time, compares the digital signal with the safety distance threshold, determines whether there is an abnormality, and generates an alarm signal if an abnormality occurs and sends the alarm signal to the sound and light alarm; The ultrasonic sensor and the laser distance measurement collect data at the same time point, and the data of the ultrasonic distance measurement can be corrected using the data of the laser distance measurement.

2. According to claim 1, an active early warning method for preventing external damage to a power transmission line, Features: The specific steps of S23 are as follows: S231, selecting a dilute basis and estimating the sparsity of the digital signal, projecting the digital signal onto the selected sparse basis, and obtaining a sparse representation vector; S232, designing a measurement matrix according to the characteristics of the digital signal and the result of the sparse representation, and multiplying the sparse representation vector by the measurement matrix to obtain a measurement value of the low-dimensional digital signal; S233, quantizing, encoding and compressing the measured value of the low-dimensional digital signal, and finally transmitting the compressed digital signal to the central processing unit through the network.

3. According to claim 2, an active early warning method for preventing external damage to a power transmission line, Features: The S231 is specifically as follows: in, is a sparse basis; A model for predicting the optimal sparse basis; Parameters of the model for predicting the optimal sparse basis; in, is the estimated sparsity; Estimation model for sparsity; is the input digital signal; are the parameters of the model; in, is a sparse representation vector; is the transpose operation.

4. According to claim 3, an active early warning method for preventing external damage to a power transmission line, Features: The S232 is specifically as follows: in, is the measurement matrix, which is used to convert the high-dimensional sparse representation vector Convert to low-dimensional measurements ; is a neural network model; are the parameters of the neural network model; in, is the measurement value of the low-dimensional digital signal.

5. According to claim 4, an active early warning method for preventing external damage to a power transmission line, Features: The safety distance threshold between the power construction equipment and the transmission line in S3 includes a primary threshold and an advanced threshold.

6. An active early warning method for preventing external damage to a power transmission line according to claim 5, Features: In S4, the digital signal is compared with the safety distance threshold to determine whether there is an abnormality, as follows: If the distance measured by either ultrasonic ranging or laser ranging is less than the preset primary threshold, a primary alarm is triggered; if both the ultrasonic ranging and laser ranging are less than the preset advanced threshold, an advanced alarm is triggered; among them, the primary alarm measures include issuing an audible and visual alarm and initiating preliminary response measures; the advanced alarm measures include issuing a strong audible and visual alarm and stopping the operation of the power construction equipment.

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