Corner reflector adaptive adjustment method, system and device based on multi-source radar remote sensing data

Abstract

The invention relates to the technical field of corner reflector adaptive adjustment, in particular to a corner reflector adaptive adjustment method, system and device based on multi-source radar remote sensing data. Comprising the following steps: S1, acquiring an installation position of a corner reflector in a terrain lifting complex scene of a power transmission line, extracting terrain parameters according to the installation position, and extracting an interference type according to the terrain parameters; the method accurately adapts to the specific requirements of the terrain uplift complex scene of the power transmission line, achieves the accurate recognition of the terrain uplift complex scene, the comprehensive extraction of terrain parameters and the directional judgment of interference types through the access of power transmission line management end data and a digital elevation model, combines the complementary advantages of multi-source radar data, and achieves the accurate recognition of the terrain uplift complex scene. Quantitative inversion and prediction of interference parameters are completed, the problems of fuzzy interference identification and insufficient data support in complex terrains in the prior art are effectively solved, and the stability of radar echo signals and the accuracy of pole and tower state monitoring data are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of corner reflector adaptive adjustment, in particular to a corner reflector adaptive adjustment method, system and device based on multi-source radar remote sensing data. BACKGROUND

[0002] As the core infrastructure of energy transmission, the safe and stable operation of the power transmission line is directly related to the reliability of energy supply, and the radar remote sensing monitoring technology has become a key technical means for the operation and maintenance of the power transmission line due to its all-weather, long-distance and non-contact advantages.

[0003] In the complex terrain lifting scene, the limitations of the prior art are more prominent. In such a scene, the airflow lifting, ice accumulation and mountain clutter interference have significant time characteristics. The traditional fixed posture corner reflector cannot dynamically adapt to the interference changes, resulting in large fluctuations in radar echo intensity and low signal-to-noise ratio, which directly affects the accuracy of the tower state parameter inversion. Therefore, a corner reflector adaptive adjustment method, system and device based on multi-source radar remote sensing data are proposed. SUMMARY

[0004] The present application aims to provide a corner reflector adaptive adjustment method, system and device based on multi-source radar remote sensing data to solve the problems raised in the background.

[0005] To solve the above technical problems, one of the purposes of the present application is to provide a corner reflector adaptive adjustment method based on multi-source radar remote sensing data, comprising the following steps: S1. In the complex terrain lifting scene of the power transmission line, the installation position of the corner reflector is obtained, the terrain parameters are extracted according to the installation position, and the interference type is extracted according to the terrain parameters, and the adjustment period is set according to the time characteristics of the terrain parameters; S2. According to the installation position and the terrain parameters, a radar list with transit window association is obtained, and the radar list is matched with its transit time and adjustment period to obtain the radar list corresponding to each adjustment period; S3. Extract the radar remote sensing data, and perform interference parameter quantitative inversion on the interference type according to the latest period of radar remote sensing data to obtain the interference parameters corresponding to each interference type, and predict the interference parameters of the adjustment period based on the obtained interference parameters to predict the interference parameters corresponding to each interference type; S4. Combine the predicted interference parameters of each interference type to set the reflection priority value of each radar of the radar list, simulate the different attitude data of the corner reflector, calculate the reflection priority total value in the adjustment period by combining the reflection priority value set by the radar, and determine the attitude data corresponding to the adjustment period, and then update the adjustment stage according to the corresponding attitude data of the adjacent adjustment period. S5, determine the attitude data of the adjustment period according to S4, control the corner reflector to adjust the posture.

[0006] As a further improvement of the technical solution, in S1, the installation position of the corner reflector in the terrain lifting complex scene is extracted at the power transmission line management end through the connection of the power transmission line management end; wherein the terrain lifting complex scene is a local steep slope section within 500m range around the installation position of the corner reflector, with slope ≥ 20°, relative altitude difference ≥ 300m, and single dominant interference.

[0007] As a further improvement of the technical solution, the latitude and longitude of the installation position of the corner reflector are extracted, the terrain parameters of the terrain lifting complex scene are extracted from the digital elevation model according to the latitude and longitude of the installation position, and then the interference type is extracted according to the terrain parameters, and the adjustment period is set according to the time period of the terrain parameters; The interference type includes airflow lifting interference type, icing interference type and mountain clutter interference type.

[0008] As a further improvement of the technical solution, in S2, the transit window of the multi-source radar at the adjustment period and the time node is obtained, the associated conditions are set, the terrain parameters and the installation position of the corner reflector are combined with the transit window and the associated conditions for associated analysis, only the transit window that meets the associated conditions is reserved, and then the corresponding radar list is extracted according to the reserved transit window to form a radar list; Wherein, the condition is that the radar beam incidence angle is 30° to 60°, and there is no mountain shelter; The transit time of the radar transit window reaching the corner reflector is extracted, and the transit time is matched according to the adjustment period, so as to obtain the radar list corresponding to each adjustment period.

[0009] As a further improvement of the technical solution, the steps of S3 are as follows: S3.1, in the power transmission line management section, the radar remote sensing data related to the corner reflector is extracted, the radar type to which the radar remote sensing data belongs is determined, and then the radar remote sensing data is combined with the interference type for data matching according to the radar type, so as to obtain the radar remote sensing data corresponding to each interference type; S3.2, set the time range of the latest period, extract the radar remote sensing data of each interference type according to the time range of the latest period, obtain the radar remote sensing data of each interference type corresponding to the latest period, and then perform interference parameter quantization inversion on the radar remote sensing data of the latest period to obtain the interference parameters corresponding to each interference type; S3.3, a prediction model is established based on the terrain parameters of the terrain lifting complex scene, and the interference parameters of each interference type are inputted, so as to output the interference parameters corresponding to each interference type for subsequent adjustment period prediction; The interference parameters correspond to the interference types, and are respectively icing interference parameters, airflow lifting interference parameters, and mountain clutter interference parameters.

[0010] As a further improvement of the technical solution, the step S4 is as follows: S4.1, according to the predicted interference parameters obtained in S3.2, determine the priority of each interference type in the adjustment period, and set a reflection priority value for each radar in the radar list by combining the priority of each interference type, the performance adaptability of each radar to the interference type, and the transit time. S4.2, simulate different attitude data of the corner reflector, and calculate the reflection priority value of the corresponding radar in each adjustment stage by combining the different attitude data, and then aggregate the obtained reflection priority values to obtain a reflection priority total value corresponding to the different attitude data; The reflection priority value is determined by the reflection duration of the radar and the corner reflector; When the transit time of the radar is consistent with the reflection duration, the total reflection priority value of the corresponding radar is obtained; When the transit time of the radar is less than the reflection duration, the reflection priority value is obtained in proportion according to the reflection duration; S4.3, set time update conditions and quantity update conditions for the adjustment stage, extract the attitude data of the highest reflection priority total value in S4.2 based on each adjustment period, and complete the determination and adjustment of the attitude data based on the time update conditions and the quantity update conditions.

[0011] As a further improvement of the technical solution, in S4.3, the time update conditions and the quantity update conditions are as follows: The time update conditions are as follows: the attitude data selected in the current adjustment period is substituted into the next adjustment period and the attitude data selected in the next adjustment period to perform a priority value scoring process comparison, if the attitude data selected in the current adjustment period has a priority value scoring period greater than the attitude data selected in the next adjustment period, the priority value scoring period corresponding to the next adjustment period is added to the current adjustment period, the time of the current adjustment period is extended, and the time of the next adjustment period is reduced. The quantity update conditions are as follows: a priority value adjustment threshold is set, and then the attitude data selected in the current adjustment period is substituted into the next adjustment period and the attitude data selected in the next adjustment period to perform a reflection priority total value comparison, when the difference in reflection priority total value between the two adjustment periods is less than the priority value adjustment threshold, no adjustment is performed, and the next adjustment period is deleted.

[0012] As a further improvement to this technical solution, in step S5, when the real-time time enters the corresponding time of the adjustment period, the attitude difference data between the real-time attitude data of the corner reflector and the attitude data determined by the adjustment period is calculated. An adjustment plan is generated based on the attitude difference data, and the corner reflector is controlled to adjust its attitude according to the adjustment plan, so that the attitude of the corner reflector is consistent with the attitude data determined by the adjustment period.

[0013] The second objective of this invention is to provide an adaptive adjustment system for corner reflectors based on multi-source radar remote sensing data, including any one of the corner reflector adaptive adjustment methods based on multi-source radar remote sensing data described above, comprising a time period setting module, a radar list acquisition module, an interference parameter analysis module, an attitude determination module, and an unmanned aerial vehicle (UAV) control module. The time period setting module is used to obtain the installation position of the corner reflector in complex scenarios of terrain elevation of the transmission line, extract terrain parameters based on the installation position, extract the interference type based on the terrain parameters, and set and adjust the time period based on the interference time period of the terrain parameters. The radar list acquisition module is used to acquire a radar list associated with a transit window based on the installation location and terrain parameters, and to match the radar list with its transit time and adjustment period to acquire the radar list corresponding to each adjustment period. The interference parameter analysis module is used to extract radar remote sensing data, perform interference parameter quantification and inversion on the interference type based on the latest radar remote sensing data, obtain the interference parameters corresponding to each interference type, and predict the interference parameters for the adjusted time period based on the obtained interference parameters, predicting the interference parameters corresponding to each interference type. The attitude determination module is used to combine the interference parameters of each interference type obtained by prediction, set the reflection priority value for each radar in the radar list, simulate different attitude data of corner reflectors, calculate the total reflection priority value in the adjustment period in combination with the reflection priority value set by the radar, thereby determining the attitude data corresponding to the adjustment period, and then update the adjustment stage according to the corresponding attitude data in adjacent adjustment periods. The UAV control module is used to determine the attitude data for the adjustment period based on the attitude determination module, and control the corner reflector to adjust the attitude.

[0014] The third objective of this invention is to provide an adaptive adjustment device for a corner reflector based on multi-source radar remote sensing data, including any one of the corner reflector adaptive adjustment methods based on multi-source radar remote sensing data described above, comprising a corner reflector and a built-in attitude adjustment component and a remote communication component. The attitude adjustment component is used to adjust the attitude of the corner reflector according to the adjustment instructions of the remote communication component; The remote communication component is used to calculate attitude adjustment parameters and drive the attitude adjustment component to move.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. A corner reflector adaptive adjustment method, system, and device based on multi-source radar remote sensing data, which accurately adapts to the specific needs of complex terrain uplift scenarios of power transmission lines. By accessing data from the power transmission line management terminal and the digital elevation model, it achieves accurate identification of complex terrain uplift scenarios, comprehensive extraction of terrain parameters, and directional determination of interference types. Combining the complementary advantages of multi-source radar data, it completes the quantitative inversion and prediction of interference parameters, effectively solving the problems of ambiguous interference identification and insufficient data support in complex terrain using traditional technologies, and significantly improving the stability of radar echo signals and the accuracy of tower status monitoring data.

[0016] 2. An adaptive adjustment method, system, and device for corner reflectors based on multi-source radar remote sensing data. The method dynamically divides the adjustment period based on the time period of interference and the radar passage pattern. By using a three-dimensional weight model of reflection priority value and multi-attitude simulation of corner reflectors, the optimal reflection attitude for each period is selected. The adjustment stage is dynamically optimized by using time update and quantity update conditions. This ensures that the reflection effect of the corner reflector is accurately matched with the radar passage window, while minimizing ineffective adjustments and mechanical wear of equipment. It achieves the optimal balance between reflection quality, adjustment efficiency, and operation and maintenance costs. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an adaptive adjustment method for corner reflectors based on multi-source radar remote sensing data according to the present invention. Figure 2 This is a flowchart illustrating the process of extracting radar remote sensing data related to corner reflectors in this invention. Figure 3 This is a flowchart illustrating the process of determining the priority of each interference type during the adjustment period in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 - Figure 3 As shown, one of the objectives of this invention is to provide an adaptive adjustment method for corner reflectors based on multi-source radar remote sensing data, comprising the following steps: S1. In complex scenarios of terrain elevation for power transmission lines, obtain the installation location of the corner reflector, extract terrain parameters based on the installation location, extract the interference type based on the terrain parameters, and set the adjustment time period based on the interference time period of the terrain parameters. Accurately identify complex terrain uplift scenarios, establish the correlation between terrain parameters, interference types, and adjustment periods, and provide a scenario basis for subsequent adjustments; In S1, by connecting to the power transmission line management terminal, the installation location of the corner reflector in a complex terrain uplift scenario is extracted from the power transmission line management terminal. The complex terrain uplift scenario is defined as a steep slope section within 500m of the corner reflector installation location, with a slope ≥20°, a relative elevation difference ≥300m, and a single dominant disturbance. The steps are as follows: Establish an encrypted communication link, access the equipment ledger system of the power transmission line management terminal, filter corner reflector equipment under the complex terrain lifting scene label, and extract key information of the target corner reflector, including tower number, installation height, and precise latitude and longitude. The latitude and longitude are extracted based on the installation location of the corner reflector. Based on the latitude and longitude of the installation location, the digital elevation model is called to extract the terrain parameters for complex terrain lifting scenarios. Then, the interference type is extracted based on the terrain parameters, and the time period is adjusted based on the time period of the interference of the terrain parameters. Centered on latitude and longitude, a 500m×500m analysis range is defined. A 1:5000 digital elevation model (DEM) is called for data cropping. The D8 algorithm is used to calculate the core terrain parameters, including the maximum slope, relative elevation difference, slope aspect, and mountain occlusion direction. Both conditions must be met simultaneously: ≥20° and elevation difference ≥300m. If both conditions are met, proceed to the next step; otherwise, it is judged as a non-target scene. The types of interference include airflow lifting interference, icing interference, and mountain clutter interference.

[0020] The adjustment period for icing interference is 40 minutes; The adjustment period for the airflow lifting disturbance is 50 minutes; Mountain clutter interference lasts for 30 minutes; S2. Obtain a list of radars associated with transit windows based on the installation location and terrain parameters. Match the radar list with its transit time and adjustment period to obtain the radar list corresponding to each adjustment period. Select radar resources that are suitable for complex scenarios and time periods to ensure that subsequent interference parameter inversion and attitude adjustment have accurate data support; In S2, the transit windows of multi-source radars at the same time node during the adjustment period are obtained. At the same time, correlation conditions are set, and the terrain parameters and the installation position of the corner reflectors are combined with the transit windows and correlation conditions for correlation analysis. Only the transit windows that meet the correlation conditions are selected. Then, the corresponding radars are extracted based on the retained transit windows to form a radar list. The steps are as follows: Construct a multi-source radar data interface, connect to the official satellite radar forecasting platform (such as Sentinel, Gaofen-3 data service system) and the radar transit plan database pre-stored at the power transmission line management terminal, input the latitude and longitude of the corner reflector installation location and the time range of the adjustment period, and extract all radar transit window data that may cover the location in the next 24 hours, including radar model, transit start time, transit end time, beam incident angle, and beam azimuth angle; Among them, the conditions are that the radar beam incident angle is between 30° and 60° and there is no mountain obstruction; The extracted terrain parameters (mountain occlusion direction, elevation of corner reflector installation location, and elevation of the highest point of the mountain) are used to determine whether there is mountain occlusion.

[0021] The transit time of the radar transit window to the corner reflector is extracted, and the transit time is matched with the adjustment period to obtain the radar list corresponding to each adjustment period.

[0022] Calculate the intersection of the transit time and the adjustment period for each reserved transit window. The intersection duration needs to be greater than 10 minutes to ensure that the radar has enough time to collect echoes, thereby determining that the transit window matches the adjustment period. Then, extract the radar information (model, beam parameters, transit time) corresponding to all matching transit windows, and establish and output the radar list corresponding to each adjustment period. S3. Extract radar remote sensing data, perform interference parameter quantification and inversion on the interference type based on the latest radar remote sensing data for the latest time period, obtain the interference parameters corresponding to each interference type, and predict the interference parameters for the adjusted time period based on the obtained interference parameters, predicting the interference parameters corresponding to each interference type. Quantify the intensity of interference and predict the interference parameters during the adjustment period, so that attitude adjustment can be transformed from a passive response to an active prediction; The steps for S3 are as follows: S3.1 In the transmission line management section, extract the radar remote sensing data related to the corner reflector and determine the radar type to which the radar remote sensing data belongs. Then, according to the radar type, combine the radar remote sensing data with the interference type to perform data matching and obtain the radar remote sensing data corresponding to each interference type. Radar remote sensing data within a 5km radius of the corner reflector installation location at the power transmission line management end are screened, including polarimetric synthetic aperture radar, Doppler weather radar, ground-based interferometric radar, etc. Establish core adaptation rules for radar type and interference type, including data on icing interference type and matching polarization radar type (polarization characteristics are sensitive to the dielectric constant of icing), data on airflow lifting interference type and matching Doppler meteorological radar type (directly measuring airflow parameters), and data on mountain clutter interference type and matching ground-based interferometric radar type (accurately distinguishing point / area target echoes). Filter the classified radar data according to the adaptation rules, remove mismatched data, and obtain radar remote sensing datasets specific to each interference type.

[0023] S3.2. Set the time range for the latest time period, extract the latest time period from the radar remote sensing data of each interference type according to the time range, obtain the radar remote sensing data of the latest time period corresponding to each interference type, and then perform interference parameter quantization and inversion on the radar remote sensing data of the latest time period to obtain the interference parameters corresponding to each interference type. The steps are as follows: Set the latest time period range: Based on the current system time, look back 1 hour. If the target dataset has insufficient data (<3 valid records) within this time period, extend it to 2 hours (maximum 3 hours). Based on the latest time period range, perform time slicing on the dedicated radar datasets for each interference type, extract the data within the corresponding time period, and remove noisy data (such as echo data with SNR < 15dB). This outputs the latest time period radar remote sensing data subsets for each interference type. Then, for each interference type's latest time period data subset, use a scene-adaptive inversion algorithm to calculate parameters, as shown in the following formula: ; in, For ice thickness, and These are the horizontal and vertical polarization backscattering coefficients (dimensionless, after radiometric calibration) from the latest polarimetric radar data. The icing calibration factor for complex terrain lifting scenarios (value 5.5, obtained by fitting icing measurement data from the past 3 years); ; in, The angle at which the airflow rises. The zenith angle of the Doppler radar beam. Radial airflow velocity of the radar The horizontal airflow velocity, For airflow correction coefficients in complex scenarios; ; in, The intensity of clutter in the mountain. This represents the average echo intensity of mountain clutter in the latest ground-based interferometric radar data. The average echo intensity of the corner reflector in the latest ground-based interferometric radar data; S3.3. Establish a prediction model based on the terrain parameters of complex terrain lifting scenarios, and input the interference parameters of each interference type to predict and output the interference parameters corresponding to each interference type for subsequent adjustment periods. The interference parameters correspond to the interference types, namely, icing interference parameters, airflow lifting interference parameters, and mountain clutter interference parameters. The steps are as follows: Static terrain parameters for complex terrain lifting scenarios are extracted, including maximum slope, relative elevation difference, slope aspect, and absolute elevation of the installation location. Historical temporal interference parameters are also obtained, including interference parameter sequences (ice thickness, airflow lifting angle, and clutter intensity) for the same season and time period over the past three years. The 3σ criterion is used to remove extreme outliers in the interference parameters. At the same time, Min-Max normalization is performed on the terrain parameters and interference parameters to eliminate dimensional differences. The input layer includes a time-series branch (the sequence of disturbance parameters for the first 6 time steps, corresponding to 1 hour of data) and a terrain branch (static terrain parameters). The feature fusion layer calculates the weights of terrain parameters on the prediction of interference parameters through an attention mechanism, thereby enhancing the influence of terrain features in complex scenes. The core prediction layer uses a 2-layer LSTM network (64 hidden neurons) to capture the temporal dependencies of the perturbation parameters; The output layer, a fully connected layer, outputs the predicted values ​​of interference parameters (ice thickness, airflow lift angle, clutter intensity) for the next adjustment period. After normalizing the latest 1-hour disturbance parameter sequence and complex scene terrain parameters, the data is input into the trained prediction model. The model outputs the normalized adjusted time-period disturbance parameter prediction values. The disturbance parameter prediction values ​​are then converted back to the real physical quantities using the inverse normalization formula to obtain the final prediction results (ice thickness, airflow lift angle, clutter intensity).

[0024] S4. Combine the interference parameters of each interference type obtained by prediction, set the reflection priority value for each radar in the radar list, simulate different attitude data of corner reflectors, calculate the total reflection priority value in the adjustment period in combination with the reflection priority value set by the radar, thereby determining the attitude data corresponding to the adjustment period, and then update the adjustment stage according to the corresponding attitude data in adjacent adjustment periods. The steps for S4 are as follows: S4.1. Based on the interference parameters predicted in S3.2, determine the priority of each interference type during the adjustment period. Then, by combining the priority of each interference type with the performance compatibility of each radar with the interference type and the transit time, set a reflection priority value for each radar in the radar list. The steps are as follows: Min-Max normalization was used to convert each interference parameter into a priority score in the 0-1 interval to eliminate the difference in units. Then, the priority scores were sorted from high to low to determine the dominant interference type (highest priority) and the secondary interference type (second highest priority) during the adjustment period. The dominant interference type had a weight of 0.7 and the secondary interference type had a weight of 0.3 (if there was no secondary interference, the dominant weight was 1.0). Reflection priority value = Interference priority weight × 0.5 + Radar-interference adaptability weight × 0.3 + Transit time matching weight × 0.2; The weighting factors are quantified, including the interference priority weight (0.7 for radars corresponding to the dominant interference type and 0.3 for radars corresponding to the secondary interference type), the radar-interference adaptability weight (according to the adaptability relationship between polarimetric radar-icing, Doppler radar-airflow lifting, and ground-based interferometric radar-clutter, 1.0 for adaptability and 0.1 for non-adaptability), and the transit time matching weight (the ratio of radar transit time to adjustment period, radar transit time / adjustment period duration, with a value range of 0-0.2). Then, the basic reflection priority value of each radar is calculated (with a value range of 0-1.0).

[0025] S4.2 Simulate different attitude data of the corner reflector (pitch angle -15°~+15°, step size 0.1°, azimuth angle 0°~360°, step size 0.5°), and calculate the reflection priority value of the corresponding radar in each adjustment stage in combination with different attitude data. Summarize the obtained reflection priority values ​​to obtain the total reflection priority value corresponding to different attitude data (the sum of the actual reflection priority values ​​of all matched radars under the same attitude). The reflection priority value is determined by the reflection duration of the radar and the corner reflector; When the radar's transit time matches the reflection duration, all reflection priority values ​​for the corresponding radar are obtained. When the radar's transit time is less than the reflection duration, the reflection priority value is obtained proportionally based on the reflection duration. S4.3 Set time update conditions and quantity update conditions for the adjustment phase. Extract attitude data based on the highest total reflection priority value in S4.2 for each adjustment period. Combine the time update conditions and quantity update conditions to complete the attitude data determination and adjustment phase update.

[0026] In S4.3, the time update conditions and quantity update conditions are as follows: The quantity update condition is set by setting a priority value adjustment threshold (based on engineering measured data, with a value of 0.05). Then, the attitude data selected in the current adjustment period is substituted into the attitude data selected in the next adjustment period and compared with the total reflection priority value. If the total reflection priority value of the different attitude data in the two adjustment periods is less than the priority value adjustment threshold, no adjustment is made and the next adjustment period is deleted. Conversely, if the total difference in reflection priority value between different attitude data in two adjustment periods is greater than the priority value adjustment threshold, it indicates that the reflection effect in the two periods is significantly different, and the next adjustment period is retained and the time update condition is determined. The time update condition involves substituting the attitude data selected in the current adjustment period into the attitude data selected in the next adjustment period and comparing them with the attitude data selected in the next adjustment period through a priority value sub-process. If the attitude data selected in the current adjustment period has a higher priority value sub-period than the attitude data selected in the next adjustment period (the next adjustment period is divided into sub-periods of 10 minutes, and the reflection priority value after substituting the optimal attitude of the current period into the next period is calculated in each sub-period), then the priority value sub-period corresponding to the next adjustment period is added to the current adjustment period, the time of the current adjustment period is extended, and the time of the next adjustment period is shortened. If the next adjustment period is at least 10 minutes long, it will be retained. If the reduction is less than 10 minutes, it will be merged into the current adjustment period and the next current adjustment period will be deleted. S5. Based on the attitude data of the adjustment period determined in S4, control the corner reflector to adjust the attitude.

[0027] In S5, when the real-time time enters the corresponding time of the adjustment period, the attitude difference data between the real-time attitude data of the corner reflector and the attitude data determined during the adjustment period is calculated. An adjustment plan is generated based on the attitude difference data, and the corner reflector is controlled to adjust its attitude according to the adjustment plan so that the attitude of the corner reflector is consistent with the attitude data determined during the adjustment period. The steps are as follows: The system time is compared with the start time of the adjustment period in real time. When the time reaches the start time of the adjustment period, the attitude adjustment process is triggered. Extract the target attitude data (target pitch angle, target azimuth angle) for the current adjustment period, calculate the pitch angle difference and azimuth angle difference respectively, and quantify the overall adjustment range by comprehensively considering the attitude differences, which serves as the basis for adjustment priority. Prioritize adjusting the elevation angle (sensitive to the radar beam incident angle), then adjust the azimuth angle (sensitive to the beam azimuth angle) to avoid cross-interference.

[0028] The second objective of this invention is to provide an adaptive adjustment system for corner reflectors based on multi-source radar remote sensing data, including any one of the above-mentioned adaptive adjustment methods for corner reflectors based on multi-source radar remote sensing data, comprising a time period setting module, a radar list acquisition module, an interference parameter analysis module, an attitude determination module, and an unmanned aerial vehicle (UAV) control module. The time period setting module is used to obtain the installation location of the corner reflector in complex scenarios of terrain elevation of transmission lines, extract terrain parameters based on the installation location, extract the interference type based on the terrain parameters, and set and adjust the time period according to the interference time period of the terrain parameters. The radar list acquisition module is used to obtain a list of radars with transit windows based on the installation location and terrain parameters. The radar list is then matched with the transit time and the adjustment period to obtain the radar list corresponding to each adjustment period. The interference parameter analysis module is used to extract radar remote sensing data, perform interference parameter quantification and inversion on the interference type based on the latest radar remote sensing data, obtain the interference parameters corresponding to each interference type, and predict the interference parameters for the adjusted time period based on the obtained interference parameters, predicting the interference parameters corresponding to each interference type. The attitude determination module is used to combine the prediction to obtain the interference parameters of each type of interference, set the reflection priority value for each radar in the radar list, simulate different attitude data of the corner reflector, calculate the total reflection priority value in the adjustment period in combination with the reflection priority value set by the radar, thereby determining the attitude data corresponding to the adjustment period, and then update the adjustment stage according to the corresponding attitude data in adjacent adjustment periods. The UAV control module is used to determine the attitude data for the adjustment period based on the attitude determination module, and control the corner reflector to adjust the attitude.

[0029] The third objective of this invention is to provide an adaptive adjustment system for corner reflectors based on multi-source radar remote sensing data, including any one of the above-mentioned adaptive adjustment methods for corner reflectors based on multi-source radar remote sensing data, comprising a corner reflector and a built-in attitude adjustment component and a remote communication component; The attitude adjustment component is used to adjust the attitude of the corner reflector according to the adjustment instructions of the remote communication component; The remote communication component is used to calculate attitude adjustment parameters and drive the attitude adjustment component to move.

[0030] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A corner reflector adaptive adjustment method based on multi-source radar remote sensing data, characterized in that: Includes the following steps: S1. In complex scenarios of terrain elevation for power transmission lines, obtain the installation location of the corner reflector, extract terrain parameters based on the installation location, extract the interference type based on the terrain parameters, and set the adjustment time period based on the interference time period of the terrain parameters. S2. Obtain a list of radars associated with transit windows based on the installation location and terrain parameters. Match the radar list with its transit time and adjustment period to obtain the radar list corresponding to each adjustment period. S3. Extract radar remote sensing data, perform interference parameter quantification and inversion on the interference type based on the latest radar remote sensing data for the latest time period, obtain the interference parameters corresponding to each interference type, and predict the interference parameters for the adjusted time period based on the obtained interference parameters, predicting the interference parameters corresponding to each interference type. S4. Combine the interference parameters of each interference type obtained by prediction, set the reflection priority value for each radar in the radar list, simulate different attitude data of corner reflectors, calculate the total reflection priority value in the adjustment period in combination with the reflection priority value set by the radar, thereby determining the attitude data corresponding to the adjustment period, and then update the adjustment stage according to the corresponding attitude data in adjacent adjustment periods. S5. Based on the attitude data of the adjustment period determined in S4, control the corner reflector to adjust the attitude.

2. The adaptive adjustment method for corner reflectors based on multi-source radar remote sensing data according to claim 1, characterized in that: In S1, by connecting to the power transmission line management terminal, the installation location of the corner reflector in the complex terrain lifting scenario is extracted at the power transmission line management terminal; wherein, the complex terrain lifting scenario is a local steep slope section within 500m around the corner reflector installation location with a slope ≥20°, a relative altitude difference ≥300m, and a single dominant interference.

3. The adaptive adjustment method for corner reflectors based on multi-source radar remote sensing data according to claim 2, characterized in that: The latitude and longitude are extracted based on the installation location of the corner reflector. Based on the latitude and longitude of the installation location, the digital elevation model is called to extract the terrain parameters of the complex terrain lifting scenario. Then, the interference type is extracted based on the terrain parameters, and the time period is adjusted based on the time period of the interference of the terrain parameters. The types of interference include airflow lifting interference, icing interference, and mountain clutter interference.

4. The adaptive adjustment method for corner reflectors based on multi-source radar remote sensing data according to claim 1, characterized in that: In step S2, the transit windows of multi-source radars at the same time node during the adjustment period are obtained. At the same time, association conditions are set, and the terrain parameters and the installation position of the corner reflector are combined with the transit window and the association conditions for association analysis. Only the transit windows that meet the association conditions are selected. Then, the corresponding radars are extracted based on the retained transit windows to form a radar list. Among them, the conditions are that the radar beam incident angle is between 30° and 60° and there is no mountain obstruction; The transit time of the radar transit window to the corner reflector is extracted, and the transit time is matched with the adjustment period to obtain the radar list corresponding to each adjustment period.

5. The adaptive adjustment method for corner reflectors based on multi-source radar remote sensing data according to claim 1, characterized in that: The steps in S3 are as follows: S3.1 In the transmission line management section, extract the radar remote sensing data related to the corner reflector and determine the radar type to which the radar remote sensing data belongs. Then, according to the radar type, combine the radar remote sensing data with the interference type to perform data matching and obtain the radar remote sensing data corresponding to each interference type. S3.

2. Set the time range of the latest time period, extract the latest time period from the radar remote sensing data of each interference type according to the time range, obtain the radar remote sensing data of the latest time period corresponding to each interference type, and then perform interference parameter quantization inversion on the radar remote sensing data of the latest time period to obtain the interference parameters corresponding to each interference type. S3.

3. Establish a prediction model based on the terrain parameters of complex terrain lifting scenarios, and input the interference parameters of each interference type to predict and output the interference parameters corresponding to each interference type for subsequent adjustment periods. Among them, the interference parameters correspond to the interference types, namely, icing interference parameters, airflow lifting interference parameters, and mountain clutter interference parameters.

6. The adaptive adjustment method for corner reflectors based on multi-source radar remote sensing data according to claim 1, characterized in that: The steps in S4 are as follows: S4.

1. Based on the interference parameters predicted in S3.2, determine the priority of each interference type during the adjustment period, and set a reflection priority value for each radar in the radar list by combining the priority of each interference type with the performance compatibility of each radar with the interference type and the transit time. S4.2 Simulate different attitude data of the corner reflector, and calculate the reflection priority value of the corresponding radar in each adjustment stage by combining different attitude data. Summarize the obtained reflection priority values ​​to obtain the total reflection priority value corresponding to different attitude data. The reflection priority value is determined by the reflection duration of the radar and the corner reflector; When the radar's transit time matches the reflection duration, all reflection priority values ​​for the corresponding radar are obtained. When the radar's transit time is less than the reflection duration, the reflection priority value is obtained proportionally based on the reflection duration. S4.3 Set time update conditions and quantity update conditions for the adjustment phase. Extract attitude data based on the highest total reflection priority value in S4.2 for each adjustment period. Combine the time update conditions and quantity update conditions to complete the attitude data determination and adjustment phase update.

7. The adaptive adjustment method for corner reflectors based on multi-source radar remote sensing data according to claim 6, characterized in that: In S4.3, the time update conditions and quantity update conditions are as follows: The time update condition involves substituting the attitude data selected in the current adjustment period into the attitude data selected in the next adjustment period and comparing them with the attitude data selected in the next adjustment period through a priority value sub-process. If the attitude data selected in the current adjustment period has a priority value sub-process greater than that of the attitude data selected in the next adjustment period, then the priority value sub-process corresponding to the next adjustment period is added to the current adjustment period, extending the time of the current adjustment period and shortening the time of the next adjustment period. The quantity update condition is set by setting a priority value adjustment threshold. Then, the attitude data selected in the current adjustment period is substituted into the attitude data selected in the next adjustment period and compared with the total reflection priority value. If the total reflection priority value of the different attitude data in the two adjustment periods is less than the priority value adjustment threshold, no adjustment is made and the next adjustment period is deleted.

8. The adaptive adjustment method for corner reflectors based on multi-source radar remote sensing data according to claim 1, characterized in that: In step S5, when the real-time time enters the corresponding time of the adjustment period, the attitude difference data between the real-time attitude data of the corner reflector and the attitude data determined by the adjustment period is calculated. An adjustment plan is generated based on the attitude difference data, and the corner reflector is controlled to adjust its attitude according to the adjustment plan so that the attitude of the corner reflector is consistent with the attitude data determined by the adjustment period.

9. A corner reflector adaptive adjustment system based on multi-source radar remote sensing data, used to implement the corner reflector adaptive adjustment method based on multi-source radar remote sensing data as described in any one of claims 1-8, characterized in that: It includes a time period setting module, a radar list acquisition module, an interference parameter analysis module, an attitude determination module, and an UAV control module; The time period setting module is used to obtain the installation position of the corner reflector in complex scenarios of terrain elevation of the transmission line, extract terrain parameters based on the installation position, extract the interference type based on the terrain parameters, and set and adjust the time period based on the interference time period of the terrain parameters. The radar list acquisition module is used to acquire a radar list associated with a transit window based on the installation location and terrain parameters, and to match the radar list with its transit time and adjustment period to acquire the radar list corresponding to each adjustment period. The interference parameter analysis module is used to extract radar remote sensing data, perform interference parameter quantification and inversion on the interference type based on the latest radar remote sensing data, obtain the interference parameters corresponding to each interference type, and predict the interference parameters for the adjusted time period based on the obtained interference parameters, predicting the interference parameters corresponding to each interference type. The attitude determination module is used to combine the interference parameters of each interference type obtained by prediction, set the reflection priority value for each radar in the radar list, simulate different attitude data of corner reflectors, calculate the total reflection priority value in the adjustment period in combination with the reflection priority value set by the radar, thereby determining the attitude data corresponding to the adjustment period, and then update the adjustment stage according to the corresponding attitude data in adjacent adjustment periods. The UAV control module is used to determine the attitude data for the adjustment period based on the attitude determination module, and control the corner reflector to adjust the attitude.

10. An adaptive adjustment device for corner reflectors based on multi-source radar remote sensing data, wherein the adaptive adjustment system for corner reflectors based on multi-source radar remote sensing data is used to execute the adaptive adjustment method for corner reflectors based on multi-source radar remote sensing data as described in any one of claims 1-8, characterized in that: Includes corner reflectors, as well as built-in attitude adjustment components and remote communication components; The attitude adjustment component is used to adjust the attitude of the corner reflector according to the adjustment instructions of the remote communication component; The remote communication component is used to calculate attitude adjustment parameters and drive the attitude adjustment component to move.

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