A method and system for monitoring conductor sag based on Beidou positioning and radar ranging

Abstract

The application provides a kind of based on compass positioning and radar ranging conductor sag monitoring method and system, applied to transmission line operation and maintenance technical field.Based on compass positioning and radar ranging conductor sag monitoring method includes: obtaining the compass positioning data sequence and radar ranging data sequence of transmission line sag;According to compass positioning data sequence, determine the dither degree of transmission line;According to the dither degree, correct the radar ranging data sequence, determine the sag information of transmission line.In the present application, compass positioning information and radar ranging information are integrated, the dither degree corresponding to compass positioning data is used to correct radar ranging data, which can avoid the disadvantages of many limiting factors and large error of monitoring results in traditional indirect measurement, and can improve the monitoring accuracy of transmission line sag, complete the online monitoring of transmission line sag instead of prediction, so as to realize the stability and high-precision detection of transmission line sag.

Description

Technical Field

[0001] This invention relates to the field of power transmission line operation and maintenance technology, specifically to a method and system for monitoring conductor sag based on BeiDou positioning and radar ranging. Background Technology

[0002] Monitoring the sag of transmission lines plays a crucial role in preventing accidents caused by excessive or insufficient sag. Both excessive and insufficient sag can affect the safe operation of the line. Excessive sag may cause the line to swing excessively in strong winds, potentially leading to short circuits. Excessive sag also reduces the distance between the conductor and the lines, pipes, and other facilities crossing below, increasing the risk of accidents. Conversely, insufficient sag causes excessive stress on the conductor, increasing the likelihood of wire breakage and other accidents.

[0003] In recent years, research focusing on online monitoring technology for transmission line sag has made significant progress in several aspects, mainly concentrated in the following three methods:

[0004] Sensor sag monitoring method: Using BeiDou positioning equipment, the positioning module fixed on the guide wire is regarded as a rover station. Differential calculation is performed through the tower or existing reference station to obtain the position of the rover station, and then the position of the guide wire can be restored and visualized in the background. However, this monitoring method is complex and easily limited by the sampling frequency;

[0005] Sag assessment based on clearance distance: By establishing an electrothermal coupling model of transmission line sag and a three-dimensional scene model of transmission corridor, combined with actual weather monitoring and weather forecast, an operational safety index assessment method is established. However, this analysis and prediction model for safety distance is relatively complex and is greatly affected by data quality.

[0006] Algorithm-based sag prediction: This method uses emerging technologies to predict the sag of transmission lines, but it does not take into account the specific operating conditions of overhead transmission lines and lacks supporting mechanical models. The limitations of intelligent algorithm models are significant, affecting the accuracy of prediction.

[0007] It is evident that current sensor-based sag monitoring methods are complex and easily limited by sampling frequency. Sag assessment based on clearance distance and sag prediction based on algorithms are predictions of sag rather than real-time monitoring, resulting in poor accuracy in the current monitoring of transmission line sag. Summary of the Invention

[0008] To overcome the shortcomings of poor accuracy in transmission line sag monitoring mentioned above, this invention provides a method for monitoring conductor sag based on BeiDou positioning and radar ranging, the method comprising:

[0009] Obtain the BeiDou positioning data sequence and radar ranging data sequence of the sag of the transmission line;

[0010] The degree of jitter of the power transmission line is determined based on the BeiDou positioning data sequence.

[0011] Based on the degree of jitter, the radar ranging data sequence is corrected to determine the sag information of the transmission line.

[0012] Optionally, the acquisition of the BeiDou positioning data sequence for the sag of the transmission line includes:

[0013] Obtain several BeiDou positioning altitudes collected at a fixed frequency;

[0014] For each of the several BeiDou positioning altitudes, the altitude difference is determined based on the BeiDou positioning altitude and the reference altitude.

[0015] Based on the altitude difference corresponding to each BeiDou positioning altitude, determine the corresponding BeiDou positioning data;

[0016] Based on each BeiDou positioning data, the BeiDou positioning data sequence is determined.

[0017] Optionally, determining the jitter level of the transmission line based on the BeiDou positioning data sequence includes:

[0018] The variance between each pair of BeiDou positioning data is determined based on the two BeiDou positioning data that are adjacent in time.

[0019] The degree of jitter in the power transmission line is determined based on the variance between every two BeiDou positioning data points.

[0020] Optionally, the step of correcting the radar ranging data sequence based on the degree of jitter to determine the sag information of the transmission line includes:

[0021] Based on the jitter level and filtering algorithm, the measurement noise in the radar ranging data sequence is filtered.

[0022] Based on the filtered radar ranging data sequence, the sag information of the transmission line is determined.

[0023] Optionally, the filtering algorithm includes the Kalman filtering algorithm.

[0024] Optionally, the step of filtering the measurement noise in the radar ranging data sequence based on the jitter level and the filtering algorithm includes:

[0025] Based on the degree of jitter, the Kalman gain coefficient of the Kalman filtering algorithm is determined;

[0026] Based on the Kalman gain coefficient, the measurement noise in the radar ranging data sequence is filtered to determine the filtered radar ranging data sequence.

[0027] Optionally, the Kalman gain coefficient satisfies the following formula:

[0028]

[0029] Wherein, K k It is the Kalman gain coefficient, P k It is the covariance matrix of the state vector, N T It is the transpose of the state matrix, N is the state matrix, and λ' k It is the optimized covariance matrix of radar measurement noise, where k represents a certain moment in the millimeter-wave radar measurement.

[0030] Optionally, the filtered radar ranging data sequence satisfies the following formula:

[0031]

[0032] Where, x' k It is the radar ranging data after filtering at time k. It is the radar ranging data predicted at time k, K k It is the Kalman gain coefficient, z k It is radar ranging data superimposed with measurement noise, N is the state matrix, and k represents a certain moment of the millimeter-wave radar measurement.

[0033] Optionally, the BeiDou positioning data sequence is collected and determined by a BeiDou positioning device, and the radar ranging data sequence is collected and determined by a millimeter-wave radar. The BeiDou positioning device and the millimeter-wave radar are integrated in a sensor, and the sensor is fixed on the power transmission line.

[0034] On the other hand, the present invention also provides a conductor sag monitoring system based on BeiDou positioning and radar ranging, comprising:

[0035] The acquisition module is used to acquire the BeiDou positioning data sequence and radar ranging data sequence of the sag of the transmission line;

[0036] The determination module is used to determine the degree of jitter of the transmission line based on the BeiDou positioning data sequence;

[0037] The monitoring module is used to correct the radar ranging data sequence according to the degree of jitter, and to determine the sag information of the transmission line.

[0038] Optionally, the acquisition module is specifically used to acquire several BeiDou positioning altitudes collected at a fixed frequency; for each BeiDou positioning altitude, determine the altitude difference based on the BeiDou positioning altitude and the reference altitude; determine each corresponding BeiDou positioning data based on the altitude difference corresponding to each BeiDou positioning altitude; and determine the BeiDou positioning data sequence based on each BeiDou positioning data.

[0039] Optionally, the determining module is specifically used to determine the variance between every two BeiDou positioning data points that are adjacent in time of acquisition; and to determine the jitter level of the transmission line based on the variance between every two BeiDou positioning data points.

[0040] Optionally, the monitoring module is specifically used to filter the measurement noise in the radar ranging data sequence according to the jitter level and the filtering algorithm; and to determine the sag information of the transmission line according to the filtered radar ranging data sequence.

[0041] Optionally, the filtering algorithm includes the Kalman filtering algorithm.

[0042] Optionally, the monitoring module is specifically used to determine the Kalman gain coefficient of the Kalman filtering algorithm based on the jitter level; and to filter the measurement noise in the radar ranging data sequence based on the Kalman gain coefficient to determine the filtered radar ranging data sequence.

[0043] Optionally, the Kalman gain coefficient satisfies the following formula:

[0044]

[0045] Wherein, K k It is the Kalman gain coefficient, P k It is the covariance matrix of the state vector, N T It is the transpose of the state matrix, N is the state matrix, and λ' k It is the optimized covariance matrix of radar measurement noise, where k represents a certain moment in the millimeter-wave radar measurement.

[0046] Optionally, the filtered radar ranging data sequence satisfies the following formula:

[0047]

[0048] Where, x' k It is the radar ranging data after filtering at time k. It is the radar ranging data predicted at time k, K k It is the Kalman gain coefficient, zk It is radar ranging data superimposed with measurement noise, N is the state matrix, and k represents a certain moment of the millimeter-wave radar measurement.

[0049] Optionally, the BeiDou positioning data sequence is collected and determined by a BeiDou positioning device, and the radar ranging data sequence is collected and determined by a millimeter-wave radar. The BeiDou positioning device and the millimeter-wave radar are integrated in a sensor, and the sensor is fixed on the power transmission line.

[0050] On the other hand, the present invention also provides a computer device, characterized in that it includes: one or more processors;

[0051] The processor is used to store one or more programs;

[0052] When the one or more programs are executed by the one or more processors, the above-described method for monitoring conductor sag based on BeiDou positioning and radar ranging is implemented.

[0053] On the other hand, the present invention also provides a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed, implements the conductor sag monitoring method based on BeiDou positioning and radar ranging as described in any one of the above.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] This invention provides a method for monitoring conductor sag based on BeiDou positioning and radar ranging, comprising: acquiring BeiDou positioning data sequences and radar ranging data sequences of transmission line sag; determining the jitter level of the transmission line based on the BeiDou positioning data sequences; and correcting the radar ranging data sequences based on the jitter level to determine the sag information of the transmission line. This invention integrates BeiDou positioning information and radar ranging information, using the jitter level corresponding to the BeiDou positioning data to correct the radar ranging data. This avoids the drawbacks of traditional indirect measurements, such as numerous limiting factors and large monitoring errors, and improves the monitoring accuracy of transmission line sag. It achieves online monitoring rather than prediction of transmission line sag, thereby realizing stable and high-precision detection of transmission line sag. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the conductor sag monitoring method based on BeiDou positioning and radar ranging of the present invention.

[0057] Figure 2 This is a schematic diagram of the sag of the transmission line according to the present invention;

[0058] Figure 3 This is a schematic diagram of the direct measurement method for the distance between the conductor and the ground according to the present invention;

[0059] Figure 4 This is a schematic diagram of the conductor sag monitoring system based on BeiDou positioning and radar ranging according to the present invention. Detailed Implementation

[0060] Monitoring the sag of transmission lines plays a crucial role in preventing accidents caused by excessive or insufficient sag. Both excessive and insufficient sag can affect the safe operation of the line. Excessive sag may cause the line to swing excessively in strong winds, potentially leading to short circuits. Excessive sag also reduces the distance between the conductor and the lines, pipelines, and other facilities crossing below, increasing the risk of accidents. Conversely, insufficient sag increases the stress on the conductor, raising the likelihood of wire breakage and other accidents. In recent years, research focusing on online monitoring technology for transmission line sag has made significant progress, primarily concentrating on the following three methods:

[0061] Sensor sag monitoring method: Using BeiDou positioning equipment, the positioning module fixed on the guide wire is regarded as a rover station, and differential calculation is performed through the tower or existing base station to obtain the position of the rover station. Then, the position of the guide wire can be restored and visualized in the background. However, this method is easily limited by the sampling frequency.

[0062] Sag Assessment Based on Clearance Distance: Sag assessment is based on clearance distance. The most direct manifestation of sag in line inspection and risk assessment is the clearance distance to objects crossing below. By establishing an electrothermal coupling model of transmission line sag and a three-dimensional scene model of the transmission corridor, combined with actual weather monitoring and forecasts, a method for assessing operational safety indicators is established. Based on safety distance regulations under different operating conditions, the clearance distance of transmission lines is verified to prevent accidents.

[0063] Algorithm-based sag prediction: This method, such as using a genetic algorithm, selects the parabolic equation as the calculation model for the sag mechanism. Combined with the temperature state equation, it models the system error and identifies nonlinear parameters to predict sag. While this method incorporates emerging technologies for predicting transmission line sag, it lacks consideration of the specific operating conditions of overhead transmission lines and has limited supporting mechanisms. The algorithm's limitations are too great, its generalization ability is weak, and the upper limit of prediction accuracy is significantly affected.

[0064] As can be seen, considering the three methods mentioned above, current sensor monitoring methods can generally accurately monitor the size and changes of sag, but these methods are very complex and easily limited by the sampling frequency. Sag prediction methods based on intelligent algorithms combine emerging technologies to predict the sag of transmission lines, but they do not consider the specific operating conditions of overhead transmission lines and lack supporting mechanical models. The limitations of the algorithm models are too great, their generalization ability is weak, and the upper limit of prediction accuracy is greatly affected. The analysis and prediction models for safe distances are all quite complex and greatly affected by data quality.

[0065] Based on this, embodiments of the present invention provide a method and system for monitoring conductor sag based on BeiDou positioning and radar ranging. The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0066] Example 1:

[0067] This invention provides a method for monitoring conductor sag based on BeiDou positioning and radar ranging, the flowchart of which is shown below. Figure 1 As shown, it includes:

[0068] Step 101: Obtain the BeiDou positioning data sequence and radar ranging data sequence of the transmission line sag.

[0069] Step 102: Determine the degree of jitter of the transmission line based on the BeiDou positioning data sequence.

[0070] Step 103: Correct the radar ranging data sequence according to the degree of jitter to determine the sag information of the transmission line.

[0071] In this embodiment of the invention, BeiDou positioning information and radar ranging information are fused. The radar ranging data is corrected by using the jitter level corresponding to the BeiDou positioning data. This can avoid the drawbacks of traditional indirect measurement, which has many limiting factors and large monitoring error. It can also improve the monitoring accuracy of transmission line sag, and complete the online monitoring of transmission line sag instead of prediction, thereby achieving stable and high-precision detection of transmission line sag.

[0072] Conductor sag refers to the sag of a transmission line, such as... Figure 2 As shown, the conductor between two adjacent towers will move downwards or upwards due to factors such as gravity and temperature, thus deviating from its original position. The sag value is the maximum distance between the lowest point of the conductor and the line connecting the two towers. Figure 1 The value corresponding to h2. The height of the connection between the two towers from the ground is H, and the height of the lowest point of the conductor from the ground is h1. Therefore, h2 = H - h1.

[0073] In this embodiment of the invention, based on BeiDou high-precision positioning and millimeter-wave radar ranging technology, millimeter-wave radar and BeiDou high-precision positioning equipment are used as the core. The equipment is fixed on a high-voltage transmission line, and the distance between the transmission line and the ground is directly detected by the millimeter-wave radar. Figure 3As shown, multiple sensors are integrated to accurately acquire sag information. After preprocessing, it is sent to the upper-level system. For example, in step 101 above, the BeiDou positioning data sequence is collected and determined by the BeiDou positioning device (or BeiDou positioning unit), and the radar ranging data sequence is collected and determined by the millimeter-wave radar (or millimeter-wave radar unit, millimeter-wave radar device). The BeiDou positioning device and the millimeter-wave radar are integrated into the sensor, which is fixed to the power transmission line. For example, after the sensor is installed, the altitude directly below the installation location is recorded as Height. After installing the sensor, the BeiDou positioning device can be initialized to obtain the longitude value (Long), latitude value (Lat), and reference altitude height output by the BeiDou positioning device. org It can also initialize millimeter-wave radar to obtain the vertical distance R from the conductor to the ground, which is directly measured by the millimeter-wave radar. org .

[0074] In one implementation, the BeiDou positioning data sequence can be determined based on real-time data collected by the BeiDou positioning device. For example, obtaining the BeiDou positioning data sequence of the transmission line sag in step 101 above includes the following process:

[0075] Obtain several BeiDou positioning altitudes collected at a fixed frequency;

[0076] For each of the several BeiDou positioning altitudes, the altitude difference is determined based on the BeiDou positioning altitude and the reference altitude.

[0077] Based on the altitude difference corresponding to each BeiDou positioning altitude, determine the corresponding BeiDou positioning data for each position.

[0078] Based on each BeiDou positioning data point, a BeiDou positioning data sequence is determined.

[0079] For example, in this implementation, several data points collected by the BeiDou positioning device at a fixed frequency are represented as follows: Long k This is the longitude value collected in the kth iteration, Lat k This is the latitude value collected in the kth iteration, height k This is the altitude obtained from the kth data collection, which is the BeiDou positioning altitude mentioned above. The altitude difference can be represented as Δh. k k = 1, 2, ..., n, where n represents the total number of data collections, and the altitude is determined by the BeiDou positioning system. k Subtracting each value from the height (Height) yields the altitude difference, which represents the vertical distance of the guide wire to the ground detected by the BeiDou positioning device. Optionally, each altitude difference can be defined as a single BeiDou positioning data point, resulting in a BeiDou positioning data sequence Δh. kk = 1, 2, ..., n, for the initial altitude difference, the height data from BeiDou positioning can be used. org Subtracting Height from Δh0 yields Δh0. Here, the k-th acquisition indicates that the acquisition occurs at time k.

[0080] In another implementation, the radar ranging data sequence can be determined based on the raw data acquired by the millimeter-wave radar. For example, the millimeter-wave radar acquires several data points at a fixed frequency, denoted as R. k k = 1, 2, ..., n, which represents the vertical distance between the wire and the ground detected by the millimeter-wave radar.

[0081] The measurement results of BeiDou positioning equipment are affected by factors such as wind speed during the measurement process, which can cause fluctuations. For example, the degree of fluctuation caused by external forces can be reflected in the variance of its distance from the ground. In step 102 above, the variance between every two adjacent BeiDou positioning data points is determined; based on the variance between every two BeiDou positioning data points, the degree of jitter in the transmission line is determined. If each altitude difference is defined as a single BeiDou positioning data point, then Δh can be calculated. k The variance δ.

[0082] Millimeter-wave radar measurements are affected by factors such as wind speed, which can cause fluctuations in the measurement results. In this embodiment of the invention, measurement noise v is introduced. k To reduce the impact of external forces on the millimeter-wave radar measurement results, in step 103 above, measurement noise in the radar ranging data sequence can be filtered based on the degree of jitter and a filtering algorithm. The sag information of the transmission line is then determined based on the filtered radar ranging data sequence. In this implementation, a filtering algorithm is used in the sensor to detect the conductor-to-ground distance Δh detected by the BeiDou positioning device. k The degree of jitter, or variance δ, is used as an optimization parameter for millimeter-wave radar measurement data. Noise is eliminated through filtering and tracking. k This study aims to mitigate the impact on millimeter-wave radar measurement data, thereby achieving stability and high accuracy in millimeter-wave radar measurement data.

[0083] For example, the filtering algorithm includes the Kalman filtering algorithm. For instance, in the process of filtering measurement noise in the radar ranging data sequence based on the jitter level and the filtering algorithm mentioned above, the Kalman gain coefficient of the Kalman filtering algorithm can be determined according to the jitter level; the measurement noise in the radar ranging data sequence is filtered according to the Kalman gain coefficient to determine the filtered radar ranging data sequence.

[0084] The Kalman gain coefficients mentioned above can satisfy the following formula:

[0085]

[0086] Among them, K k It is the Kalman gain coefficient, P k It is the covariance matrix of the state vector, N T It is the transpose of the state matrix, N is the state matrix, and λ' k It is the optimized covariance matrix of radar measurement noise, where k represents a certain moment in the millimeter-wave radar measurement.

[0087] The filtered radar ranging data sequence described above satisfies the following formula:

[0088]

[0089] x' k It is the radar ranging data after filtering at time k. It is the radar ranging data predicted at time k, K k It is the Kalman gain coefficient, z k It is radar ranging data superimposed with measurement noise, N is the state matrix, and k represents a certain moment of the millimeter-wave radar measurement.

[0090] For example, the process of optimizing and processing data using the Kalman filter algorithm includes the following steps ① to ④:

[0091] ① Establish a prediction model:

[0092] Since millimeter radar wave data consists of a series of discrete data, the following formula applies:

[0093]

[0094] in, Let x' be the predicted value of the radar ranging data at time k. k-1 This is the radar measurement result after filtering at time k-1; A is the state transition matrix; u k-1 B is the control vector; B is the input control matrix; w k-1 This refers to process noise, specifically the error introduced during the calculation process. No additional control is introduced in this model, therefore u k-1 =0, therefore:

[0095]

[0096] z k =Nx k +v k (1.3)

[0097] in, Let x' be the predicted value of the radar ranging data at time k, A be the state transition matrix, and x' be the predicted value. k-1 This is the radar measurement result after filtering at time k-1, w k-1 It is process noise, v k It refers to measurement noise, specifically the noise introduced by external forces during the measurement of radar data; k It is the measurement result of millimeter-wave radar, that is, radar ranging data superimposed with measurement noise; x k =[R k ] represents the actual measurement results from millimeter-wave radar, where R k It is the actual distance information detected by the radar; N is the transformation matrix, which transforms x k Mapped to the vector space z where the measured values ​​reside k .

[0098] ② Make predictions:

[0099] This step can be completed using the following formula:

[0100] P k =AP k-1 A T +Q k (1.4)

[0101] Among them, P k Let P be the covariance matrix of the state vector at time k, representing the relationship between each element of the state vector. Let A be the state transition matrix. k-1 Let A be the covariance matrix of the state vector at time k-1. T It is the transpose of the state transition matrix, Q k The covariance matrix represents the Gaussian noise of the predicted state.

[0102] ③ Solve for the Kalman gain coefficient K k :

[0103]

[0104] Among them, K k It is the Kalman gain coefficient, P k It is the covariance matrix of the state vector, N T It is the transpose of the state matrix, N is the state matrix, k represents a certain moment in the millimeter-wave radar measurement, and λ k For radar noise measurement v k The covariance matrix represents the impact of measurement noise on the radar measurement system, while the ground distance Δh measured by the BeiDou positioning equipment... k The variance δ has the same mathematical meaning, and the reason for the introduced error is the sensor vibration caused by wind. Therefore, λ k The values ​​in the matrix are replaced by δ to obtain a new noise covariance matrix λ'.k Therefore:

[0105]

[0106] Among them, K k It is the Kalman gain coefficient, P k It is the covariance matrix of the state vector, N T It is the transpose of the state matrix, N is the state matrix, and λ' k It is the optimized covariance matrix of radar measurement noise, where k represents a certain moment in the millimeter-wave radar measurement.

[0107] ④ Correction of millimeter-wave radar measurement results:

[0108] Based on the actual situation of the technology and application scenario used in this patent, the variance δ of the measurement results from the BeiDou positioning equipment was used to optimize the key parameter of the Kalman filter algorithm, the Kalman gain coefficient. Therefore, the measurement results of the millimeter-wave radar after correction are as follows:

[0109]

[0110] Where, x' k It is the radar ranging data after filtering at time k. k It is the radar ranging data predicted at time k, K k It is the Kalman gain coefficient, z k This is radar ranging data superimposed with measurement noise, where N is the state matrix, k represents a certain moment in the millimeter-wave radar measurement, and x' k Satisfy the following formula:

[0111] x' k =R' k (1.8)

[0112] Where, x' k R' is the filtered radar ranging data at time k. k It is the radar ranging result obtained after filtering at time k.

[0113] When determining the sag information of the transmission line based on the filtered radar ranging data sequence, the conductor sag value can be obtained using the formula h2 = H - h1 above. Here, k represents a specific moment measured by the millimeter-wave radar. Where h2 and h... 2k These are the conductor sag parameters, where H is the height of the connecting line at the junction of the two towers from the ground, and h1 and R' are also relevant parameters. k It is the height parameter of the lowest point of the conductor from the ground.

[0114] After correcting the millimeter-wave radar measurement results, the corrected millimeter-wave radar measurement results at time k (k = 1, 2, ..., n) can be used to obtain the conductor sag value. Data is transmitted to an edge smart gateway via IEEE 802.15.4 communication (for example only), enabling autonomous computation at the sensor front end and significantly reducing data transmission volume. Stable data transmission is achieved through standardized low-power wireless communication technology for power transmission scenarios, avoiding the drawbacks of traditional indirect measurements such as numerous limiting factors and large detection errors, thus realizing intelligent and unmanned monitoring and early warning.

[0115] The following is a specific embodiment of the present invention: This embodiment is based on BeiDou high-precision positioning and millimeter-wave radar ranging technology. Using millimeter-wave radar and BeiDou high-precision positioning equipment as the core, the equipment is fixed to a high-voltage transmission line. The millimeter-wave radar directly detects the distance between the conductor and the ground, and multiple sensors are integrated to accurately acquire sag information. After preprocessing, the information is sent to the upper-level system. Specifically, the steps include the following:

[0116] 1) First, based on the positioning data, obtain the altitude (Height) directly below the device installation location.

[0117] 2) Initialize the BeiDou positioning unit to obtain the longitude, latitude, and altitude reference values ​​output by the BeiDou device, which are respectively Long, Lat, and height. org Initialize the millimeter-wave radar equipment to obtain the vertical distance R from the conductor to the ground, which is directly measured by the millimeter-wave radar. org ; the height of the Beidou positioning data org Subtracting Height from Δh0 gives us Δh0.

[0118] 3) Data detected by BeiDou equipment and millimeter-wave radar at fixed frequencies are as follows:

[0119]

[0120] R k k = 1, 2, ..., n

[0121] The collected BeiDou positioning data height k Subtracting each value from Height yields Δh. k k = 1, 2, ..., n, which represents the vertical distance between the conductor and the ground detected by the BeiDou equipment. Calculate Δh. k The variance δ.

[0122] 4) Both BeiDou positioning equipment and millimeter-wave radar measurements are affected by factors such as wind speed, causing fluctuations in the measurement results. The degree of fluctuation in BeiDou equipment due to external forces is reflected in the variance of its distance to the ground, while the millimeter-wave radar data introduces measurement noise v. k To reduce the impact of these factors on radar measurement results, an optimized Kalman filter algorithm is used in the sensor. This algorithm calculates the distance between the device and the ground detected by the BeiDou positioning equipment, Δh. k The degree of jitter, or variance δ, is used as an optimization parameter for millimeter-wave radar measurement data. Noise is eliminated through filtering and tracking. k The algorithm optimizes and processes the data to achieve stability and high accuracy in millimeter-wave radar measurement data, thus mitigating the impact on this data. The optimization process is described in steps ① to ④ above.

[0123] 5) According to the formula h2=H-h1, the conductor sag value can be obtained. Here, k represents a specific moment measured by the millimeter-wave radar.

[0124] 6) Millimeter-wave radar measurement results at time k (k = 1, 2, ..., n) after correction: conductor sag value The data is transmitted to the edge smart gateway via IEEE 802.15.4 communication, enabling autonomous calculations at the sensor front end and significantly reducing the amount of data transmission.

[0125] This invention, based on millimeter-wave radar ranging information and BeiDou high-precision positioning information, utilizes an optimized Kalman filter algorithm to fuse the measurements from the BeiDou positioning device and the millimeter-wave radar ranging results, achieving stable and high-precision detection of conductor sag characteristics. All calculations are performed within the sensor, significantly reducing the amount of radar and BeiDou positioning information transmitted. This enables adaptation to standardized low-power wireless communication technology for power transmission scenarios, while avoiding the drawbacks of traditional indirect measurements, such as numerous limiting factors and large errors in detection results. Specifically, in this invention, the sag monitoring sensor is reliably fixed to the conductor of the power transmission line. The vertical distance between the conductor and the ground is measured by the millimeter-wave radar and BeiDou positioning device inside the sensor. The optimized Kalman filter algorithm fuses the radar data and BeiDou data, further improving the sensor's monitoring accuracy and enabling online monitoring of conductor sag.

[0126] Example 2:

[0127] Based on the same inventive concept, this invention also provides a conductor sag monitoring system based on BeiDou positioning and radar ranging, as shown in the schematic diagram below. Figure 4 As shown, it includes:

[0128] The acquisition module is used to acquire the BeiDou positioning data sequence and radar ranging data sequence of the sag of the transmission line;

[0129] The determination module is used to determine the degree of jitter in the transmission line based on the BeiDou positioning data sequence;

[0130] The monitoring module is used to correct the radar ranging data sequence based on the degree of jitter and determine the sag information of the transmission line.

[0131] In one possible implementation, the acquisition module is specifically used to acquire several BeiDou positioning altitudes collected at a fixed frequency; for each BeiDou positioning altitude, determine the altitude difference based on the BeiDou positioning altitude and the reference altitude; determine each corresponding BeiDou positioning data based on the altitude difference corresponding to each BeiDou positioning altitude; and determine the BeiDou positioning data sequence based on each BeiDou positioning data.

[0132] In one possible implementation, the determining module is specifically used to determine the variance between every two BeiDou positioning data points that are adjacent in time of acquisition; and to determine the degree of jitter of the transmission line based on the variance between every two BeiDou positioning data points.

[0133] In one possible implementation, the monitoring module is specifically used to filter the measurement noise in the radar ranging data sequence based on the degree of jitter and a filtering algorithm; and to determine the sag information of the transmission line based on the filtered radar ranging data sequence.

[0134] In one possible implementation, the filtering algorithm includes the Kalman filter algorithm.

[0135] In one possible implementation, the monitoring module is specifically used to determine the Kalman gain coefficient of the Kalman filtering algorithm based on the degree of jitter; and to filter the measurement noise in the radar ranging data sequence based on the Kalman gain coefficient to determine the filtered radar ranging data sequence.

[0136] In one possible implementation, the Kalman gain coefficient satisfies the following formula:

[0137]

[0138] Among them, K k It is the Kalman gain coefficient, P k It is the covariance matrix of the state vector, N T It is the transpose of the state matrix, N is the state matrix, and λ' k It is the optimized covariance matrix of radar measurement noise, where k represents a certain moment in the millimeter-wave radar measurement.

[0139] In one possible implementation, the filtered radar ranging data sequence satisfies the following formula:

[0140]

[0141] x' k It is the radar ranging data after filtering at time k. It is the radar ranging data predicted at time k, K k It is the Kalman gain coefficient, z k It is radar ranging data superimposed with measurement noise, N is the state matrix, and k represents a certain moment of the millimeter-wave radar measurement.

[0142] In one possible implementation, the BeiDou positioning data sequence is collected and determined by the BeiDou positioning device, and the radar ranging data sequence is collected and determined by the millimeter-wave radar. The BeiDou positioning device and the millimeter-wave radar are integrated into a sensor, which is fixed to the power transmission line.

[0143] Example 3:

[0144] Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the conductor sag monitoring method based on Beidou positioning and radar ranging in the above embodiments.

[0145] Example 4:

[0146] Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory). A computer-readable storage medium is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the conductor sag monitoring method based on BeiDou positioning and radar ranging in the above embodiments.

[0147] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0149] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A method for monitoring conductor sag based on BeiDou positioning and radar ranging, characterized in that, include: Obtain the BeiDou positioning data sequence and radar ranging data sequence of the sag of the transmission line; The degree of jitter of the power transmission line is determined based on the BeiDou positioning data sequence. Based on the degree of jitter, the radar ranging data sequence is corrected to determine the sag information of the transmission line; The step of determining the jitter level of the transmission line based on the BeiDou positioning data sequence includes: Based on every two BeiDou positioning data points with adjacent acquisition times in the BeiDou positioning data sequence, determine the variance between each pair of BeiDou positioning data points. The degree of jitter of the power transmission line is determined based on the variance between every two BeiDou positioning data. The step of correcting the radar ranging data sequence based on the degree of jitter to determine the sag information of the transmission line includes: Based on the degree of jitter, determine the Kalman gain coefficient of the Kalman filtering algorithm; Based on the Kalman gain coefficient, the measurement noise in the radar ranging data sequence is filtered to determine the filtered radar ranging data sequence; Based on the filtered radar ranging data sequence, the sag information of the transmission line is determined; The Kalman gain coefficient satisfies the following formula: Among them, the It is the Kalman gain coefficient. It is the covariance matrix of the state vector. It is the transpose of the state matrix. It is a state matrix. It is the covariance matrix of radar measurement noise. The value in the middle is the variance of the distance to the ground measured by Beidou positioning equipment. The replacement yields a new noise covariance matrix. Represents a specific moment measured by millimeter-wave radar; The filtered radar ranging data sequence satisfies the following formula: in, This is filtered radar ranging data. It is predicted radar ranging data. It is the Kalman gain coefficient. It is radar ranging data with measurement noise superimposed. It is a state matrix. This represents a specific moment measured by millimeter-wave radar.

2. The method as described in claim 1, characterized in that, The BeiDou positioning data sequence for obtaining the sag of the transmission line includes: Obtain several BeiDou positioning altitudes collected at a fixed frequency; For each of the several BeiDou positioning altitudes, the altitude difference is determined based on the BeiDou positioning altitude and the reference altitude. Based on the altitude difference corresponding to each BeiDou positioning altitude, determine the corresponding BeiDou positioning data; Based on each BeiDou positioning data, the BeiDou positioning data sequence is determined.

3. The method as described in claim 1 or 2, characterized in that, The BeiDou positioning data sequence is collected and determined by the BeiDou positioning device, and the radar ranging data sequence is collected and determined by the millimeter-wave radar. The BeiDou positioning device and the millimeter-wave radar are integrated in a sensor, and the sensor is fixed on the power transmission line.

4. A conductor sag monitoring system based on BeiDou positioning and radar ranging, characterized in that, include: The acquisition module is used to acquire the BeiDou positioning data sequence and radar ranging data sequence of the sag of the transmission line; The determination module is used to determine the degree of jitter of the transmission line based on the BeiDou positioning data sequence; The monitoring module is used to correct the radar ranging data sequence according to the degree of jitter, and determine the sag information of the transmission line; The determining module is specifically used to determine the variance between every two BeiDou positioning data points that are adjacent in time in the BeiDou positioning data sequence; and to determine the jitter level of the transmission line based on the variance between every two BeiDou positioning data points. The monitoring module is specifically used to determine the Kalman gain coefficient of the Kalman filter algorithm based on the jitter level; to filter the measurement noise in the radar ranging data sequence based on the Kalman gain coefficient, and to determine the filtered radar ranging data sequence; and to determine the sag information of the transmission line based on the filtered radar ranging data sequence. The Kalman gain coefficient satisfies the following formula: Among them, the It is the Kalman gain coefficient. It is the covariance matrix of the state vector. It is the transpose of the state matrix. It is a state matrix. It is the covariance matrix of radar measurement noise. The value in the middle is the variance of the distance to the ground measured by Beidou positioning equipment. The replacement yields a new noise covariance matrix. Represents a specific moment measured by millimeter-wave radar; The filtered radar ranging data sequence satisfies the following formula: in, This is filtered radar ranging data. It is predicted radar ranging data. It is the Kalman gain coefficient. It is radar ranging data with measurement noise superimposed. It is a state matrix. This represents a specific moment measured by millimeter-wave radar.

5. The system as described in claim 4, characterized in that, The acquisition module is specifically used to acquire several BeiDou positioning altitudes collected at a fixed frequency; for each BeiDou positioning altitude, determine the altitude difference based on the BeiDou positioning altitude and the reference altitude; determine each corresponding BeiDou positioning data based on the altitude difference corresponding to each BeiDou positioning altitude; and determine the BeiDou positioning data sequence based on each BeiDou positioning data.

6. The system as described in claim 4 or 5, characterized in that, The BeiDou positioning data sequence is collected and determined by the BeiDou positioning device, and the radar ranging data sequence is collected and determined by the millimeter-wave radar. The BeiDou positioning device and the millimeter-wave radar are integrated in a sensor, and the sensor is fixed on the power transmission line.

7. A computer device, characterized in that, include: One or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the method for monitoring conductor sag based on BeiDou positioning and radar ranging as described in any one of claims 1 to 3 is implemented.

8. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the conductor sag monitoring method based on BeiDou positioning and radar ranging as described in any one of claims 1 to 3.

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