Lead sag monitoring method and system based on Beidou positioning and radar ranging

Abstract

The invention provides a lead sag monitoring method and system based on Beidou positioning and radar ranging, and is applied to the technical field of operation and maintenance of power transmission lines. The lead sag monitoring method based on Beidou positioning and radar ranging comprises the following steps: acquiring a Beidou positioning data sequence and a radar ranging data sequence of a power transmission line sag; determining the jitter degree of the power transmission line according to the Beidou positioning data sequence; and correcting the radar ranging data sequence according to the jitter degree, and determining sag information of the power transmission line. According to the invention, the Beidou positioning information and the radar ranging information are fused, and the radar ranging data are corrected by using the jitter degree corresponding to the Beidou positioning data, so that the defects of many limited factors and large monitoring result error in the traditional indirect measurement can be avoided, and the monitoring precision of the sag of the power transmission line can be improved. Therefore, on-line monitoring rather than prediction of the sag of the power transmission line is completed, so that stable and high-precision detection of the sag of the power transmission line is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line operation, maintenance and overhaul, and in particular to a conductor sag monitoring method and system based on Beidou positioning and radar ranging. Background Art

[0002] Transmission line sag monitoring plays a vital 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 can cause the line to swing excessively during strong winds, potentially causing a short circuit. Excessive sag also reduces the distance between the conductors and the lines, pipelines, and other facilities they cross below, making them more susceptible to accidents. Excessive sag can lead to excessive stress on the conductors, increasing the likelihood of breakage and other accidents.

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

[0004] Sensor sag monitoring method: Using Beidou positioning equipment, the positioning module fixed on the conductor is regarded as a rover. Differential analysis is performed on the tower or an existing base station to obtain the rover's position, and the conductor's position 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: This approach establishes an electrothermal coupling model of transmission line sag and a three-dimensional scenario model of the transmission corridor, combined with actual weather monitoring and forecasting, to develop an operational safety index assessment method. However, this safety distance analysis and prediction model is relatively complex and significantly affected by data quality.

[0006] Algorithm-based sag prediction: The sag of transmission lines is predicted by combining emerging technologies. However, the specific operating conditions of overhead transmission lines are not taken into account, and there is little support from mechanical models. The intelligent algorithm model has great limitations, which affects the prediction accuracy.

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

[0008] In order to overcome the above-mentioned defect of poor accuracy in transmission line sag monitoring, the present invention provides a conductor sag monitoring method based on Beidou positioning and radar ranging, the method comprising:

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

[0010] determining a degree of jitter of the power transmission line according to the Beidou positioning data sequence;

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

[0012] Optionally, obtaining a Beidou positioning data sequence for a transmission line sag includes:

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

[0014] For each of the plurality of Beidou positioning altitudes, determining an altitude difference according to the Beidou positioning altitude and a reference altitude;

[0015] Determine each corresponding Beidou positioning data according to the altitude difference corresponding to each Beidou positioning altitude;

[0016] Determine the Beidou positioning data sequence according to each Beidou positioning data.

[0017] Optionally, determining the jitter degree of the power transmission line according to the Beidou positioning data sequence includes:

[0018] Determine the variance between each two Beidou positioning data according to each two Beidou positioning data with adjacent acquisition times;

[0019] The jitter degree of the power transmission line is determined according to the variance between each two Beidou positioning data.

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

[0021] performing filtering processing on the measurement noise in the radar ranging data sequence according to the jitter degree and the filtering algorithm;

[0022] The sag information of the power transmission line is determined according to the radar ranging data sequence after filtering.

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

[0024] Optionally, the filtering the measurement noise in the radar ranging data sequence according to the jitter degree and the filtering algorithm includes:

[0025] Determining a Kalman gain coefficient of the Kalman filter algorithm according to the jitter degree;

[0026] 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.

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

[0028]

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

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

[0031]

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

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

[0035] An acquisition module is used to obtain the Beidou positioning data sequence and radar ranging data sequence of the transmission line sag;

[0036] a determination module, configured to determine a degree of jitter of the power transmission line according to the Beidou positioning data sequence;

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

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

[0039] Optionally, the determination module is specifically used to determine the variance between each two Beidou positioning data with adjacent collection times; and determine the jitter degree of the transmission line based on the variance between each two Beidou positioning data.

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

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

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

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

[0044]

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

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

[0047]

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

[0050] On the other hand, the present invention further provides a computer device, characterized by comprising: one or more processors;

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

[0052] When the one or more programs are executed by the one or more processors, any one of the above-mentioned methods for monitoring wire 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 a computer program is stored thereon, and when the computer program is executed, it implements any one of the above-mentioned wire sag monitoring methods based on Beidou positioning and radar ranging.

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

[0055] The present invention provides a method for monitoring conductor sag based on Beidou positioning and radar ranging, comprising: obtaining a Beidou positioning data sequence and a radar ranging data sequence of transmission line sag; determining the degree of jitter of the transmission line based on the Beidou positioning data sequence; and correcting the radar ranging data sequence based on the jitter degree to determine the sag information of the transmission line. The present invention integrates Beidou positioning information with radar ranging information and corrects the radar ranging data using the jitter degree corresponding to the Beidou positioning data. This method can avoid the drawbacks of traditional indirect measurement, which suffers from multiple limiting factors and large errors in monitoring results. It can also improve the monitoring accuracy of transmission line sag, enabling online monitoring of transmission line sag rather than prediction, thereby achieving stable and high-precision detection of transmission line sag. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the flow of the conductor sag monitoring method based on Beidou positioning and radar ranging of the present invention;

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

[0058] Figure 3 Schematic diagram of the method for directly measuring the distance between a conductor and the ground according to the present invention;

[0059] Figure 4 It is a structural schematic diagram of the conductor sag monitoring system based on Beidou positioning and radar ranging of the present invention. DETAILED DESCRIPTION

[0060] Transmission line sag monitoring plays an important role in preventing accidents caused by excessive or insufficient line sag. Both excessive and insufficient sag will affect the safe operation of the line. Excessive sag may cause the line to swing too much during strong winds and hit the line, resulting in a short circuit accident. At the same time, excessive sag also reduces the distance between the conductor and the lines, pipelines and other facilities crossing below it, making accidents more likely to occur. Excessive sag will cause excessive stress on the conductor, increasing the possibility of accidents such as line breakage. In recent years, research focusing on online sag monitoring technology for transmission lines has made progress in many aspects, mainly focusing on the following three methods:

[0061] Sensor sag monitoring method: Using Beidou positioning equipment, the positioning module fixed on the conductor is regarded as a rover. The position of the rover is obtained by differential analysis using a tower or an existing base station. The conductor position can then be restored and visualized in the background. However, this method is susceptible to sampling frequency limitations.

[0062] Sag assessment based on clearance distance: Sag assessment based on clearance distance. The most direct manifestation of sag in line inspection and risk assessment is the clearance distance of the underlying crossing objects. By establishing an electrothermal coupling model of transmission line sag and a three-dimensional scenario model of the transmission corridor, combined with actual weather monitoring and weather forecasts, an operation safety index assessment method is established. According to the safety distance regulations under different operating conditions, the clearance distance of the transmission line is verified to avoid accidents.

[0063] Algorithm-based sag prediction: For example, using a genetic algorithm, the parabolic equation is selected as the sag mechanism calculation model. Combined with the temperature equation of state, the system error is modeled and nonlinear parameters are identified for the model to achieve sag prediction. This method combines emerging technologies to predict transmission line sag, but it does not incorporate the specific operating conditions of overhead transmission lines and lacks the support of a mechanistic model. The algorithmic model is very limited, generalization is weak, and the upper limit of prediction accuracy is significantly affected.

[0064] As can be seen from the above three methods, current sensor monitoring methods can basically accurately monitor the size and changes of sag, but the methods are very complex and easily limited by sampling frequency. Intelligent algorithm-based sag prediction methods combine emerging technologies to predict transmission line sag, but they do not consider the specific operating conditions of overhead transmission lines and lack the support of mechanical models. The algorithmic models are very limited and lack strong generalization, which greatly affects the upper limit of prediction accuracy. Analytical prediction models for safety distance are relatively complex and are greatly affected by data quality.

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

[0066] Example 1:

[0067] The present invention provides a method for monitoring conductor sag based on Beidou positioning and radar ranging, the flow chart of which is as follows: Figure 1 Shown, including:

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

[0069] Step 102: Determine the degree of jitter of the power 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 the embodiment of the present invention, Beidou positioning information is integrated with radar ranging information, and the radar ranging data is corrected using the jitter degree corresponding to the Beidou positioning data. This can avoid the disadvantages of many limiting factors and large errors in monitoring results in traditional indirect measurement, and can improve the monitoring accuracy of transmission line sag, complete online monitoring of transmission line sag rather than prediction, thereby achieving stable and high-precision detection of transmission line sag.

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

[0073] In the embodiment of the present invention, based on Beidou high-precision positioning technology and millimeter-wave radar ranging technology, with millimeter-wave radar and Beidou high-precision positioning equipment as the core, the equipment is fixed on the high-voltage transmission line, and the distance between the wire and the ground is directly detected by millimeter-wave radar. Figure 3As shown, multiple sensors are integrated to accurately obtain sag information. After preprocessing, it is sent to the upper system. For example, in the above step 101, 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 in the sensor, and the sensor is fixed on the transmission line. For example, after the sensor is installed, the altitude directly below the installation location is recorded as Height. After the sensor is installed, 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 , the millimeter wave radar can also be initialized to obtain the vertical distance R from the wire to the ground directly measured by the millimeter wave radar org .

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

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

[0076] For each of the plurality of Beidou positioning altitudes, determining an altitude difference according to the Beidou positioning altitude and a reference altitude;

[0077] Determine each corresponding Beidou positioning data according to the altitude difference corresponding to each Beidou positioning altitude;

[0078] Determine the Beidou positioning data sequence based on each Beidou positioning data.

[0079] For example, in this implementation, the data collected by the Beidou positioning device at a fixed frequency is represented as Long k is the longitude value collected for the kth time, Lat k is the latitude value collected for the kth time, height k is the altitude collected for the kth time, that is, the Beidou positioning altitude mentioned above. The altitude difference can be expressed as Δh k k=1,2,....,n,n represents the total number of times data is collected, according to the Beidou positioning altitude height k The altitude difference is obtained by subtracting it from Height, which is the vertical distance of the wire to the ground detected by the Beidou positioning device. Optionally, each altitude difference can be determined as each Beidou positioning data to obtain the Beidou positioning data sequence Δh kk=1,2,......,n,for the initial altitude difference, the Beidou positioning data height org Subtracting Δh0 from Height yields Δh0. The k-th acquisition indicates acquisition at the k-th moment.

[0080] In another implementation, the radar ranging data sequence can be determined based on the original data collected by the millimeter wave radar. For example, the millimeter wave radar collects a number of data at a fixed frequency, which is represented by R k k=1, 2, ..., n, which is the vertical distance between the conductor and the ground detected by the millimeter wave radar.

[0081] During the measurement process, the Beidou positioning device is affected by factors such as wind speed, which can cause fluctuations in the measurement results. For example, the degree of fluctuation caused by the external force on the Beidou positioning device can be reflected in the variance of its distance to the ground. For example, in the above step 102, the variance between each two Beidou positioning data with adjacent acquisition times is determined; and the degree of jitter of the power transmission line is determined based on the variance between each two Beidou positioning data. If each altitude difference is determined as each Beidou positioning data, Δh can be calculated k The variance δ of .

[0082] Millimeter wave radar is affected by factors such as wind speed during measurement, which may cause fluctuations in measurement results. In the embodiment of the present invention, measurement noise v is introduced. k In order to reduce the impact of external forces on the millimeter wave radar measurement results, in the above step 103, the measurement noise in the radar ranging data sequence can be filtered according to the jitter level and the filtering algorithm; the sag information of the transmission line is determined based on the radar ranging data sequence after filtering. In this implementation, the filtering algorithm is used in the sensor to convert the wire-to-ground distance Δh detected by the Beidou positioning device into k The jitter degree, namely the variance δ, is used as the optimization parameter of the millimeter wave radar measurement data to eliminate the noise v by filtering and tracking. k The impact on millimeter-wave radar measurement data is reduced, thereby achieving stability and high precision of millimeter-wave radar measurement data.

[0083] For example, the filtering algorithm includes a Kalman filtering algorithm. For example, in the process of filtering the measurement noise in the radar ranging data sequence according to the jitter degree and the filtering algorithm, the Kalman gain coefficient of the Kalman filtering algorithm can be determined according to the jitter degree; 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.

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

[0085]

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

[0087] The radar ranging data sequence after the above filtering satisfies the following formula:

[0088]

[0089] x' k is the radar ranging data after filtering at time k, is the radar ranging data predicted at time k, K k is the Kalman gain coefficient, z k It is the radar ranging data superimposed with measurement noise, N is the state matrix, and k represents a certain moment of 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] ①Build a prediction model:

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

[0093]

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

[0095]

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

[0097] in, is the predicted value of the radar ranging data at time k, A is the state transfer matrix, x' k-1 is the radar measurement result after filtering at time k-1, w k-1 is the process noise, v k is the measurement noise, i.e. the noise introduced by external forces during the measurement of radar data; k is the measurement result of the millimeter-wave radar, that is, the radar ranging data superimposed with measurement noise; x k =[R k ] is the actual measurement result of the millimeter wave radar, where R k is the distance information actually detected by the radar; N is the conversion matrix, which converts x k Mapped to the vector space z where the measurement value is located k .

[0098] ② Make predictions:

[0099] Use the following formula to complete this step:

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

[0101] Among them, P k is the covariance matrix of the state vector at time k, representing the relationship between each element of the state vector, A is the state transfer matrix, P k-1 is the covariance matrix of the state vector at time k-1, A T is the transposed matrix of the state transfer matrix, Q k Covariance matrix representing the Gaussian noise of the predicted states.

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

[0103]

[0104] Among them, K k is the Kalman gain coefficient, P k is the covariance matrix of the state vector, N T is the transposed matrix of the state matrix, N is the state matrix, k represents a certain moment of millimeter wave radar measurement, λ k The radar measurement noise 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 of has the same mathematical meaning, and the reason for the error is that the wind causes the sensor to shake, so λ k The values ​​in are replaced by δ to obtain the new noise covariance matrix λ'k . So we have:

[0105]

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

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

[0108] According to the actual situation of the technology used in this patent and the application scenario, the variance δ of the measurement results of the Beidou positioning equipment is used to optimize the Kalman gain coefficient, a key parameter of the Kalman filter algorithm. The measurement results of the millimeter-wave radar after correction are:

[0109]

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

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

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

[0113] When determining the sag information of the transmission line based on the radar ranging data sequence after filtering, the conductor sag value can be obtained according to the above formula h2=H-h1. Here, k represents a certain moment of millimeter wave radar measurement. 2k is the conductor sag parameter, H is the height of the connection line between the two towers from the ground, h1, R' k It is the height parameter of the lowest point of the wire from the ground.

[0114] After the millimeter wave radar measurement result is corrected, the measurement result of the millimeter wave radar at the corrected time k (k=1, 2, ..., n): the wire sag value Data is transmitted to the edge intelligent gateway via IEEE802.15.4 communication (for example only), completing autonomous computations at the sensor front end and significantly reducing data transmission volume. This stable data transmission is achieved through standardized, low-power wireless communication technology tailored to power transmission scenarios, avoiding the numerous limitations and large errors associated with traditional indirect measurement, enabling intelligent, unmanned monitoring and early warning.

[0115] The following is a specific example to illustrate the present invention: This example is based on Beidou high-precision positioning and millimeter-wave radar ranging technology, with millimeter-wave radar and Beidou high-precision positioning equipment as the core. The equipment is fixed on the high-voltage transmission line, and the millimeter-wave radar is used to directly detect the distance between the wire and the ground. Multiple sensors are integrated to accurately obtain sag information, which is sent to the upper system after preprocessing. The specific steps include:

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

[0117] 2) Initialize the Beidou positioning unit and obtain the longitude, latitude and altitude reference output by the Beidou device, which are Long, Lat and height respectively. org Initialize the millimeter wave radar device and obtain the vertical distance R from the wire to the ground directly measured by the millimeter wave radar org ; The Beidou positioning data height org Subtract it from Height to get Δh0.

[0118] 3) The data detected by Beidou equipment and millimeter wave radar at fixed frequencies are:

[0119]

[0120] R k k=1,2,……,n

[0121] The collected Beidou positioning data height k Subtract them from Height to get Δh k k=1,2,……,n,that is, the vertical distance between the wire and the ground detected by the Beidou equipment, calculate Δh k The variance δ of .

[0122] 4) Beidou positioning equipment and millimeter wave radar are affected by factors such as wind speed during measurement, which can cause fluctuations in measurement results. The degree of fluctuation caused by external forces on Beidou equipment is reflected in the variance of its distance to the ground, while millimeter wave radar data introduces measurement noise v k In order to reduce the impact of these factors on the radar measurement results, an optimized Kalman filter algorithm is used in the sensor. This algorithm converts the distance between the device and the ground detected by the Beidou positioning device, i.e., Δh k The jitter degree, namely the variance δ, is used as the optimization parameter of the millimeter wave radar measurement data to eliminate the noise v by filtering and tracking. k The impact of the millimeter-wave radar measurement data is achieved, thereby achieving the stability and high accuracy of the millimeter-wave radar measurement data. The algorithm optimizes and processes the data as shown in steps ① to ④ above.

[0123] 5) According to the formula h2=H-h1, the conductor sag value is Here, k represents a moment of millimeter-wave radar measurement.

[0124] 6) The measurement results of the millimeter-wave radar at the corrected time k (k = 1, 2, ..., n): the conductor sag value The data is transmitted to the edge intelligent gateway via the IEEE802.15.4 communication method, completing autonomous calculations at the sensor front end and significantly reducing the amount of data transmission.

[0125] The embodiment of the present invention is based on millimeter-wave radar ranging information and Beidou high-precision positioning information, and uses an optimized Kalman filter algorithm to fuse the Beidou positioning device measurement value and the millimeter-wave radar ranging result, thereby achieving stable and high-precision detection of the conductor sag characteristics. All calculation processes are completed in the sensor, which greatly reduces the transmission volume of radar and Beidou positioning information, realizes the adaptation of standardized low-power wireless communication technology for power transmission scenarios, and avoids the drawbacks of many limiting factors and large errors in detection results in traditional indirect measurements. Specifically, the sag monitoring sensor in the embodiment of the present invention is reliably fixed on the conductor of the transmission line, and the vertical distance of the conductor to the ground is measured by the millimeter-wave radar inside the sensor and the Beidou positioning device, and the radar data and Beidou data are fused through the optimized Kalman filter algorithm, further improving the monitoring accuracy of the sensor and completing the online monitoring of the conductor sag.

[0126] Example 2:

[0127] Based on the same inventive concept, the present invention also provides a wire sag monitoring system based on Beidou positioning and radar ranging, the structural diagram of which is shown in FIG. Figure 4 Shown, including:

[0128] An acquisition module is used to obtain the Beidou positioning data sequence and radar ranging data sequence of the transmission line sag;

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

[0130] 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.

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

[0132] In a possible implementation, the determination module is specifically used to determine the variance between each two Beidou positioning data based on each two Beidou positioning data with adjacent collection times; and determine the jitter degree of the transmission line based on the variance between each two Beidou positioning data.

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

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

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

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

[0137]

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

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

[0140]

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

[0142] In a possible implementation, 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 into a sensor, and the sensor is fixed on the transmission line.

[0143] Example 3:

[0144] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, the memory being used to store a computer program, the computer program including program instructions, and the processor being used to execute the program instructions stored in a computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or corresponding function, so as to implement the steps of a wire sag monitoring method based on Beidou positioning and radar ranging in the above embodiment.

[0145] Example 4:

[0146] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). 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 memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a wire sag monitoring method based on Beidou positioning and radar ranging in the above embodiment.

[0147] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function 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 are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may 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 pending claims.

Claims

1. A wire sag monitoring method based on Beidou positioning and radar ranging, characterized in that: include: Obtain Beidou positioning data sequence and radar ranging data sequence of transmission line sag; determining a degree of jitter of the power transmission line according to the Beidou positioning data sequence; The radar ranging data sequence is corrected according to the jitter degree to determine the sag information of the transmission line.

2. The method according to claim 1, wherein The Beidou positioning data sequence for obtaining the transmission line sag includes: Obtain several Beidou positioning altitudes collected at a fixed frequency; For each of the plurality of Beidou positioning altitudes, determining an altitude difference according to the Beidou positioning altitude and a reference altitude; Determine each corresponding Beidou positioning data according to the altitude difference corresponding to each Beidou positioning altitude; Determine the Beidou positioning data sequence according to each Beidou positioning data.

3. The method according to claim 2, wherein Determining the jitter degree of the power transmission line according to the Beidou positioning data sequence includes: Determine the variance between each two Beidou positioning data according to each two Beidou positioning data with adjacent acquisition times; The jitter degree of the power transmission line is determined according to the variance between each two Beidou positioning data.

4. The method according to any one of claims 1 to 3, wherein Correcting the radar ranging data sequence according to the jitter degree to determine the sag information of the transmission line includes: performing filtering processing on the measurement noise in the radar ranging data sequence according to the jitter degree and the filtering algorithm; The sag information of the power transmission line is determined according to the radar ranging data sequence after filtering.

5. The method according to claim 4, wherein The filtering algorithm includes a Kalman filtering algorithm.

6. The method according to claim 5, wherein The filtering of the measurement noise in the radar ranging data sequence according to the jitter degree and the filtering algorithm includes: Determining a Kalman gain coefficient of the Kalman filter algorithm according to the jitter degree; 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.

7. The method according to claim 6, wherein The Kalman gain coefficient satisfies the following formula: Among them, the K k is the Kalman gain coefficient, P k is the covariance matrix of the state vector, N T is the transposed matrix of the state matrix, N is the state matrix, λ′ k is the covariance matrix of the optimized radar measurement noise, and k represents a certain moment of millimeter-wave radar measurement.

8. The method according to claim 6, wherein The radar ranging data sequence after filtering satisfies the following formula: Among them, x' k is the filtered radar ranging data, is the predicted radar ranging data, K k is the Kalman gain coefficient, z k It is the radar ranging data superimposed with measurement noise, N is the state matrix, and k represents a certain moment of millimeter-wave radar measurement.

9. The method according to any one of claims 1 to 3, wherein: 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 transmission line.

10. A conductor sag monitoring system based on Beidou positioning and radar ranging, characterized in that: include: An acquisition module is used to obtain the Beidou positioning data sequence and radar ranging data sequence of the transmission line sag; a determination module, configured to determine a degree of jitter of the power transmission line according to the Beidou positioning data sequence; A monitoring module is used to correct the radar ranging data sequence according to the jitter degree and determine the sag information of the transmission line.

11. The system according to claim 10, wherein: The acquisition module is specifically used to obtain several Beidou positioning altitudes collected at a fixed frequency; for each Beidou positioning altitude among the several Beidou positioning altitudes, determine the altitude difference according to the Beidou positioning altitude and the reference altitude; determine each corresponding Beidou positioning data according to the altitude difference corresponding to each Beidou positioning altitude; and determine the Beidou positioning data sequence according to each Beidou positioning data.

12. The system according to claim 11, wherein The determination module is specifically used to determine the variance between each two Beidou positioning data with adjacent collection times; and determine the jitter degree of the transmission line based on the variance between each two Beidou positioning data.

13. The system according to any one of claims 10 to 12, wherein: The monitoring module is specifically configured to filter the measurement noise in the radar ranging data sequence according to the jitter degree and the filtering algorithm; and determine the sag information of the transmission line according to the radar ranging data sequence after the filtering process.

14. The system according to claim 13, wherein: The filtering algorithm includes a Kalman filtering algorithm.

15. The system according to claim 14, wherein: The monitoring module is specifically configured to determine a Kalman gain coefficient of the Kalman filter algorithm according to the degree of jitter; filter the measurement noise in the radar ranging data sequence according to the Kalman gain coefficient, and determine the filtered radar ranging data sequence.

16. The system according to claim 15, wherein: The Kalman gain coefficient satisfies the following formula: Among them, the K k is the Kalman gain coefficient, P k is the covariance matrix of the state vector, N is the state matrix, N T is the transposed matrix of the state matrix, λ′ k is the covariance matrix of the optimized radar measurement noise, and k represents a certain moment of millimeter-wave radar measurement.

17. The system according to claim 15, wherein: The radar ranging data sequence after filtering satisfies the following formula: Among them, x' k is the filtered radar ranging data, is the predicted radar ranging data, K k is the Kalman gain coefficient, z k It is the radar ranging data superimposed with measurement noise, N is the state matrix, and k represents a certain moment of millimeter-wave radar measurement.

18. The system according to any one of claims 10 to 12, wherein: 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 transmission line.

19. A computer device, characterized in that: include: one or more processors; The processor is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the wire sag monitoring method based on Beidou positioning and radar ranging as described in any one of claims 1 to 9 is implemented.

20. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the wire sag monitoring method based on Beidou positioning and radar ranging as described in any one of claims 1 to 9 is implemented.

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