Ground clamp positioning method and system using satellite positioning and landmark calibration

Abstract

The invention discloses a ground clamp positioning method and system using satellite positioning and landmark calibration, and relates to the technical field of monitoring and positioning. Comprising the following steps: acquiring an initial position of a grounding wire clamp, and determining through combination of a wireless identification signal and a satellite positioning system; determining a search area by utilizing the initial position, acquiring three-dimensional coordinates of landmark points in the area, performing difference comparison on the three-dimensional coordinates and data in a historical landmark database, and calculating space-time deviation values, including seasonal change parameters and geographical deformation parameters, of the landmark points; correcting the initial position based on the space-time deviation value to obtain calibrated grounding wire clamp position data; and inputting the calibrated data into a neural network model trained based on the historical positioning record of the grounding wire clamp, and calculating to obtain a final position coordinate. According to the method, through multi-technology fusion, dynamic calibration and deep learning optimization, the positioning precision in a complex electric power environment is remarkably improved, and high-precision and all-weather technical support is provided for safety inspection and maintenance of an electric power circuit.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and positioning, and particularly to a positioning method and system for earthing clamps using satellite positioning and landmark calibration. Background Art

[0002] The positioning technology of earthing clamps plays a crucial role in the safe operation and maintenance of power systems. Its development process reflects the progressive trajectory of the continuous integration of positioning technology and power engineering practice. In the early stage, the positioning of earthing clamps mainly relied on manual inspections and simple markings, with a crude and inefficient method. With the maturity of the Global Navigation Satellite System (GNSS) technology, single satellite positioning methods have gradually been applied to this field, using satellite navigation systems such as GPS and Beidou to provide basic coordinate positioning services. Subsequently, the introduction of Differential Global Positioning System (DGPS) technology has improved the positioning accuracy, and Real-Time Kinematic (RTK) technology has further increased the accuracy to the centimeter level. In recent years, multi-source information fusion positioning has become a research hotspot. By combining multiple technologies such as inertial navigation and wireless sensor networks, a more complex positioning architecture has been constructed, significantly enhancing the adaptability of earthing clamps in complex environments.

[0003] However, the existing technologies still face many challenges, especially the positioning problems unique to the power environment have not been fully solved. For example, problems such as electromagnetic interference in high-voltage lines, signal blockage in mountain valleys, and seasonal surface deformation pose severe tests to the positioning accuracy and reliability.

[0004] The current positioning technology of earthing clamps has the following technical problems to be solved urgently:

[0005] 1. Insufficient anti-interference ability: A single satellite positioning system is vulnerable to electromagnetic interference and multipath effects in the high-voltage transmission line environment. Especially in complex terrains such as urban canyons or mountain forests, the positioning error is large, making it difficult to meet the high-precision requirements of power safety maintenance.

[0006] 2. Lack of time dimension: Most of the existing positioning methods are static positioning, and do not consider the influence of seasonal changes in the surface and geological structure deformation on the positioning reference system, resulting in systematic deviations in long-term monitoring results.

[0007] 3. Lack of adaptability: The existing technologies lack the ability of adaptive learning, cannot use historical positioning data to optimize the current positioning results, nor can they intelligently adjust the positioning strategy according to changes in environmental conditions, resulting in poor positioning reliability in adverse weather or signal-limited situations.

[0008] 4. Low data integration: The integration degree of positioning data and the power facility management system is insufficient, making it difficult to achieve the efficient utilization and visual management of positioning information, increasing the operation and maintenance costs and operation complexity.

[0009] In summary, the existing grounding wire clamp positioning technology has obvious deficiencies in aspects such as anti-interference ability, time dimension modeling, adaptive learning, and data integration. There is an urgent need for a new positioning technology that can comprehensively solve the above problems to improve positioning accuracy, reliability, and operation and maintenance efficiency, and meet the requirements of safe operation and maintenance of the power system. Summary of the Invention

[0010] To solve the above problems, the present invention provides a grounding wire clamp positioning method and system using satellite positioning and landmark calibration.

[0011] The technical solution adopted by the present invention is as follows:

[0012] A grounding wire clamp positioning method using satellite positioning and landmark calibration, the grounding wire clamp positioning method includes the following steps:

[0013] Step 1: Initial position acquisition: Obtain the initial position of the grounding wire clamp;

[0014] Step 2: Landmark point collection and calibration: Use the initial position of the grounding wire clamp to determine the search area around the grounding wire clamp, collect the three-dimensional coordinates of the landmark points in the search area, and compare the differences between the three-dimensional coordinates and the historical landmark data in the historical landmark database to obtain the spatio-temporal offset value of the landmark points; the spatio-temporal offset value includes the seasonal change parameter and geographical deformation parameter of the landmark points;

[0015] Step 3: Position correction: Use the spatio-temporal offset value to correct the initial position of the grounding wire clamp to obtain the calibrated grounding wire clamp position data;

[0016] Step 4: Final position calculation: Input the calibrated grounding wire clamp position data into a neural network model trained based on the historical positioning records of the grounding wire clamp, and calculate the final position coordinates of the grounding wire clamp.

[0017] Further, in step 1, deploy multiple receivers to detect the wireless identification signal emitted by the grounding wire clamp, and combine with the satellite positioning system to obtain the initial position of the grounding wire clamp;

[0018] Determine the installation positions of multiple receivers according to the power line distribution, and select a suitable communication frequency according to the environmental characteristics and electromagnetic interference conditions;

[0019] Extract the unique identification information and signal strength indication of the grounding wire clamp from the wireless identification signal, obtain satellite positioning data from the satellite positioning module built in the grounding wire clamp, and fuse the wireless identification signal positioning result and the satellite positioning result; the fusion methods include weighted average method, Kalman filtering algorithm, and non-linear filtering algorithm;

[0020] Finally, combine the wireless identification signal positioning result and the satellite positioning result to generate the initial position of the grounding wire clamp.

[0021] Further, in step 2, determining the search area around the grounding wire clamp includes the following steps:

[0022] Set the search area centered on the initial position: Dynamically determine the search range according to the estimated positioning accuracy of the grounding wire clamp, and adopt a multi-layer zoning strategy for the search area;

[0023] Obtain the terrain data and ground object distribution data of the search area: Use multiple geographical information data sources to construct the geographical environment background information of the search area;

[0024] Divide the landmark collection sub-areas: According to the terrain complexity, ground object density, and power line orientation, use a spatial clustering algorithm to divide areas with similar geographical features into the same sub-areas;

[0025] Set the set of sampling point positions: In each sub-area, use the optimal coverage algorithm to scientifically distribute the sampling points;

[0026] Dynamically adjust the search strategy: When it is detected that the satellite signal is blocked, calculate the signal attenuation degree; Dynamically expand the search area range and increase the number of sampling points according to the signal attenuation degree.

[0027] Further, in step 2, collecting the three-dimensional coordinates of landmark points includes the following steps:

[0028] Select landmark points: Select ground objects with clear geometric features and visual features and fixed positions within the search area as landmark points;

[0029] Measure the three-dimensional coordinates of landmark points: Use multiple measurement devices to obtain the longitude, latitude, and elevation data of landmark points;

[0030] Create landmark point identification information: Generate unified and standardized identification information for each landmark point, including position information and feature description information.

[0031] Further, in step 2, comparing with the historical landmark database and calculating the spatio-temporal offset value includes the following steps:

[0032] Match landmark points with historical records: Adopt a multi-feature fusion recognition method, comprehensively considering the spatial position similarity, geometric feature similarity, and attribute feature similarity of landmark points; For each newly collected landmark point, search for possible matching objects in the historical landmark database and calculate the matching degree score; The condition for successful matching is: the score exceeds the preset threshold;

[0033] Calculate the difference: Calculate the difference between the current three-dimensional coordinates of the landmark point and the historical coordinates recorded in the historical landmark database; The difference calculation uses vector subtraction operations to obtain the displacement components of the landmark point in the eastward, northward, and vertical directions;

[0034] Decomposition difference variation pattern over time: Using the time series analysis method, namely wavelet transform + trend extraction algorithm, to decompose the difference variation pattern;

[0035] Coordinate offset where the seasonal change component repeats at fixed time intervals; The mathematical expression is:

[0036]

[0037] In the formula, P s (t) represents the seasonal change component of the landmark point at time t; A i represents the amplitude of the i-th periodic component; ω i represents the angular frequency of the i-th periodic component; t represents the time variable; φ i represents the phase of the i-th periodic component; n represents the number of periodic components considered;

[0038] Coordinate offset where the geographical deformation component shows a one-way development trend; The mathematical expression is:

[0039] P g (t) = a0 + a1t + a2t 2 +... + a m t m ;

[0040] In the formula, P g (t) represents the geographical deformation component of the landmark point at time t; a0 represents the constant term of the polynomial; a1, a2,..., a m represent the polynomial coefficients; t m represents the power term of the time variable, t 1 describes uniform change, t 2 describes accelerating or decelerating change, t 3 describes more complex non-linear change, m represents the order of the polynomial;

[0041] Determine the spatio-temporal offset value: Determine the seasonal change component as the seasonal change parameter, reflecting the periodic law of the landmark point's position; Determine the geographical deformation component as the geographical deformation parameter, reflecting the long-term change trend of the landmark point's position;

[0042] Handle the situation of no matching record: If there is no record matching the landmark point in the historical landmark database, then add the current three-dimensional coordinates of the landmark point as the initial record to the database; During the addition process, create a complete attribute record for the new landmark point, including the acquisition time, coordinate accuracy assessment, description of the surrounding environment, and assign a unique identifier.

[0043] Furthermore, in step 3, use the spatio-temporal offset value to correct the preliminary position of the grounding clamp, including the following steps:

[0044] Build a correction model reflecting the distribution of spatio-temporal offset values: within the search area, construct a spatial interpolation model based on the spatio-temporal offset values of landmark points; use the improved Kriging interpolation algorithm to describe the spatial autocorrelation between landmark points through the semi-variance function and predict the correction values of unsampled points;

[0045] The Kriging interpolation formula is:

[0046]

[0047] In the formula, C(x) represents the correction value at position x; C(x k ) represents the spatio-temporal offset value of the k-th landmark point; ω k represents the corresponding weight coefficient; m represents the number of landmark points used for interpolation calculation;

[0048] The semi-variance function is:

[0049]

[0050] In the formula, γ(h) represents the semi-variance between two points; h represents the Euclidean distance between two points; C0 represents the nugget effect; C represents the sill value; a represents the range;

[0051] The time evolution model is based on Fourier analysis and trend decomposition, and decomposes the time series of the position offset of landmark points into two parts: periodic variation and long-term trend;

[0052] The formula is expressed as:

[0053] C(x,t) = C s (x,t) + C g (x,t);

[0054] In the formula, C(x,t) represents the correction value at position x at time t; C s (x,t) represents the seasonal variation part; C g (x,t) represents the geographical deformation part;

[0055] Calculate the correction vector at the preliminary position: input the preliminary position coordinates of the grounding clamp and the current time into the correction model to obtain the correction amount to be applied at this position at the current time; the correction vector includes three components: eastward, northward, and vertical, respectively representing the correction offsets of the grounding clamp position in three directions;

[0056] The calculation formula of the correction vector is:

[0057]

[0058] In the formula, represents the correction vector of the preliminary position p0 at time t0; C E 、CN and C U represent the correction values in the east, north, and vertical directions respectively;

[0059] Apply the correction vector to obtain the calibrated position data: Apply the correction vector to the preliminary position to obtain the calibrated position data of the grounding wire clamp;

[0060] The calculation formula is:

[0061]

[0062] In the formula, p cal represents the calibrated position coordinates; p0 represents the preliminary position coordinates; represents the correction vector;

[0063] Record the calibration results and intermediate data: Record the calibrated position data of the grounding wire clamp and its corresponding correction vector.

[0064] Furthermore, in step 4, calculating the final position coordinates of the grounding wire clamp includes the following steps:

[0065] Convert the calibrated position data of the grounding wire clamp into a feature vector: Extract and construct a multi-dimensional vector that can comprehensively reflect the position characteristics of the grounding wire clamp; Normalize all features to form a feature vector with a fixed dimension as the input of the neural network;

[0066] The construction formula of the feature vector is:

[0067] F = [f1, f2,..., f d ;

[0068] In the formula, F represents the feature vector; f r represents the r-th feature component, r ∈ [1, d]; d represents the dimension of the feature vector;

[0069] Input the feature vector into the neural network model for processing: The neural network model adopts a deep neural network combining long short-term memory network LSTM and attention mechanism;

[0070] The forward propagation process is:

[0071] H = LSTM(F);

[0072] A = Attention(H);

[0073] O = FC(A);

[0074] In the formula, F represents the input feature vector; H represents the output of the LSTM layer; A represents the output of the attention layer; O represents the final output result; LSTM(·), Attention(·), and FC(·) respectively represent the operations of the LSTM layer, the attention layer, and the fully connected layer;

[0075] Extract the final position coordinates from the output result of the neural network model: The model output is a multi-dimensional vector, which contains the final position coordinates and other additional information; Extract the position coordinate part in the output vector to obtain the final position coordinates of the grounding wire clamp;

[0076] Finally, record the final position coordinates and positioning parameters in the historical positioning record of the grounding wire clamp;

[0077] When the neural network model cannot generate a valid position result, start the alternative positioning method to recalculate the final position coordinates of the grounding wire clamp.

[0078] A grounding wire clamp positioning system using satellite positioning and landmark calibration, comprising:

[0079] A satellite positioning module for obtaining the preliminary position of the grounding wire clamp;

[0080] A calibration analysis module, which uses the preliminary position to determine the search area around the grounding wire clamp, collects the three-dimensional coordinates of landmark points in the search area, and compares the three-dimensional coordinates with the historical landmark data in the historical landmark database to obtain the spatio-temporal offset value of the landmark points;

[0081] A correction and optimization module, which uses the spatio-temporal offset value to correct the preliminary position, obtains the calibrated grounding wire clamp position data, and inputs the calibrated grounding wire clamp position data into a neural network model trained based on the historical positioning record of the grounding wire clamp to calculate the final position coordinates of the grounding wire clamp.

[0082] A computer device, comprising a memory and a processor, wherein the memory stores, and it is characterized in that: when the processor executes a computer program, it implements the above-mentioned grounding wire clamp positioning method using satellite positioning and landmark calibration.

[0083] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned grounding wire clamp positioning method using satellite positioning and landmark calibration.

[0084] Compared with the prior art, the beneficial effects of the present invention are:

[0085] 1. Overcoming the limitations of single positioning technology: This positioning method for grounding wire clamps using satellite positioning and landmark calibration effectively solves the problem of insufficient accuracy in traditional single satellite positioning due to signal occlusion or electromagnetic interference in complex power environments by integrating wireless identification signals and satellite positioning technology, providing stable and reliable preliminary position information for subsequent calibration.

[0086] 2. Introducing the concept of landmark spatio-temporal offset values: This positioning method for grounding wire clamps using satellite positioning and landmark calibration proposes a decomposition and modeling method for seasonal change parameters and geographical deformation parameters, successfully capturing the dynamic law of landmark point positions changing over time. It solves the systematic deviation problem caused by traditional static positioning methods ignoring the time dimension, significantly improving the accuracy and robustness of positioning results.

[0087] 3. Achieving high-precision dynamic correction: This positioning method for grounding wire clamps using satellite positioning and landmark calibration adopts a correction mechanism combining Kriging spatial interpolation and time evolution model, comprehensively reflecting the spatial distribution and time variation law of landmark point position offsets. Combining with a weight decay mechanism, it focuses on reflecting the local deformation characteristics around the grounding wire clamp, further improving the correction accuracy.

[0088] 4. Optimizing positioning performance through deep learning: This positioning method for grounding wire clamps using satellite positioning and landmark calibration is based on a neural network model of long short-term memory network (LSTM) and attention mechanism, which can effectively process the temporal characteristics and spatial correlation of the grounding wire clamp position, extracting more accurate position estimates. Using an incremental learning mechanism to continuously update the historical positioning database, enabling the system to have the ability of "experience accumulation", and the positioning performance gradually improves with the running time and data accumulation.

[0089] 5. Ensuring reliability through multiple mechanisms: When the neural network model fails in this positioning method for grounding wire clamps using satellite positioning and landmark calibration, it activates an alternative positioning method based on particle filter to ensure the reliable operation of the system under extreme conditions, significantly enhancing the fault tolerance and adaptability of the system.

[0090] 6. Significantly improving positioning accuracy: Compared with traditional positioning methods in complex environments, the positioning accuracy of this positioning method for grounding wire clamps using satellite positioning and landmark calibration is greatly improved, meeting the requirements of high-precision positioning for power line safety inspection and maintenance. The improvement of positioning accuracy not only helps to improve the management efficiency of power facilities, but also provides important technical support for the intelligent operation and maintenance of power systems.

[0091] 7. All-weather and full-scenario applicability: This positioning method for grounding wire clamps using satellite positioning and landmark calibration is applicable to various types of power lines and complex terrain environments, and can achieve high-precision and all-weather determination of the grounding wire clamp position, providing a strong technical guarantee for the safety monitoring and fault troubleshooting of power lines.

[0092] In summary, the grounding wire clamp positioning method using satellite positioning and landmark calibration breaks through the technical bottlenecks of traditional positioning methods through multi-technology integration, dynamic calibration, deep learning optimization, and multi-mechanism guarantee, achieving high-precision and intelligent positioning of the grounding wire clamp position, and has important engineering application value and technological progress significance. Brief Description of the Drawings

[0093] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0094] Figure 1 Schematic diagram of the application scenario of the grounding wire clamp positioning method using satellite positioning and landmark calibration of the present invention;

[0095] Figure 2 Schematic diagram of the overall process of the grounding wire clamp positioning method using satellite positioning and landmark calibration of the present invention;

[0096] Figure 3 Schematic diagram of the search area partition and landmark point collection of the grounding wire clamp positioning method using satellite positioning and landmark calibration of the present invention;

[0097] Figure 4 Schematic diagram of the overall structure of the grounding wire clamp positioning system using satellite positioning and landmark calibration of the present invention;

[0098] Figure 5 Schematic diagram of the computer device of the grounding wire clamp positioning method using satellite positioning and landmark calibration of the present invention. Detailed Description of the Embodiments

[0099] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can be obtained based on these drawings.

[0100] The grounding wire clamp is an important safety device in power line maintenance, which can effectively ensure the safety of line maintenance work. However, in actual applications, traditional grounding wire clamp positioning methods mainly rely on manual inspections and simple satellite positioning, and are easily interfered by environmental factors, resulting in low positioning accuracy.

[0101] In view of the problem of insufficient accuracy in the traditional grounding wire clamp positioning method, this embodiment proposes a grounding wire clamp positioning method using satellite positioning and landmark calibration. This grounding wire clamp positioning method using satellite positioning and landmark calibration can combine multiple positioning technologies to effectively improve the positioning accuracy of the grounding wire clamp and ensure the safe maintenance of power lines.

[0102] The grounding wire clamp positioning method provided in this embodiment can be applied to an application environment such as Figure 1 shown. Among them, multiple receivers 102 communicate with the central processing system 104 through a network. The data storage system can store the data that the central processing system 104 needs to process. The data storage system can be integrated on the central processing system 104, or placed in the cloud or on other network servers. The grounding wire clamp establishes a connection with the receiver by transmitting a wireless identification signal. The system detects the wireless identification signal emitted by the grounding wire clamp by deploying multiple receivers, and combines the satellite positioning system to obtain the preliminary position of the grounding wire clamp; uses the preliminary position to determine the search area around the grounding wire clamp, collects the three-dimensional coordinates of landmark points in the search area, and compares the three-dimensional coordinates with the historical landmark data in the historical landmark database to obtain the spatio-temporal offset value of the landmark points; uses the spatio-temporal offset value to correct the preliminary position to obtain the calibrated grounding wire clamp position data, and inputs the calibrated grounding wire clamp position data into a neural network model trained based on the historical positioning records of the grounding wire clamp to calculate the final position coordinates of the grounding wire clamp.

[0103] Among them, the receiver 102 can be, but is not limited to, various fixed receiving stations, portable receiving devices, vehicle-mounted receiving units, tower-mounted receivers, and pole-mounted receiving devices, etc. The fixed receiving station can be an all-weather monitoring site designed to prevent wind and rain; the portable receiving device can be a handheld terminal carried by staff; the vehicle-mounted receiving unit can be installed on an inspection vehicle to form a mobile monitoring network; the tower-mounted receiver is designed specifically for high-voltage transmission line towers and can be directly installed on the tower; the pole-mounted receiving device is suitable for medium- and low-voltage distribution networks and can be quickly mounted on poles. The central processing system 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0104] In an exemplary embodiment, as Figure 2 shown, a grounding wire clamp positioning method using satellite positioning and landmark calibration is provided. Taking the application of this method to the Figure 1 terminal as an example for illustration, it includes the following steps 1 to 4. Among them:

[0105] Step 1: Obtain the preliminary position of the grounding wire clamp.

[0106] In this embodiment, the specific operation of obtaining the preliminary position of the grounding wire clamp can be to deploy multiple receivers to detect the wireless identification signals emitted by the grounding wire clamp and combine with the satellite positioning system to obtain the preliminary position of the grounding wire clamp.

[0107] Among them, the wireless identification signal includes a radio frequency identification signal and a signal strength indication. The radio frequency identification signal is used to identify different grounding wire clamps, and the signal strength indication is used to assist in judging the distance between the grounding wire clamp and the receiver.

[0108] Specifically, deploy multiple receivers to detect the wireless identification signals emitted by the grounding wire clamp and combine with the satellite positioning system to obtain the preliminary position of the grounding wire clamp. The specific implementation is as follows: First, determine the installation positions of multiple receivers according to the distribution of the power line; then, configure multiple receivers to receive the wireless identification signals emitted by the grounding wire clamp at a specific communication frequency; finally, extract the unique identification information and signal strength indication of the grounding wire clamp from the wireless identification signals, and combine the position information of multiple receivers to determine the preliminary position of the grounding wire clamp.

[0109] Furthermore, when determining the installation positions of multiple receivers according to the distribution of the power line, it is necessary to consider the trend of the power line, terrain features, and signal coverage. Generally speaking, the receivers should be evenly distributed along the power line, and it is ensured that the grounding wire clamp at any position can be detected by at least three or more receivers simultaneously, so as to improve the positioning accuracy through the triangulation principle. At the same time, the installation positions of the receivers also need to consider the power supply convenience and communication network coverage to ensure that the receivers can work stably and transmit data in real time.

[0110] In the area of high-voltage transmission lines, the receiver spacing can usually be set to 1 - 3 kilometers, while in mountainous areas with complex terrain, it needs to be shortened to 500 - 800 meters to ensure the continuity of signal coverage. For ultra-high voltage lines, due to stronger electromagnetic interference, the receivers should be installed at a certain distance from the line. Usually, it is recommended to maintain a safety distance of 50 - 100 meters, and at the same time, a shielding design is adopted to reduce the interference impact.

[0111] When configuring multiple receivers to receive the wireless identification signals emitted by the grounding wire clamp at a specific communication frequency, it is necessary to select a suitable communication frequency according to the environmental characteristics and electromagnetic interference conditions. In the power environment, the electromagnetic field generated by high-voltage lines may interfere with wireless signals. Therefore, a low-frequency band with strong anti-interference ability or a specific industrial frequency band is usually selected for communication. At the same time, the receivers need to be configured with corresponding signal processing algorithms to filter out environmental noise and improve the signal reception quality.

[0112] For different application scenarios, multiple radio frequency identification technologies can be selected. For example, in cases where a smaller coverage range is required, a short-range identification solution based on RFID technology can be adopted, with a working frequency usually of 13.56 MHz or 433 MHz; for scenarios that require a larger coverage range, Bluetooth Low Energy technology can be used, operating in the 2.4 GHz frequency band, with a communication distance of up to about 100 meters; and in the case of power lines crossing complex terrains such as mountains, low-power wide-area network technologies such as LoRa or NB-IoT can be considered; these technologies operate in the 433 MHz or 868 MHz frequency band, with a communication distance of up to several kilometers, and have strong penetration ability and anti-interference ability.

[0113] When extracting the unique identification information and signal strength indication of the grounding wire clamp from the wireless identification signal and determining the preliminary position of the grounding wire clamp by combining the position information of multiple receivers, the system first analyzes the received wireless identification signal to obtain the unique identification code of the grounding wire clamp to ensure that the target device is being located. Then, by analyzing the intensity difference of the same grounding wire clamp signal received by multiple receivers and combining the known position information of the receivers, a signal strength triangulation algorithm is used to calculate the spatial position of the grounding wire clamp. At the same time, the system also obtains satellite positioning data from the satellite positioning module built into the grounding wire clamp and fuses the signal strength positioning result with the satellite positioning result to obtain a more accurate preliminary position.

[0114] For the calculation of the preliminary position, multiple specific algorithms can be used. For example: The basic weighted average method is to perform weighted averaging on the radio frequency positioning result and the satellite positioning result according to a certain weight, and the weight can be dynamically adjusted according to environmental conditions. In an environment where the signal changes are relatively complex, the Kalman filter algorithm can be used. This algorithm can effectively process data containing random noise and improve the positioning accuracy through a prediction-correction iterative process. For scenarios where the grounding wire clamp is in a moving state, non-linear filtering algorithms such as the extended Kalman filter or particle filter can also be used to better track the position change of the grounding wire clamp.

[0115] When the grounding wire clamp is in the complex environment of the power line, a single positioning technology often fails to provide sufficient accuracy. By combining the wireless identification signal positioning and satellite positioning technologies, they can complement each other and reduce their respective limitations. For example, in mountainous or canyon environments where satellite signals are blocked, wireless identification signal positioning can provide effective position information; while in areas with strong electromagnetic interference, satellite positioning may provide more stable reference data.

[0116] Under different types of power lines, there are also differences in the deployment plans of the positioning system. For 110kV - 220kV high-voltage lines, the electromagnetic interference is relatively small, and the satellite positioning system can be preferentially adopted, combined with a medium-density receiver network; for ultra-high-voltage lines of 500kV and above, the electromagnetic field intensity increases significantly, and the radio frequency signal is vulnerable to interference. At this time, the receiver deployment should be encrypted, and special signal processing algorithms, such as spectrum spreading technology or adaptive noise cancellation algorithms, should be adopted to improve the anti-interference ability of the signal.

[0117] It should be noted that the grounding wire clamp can integrate a low-power wireless transmission module and a satellite positioning receiving module, and adopt a suitable power management strategy to ensure long-term operation. The wireless transmission module regularly broadcasts signals containing unique identifiers, while the satellite positioning receiving module collects and preliminarily processes the data of the satellite positioning system. The two parts of data can be transmitted to the central processing system through the receiver network for subsequent position calculation and optimization.

[0118] In the design of the grounding wire clamp, different types of power supply schemes can be selected according to the actual application scenarios. For portable grounding wire clamps, high-energy-density lithium batteries are usually combined with an ultra-low-power design to achieve continuous operation for several months; for grounding wire clamps that are used for a long time and fixed, integrating solar panels or using electric field energy harvesting technology can be considered to achieve energy self-sufficiency and greatly extend the maintenance cycle.

[0119] Step 2: Use the preliminary position to determine the search area around the grounding wire clamp, collect the three-dimensional coordinates of landmark points within the search area, and compare the three-dimensional coordinates with the historical landmark data in the historical landmark database to obtain the spatio-temporal offset value of the landmark points.

[0120] In this embodiment, the spatio-temporal offset value of the landmark points includes the seasonal change parameters and geographical deformation parameters of the landmark points. Through these two parameters, the regular characteristics of the landmark point position changing with time and environment can be comprehensively reflected, thus providing a reliable reference benchmark for the calibration of the grounding wire clamp position.

[0121] Specifically, the operation process of using the preliminary position to determine the search area around the grounding wire clamp includes multiple steps: First, set the search area with the preliminary position as the center; then, obtain the terrain data and ground object distribution data of the search area; next, divide the landmark collection sub-areas within the search area according to the terrain data and ground object distribution data; after that, set the position set of sampling points for the landmark collection sub-areas; finally, when it is detected that the satellite positioning system signal is blocked, expand the search area range and increase the number of the position set of sampling points.

[0122] When setting the search area centered around the initial position, the search range can be dynamically determined according to the estimated positioning accuracy of the grounding clamp. In the case where the power line is located in an open area with good signal reception, the search area can be set to a relatively small range; while in complex environments such as mountainous areas or building-dense areas, the search range can be automatically expanded to ensure that sufficient landmark points are included. The search area usually adopts a multi-layer zoning strategy, which is divided into a core area, a buffer area, and a peripheral area from the inside out. Landmark collection is preferentially carried out in the core area and gradually expanded outwards as needed.

[0123] Exemplarily, as Figure 3 shown, Figure 3 FIG. is a schematic diagram of the search area zoning and landmark point collection. The blue dots represent landmark points, indicating the fixed reference points within the search area; the red dots represent the initial position of the grounding clamp, which is the core target for positioning; the purple squares represent the receivers, which are used for signal reception and positioning; the simplified tower structure represents the power tower, which is a key facility on the power line; the black dashed line represents the power line, indicating the line direction; and the four-layer different search area zoning boundaries, including the core area circled by the red dashed line, the buffer area circled by the orange dashed line, the peripheral area circled by the green dashed line, and the extended search area when the signal is limited circled by the blue dashed line. This multi-layer zoning strategy enables the system to dynamically adjust and optimize landmark point collection according to different environmental conditions.

[0124] In the link of obtaining the terrain data and ground object distribution data of the search area, this embodiment comprehensively utilizes a variety of geographical information data sources, including digital elevation model DEM, remote sensing image data, geographical information system GIS database, and the existing power facility distribution map. These data together constitute the geographical environment background information of the search area, providing a basis for subsequent landmark point selection. The terrain data mainly includes parameters such as elevation, slope, aspect, and surface roughness, and the ground object distribution data includes the spatial distribution characteristics of elements such as buildings, roads, rivers, vegetation, and power facilities.

[0125] When dividing the landmark collection sub-areas within the search area according to the terrain data and ground object distribution data, factors such as the terrain complexity, ground object density, and the direction of the power line in the area need to be considered. The specific division method uses a spatial clustering algorithm to group areas with similar geographical features into the same sub-area to optimize the distribution of landmark points. For areas with large terrain undulations, the sub-area division is more refined to ensure that sufficient landmark points are set at positions with significant elevation changes; for flat and open areas with dense power facilities, the sub-area division is mainly based on the distribution characteristics of the power facilities.

[0126] In the process of setting the set of sampling point positions for the landmark acquisition sub-region, the optimal coverage algorithm is adopted to scientifically distribute the sampling points within each sub-region. This algorithm takes into account factors such as possible landmark point candidate objects, observation line-of-sight distances, and satellite signal quality within the region to generate an optimal set of sampling point positions. In practical applications, different sampling point densities are assigned to each sub-region, and regions with complex terrain or dense power facilities will be assigned more sampling points to improve the positioning accuracy. At the same time, the algorithm also considers the mutual relationship between sampling points to ensure that there is sufficient spatial distribution between sampling points and an effective triangulation network can be formed.

[0127] When it is detected that the satellite positioning system signal is blocked, the search area range is expanded and the number of the set of sampling point positions is increased. According to the satellite signal quality indicators received in real time, the search strategy is dynamically adjusted. When the satellite signal is blocked by trees, buildings or terrain, the signal attenuation degree is calculated, and based on this, the range of the expanded search area and the number of sampling points to be increased are determined. The more severe the signal attenuation degree is, the larger the expansion range and the number of increased sampling points, so as to ensure that sufficient landmark information can still be obtained in a signal-limited environment.

[0128] When collecting the three-dimensional coordinates of landmark points within the search area, it is first necessary to select features on the ground within the search area that can be recognized by the measuring device and have fixed positions as landmark points. Features that can be recognized by the measuring device refer to objects with clear geometric and visual features; such as: the foundation of a power tower, fixed structures of a substation, road intersections, corner points of independent buildings, etc. Being in a fixed position means that the feature maintains a relatively stable spatial position on a long time scale and is not easily moved or deformed due to environmental changes or human factors. When selecting landmark points, fixed facilities within the power system are given priority, such as tower base identification stakes, substation reference points, etc. These landmark points not only have fixed positions but also have clear management records, which are convenient for subsequent maintenance and updating.

[0129] When using a measuring device to measure the three-dimensional coordinates of landmark points, a satellite positioning receiver and a ranging device are comprehensively used. The satellite positioning receiver mainly adopts multi-frequency point and multi-constellation reception, simultaneously receiving signals from multiple satellite navigation systems such as GPS, Beidou, and GLONASS, and improving the coordinate accuracy through differential positioning technology. The ranging device includes high-precision measuring tools such as laser rangefinders and total stations, which are used to obtain the relative distance and angle information of landmark points. In the actual measurement process, the method of averaging multiple observations is adopted to reduce the influence of random errors on the measurement results. For each landmark point, the longitude, latitude, and elevation values in the WGS84 coordinate system are recorded to form complete three-dimensional coordinate data.

[0130] When creating identification information for each landmark point, identification data containing location information and feature description information is generated according to a unified specification. The location information includes the absolute coordinates of the landmark point, the relative coordinates with respect to the preliminary position of the grounding clamp, and the coordinate acquisition time; the feature description information includes the type identification of the landmark point such as power facility type, natural feature type, artificial sign type, physical feature descriptions such as shape, size, and material, and recognizable features such as color, texture, and contour. These identification information not only helps with subsequent landmark point matching and recognition, but also provides a basis for the establishment and maintenance of the historical landmark database.

[0131] The process of comparing the three-dimensional coordinates with the historical landmark data in the historical landmark database to obtain the spatio-temporal offset value of the landmark point includes multiple steps. First, match the landmark point with the corresponding record in the historical landmark database. The matching process uses a multi-feature fusion recognition method, comprehensively considering the spatial position similarity, geometric feature similarity, and attribute feature similarity of the landmark point. For each newly collected landmark point, search for possible matching objects in the historical database and calculate the matching degree score. When the score exceeds the preset threshold, confirm that the matching is successful.

[0132] After successful matching, calculate the difference between the current three-dimensional coordinates of the landmark point and the historical coordinates recorded in the historical landmark database. The difference calculation uses vector subtraction operation to obtain the displacement components of the landmark point in the eastward, northward, and vertical directions. To improve the calculation accuracy, consider the conversion relationship between different coordinate systems to ensure that the difference calculation is carried out under a unified reference coordinate system.

[0133] Next, analyze the change pattern of the difference over time and decompose it into two main components: the coordinate offset part that repeats at fixed time intervals and the coordinate offset part that shows a one-way development trend. This decomposition process uses time series analysis methods. Specifically, when implementing, a combination of wavelet transform and trend extraction algorithms can be applied. For the time series data of the landmark point position change, first perform time-domain and frequency-domain transformation to extract the change components of different frequencies, and then identify the periodic part and the trend part among them.

[0134] The mathematical expression of the time series decomposition algorithm can be simplified as:

[0135] P(t) = P s (t) + P g (t) + ∈(t);

[0136] In the formula, P(t) represents the position offset vector of the landmark point at time t; P s (t) represents the seasonal change component of the landmark point at time t; P g (t) represents the geographical deformation component of the landmark point at time t; ∈(t) represents the random error term of the landmark point at time t.

[0137] Seasonal variation component P s (t) can be further expressed as:

[0138]

[0139] In the formula, A i represents the amplitude of the i-th periodic component, ω i represents the angular frequency of the i-th periodic component; t represents the time variable; φ i represents the phase of the i-th periodic component; n represents the number of periodic components considered. This expression is derived from the principle of Fourier analysis and is particularly suitable for describing periodic surface deformations caused by temperature changes, seasonal fluctuations of the groundwater level, etc. In the embodiment, by identifying the main frequency components, the seasonal variation law of the landmark point position can be effectively captured.

[0140] Geographical deformation component P g (t) can then be represented using a polynomial trend model:

[0141] P g (t) = a0 + a1t + a2t 2 +... + a m t m ;

[0142] In the formula, a0 represents the constant term of the polynomial; a1, a2,..., a m represent the polynomial coefficients, m is the order of the polynomial, usually taking values from 1 to 3, and is used to describe linear, quadratic or cubic trends; t m represents the power term of the time variable, t 1 describes uniform change, t 2 describes accelerating or decelerating change, t 3 describes more complex non-linear change, and m represents the order of the polynomial. This model is suitable for describing continuous surface deformations caused by factors such as crustal movement, underground mining, soil erosion, etc. In practical applications, the appropriate order of the polynomial is adaptively selected according to the time span and change characteristics of the historical data.

[0143] The coordinate offset part that repeats at fixed time intervals is determined as the seasonal variation parameter, and the coordinate offset part showing a one-way development trend is determined as the geographical deformation parameter. The seasonal variation parameter mainly reflects the periodic law of the landmark point position changing with seasons, including characteristic quantities such as amplitude, period and phase; the geographical deformation parameter reflects the long-term change trend of the landmark point position, including characteristic quantities such as deformation rate, acceleration and direction. These two sets of parameters together constitute the spatio-temporal offset value of the landmark point, providing a scientific basis for subsequent position correction.

[0144] When there is no matching record for the landmark point in the historical landmark database, the current three-dimensional coordinates of the landmark point are added to the historical landmark database as an initial record. During the addition process, a complete attribute record is created for the new landmark point, including information such as the acquisition time, coordinate accuracy assessment, and surrounding environment description, and a unique identifier is assigned to it. These newly added landmark point records will gradually accumulate position change data during subsequent positioning processes, enabling them to participate in the calculation of spatio-temporal offset values.

[0145] In step 2 above, by establishing a grounding wire clamp positioning method centered on landmark calibration, the problem of insufficient accuracy of traditional satellite positioning in complex power environments is effectively solved. This solution comprehensively considers the seasonal changes and geographical deformation factors of landmark points, introduces the concept of spatio-temporal offset values, and transforms static positioning into dynamic calibration, significantly improving the accuracy of determining the position of the grounding wire clamp. Especially in cases where satellite signals are restricted or the surface environment changes complexly, this method can continuously optimize the positioning accuracy through the analysis and comparison of historical landmark data, providing more reliable technical support for the safety maintenance of power lines. Compared with traditional single satellite positioning, this landmark calibration-based positioning method not only improves the spatial accuracy but also increases the monitoring of changes in the time dimension, achieving a technological leap from point position determination to dynamic tracking, with obvious technological progressiveness.

[0146] Step 3: Use the spatio-temporal offset value to correct the preliminary position to obtain the calibrated grounding wire clamp position data, and input the calibrated grounding wire clamp position data into a neural network model trained based on the historical positioning records of the grounding wire clamp to calculate the final position coordinates of the grounding wire clamp.

[0147] In this embodiment, using the spatio-temporal offset value to correct the preliminary position includes: establishing a correction model in the search area that reflects the distribution of spatio-temporal offset values; calculating the correction vector at the preliminary position based on the correction model; applying the correction vector to the preliminary position to obtain the calibrated grounding wire clamp position data; and recording the calibrated grounding wire clamp position data and its corresponding correction vector.

[0148] When establishing a correction model in the search area that reflects the distribution of spatio-temporal offset values, a method combining spatial interpolation and time evolution is adopted. For the spatio-temporal offset values of multiple landmark points in the search area, first, a spatial interpolation model is constructed according to their spatial distribution characteristics to reflect the spatial distribution law of the position offset of landmark points; then, through time series analysis, a time evolution model is established for each spatial position to reflect the variation law of the position offset over time. In this way, the correction model can be expressed as a two-dimensional spatio-temporal function, capable of predicting the position offset amount at any position in the search area at any time point.

[0149] The spatial interpolation model uses an improved Kriging interpolation algorithm. This algorithm is based on the principles of geostatistics, takes into account the spatial autocorrelation between data points, describes the spatial structure characteristics through the semivariogram function, and can perform an optimal linear unbiased estimation of unsampled points. For the scenario of this application, the following modified Kriging interpolation formula is adopted:

[0150]

[0151] In the formula, C(x) represents the correction value at position x; C(x k ) represents the spatio-temporal offset value of the kth landmark point; ω k represents the corresponding weight coefficient; m represents the number of landmark points used for interpolation calculation.

[0152] The weight coefficient ω k is determined by solving the following Kriging equations:

[0153]

[0154] Among them, γ(x k ,x l ) represents the semivariance between positions x k and x l ; λ is the Lagrange multiplier, which constrains the sum of the weight coefficients to be 1; γ(x k ,x) represents the semivariance between the sampling point x k and the point x to be estimated.

[0155] The semivariogram function adopts an anisotropic spherical model to adapt to the distribution characteristics of terrain and features along the power line:

[0156]

[0157] In the formula, γ(h) represents the semivariance between two points; h represents the distance between two points; C0 represents the nugget effect, that is, the measurement error; C represents the sill value, that is, the spatial structure variance; a represents the range, that is, the spatial correlation range. In this embodiment, these parameters are determined by analyzing the spatial distribution characteristics of the spatio-temporal offset values of the landmark points, and can better adapt to the distribution characteristics of the landmark points in the power line environment.

[0158] The time evolution model is based on Fourier analysis and trend decomposition, decomposes the time series of the position offset of the landmark points into two parts: periodic variation and long-term trend, models them separately and makes predictions. The model expression is:

[0159] C(x,t)=C s (x,t)+C g (x,t);

[0160] where, C(x,t) represents the corrected value at position x at time t, C s (x,t) represents the seasonal variation part; C g (x,t) represents the geographical deformation part. Through the above method combining spatial interpolation and time evolution, a complete correction model that can reflect the distribution law of spatio-temporal offset values within the search area is formed.

[0161] When calculating the correction vector at the preliminary position based on the correction model, the preliminary position coordinates of the ground wire clamp and the current time are input into the correction model to obtain the correction amount that should be applied at this position at the current time. The correction vector includes three components: eastward, northward, and vertical, which respectively represent the correction offsets required for the ground wire clamp position in the three directions. The calculation formula for the correction vector is:

[0162]

[0163] where, represents the correction vector at the preliminary position p0 at time t0; C E , C N and C U represent the correction values in the eastward, northward, and vertical directions respectively. These correction values are obtained through interpolation calculation of the correction model at the preliminary position. To improve the accuracy of the correction vector, this embodiment also introduces a weight decay mechanism, that is, the farther the landmark point is from the preliminary position, the smaller its influence on the calculation of the correction vector, which can better reflect the local deformation characteristics around the ground wire clamp.

[0164] The process of applying the correction vector to the preliminary position to obtain the calibrated position data of the ground wire clamp is to add the preliminary position coordinates and the correction vector to obtain the calibrated three-dimensional coordinates. The calculation formula is:

[0165]

[0166] where, p cal represents the calibrated position coordinates; p0 represents the preliminary position coordinates; represents the correction vector. The calibrated position coordinates include three components: longitude, latitude, and elevation, forming a complete three-dimensional position representation.

[0167] When recording the calibrated position data of the ground wire clamp and its corresponding correction vector, not only the calibration result is stored, but also the intermediate data of the correction process is retained, including the preliminary position, each component of the correction vector, the calibration time, and the landmark point information involved in the calculation, etc. These records can be used for subsequent position tracking and change analysis on the one hand, and also provide valuable data resources for the training and optimization of the neural network model on the other hand.

[0168] Step 4: In this embodiment, the calibrated ground wire clamp position data is input into a neural network model trained based on the historical positioning records of the ground wire clamp, and the final position coordinates of the ground wire clamp are calculated, including: converting the calibrated ground wire clamp position data into a feature vector; inputting the feature vector into the neural network model for processing; extracting the final position coordinates of the ground wire clamp from the output result of the neural network model; recording the final position coordinates and positioning parameters in the historical positioning records of the ground wire clamp; when the neural network model fails to generate a valid position result, starting an alternative positioning method to recalculate the final position coordinates of the ground wire clamp.

[0169] When converting the calibrated ground wire clamp position data into a feature vector, it is necessary to extract and construct a multi-dimensional vector that can reflect the position characteristics of the ground wire clamp. The feature vector not only includes the calibrated three-dimensional coordinates, but also includes time characteristics such as time periods of the day, seasons, etc., environmental characteristics such as temperature, humidity, weather conditions, etc., historical trajectory characteristics such as historical position change trends, speeds, etc., and power line characteristics such as line types, voltage levels, etc. After these features are normalized, a feature vector with a fixed dimension is formed as the input of the neural network. The construction formula of the feature vector is:

[0170] F=[f1,f2,...,f d ;

[0171] In the formula, F represents the feature vector; f r represents the r-th feature component, r ∈ [1, d]; d represents the dimension of the feature vector. In this embodiment, the feature vector contains various factors that comprehensively reflect the position characteristics of the ground wire clamp.

[0172] When inputting the feature vector into the neural network model for processing, this embodiment adopts a deep neural network model that combines a long short-term memory network (LSTM) and an attention mechanism. This model consists of an input layer, an LSTM layer, an attention layer, and an output layer, and can effectively process the temporal characteristics and spatial correlations of the ground wire clamp position.

[0173] Specifically, the LSTM layer is responsible for capturing the temporal patterns of the ground wire clamp position changes, and the attention layer is responsible for identifying the most important factors for the current position among various influencing factors and assigning higher weights. The forward propagation process of the model can be expressed as:

[0174] H=LSTM(F);

[0175] A=Attention(H);

[0176] O=FC(A);

[0177] In the formula, F represents the input feature vector, H represents the output of the LSTM layer, A represents the output of the attention layer, O represents the final output result, and LSTM(·), Attention(·), and FC(·) represent the operations of the LSTM layer, the attention layer, and the fully connected layer, respectively.

[0178] When extracting the final position coordinates of the ground wire clamp from the output result of the neural network model, the multi-dimensional vector output by the model is mapped into a three-dimensional coordinate representation. The output vector not only contains the final position coordinates, but also additional information such as positioning accuracy evaluation and reliability index. By parsing the output vector, three components of longitude, latitude, and elevation are extracted to form the final position coordinates of the ground wire clamp.

[0179] When recording the final position coordinates and positioning parameters into the historical positioning record of the ground wire clamp, this embodiment adopts the idea of incremental learning to continuously enrich and update the historical positioning database. The recorded content includes the final position coordinates, positioning time, positioning accuracy, environmental conditions, and key parameters of the positioning process, etc. These records can be used as a reference for subsequent positioning on the one hand, and provide training data for the iterative optimization of the neural network model on the other hand.

[0180] When the neural network model fails to generate a valid position result, an alternative positioning method is started to recalculate the final position coordinates of the ground wire clamp. The alternative positioning method can adopt a positioning algorithm based on Particle Filter. This algorithm does not rely on the neural network model, but gradually approaches the true position of the ground wire clamp through an iterative process of prediction-observation-update. The particle filter algorithm first randomly generates multiple particles in the possible position space, then calculates the weight of each particle according to the observation data, and then generates a new generation of particle sets through resampling, and iterates until convergence. Finally, the final position estimate of the ground wire clamp is obtained through weighted average.

[0181] In step 4 above, a calibration model is first established based on the spatio-temporal offset distribution of landmark points to systematically calibrate the preliminary position and eliminate the influence of seasonal changes and geographical deformations. Then, using deep learning technology, a neural network model is trained based on historical positioning records to further optimize the calibrated position and extract a more accurate position estimate. Finally, an alternative positioning mechanism is used to ensure the reliable operation of the system under extreme conditions. This multi-level and multi-mechanism positioning method breaks through the accuracy bottleneck of traditional satellite positioning methods in the power environment, realizes the high-precision and all-weather determination of the position of the grounding clamp, and provides strong support for the safety inspection and maintenance of power lines. Compared with traditional methods, the positioning accuracy of this application is significantly improved, especially in environments such as mountainous areas and urban canyons, where the positioning accuracy is greatly improved. At the same time, through the adaptive learning mechanism, the positioning performance will continuously improve with the increase in the running time, demonstrating the ability to accumulate experience that traditional methods do not possess, and reflecting the great potential of artificial intelligence technology in the application of power systems.

[0182] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but is executed alternately or alternately with at least a part of other steps or steps in other steps.

[0183] Based on the same inventive concept, this embodiment also provides a grounding clamp positioning system using satellite positioning and landmark calibration. The implementation solution for solving the problem of this system is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the grounding clamp positioning system using satellite positioning and landmark calibration provided below can refer to the limitations on the processing method of graphic and text questions in the above text, and will not be repeated here.

[0184] In an exemplary embodiment, as Figure 4 shown, a grounding clamp positioning system using satellite positioning and landmark calibration is provided, including:

[0185] A satellite positioning module for obtaining the preliminary position of the grounding clamp;

[0186] The calibration analysis module uses the preliminary position to determine the search area around the grounding wire clamp, collects the three-dimensional coordinates of landmark points within the search area, and compares the differences between the three-dimensional coordinates and the historical landmark data in the historical landmark database to obtain the spatio-temporal offset value of the landmark points.

[0187] The calibration and optimization module uses the spatio-temporal offset value to correct the preliminary position, obtains the calibrated position data of the grounding wire clamp, and inputs the calibrated position data of the grounding wire clamp into the neural network model trained based on the historical positioning records of the grounding wire clamp to calculate the final position coordinates of the grounding wire clamp.

[0188] The above grounding wire clamp positioning system using satellite positioning and landmark calibration first obtains the preliminary position of the grounding wire clamp through the satellite positioning module to provide basic positioning information; then, the calibration analysis module collects the three-dimensional coordinates of landmark points within the search area and calculates the spatio-temporal offset value to provide a basis for position correction; finally, the calibration and optimization module uses the spatio-temporal offset value to correct the preliminary position and further optimizes it through the neural network model to obtain the final position coordinates.

[0189] Compared with the traditional single satellite positioning system, the traditional satellite positioning system is often affected by factors such as signal occlusion and multipath effects, and the positioning accuracy is limited; while this application effectively overcomes these limitations by introducing landmark calibration and neural network optimization mechanisms, achieving higher-precision positioning. Especially in areas with complex terrain and dense vegetation, this embodiment can effectively correct the preliminary position using the spatio-temporal variation law of landmark points, greatly improving the positioning accuracy. At the same time, this embodiment realizes the adaptive improvement of the positioning performance through the continuous learning and optimization of the neural network model. With the continuous accumulation of historical positioning records, the neural network model can capture more complex position variation laws and provide more accurate predictions for the positioning of the grounding wire clamp. This ability to accumulate experience is not possessed by the traditional positioning system, reflecting the innovative value of artificial intelligence technology in the application of the power system. In addition, this embodiment also designs an alternative positioning mechanism to ensure the reliable operation of the system under extreme conditions. When the neural network model cannot generate an effective position result, the system will automatically start the alternative positioning method based on particle filtering to calculate the position of the grounding wire clamp through different technical paths, ensuring the all-weather and high-reliability operation of the system.

[0190] Each module in the above grounding wire clamp positioning system using satellite positioning and landmark calibration can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0191] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in Figure 5 . The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be implemented through Wi-Fi, a mobile cellular network, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for processing graphic and text questions and answers. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0192] Those skilled in the art can understand that Figure 5 the structure shown in is only a block diagram of some structures related to the solution of this embodiment, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0193] In an embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the steps in the above method embodiments.

[0194] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0195] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0196] It should be noted that the user information involved in this application includes, but is not limited to, user device information, user personal information, etc., and the data includes, but is not limited to, data for analysis, stored data, displayed data, etc. All of them are information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0197] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (RRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this embodiment can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., and are not limited thereto.

[0198] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in this application.

[0199] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A positioning method for grounding wire clamps using satellite positioning and landmark calibration, characterized in that, The method for positioning the grounding wire clamp includes the following steps: Step 1: Obtaining the preliminary position: Obtain the preliminary position of the grounding wire clamp; Step 2: Collecting and calibrating landmark points: Determine the search area around the grounding wire clamp using the preliminary position of the grounding wire clamp, collect the three-dimensional coordinates of the landmark points within the search area, and compare the differences between the three-dimensional coordinates and the historical landmark data in the historical landmark database to obtain the spatio-temporal offset value of the landmark points; the spatio-temporal offset value includes the seasonal change parameters and geographical deformation parameters of the landmark points; Step 3: Position correction: Use the spatio-temporal offset value to correct the preliminary position of the grounding wire clamp to obtain the calibrated position data of the grounding wire clamp; Step 4: Calculating the final position: Input the calibrated position data of the grounding wire clamp into the neural network model trained based on the historical positioning records of the grounding wire clamp to calculate the final position coordinates of the grounding wire clamp.

2. The method for positioning a grounding wire clamp using satellite positioning and landmark calibration according to claim 1, wherein: In step 1, detect the wireless identification signal emitted by the grounding wire clamp by deploying multiple receivers, and combine with the satellite positioning system to obtain the preliminary position of the grounding wire clamp; Determine the installation positions of multiple receivers according to the distribution of power lines, and select appropriate communication frequencies according to environmental characteristics and electromagnetic interference conditions; Extract the unique identification information and signal strength indication of the grounding wire clamp from the wireless identification signal, obtain satellite positioning data from the satellite positioning module built in the grounding wire clamp, and fuse the wireless identification signal positioning result and the satellite positioning result; the fusion methods include the weighted average method, the Kalman filtering algorithm, and the non-linear filtering algorithm; Finally, combine the wireless identification signal positioning result and the satellite positioning result to generate the preliminary position of the grounding wire clamp.

3. The method for positioning the grounding wire clamp using satellite positioning and landmark calibration according to claim 1, characterized in that: In step 2, determining the search area around the grounding wire clamp includes the following steps: Setting the search area centered on the preliminary position: Dynamically determine the search range according to the estimated positioning accuracy of the grounding wire clamp, and the search area adopts a multi-layer zoning strategy; Obtaining the terrain data and ground object distribution data of the search area: Use multiple geographical information data sources to construct the geographical environment background information of the search area; Dividing the landmark collection sub-areas: According to the terrain complexity, ground object density, and power line direction, use the spatial clustering algorithm to divide the areas with similar geographical features into the same sub-area; Setting the set of sampling point positions: In each sub-area, use the optimal coverage algorithm to scientifically distribute the sampling points; Dynamically adjusting the search strategy: When it is detected that the satellite signal is blocked, calculate the signal attenuation degree; Dynamically expand the search area range and increase the number of sampling points according to the signal attenuation degree.

4. The method for positioning a grounding wire clamp using satellite positioning and landmark calibration according to claim 1, wherein: In step 2, collecting the three-dimensional coordinates of the landmark points includes the following steps: Selecting the landmark points: Select the ground objects with clear geometric features and visual features and fixed positions within the search area as the landmark points; Measuring the three-dimensional coordinates of the landmark points: Use multiple measuring devices to obtain the longitude, latitude, and elevation data of the landmark points; Creating the identification information of the landmark points: Generate unified and standardized identification information for each landmark point, including position information and feature description information.

5. The method for positioning a grounding wire clamp using satellite positioning and landmark calibration according to claim 1, characterized in that: In step 2, comparing with the historical landmark database and calculating the spatio-temporal offset value includes the following steps: Match landmark points with historical records: Adopt an identification method that integrates multiple features, comprehensively considering the spatial position similarity, geometric feature similarity, and attribute feature similarity of landmark points; for each newly collected landmark point, search for possible matching objects in the historical landmark database and calculate the matching degree score; the condition for successful matching is that the score exceeds a preset threshold. Calculate the difference: Calculate the difference between the current three-dimensional coordinates of the landmark point and the historical coordinates recorded in the historical landmark database; the difference calculation uses vector subtraction operations to obtain the displacement components of the landmark point in the eastward, northward, and vertical directions. Decompose the change pattern of the difference over time: Adopt a time series analysis method, namely the wavelet transform + trend extraction algorithm, to decompose the change pattern of the difference. The seasonal change component is the coordinate offset that repeats at fixed time intervals; the mathematical expression is: where P s (t) represents the seasonal variation component of the landmark point at time t; A i represents the amplitude of the i-th periodic component; ω i represents the angular frequency of the i-th periodic component; t represents the time variable; φ i represents the phase of the i-th periodic component; n represents the number of periodic components considered; The geographical deformation component is the coordinate offset that shows a one-way development trend; the mathematical expression is: P g (t) = a0 + a1t + a2t 2 +... + a m t m ; where P g (t) represents the geographical deformation component of the landmark point at time t; a0 represents the constant term of the polynomial; a1, a2,..., a m represent the polynomial coefficients; t m represents the power term of the time variable, t 1 describes uniform change, t 2 describes acceleration or deceleration change, t 3 describes more complex non-linear change, and m represents the order of the polynomial; Determine the spatio-temporal offset value: Determine the seasonal change component as the seasonal change parameter, which reflects the periodic law of the landmark point's position; determine the geographical deformation component as the geographical deformation parameter, which reflects the long-term change trend of the landmark point's position. Handle the case of no matching record: If there is no record in the historical landmark database that matches the landmark point, add the current three-dimensional coordinates of the landmark point as an initial record to the database; during the addition process, create a complete attribute record for the new landmark point, including the acquisition time, coordinate accuracy assessment, description of the surrounding environment, and assign a unique identifier.

6. The method for positioning a grounding wire clamp using satellite positioning and landmark calibration according to claim 1, wherein: In step 3, use the spatio-temporal offset value to correct the preliminary position of the grounding clamp, including the following steps: Establish a correction model that reflects the distribution of spatio-temporal offset values: Within the search area, construct a spatial interpolation model based on the spatio-temporal offset values of the landmark points; use an improved Kriging interpolation algorithm to describe the spatial autocorrelation between landmark points through the semi-variance function and predict the correction values of unsampled points. The Kriging interpolation formula is: where C(x) represents the correction value at position x; C(x k ) represents the spatio-temporal offset value of the k-th landmark point; ω k represents the corresponding weight coefficient; m represents the number of landmark points used for interpolation calculation; The semi-variance function is: In the formula, γ(h) represents the semi-variance between two points; h represents the Euclidean distance between two points; C0 represents the nugget effect; C represents the sill value; a represents the range. The time evolution model is based on Fourier analysis and trend decomposition, decomposing the time series of the landmark point position offset into two parts: periodic change and long-term trend. The formula expression is: C(x,t) = C s (x,t) + C g (x,t); Wherein, C(x, t) represents the corrected value at position x at time t; C s (x, t) represents the seasonal variation part; C g (x, t) represents the geographical distortion part; Calculate the correction vector at the preliminary position: Input the preliminary position coordinates of the grounding clamp and the current time into the correction model to obtain the correction amount that should be applied to this position at the current time; the correction vector contains three components: eastward, northward, and vertical, respectively representing the correction offsets of the grounding clamp position in the three directions. The calculation formula of the correction vector is: In the formula, represents the correction vector of the initial position p0 at time t0; C E , C N and C U respectively represent the correction values in the east, north, and vertical directions; Apply the correction vector to obtain the calibrated position data: Apply the correction vector to the preliminary position to obtain the calibrated position data of the grounding clamp. The calculation formula is: where p cal represents the calibrated position coordinate; p0 represents the preliminary position coordinate; represents the correction vector; Record the calibration results and intermediate data: Record the calibrated position data of the grounding clamp and its corresponding correction vector.

7. The method for positioning a ground wire clamp using satellite positioning and landmark calibration according to claim 1, wherein: In step 4, calculate the final position coordinates of the grounding clamp, including the following steps: Convert the calibrated ground wire clamp position data into feature vectors: Extract and construct multi-dimensional vectors that can comprehensively reflect the position characteristics of the ground wire clamp; normalize all features to form feature vectors of a fixed dimension as the input of the neural network; The construction formula of the feature vector is: F = [f1, f2,..., f d ; where \(F\) represents the feature vector; \(f\) r represents the \(r\)-th feature component, \(r\in[1,d]\); \(d\) represents the dimension of the feature vector; Input the feature vectors into the neural network model for processing: The neural network model adopts a deep neural network combining long short-term memory network (LSTM) and attention mechanism; The forward propagation process is: H = LSTM(F); A = Attention(H); O = FC(A); In the formula, F represents the input feature vector; H represents the output of the LSTM layer; A represents the output of the attention layer; O represents the final output result; LSTM(·), Attention(·) and FC(·) respectively represent the operations of the LSTM layer, the attention layer and the fully connected layer; Extract the final position coordinates from the output result of the neural network model: The model output is a multi-dimensional vector, which contains the final position coordinates and other additional information; extract the position coordinate part in the output vector to obtain the final position coordinates of the ground wire clamp; Finally, record the final position coordinates and positioning parameters in the historical positioning record of the ground wire clamp; When the neural network model cannot generate valid position results, start the alternative positioning method to recalculate the final position coordinates of the ground wire clamp.

8. A grounding wire clamp positioning system using satellite positioning and landmark calibration, characterized in that, It includes: A satellite positioning module for obtaining the preliminary position of the ground wire clamp; A calibration analysis module that uses the preliminary position to determine the search area around the ground wire clamp, collects the three-dimensional coordinates of landmark points in the search area, and compares the three-dimensional coordinates with the historical landmark data in the historical landmark database to obtain the spatio-temporal offset value of the landmark points; A correction and optimization module that uses the spatio-temporal offset value to correct the preliminary position to obtain the calibrated ground wire clamp position data, and inputs the calibrated ground wire clamp position data into the neural network model trained based on the historical positioning record of the ground wire clamp to calculate the final position coordinates of the ground wire clamp.

9. A computer device, characterized in that: It includes a memory and a processor, and the memory stores. It is characterized in that: when the processor executes the computer program, it implements the ground wire clamp positioning method using satellite positioning and landmark calibration according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the ground wire clamp positioning method using satellite positioning and landmark calibration according to any one of claims 1 to 7.

Images