A distributed multi-objective microwave transmission control method and system

Abstract

The present invention provides a distributed multi-target microwave transmission control method and system, which relates to the field of data processing technology. The method includes: obtaining geographic coordinate information of each target device, constructing a motion trajectory of each target device, sorting the coordinate information of each target device according to the time node sequence, and accumulating the coordinate differences between adjacent time nodes to obtain a predicted coordinate data set; extracting the coordinate distance values ​​between each target device; grouping and counting the coordinate distance values ​​to obtain a position merged data set; extracting beam direction range information and beam angle interval parameters for the current time period to obtain a beam candidate data set; performing coordinate comparison on each target device to obtain beam binding data; generating control instructions; and ensuring that the beam adjusts the beam emission direction according to the control instructions. The present invention can predict the coordinates of the target device and control the beam emission direction, ensuring stable microwave transmission to the target device.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a distributed multi-target microwave transmission control method and system. Background Art

[0002] Existing microwave wireless power transmission systems often employ static, single-point directional transmission. For example, space-based solar power stations transmit microwaves in a targeted manner to a fixed receiving device, providing remote power supply. These systems typically rely on high-gain antennas onboard satellites for beam steering, and cooperate with ground stations for target pointing confirmation. When the target location is known and stable, they can achieve a certain degree of remote charging. However, this approach primarily serves fixed receiving devices and may not be suitable for the practical needs of multiple targets, multiple paths, and dynamic changes.

[0003] For example, in distant-water fishing fleet operations, multiple fishing vessels move in a relatively dispersed state on the sea surface, and their positions change in real time with ocean currents and wind speeds. In this scenario, existing microwave transmission systems mainly use satellite antennas to switch beam directions in sequence to charge multiple targets in turn. However, due to the beam reconstruction time of the antenna, if the target vessel has shifted its position during the beam switching process, the beam and the receiving device may be misaligned, resulting in the microwave energy not being accurately aimed at the target, and thus transmission failure. Summary of the Invention

[0004] The object of the present invention is to provide a distributed multi-objective microwave transmission control method and system, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] In a first aspect, a distributed multi-objective microwave transmission control method is provided, the method comprising:

[0007] Obtaining geographic coordinate information of each target device and associating it with the device identification of each target device, constructing a motion trajectory of each target device, and obtaining motion trajectory data;

[0008] According to the motion trajectory data, the coordinate information of each target device is sorted in the order of time nodes, and the coordinate differences between adjacent time nodes are accumulated to obtain the predicted coordinate data set;

[0009] According to the predicted coordinate data set, the coordinate distance values ​​between each target device are extracted, the coordinate distance values ​​are grouped and counted, and the target devices with distance constraints are merged to obtain a position merged data set;

[0010] Merge the dataset based on the location, extract the beam direction range information and beam angle interval parameters for the current time period, and use them to match and index each target device to obtain the beam candidate dataset;

[0011] Based on the beam candidate dataset and the predicted coordinate dataset, coordinates of each target device are compared to obtain direction offset data, which is then compared with a preset offset threshold to obtain beam binding data.

[0012] Generate control instructions based on the beam binding data and ensure that the beam adjusts the beam transmission direction according to the control instructions.

[0013] Furthermore, the geographic coordinate information of each target device is obtained and associated with the device identification of each target device to construct the motion trajectory of each target device and obtain motion trajectory data, including:

[0014] According to the geographic coordinate information of the target device, the geographic coordinates of each device at different time nodes are extracted to obtain a geographic coordinate data set;

[0015] According to the geographic coordinate data set, the spatial displacement between the geographic coordinates of each target device is calculated to obtain the displacement change data;

[0016] By filtering the displacement change data, abnormal data caused by signal noise or environmental interference is removed to obtain denoised displacement change data;

[0017] According to the denoised displacement change data, the motion trajectory of each target device is fitted, the trajectory curve is constructed, and a smooth trajectory path is obtained;

[0018] According to the smooth trajectory path, the motion speed and acceleration of each target device are extracted and time-sorted to obtain the motion trajectory data.

[0019] Furthermore, based on the motion trajectory data, the coordinate information of each target device is sorted in the order of time nodes, and the coordinate differences between adjacent time nodes are accumulated to obtain a predicted coordinate data set, including:

[0020] According to the time node information of each target device in the motion trajectory data, the geographical coordinates of each target device are sorted in ascending time order to obtain ordered coordinate sequence data;

[0021] According to the ordered coordinate sequence data, the coordinate values ​​between two adjacent time nodes of the target device are extracted, and their coordinate differences are calculated to obtain a coordinate difference vector set;

[0022] Normalizing each coordinate difference vector in the coordinate difference vector set according to the time interval between the coordinate difference values ​​to obtain a normalized difference vector data set;

[0023] According to the normalized difference vector data set, the coordinate difference vectors of each target device are sequentially superimposed to obtain a cumulative displacement vector data set;

[0024] According to the cumulative displacement vector data set, the real-time coordinates of each target device and the corresponding cumulative displacement vector are vector-superimposed to obtain a predicted coordinate data set.

[0025] Furthermore, based on the predicted coordinate data set, the coordinate distance values ​​between the target devices are extracted, the coordinate distance values ​​are grouped and counted, and the target devices with distance constraints are merged to obtain a position merged data set, including:

[0026] According to the coordinate information of each target device in the predicted coordinate data set, a pairwise target device combination is constructed to obtain a target pairing set;

[0027] According to each pair of target devices in the target pairing set, the coordinate distance value between the coordinates is calculated to obtain a distance value data set;

[0028] Based on the distance value dataset, all coordinate distance values ​​are classified according to a preset distance threshold, and target device pairs whose distance is not greater than the preset distance threshold are marked as valid merge pairs to obtain a valid merge pair set;

[0029] According to the valid merged pairing set, multiple target devices with overlapping pairing relationships are connected and aggregated to obtain a merged target group set;

[0030] Calculate the center of gravity coordinates of each group based on the predicted coordinates of each target device in the merged target group set to obtain a center of gravity coordinate set;

[0031] The centroid coordinates of each group in the centroid coordinate set are used as representative coordinates to replace the coordinates of each target device in the original group, and a position merged data set is obtained.

[0032] Furthermore, based on the position-merged dataset, the beam direction range information and beam angle interval parameters of the current time period are extracted, and the target devices are matched and indexed based on them to obtain the beam candidate dataset, including:

[0033] According to the satellite's spatial position information, the center reference direction of the satellite platform is determined, and the left and right extreme transmission directions within its coverage area are extracted to obtain the beam direction range information;

[0034] Based on the minimum spatial distance between the coordinates of each center of gravity in the position merged data set and the spatial distance from the satellite to the target area, the angle accuracy required for differentiation is calculated to obtain the target resolution angle data;

[0035] Adjust the unit according to the minimum angle of the satellite beam to determine the control accuracy data;

[0036] According to the target resolution angle data and the control accuracy data, the larger value of the two is selected as the angle interval parameter to obtain the beam angle interval parameter;

[0037] According to the beam direction range information and beam angle interval parameters, the satellite beam is discretized into sections to obtain a beam direction sequence data set;

[0038] According to the beam direction sequence data set, the transmission direction offset value of each center of gravity coordinate in the position merged data set is calculated to obtain the direction offset data set;

[0039] According to the direction offset dataset, each centroid coordinate is matched to the nearest beam direction, the beam index mapping relationship is determined, and the beam candidate dataset is obtained.

[0040] Furthermore, based on the beam candidate dataset and the predicted coordinate dataset, coordinates of each target device are compared to obtain direction offset data, which is then compared with a preset offset threshold to obtain beam binding data, including:

[0041] According to the coordinates of each beam direction in the beam candidate data set, the target device identifier associated therewith is extracted to obtain the beam target pairing data;

[0042] According to the target device identifier in the beam target pairing table, the corresponding real-time coordinates in the predicted coordinate data set are extracted to obtain the target real-time coordinate data;

[0043] According to the real-time coordinates of each beam direction and its associated target device, the space vector between the satellite and the target device is calculated to obtain the target direction vector dataset;

[0044] According to the angle between each beam direction and the target direction vector, the spatial offset angle value is calculated to obtain the direction offset angle data set;

[0045] By normalizing the spatial offset angle values ​​in the direction offset angle dataset, a standard offset dataset is obtained;

[0046] According to the standard offset data set, the offset data of each target device is compared with the preset offset threshold, and the target devices that meet the offset threshold conditions are screened out to obtain the beam binding data.

[0047] Furthermore, based on the beam binding data, a control instruction is generated, and the beam is ensured to adjust the beam transmission direction according to the control instruction, including:

[0048] According to the target device corresponding to each beam direction in the beam binding data, the transmission coordinates under each beam direction are extracted to obtain the control target data set;

[0049] According to the control target data set, the spatial direction parameters of the launch coordinates of each target device relative to the satellite platform are calculated to obtain the direction parameter data set;

[0050] According to the direction parameter data set, the spatial direction parameter of each target device is converted into a corresponding beam steering angle value to obtain a beam steering angle data set;

[0051] According to the beam steering angle data set, the time tag is determined in combination with the current time node to obtain the control time synchronization data set;

[0052] According to the control time synchronization data set, the beam number, control angle and time tag are determined, and the control instructions are generated. The control instructions are transmitted to the beam steering module in turn to adjust the beam transmission direction.

[0053] In a second aspect, a distributed multi-target microwave transmission control system is provided, the system comprising:

[0054] The target trajectory module is used to obtain the geographic coordinate information of each target device and associate it with the device identification of each target device to construct the motion trajectory of each target device and obtain motion trajectory data;

[0055] The predicted coordinate module is used to sort the coordinate information of each target device in the order of time nodes based on the motion trajectory data, and accumulate the coordinate differences between adjacent time nodes to obtain a predicted coordinate data set;

[0056] The target merging module is used to extract the coordinate distance values ​​between target devices based on the predicted coordinate data set, group and count the coordinate distance values, and merge the target devices with distance constraints to obtain a position merged data set;

[0057] The beam matching module is used to merge the data sets according to the location, extract the beam direction range information and beam angle interval parameters of the current time period, and match the index of each target device based on them to obtain the beam candidate data set;

[0058] The beam binding module is used to compare the coordinates of each target device based on the beam candidate data set and the predicted coordinate data set to obtain direction offset data, and compare it with the preset offset threshold to obtain beam binding data;

[0059] The control instruction module is used to generate control instructions according to the beam binding data and ensure that the beam adjusts the beam transmission direction according to the control instructions;

[0060] The beam control module is used to adjust the beam transmission direction according to the control instructions.

[0061] The above solution of the present invention includes at least the following beneficial effects:

[0062] The present invention constructs a trajectory curve over continuous time and introduces cumulative coordinate differences for forward prediction to obtain a more continuous target predicted position. It is highly adaptable to the position change trend of multiple target devices in simultaneous motion. It is particularly suitable for the constantly changing operating states of multiple entities in complex environments, such as maritime fishing boat formations, unmanned aerial vehicle groups, or vehicle networking systems. Traditional beam control methods often cause the microwave beam to fail to accurately track the target due to hysteresis problems, resulting in beam drift or energy loss. After introducing motion prediction, the system can adjust the beam pointing in advance to avoid alignment deviation caused by transmission delay, significantly improve transmission stability and continuity, and reduce transmission failure rate.

[0063] The present invention calculates and predicts the minimum distance between targets, combines the spatial geometric relationship between the satellite platform and the target, determines the angular accuracy required for beam resolution, and sets the beam angle interval parameters accordingly to achieve detailed segmented discrete processing of the entire beam coverage range. Unlike existing technical solutions based on fixed angles or static angle control, this method can adaptively adjust the angle interval according to the actual distribution of the task to optimize the beam control granularity. When the target devices are densely distributed and the spatial differentiation demand is high, the system can automatically compress the angle interval, thereby improving the target differentiation resolution and avoiding the problem of inaccurate pointing caused by multiple targets falling into the same angle sector at the same time. When the targets are sparse, the angle interval can be appropriately relaxed to reduce control overhead and redundant processing, significantly enhancing the system's flexible regulation capability in the face of the trend of complex target distribution, and can adapt to the needs of various dynamic multi-target tasks, providing more accurate basic data support for subsequent beam binding and emission direction correction.

[0064] The present invention ensures that the spatial error between the actual coordinates of each target device and the beam direction does not exceed the system's allowable range through a direction offset angle detection and a preset offset threshold comparison mechanism. The system calculates the spatial vector angle between the predicted coordinates and the candidate beam direction to obtain an accurate direction offset value, and then normalizes the offset value to adapt to the comparison requirements under multi-target conditions. Finally, target devices that can be accepted for binding are screened out based on the set threshold. Due to environmental disturbances, platform motion or device inertia factors, there are certain errors in the predicted coordinates. If effective verification is not performed, beam binding errors are very likely to occur, resulting in directional misalignment, energy waste and even accidental target contact. Through this offset detection mechanism, the system can dynamically eliminate abnormal points with excessive offsets, and only retain target devices that meet the accuracy requirements for beam binding, ensuring the accuracy of beam control matching and providing a reliable premise for the generation of control instructions and beam adjustment.

[0065] The present invention can effectively identify targets with close distances in physical space and aggregate them into the same merged group through the grouping statistics of coordinate distance values ​​and the spatial merging processing technology of target devices, and perform control operations by replacing the original multi-point coordinates with the center of gravity coordinates. Multiple target devices often lead to complex beam scheduling due to large differences in spatial density. If a beam is allocated to each target independently, it will not only waste beam resources but also greatly increase the complexity of system control. The merging mechanism identifies groups of devices that can be covered simultaneously through coordinate distance constraints, represents these targets through center of gravity coordinates, and uniformly uses them as beam control targets. Without reducing pointing accuracy, spatial multiplexing of multiple targets in one beam is achieved. Especially in mission scenarios with a large number of targets and tight resources, this solution can maximize the utilization of limited beam resources, improve the system's throughput and energy supply efficiency in large-scale missions, reduce the frequency of beam switching, and thus reduce energy loss and scheduling delays during dynamic reconstruction.

[0066] The present invention supports the simultaneous management of multiple heterogeneous target devices and parallel beam control through a distributed structured operation process design, demonstrating good system scalability. In an environment where the number of targets continues to grow, the system can add beam channels and processing nodes as needed without major adjustments to the original structure. It also provides multiple redundant protections through mechanisms such as coordinate prediction, target merging, and direction offset judgment. Even if some coordinate data is lost or the target mutates, the control command can continue to be transmitted through the remaining path data and prediction mechanism. Traditional microwave control systems usually only support a small number of targets and rely on manual intervention. The system has a weak response capability to sudden changes. However, this system achieves stable control of dynamic multiple targets through algorithm drive, process optimization and prediction mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of a distributed multi-objective microwave transmission control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0069] like Figure 1 As shown, an embodiment of the present invention provides a distributed multi-objective microwave transmission control method, the method comprising:

[0070] Obtaining geographic coordinate information of each target device and associating it with the device identification of each target device, constructing a motion trajectory of each target device, and obtaining motion trajectory data;

[0071] According to the motion trajectory data, the coordinate information of each target device is sorted in the order of time nodes, and the coordinate differences between adjacent time nodes are accumulated to obtain the predicted coordinate data set;

[0072] According to the predicted coordinate data set, the coordinate distance values ​​between each target device are extracted, the coordinate distance values ​​are grouped and counted, and the target devices with distance constraints are merged to obtain a position merged data set;

[0073] Merge the dataset based on the location, extract the beam direction range information and beam angle interval parameters for the current time period, and use them to match and index each target device to obtain the beam candidate dataset;

[0074] Based on the beam candidate dataset and the predicted coordinate dataset, coordinates of each target device are compared to obtain direction offset data, which is then compared with a preset offset threshold to obtain beam binding data.

[0075] Generate control instructions based on the beam binding data and ensure that the beam adjusts the beam transmission direction according to the control instructions.

[0076] In an embodiment of the present invention, the geographic coordinate information of each target device is obtained and associated with the device identification of each target device to construct the motion trajectory of each target device and obtain motion trajectory data. By continuously collecting coordinate information and fitting the trajectory, the system can fully understand the spatiotemporal distribution and motion trends of the target devices, providing an accurate data basis for subsequent prediction and control operations.

[0077] According to the motion trajectory data, the coordinate information of each target device is sorted in the order of time nodes, and the coordinate differences between adjacent time nodes are accumulated to obtain a predicted coordinate data set. The possible position of the target device is judged in advance through forward prediction, and a dynamic connection is established between the beam emission and the actual position of the device; according to the predicted coordinate data set, the coordinate distance values ​​between each target device are extracted, the coordinate distance values ​​are grouped and counted, and the target devices with distance constraints are merged to obtain a position merged data set, which significantly reduces the number of beam targets that the system needs to control at the same time; according to the position merged data set, the beam direction range information and beam angle interval parameters of the current time period are extracted, and each target device is controlled based on them The row matching index is used to obtain the beam candidate data set, ensuring that the beam pointing has sufficient resolution to accurately cover each target or target group, while providing the infrastructure for dynamic allocation and reconstruction; based on the beam candidate data set and the predicted coordinate data set, the coordinates of each target device are compared to obtain the direction offset data, which is then compared with the preset offset threshold to obtain the beam binding data, effectively eliminating matching anomalies caused by prediction errors and position fluctuations, and ensuring that each beam is only bound to truly controllable and coverable targets in the physical space; based on the beam binding data, control instructions are generated to ensure that the beam adjusts the beam emission direction according to the control instructions, forming the closed-loop end of the control chain, and realizing the unified deployment and time-series distribution of multiple beams.

[0078] In a preferred embodiment of the present invention, the geographic coordinate information of each target device is obtained and associated with the device identification of each target device to construct the motion trajectory of each target device to obtain the motion trajectory data, including:

[0079] According to the geographic coordinate information of the target device, the geographic coordinates of each device at different time nodes are extracted to obtain a geographic coordinate data set;

[0080] According to the geographic coordinate data set, the spatial displacement between the geographic coordinates of each target device is calculated to obtain the displacement change data;

[0081] By filtering the displacement change data, abnormal data caused by signal noise or environmental interference is removed to obtain denoised displacement change data;

[0082] According to the denoised displacement change data, the motion trajectory of each target device is fitted, the trajectory curve is constructed, and a smooth trajectory path is obtained;

[0083] According to the smooth trajectory path, the motion speed and acceleration of each target device are extracted and time-sorted to obtain the motion trajectory data.

[0084] In an embodiment of the present invention, based on the geographic coordinate information of the target device, the geographic coordinates of each device at different time nodes are extracted to obtain a geographic coordinate data set, ensuring that the system establishes a unified and continuous positioning data source, constructs a time series position record of the target device, and provides high-precision and continuous basic data support for subsequent trajectory fitting and motion state judgment; based on the geographic coordinate data set, the spatial displacement between the geographic coordinates of each target device is calculated to obtain displacement change data, quantitatively describe the movement behavior of the target device, and generate spatial motion state data with time interval labels, which is convenient for subsequent analysis of whether the device is in a stationary, uniform or accelerated state; by filtering the displacement change data, anomalies caused by signal noise or environmental interference are removed. The system can obtain denoised displacement change data from constant data, which can effectively eliminate non-real displacement data caused by positioning errors, signal blocking, equipment failure and other factors; fit the motion trajectory of each target device based on the denoised displacement change data, construct a trajectory curve, and obtain a smooth trajectory path, which converts discrete motion data into a continuous and differentiable path representation, making the path data more intuitive and easy to use, and also providing a mathematical basis for further calculation of speed and acceleration; extract the motion speed and acceleration of each target device based on the smooth trajectory path, and sort them by time to obtain motion trajectory data. Through the continuous calculation of speed and acceleration, the system can identify whether the target is accelerating, decelerating or traveling at a constant speed, and thus determine whether the motion state is stable.

[0085] The displacement change data is filtered to remove abnormal data caused by signal noise or environmental interference to obtain denoised displacement change data, which specifically includes:

[0086] First, displacement change data is organized into a time-series-indexed data stream. Each data record includes a timestamp, device ID, and corresponding displacement value. To eliminate abnormal sudden changes or deviations caused by environmental interference, signal obstruction, and satellite multipath effects, the system employs multiple filtering mechanisms to cleanse the data. First, a sliding average filter is used to smooth the data within a specified time window. For example, a sliding window of five time nodes is set, and the displacement values ​​at each central node are replaced with a local mean. Second, a median filter is introduced to remove the maximum and minimum values ​​within the sliding window, retaining only the median value, improving its ability to mitigate anomalies. Building on the sliding average and median filters, the system further employs discriminant filtering. This involves calculating the gradient difference between each data point and its adjacent data points. When this gradient exceeds a preset threshold (e.g., three times the average displacement change), the point is considered an outlier and replaced with a local interpolation. If the device is located in a highly dynamic area, the threshold tolerance can be appropriately increased to avoid filtering out normal sudden changes. After this series of filtering operations, the displacement change data will no longer contain obvious fluctuation anomalies or noise points, which is the denoised displacement change data.

[0087] Among them, according to the denoised displacement change data, the motion trajectory of each target device is fitted, a trajectory curve is constructed, and a smooth trajectory path is obtained, which specifically includes:

[0088] After receiving the denoised displacement change data, the system groups them according to the device identifier and arranges each data set in ascending order according to the timestamp to form a time-displacement series curve for a single device. For each device data set, the system uses the B-spline curve fitting method to reconstruct the curve. First, a fixed number of control nodes in each group are selected, and the data is uniformly segmented according to the length of each spline segment, and the control weights between the nodes are calculated. Then, a fitting function is generated using a cubic spline interpolation function, so that it has continuous first-order and second-order derivatives at each control node to ensure the smoothness and differentiability of the curve. The system verifies the fitting results through a fitting error evaluation mechanism. When the error exceeds the set threshold (such as within 0.5m), the number of spline segments is automatically refined to improve the fitting accuracy. All spline segments are connected to obtain a smooth trajectory path.

[0089] Among them, according to the smooth trajectory path, the movement speed and acceleration of each target device are extracted and time-sorted to obtain the movement trajectory data, which specifically includes:

[0090] Based on the generated smooth trajectory path, the system first solves the first-order derivative and second-order derivative of the trajectory curve. Specifically, for any point in the trajectory , the system calculates its and The corresponding spatial position vector and , use the difference method to approximate the velocity vector of the point , is the time difference between two adjacent time nodes, and the velocity vector modulus is normalized to express it as the instantaneous velocity value Similarly, the system performs a differential operation on the velocity value sequence again to obtain the acceleration value ,Right now The velocity and acceleration values ​​at each moment are recorded in the data format of "device number + timestamp + velocity value + acceleration value" and stored in the distributed trajectory database. The system sorts this data by time and generates a time-retrievable velocity-acceleration joint sequence, thereby constructing complete motion trajectory data.

[0091] In a preferred embodiment of the present invention, based on the motion trajectory data, the coordinate information of each target device is sorted in the order of time nodes, and the coordinate differences between adjacent time nodes are accumulated to obtain a predicted coordinate data set, including:

[0092] According to the time node information of each target device in the motion trajectory data, the geographical coordinates of each target device are sorted in ascending time order to obtain ordered coordinate sequence data;

[0093] According to the ordered coordinate sequence data, the coordinate values ​​between two adjacent time nodes of the target device are extracted, and their coordinate differences are calculated to obtain a coordinate difference vector set;

[0094] Normalizing each coordinate difference vector in the coordinate difference vector set according to the time interval between the coordinate difference values ​​to obtain a normalized difference vector data set;

[0095] According to the normalized difference vector data set, the coordinate difference vectors of each target device are sequentially superimposed to obtain a cumulative displacement vector data set;

[0096] According to the cumulative displacement vector data set, the real-time coordinates of each target device and the corresponding cumulative displacement vector are vector-superimposed to obtain a predicted coordinate data set.

[0097] In an embodiment of the present invention, the geographic coordinates of each target device are sorted in ascending time order based on the time node information of each target device in the motion trajectory data to obtain ordered coordinate sequence data, thereby restoring the true order of the device's movement path in real space and providing a basis for subsequent calculations. Based on the ordered coordinate sequence data, the coordinate values ​​between two adjacent time nodes of the target device are extracted, and their coordinate differences are calculated to obtain a coordinate difference vector set, which reflects the movement trend and speed direction of the target device between each two consecutive time nodes and captures dynamic behavior characteristics. Based on the time interval between the coordinate differences, each coordinate difference vector in the coordinate difference vector set is normalized to obtain a normalized difference vector dataset, thereby removing interference caused by velocity amplitude differences and preventing prediction deviation caused by large displacements in a certain section. Based on the normalized difference vector dataset, the coordinate difference vectors of each target device are sequentially superimposed to obtain a cumulative displacement vector dataset, thereby accumulating short-term local motion trends into an overall movement trend and ensuring smoother and more stable prediction results. Based on the cumulative displacement vector dataset, the real-time coordinates of each target device and the corresponding cumulative displacement vector are vector-superimposed to obtain a predicted coordinate dataset, which provides a basic coordinate basis for subsequent beam direction calculation, beam binding, and beam steering.

[0098] According to the time node information of each target device in the motion trajectory data, the geographic coordinates of each target device are sorted in ascending time order to obtain ordered coordinate sequence data, which specifically includes:

[0099] For each target device, its geographic coordinate records, including multiple time nodes, are extracted from the motion trajectory data. Each record includes the target device's device ID, a timestamp, and the corresponding geographic coordinate data. The geographic coordinates can be three-dimensional coordinates consisting of longitude, latitude, and altitude information. After extraction, the target devices are first classified and grouped according to their device IDs. Within each group, they are sorted in ascending order based on the timestamp field. A time sorting algorithm, such as a stability-based merge sort algorithm, is used to ensure monotonicity of the sorting result. The sorting result forms a set of trajectory points that evolve over time for the target device, resulting in an ordered coordinate sequence data.

[0100] According to the time interval between the coordinate differences, each coordinate difference vector in the coordinate difference vector set is normalized to obtain a normalized difference vector data set, specifically including:

[0101] After completing the ascending time sorting and constructing the ordered coordinate sequence data, the coordinate pairs between two adjacent time nodes in the sequence are processed, the three-dimensional difference between the two coordinates is extracted, and a difference vector set is constructed. Each difference vector consists of the coordinate difference in each axis direction in the three-dimensional space, which is recorded as 、 and , forming a vector Then, for each difference vector, based on the interval value of the previous and next time nodes Calculate the displacement rate per unit time as the normalization factor. The normalization formula is: If a unified standard length scale is required, the normalized result vectors can be further normalized to make the lengths of all vectors normalized to 1, preserving their directional characteristics. The obtained normalized difference vector dataset can be used as a standard representation of the target device's short-term movement trend.

[0102] According to the accumulated displacement vector data set, the real-time coordinates of each target device and the corresponding accumulated displacement vector are vector-superimposed to obtain a predicted coordinate data set, which specifically includes:

[0103] The system linearly accumulates all normalized vectors in time sequence to construct the cumulative displacement vector of the device. Specifically, for the normalized vector sequence , calculate its synthetic displacement vector by sequential accumulation and summation , is the index of the displacement vector, is the total number of displacement vectors. The accumulation can introduce weighting factors as needed, such as target speed, environmental confidence, etc. After completing the calculation of the cumulative displacement vector, the real-time geographic coordinates of the target device at the current moment are obtained. , and perform a space vector addition operation with the above synthetic vector, that is, execute: The predicted coordinate point is the estimated location coordinate of the current target device within a short time window in the future. By performing the above operation on each target device, a predicted coordinate dataset covering multiple target devices can be constructed.

[0104] According to the accumulated displacement vector data set, the real-time coordinates of each target device and the corresponding accumulated displacement vector are vector-superimposed to obtain a predicted coordinate data set, which specifically includes:

[0105] First, the cumulative displacement vector of each target device is extracted from the cumulative displacement vector dataset , and combined with real-time coordinates To ensure consistent accuracy, the overlay operation is performed dimension by dimension in the three-dimensional geographic coordinate space, where vector addition is performed independently on the X, Y, and Z axes. At the same time, to improve the credibility of the predicted coordinates, the system can also perform error correction on the overlay results. The error can be dynamically adjusted based on the historical position error offset vector, introducing a dynamic correction vector , the corrected predicted coordinates are This correction step can be dynamically determined based on environmental characteristics to improve prediction accuracy and enhance system robustness. The final output prediction coordinate points are encapsulated through a set of unified data structures to form a prediction coordinate data set.

[0106] in, The acquisition process of a dynamic correction vector is an important means of correcting errors in predicted coordinates. Essentially, it introduces a set of compensation vectors derived from historical deviations, environmental disturbances, or statistical learning models based on existing trajectory prediction results to further improve the accuracy and robustness of the prediction results. First, the system obtains the predicted coordinates of a target device and its true coordinates at each time point and calculates a three-dimensional difference vector as the original error sample. To avoid amplification of deviations caused by short-term disturbances, the system can set a sliding time window, selecting only error data from the most recent time periods. Next, the system constructs an error correction based on the historical error samples collected above. Linear statistical analysis methods or physical perturbation function models can be used. For example, in a statistical regression model, the system can perform weighted averaging or least squares fitting on historical errors to extract representative error trends as correction vectors. Finally, based on the constructed error model or current external input, the system generates a dynamic correction vector for the current time point. This vector can be directly superimposed with the cumulative displacement vector output by the trajectory prediction module to form the updated predicted coordinates.

[0107] In a preferred embodiment of the present invention, based on the predicted coordinate data set, coordinate distance values ​​between target devices are extracted, the coordinate distance values ​​are grouped and counted, and target devices with distance constraints are merged to obtain a position merged data set, including:

[0108] According to the coordinate information of each target device in the predicted coordinate data set, a pairwise target device combination is constructed to obtain a target pairing set;

[0109] According to each pair of target devices in the target pairing set, the coordinate distance value between the coordinates is calculated to obtain a distance value data set;

[0110] Based on the distance value dataset, all coordinate distance values ​​are classified according to a preset distance threshold, and target device pairs whose distance is not greater than the preset distance threshold are marked as valid merge pairs to obtain a valid merge pair set;

[0111] According to the valid merged pairing set, multiple target devices with overlapping pairing relationships are connected and aggregated to obtain a merged target group set;

[0112] Calculate the center of gravity coordinates of each group based on the predicted coordinates of each target device in the merged target group set to obtain a center of gravity coordinate set;

[0113] The centroid coordinates of each group in the centroid coordinate set are used as representative coordinates to replace the coordinates of each target device in the original group, and a position merged data set is obtained.

[0114] In an embodiment of the present invention, based on the coordinate information of each target device in the predicted coordinate data set, a pairwise combination of target devices is constructed to obtain a target pairing set, thereby achieving a systematic organization of the spatial relative position relationship of all target devices at the predicted time point, providing structured input for subsequent distance calculations, ensuring that the spatial differences between target devices can be comprehensively evaluated, and preventing the omission of potential mergeable objects; based on each pair of target devices in the target pairing set, the coordinate distance value between their coordinates is calculated to obtain a distance value data set, and the actual spatial distance between all device pairs is accurately calculated, which can comprehensively obtain the physical proximity of the target devices at the predicted moment. This step not only provides a quantitative standard for subsequent distance threshold classification, but also lays a numerical foundation for the merging and clustering of target devices. Based on the distance value data set, all coordinate distance values ​​are classified according to the preset distance threshold, and the target device pairs that are not greater than the preset distance threshold are marked as valid merged pairs to obtain an effective merged pairing set, which effectively screens out target devices that are physically far away and difficult to cover simultaneously. Based on the effective merged pairing set, multiple target devices with overlapping pairing relationships are connected and aggregated to obtain a merged target group set, realizing automatic aggregation of merging relationships between devices and providing structural support for multi-device group control. Based on the predicted coordinates of each target device in the merged target group set, the center of gravity coordinates of each group are calculated to obtain a center of gravity coordinate set, which retains the original spatial distribution structure while simplifying the data, allowing the system to complete beam pointing control with fewer data points. The center of gravity coordinates of each group in the center of gravity coordinate set are used as representative coordinates to replace the coordinates of each target device in the original group to obtain a position merged data set, which significantly reduces the number of spatial points that the control system needs to process and alleviates the data processing load in large-scale target scenarios.

[0115] According to the distance value dataset, all coordinate distance values ​​are classified according to a preset distance threshold, and target device pairs whose distance is not greater than the preset distance threshold are marked as valid merged pairs, thereby obtaining a valid merged pairing set, which specifically includes:

[0116] The system first reads the spatial coordinate data of all target devices in the predicted coordinate data set, and traverses them in pairs. For each target device in each pair, the Euclidean space distance between their predicted coordinates is calculated. Set the preset distance threshold to The system compares the distance between each group of devices in turn to see if they meet conditions, and are the device indexes. If they meet the criteria, the target device pair is marked as a valid merge pair and added to the valid merge pair set. If they do not meet the criteria, the pair is discarded and does not participate in subsequent merge analysis. Ultimately, all target device combinations that meet the preset spatial proximity criteria constitute a valid merge pair set.

[0117] According to the valid merged pairing set, multiple target devices with overlapping pairing relationships are connected and aggregated to obtain a merged target group set, which specifically includes:

[0118] The system first constructs an undirected graph, in which each target device is a node in the graph, and each device pair in the valid merged pairing set is an edge connecting two nodes. Next, the system extracts the connected subgraph of the graph and uses a depth-first search to identify all connected components. Each connected component is a merged target group, and all target devices within it have a direct or indirect spatial proximity relationship, which meets the conditions of being covered by a beam at the same time. For example, if device A is effectively paired with device B, and device B is also effectively paired with device C, then although device A and device C do not directly form a valid pairing relationship, since they are connected to B, A, B, and C will be merged into the same target group.

[0119] The center of gravity coordinates of each group are calculated based on the predicted coordinates of each target device in the merged target group set to obtain a center of gravity coordinate set, which specifically includes:

[0120] For each merged target group, extract the predicted coordinate dataset of its member target devices The system calculates the spatial geometric center of gravity of the group by performing weighted averaging of all coordinate points in each dimension. The calculation formula is as follows: , ,in, is the number of target devices in the group. The obtained centroid coordinates are used as the representative spatial point of the group for subsequent beam matching, direction indexing, and binding processing.

[0121] In a preferred embodiment of the present invention, based on the position-merged dataset, the beam direction range information and beam angle interval parameters of the current time period are extracted, and matching indexes are performed on each target device based on the information to obtain a beam candidate dataset, including:

[0122] According to the satellite's spatial position information, the center reference direction of the satellite platform is determined, and the left and right extreme transmission directions within its coverage area are extracted to obtain the beam direction range information;

[0123] Based on the minimum spatial distance between the coordinates of each center of gravity in the position merged data set and the spatial distance from the satellite to the target area, the angle accuracy required for differentiation is calculated to obtain the target resolution angle data;

[0124] Adjust the unit according to the minimum angle of the satellite beam to determine the control accuracy data;

[0125] According to the target resolution angle data and the control accuracy data, the larger value of the two is selected as the angle interval parameter to obtain the beam angle interval parameter;

[0126] According to the beam direction range information and beam angle interval parameters, the satellite beam is discretized into sections to obtain a beam direction sequence data set;

[0127] According to the beam direction sequence data set, the transmission direction offset value of each center of gravity coordinate in the position merged data set is calculated to obtain the direction offset data set;

[0128] According to the direction offset dataset, each centroid coordinate is matched to the nearest beam direction, the beam index mapping relationship is determined, and the beam candidate dataset is obtained.

[0129] In an embodiment of the present invention, the central reference direction of the satellite platform is determined based on the satellite spatial position information, and the left and right extreme emission directions within its coverage area are extracted to obtain beam direction range information, accurately identify the spatial boundary of the current beam emission, and limit the feasible range of subsequent beam pointing calculations; based on the minimum spatial distance value between the center of gravity coordinates in the position merged data set, combined with the spatial distance from the satellite to the target area, the angular accuracy required for differentiation is calculated to obtain target resolution angle data, which provides the minimum control accuracy index required for spatial differentiation between target groups and provides a theoretical basis for the subsequent setting of beam adjustment granularity; based on the minimum angular adjustment unit of the satellite beam, the control accuracy data is determined, and the minimum control accuracy parameter of the satellite antenna system is extracted to facilitate the subsequent selection of a reasonable angular interval; based on the target resolution angle data and the control accuracy data, the larger value of the two is selected as the angular interval parameter to obtain the beam angle interval parameter, while considering the system control capability and the target spatial differentiation requirements to avoid excessive control load caused by setting the angular interval too small, or confusion between targets caused by setting it too large.

[0130] Based on the beam direction range information and beam angle interval parameters, the satellite beam is segmented and discretized to obtain a beam direction sequence data set. The continuous spatial angle is converted into a finite set of controllable beam directions to ensure that each discrete angle has sufficient pointing capability and control accessibility, providing the basis for subsequent precise binding and dynamic control. Based on the beam direction sequence data set, the transmission direction offset value of each center of gravity coordinate in the position merged data set is calculated to obtain a direction offset data set. The most matching transmission direction is determined for each target group, and the offset is used as the matching optimization indicator to achieve direction binding with minimized deviation within the control accuracy allowed by the system. Based on the direction offset data set, each center of gravity coordinate is matched to the nearest beam direction, and the beam index mapping relationship is determined to obtain beam candidate data. By establishing a one-to-many beam-target relationship, it supports multiple centers of gravity sharing beam directions, maximizes utilization efficiency under limited resources, and provides input basis for subsequent beam transmission control.

[0131] Among them, according to the minimum spatial distance value between the coordinates of each center of gravity in the position merged data set, combined with the spatial distance from the satellite to the target area, the angle accuracy required for differentiation is calculated to obtain the target resolution angle data, specifically including:

[0132] First, the centroid coordinates of all target groups in the location merged dataset are extracted, and all centroid coordinates are combined into a target group pairing set. For each pair, the three-dimensional Euclidean distance in geographic space is calculated, and the minimum spatial distance value is selected, which is recorded as ; Then, according to the vertical or approximate vertical distance from the current satellite platform to the ground target area (can be obtained through remote sensing altitude information or ephemeris data), and the minimum angle accuracy required to resolve the minimum spacing is calculated using trigonometric functions. The calculation formula is: The obtained angle value is the target resolution angle data.

[0133] According to the beam direction range information and beam angle interval parameters, the satellite beam is discretized into segments to obtain a beam direction sequence data set, which specifically includes:

[0134] First, according to the target resolution angle obtained in the previous steps The minimum control unit angle with the satellite antenna itself Compare and select the larger value of the two as the beam angle interval parameter , ensuring that the system has a practical controllable directional resolution capability; then, according to the preset beam emission coverage boundary of the satellite platform, the angle boundary values ​​corresponding to the left and right extreme directions are extracted and recorded as the left boundary angle Angle with right boundary , thereby constructing the beam direction range interval; then, using the beam angle interval parameter as the step size, the entire direction range is divided into intervals according to the following formula: , the final discrete angle set That is the beam direction sequence dataset.

[0135] According to the beam direction sequence data set, the transmission direction offset value of each center of gravity coordinate in the position merged data set is calculated to obtain the direction offset data set, which specifically includes:

[0136] First, a spherical coordinate reference system is established for the satellite platform in three-dimensional space, and each angle value in the beam direction sequence data set is converted into a corresponding unit direction vector. Then, the centroid coordinate point of each merged target group is extracted and its vector form is normalized to a unit target direction vector. The angle between each target direction vector and all beam direction vectors is calculated using the following formula: ,in is the direction vector of the target group’s center of gravity, For the The unit vector of the beam direction, is the directional offset angle value between the two; the above operation traverses all the center of gravity coordinates and all the beam directions to construct a directional offset data set with a two-dimensional matrix structure, where each matrix element represents the angular deviation between a center of gravity and a certain beam direction.

[0137] Among them, according to the direction offset data set, each center of gravity coordinate is matched to the nearest beam direction, the beam index mapping relationship is determined, and the beam candidate data is obtained, which specifically includes:

[0138] For each target group centroid in the direction offset dataset, we traverse the offset angles between it and all beam directions, select the one with the smallest offset angle, and record its corresponding beam direction index as the matching direction for the target group. Next, we classify and aggregate the target group numbers pointing to the same beam direction into a set, ultimately forming the following mapping structure: Beam={ θ k :[ C i1 , C i2 ,...]} ,in is the discrete beam direction index, is the center of gravity number of the target group belonging to this direction, is the index of the center of gravity number; the structure is the beam candidate data, which represents the set of beam directions to which all target centers of gravity should be bound in the current time period.

[0139] In a preferred embodiment of the present invention, coordinates of each target device are compared based on the beam candidate dataset and the predicted coordinate dataset to obtain direction offset data, which is then compared with a preset offset threshold to obtain beam binding data, including:

[0140] According to the coordinates of each beam direction in the beam candidate data set, the target device identifier associated therewith is extracted to obtain the beam target pairing data;

[0141] According to the target device identifier in the beam target pairing table, the corresponding real-time coordinates in the predicted coordinate data set are extracted to obtain the target real-time coordinate data;

[0142] According to the real-time coordinates of each beam direction and its associated target device, the space vector between the satellite and the target device is calculated to obtain the target direction vector dataset;

[0143] According to the angle between each beam direction and the target direction vector, the spatial offset angle value is calculated to obtain the direction offset angle data set;

[0144] By normalizing the spatial offset angle values ​​in the direction offset angle dataset, a standard offset dataset is obtained;

[0145] According to the standard offset data set, the offset data of each target device is compared with the preset offset threshold, and the target devices that meet the offset threshold conditions are screened out to obtain the beam binding data.

[0146] In an embodiment of the present invention, according to the coordinates of each beam direction in the beam candidate data set, the target device identifier associated therewith is extracted to obtain beam target pairing data, and an initial association between the beam direction and the target device is established, providing an accurate spatial reference system for angle offset calculation; according to the target device identifier in the beam target pairing table, the corresponding real-time coordinates in the predicted coordinate data set are extracted to obtain target real-time coordinate data, ensuring that each target device uses the most recent predicted position for beam calculation; according to the real-time coordinates of each beam direction and its associated target device, the space vector between the satellite and the target device is calculated to obtain a target direction vector data set, thereby realizing relative direction modeling in three-dimensional space and laying the foundation for subsequent angle calculation; according to the real-time coordinates of each beam direction and the target direction, the space vector between the satellite and the target device is calculated to obtain a target direction vector data set, thereby realizing relative direction modeling in three-dimensional space and laying the foundation for subsequent angle calculation; The angle between the two quantities is calculated, the spatial offset angle value is calculated, and the directional offset angle data set is obtained, which realizes the quantitative evaluation of the offset degree of each target device relative to the beam direction and provides an objective and measurable basis for judgment; by normalizing the spatial offset angle values ​​in the directional offset angle data set, a standard offset data set is obtained, which solves the problem of different angles and non-uniform scales of multiple target devices, so that the system can perform offset threshold screening under a unified standard; according to the standard offset data set, the offset data of each target device is compared with the preset offset threshold, and the target devices that meet the offset threshold conditions are screened out to obtain the output beam binding data, realizing the target screening and filtering operation, and providing key data support for the system to automatically transmit commands and perform precise beam control.

[0147] According to the real-time coordinates of each beam direction and its associated target device, the space vector between the satellite and the target device is calculated to obtain the target direction vector dataset, which specifically includes:

[0148] First, the spatial reference point of each beam is extracted from the beam direction coordinate values ​​recorded in the beam candidate data set as the starting point of the vector. Subsequently, the system accesses the predicted coordinate data set based on the target device number associated with each beam direction, and retrieves the real-time coordinate information of the corresponding target device as the end point of the vector. The system then performs a three-dimensional coordinate difference operation on each set of "beam direction-target device" correspondences, subtracting the beam direction reference point coordinates from the target real-time coordinates to calculate a set of three-dimensional vectors that describe the direction from the beam starting point to the target device. The system performs a normalization process on all the obtained direction vectors, that is, dividing each vector by its own modulus to obtain a set of standard unit direction vectors. These sets of unit vectors constitute the target direction vector data set.

[0149] The spatial offset angle value is calculated based on the angle between each beam direction and the target direction vector to obtain the direction offset angle dataset, which specifically includes:

[0150] The system constructs a corresponding unit direction vector for each beam direction. This unit vector is obtained by normalizing the original beam direction vector to ensure uniformity in vector comparison. For each set of beam unit vectors and target direction unit vectors, the system performs a standard cosine angle calculation. The specific calculation process involves first performing a dot product operation on the two vectors to obtain the cosine value of the angle, and then using the inverse cosine function to obtain the actual angle value. The calculation formula is: ,in represents the beam unit direction vector, Represents the target device unit direction vector, The system uses a vectorized computing engine to process all beam-target pairs in parallel, and the final output angle value set constitutes the direction offset angle data set.

[0151] The standard offset dataset is obtained by normalizing the spatial offset angle values ​​in the direction offset angle dataset, which specifically includes:

[0152] The system performs normalization on each angle value. Normalization uses a linear mapping function to convert the original angle value According to the set maximum acceptable deviation angle Perform linear compression processing, the calculation formula is: ,in The maximum direction deviation allowed by the system is set, such as 15 degrees or 30 degrees. When the angle value exceeds When the system is not able to perform normalization, it can choose to perform clipping, that is, setting the excess value to 1 to ensure that the normalized value is always in the range [0,1]. After the normalization operation is completed, the system stores all the normalized values ​​in a new data structure to form a standard offset dataset.

[0153] Among them, according to the standard offset data set, the offset data of each target device is compared with the preset offset threshold, the target devices that meet the offset threshold conditions are screened out, and the output beam binding data is obtained, which specifically includes:

[0154] The system compares the offset value of each target device in the standard offset data set with the offset threshold set by the system one by one. For comparison, usually the threshold The setting is based on the beam emission control accuracy requirements and the scene dynamic characteristics, for example, it is set to 0.25 or 0.30. For each beam direction, among the target devices associated with it, only when the standard offset value corresponding to a target device is less than or equal to When a target device is found, the system marks it as a bindable target and adds it to the list of binding candidates. The system then traverses all beam directions and their corresponding target devices, generating a mapping between beam directions and target sets that meet the binding criteria through offset filtering. This mapping table is then formatted and stored uniformly to form the final beam binding data.

[0155] In a preferred embodiment of the present invention, generating a control instruction according to the beam binding data and ensuring that the beam adjusts the beam transmission direction according to the control instruction includes:

[0156] According to the target device corresponding to each beam direction in the beam binding data, the transmission coordinates under each beam direction are extracted to obtain the control target data set;

[0157] According to the control target data set, the spatial direction parameters of the launch coordinates of each target device relative to the satellite platform are calculated to obtain the direction parameter data set;

[0158] According to the direction parameter data set, the spatial direction parameter of each target device is converted into a corresponding beam steering angle value to obtain a beam steering angle data set;

[0159] According to the beam steering angle data set, the time tag is determined in combination with the current time node to obtain the control time synchronization data set;

[0160] According to the control time synchronization data set, the beam number, control angle and time tag are determined, and the control instructions are generated. The control instructions are transmitted to the beam steering module in turn to adjust the beam transmission direction.

[0161] In an embodiment of the present invention, according to the target device corresponding to each beam direction in the beam binding data, the transmission coordinates under each beam direction are extracted to obtain a control target data set, thereby realizing the transition from macro-control to micro-point control, ensuring that the target pointed by each microwave beam has accurate spatial positioning information, and providing data support for subsequent angle conversion and emission control; according to the control target data set, the spatial direction parameters of the emission coordinates of each target device relative to the satellite platform are calculated to obtain a direction parameter data set, completing the conversion from Cartesian coordinates to transmission direction parameters, and providing a direction parameter input that can be directly used for subsequent beam control hardware; according to the direction parameter data set, the spatial direction parameters of each target device are converted into the corresponding The beam steering angle value is obtained to obtain the beam steering angle dataset, which effectively realizes the data coupling between the spatial direction parameters and the antenna control system, and solves the problem that the direction resolution parameters cannot directly drive the hardware execution; according to the beam steering angle dataset, the time tag is determined in combination with the current time node to obtain the control time synchronization dataset, which provides a unified time base, so that all beam control actions are synchronized and coordinated, and effectively avoid problems such as control delay, instruction overlap or execution misalignment; according to the control time synchronization dataset, the beam number, control angle and time tag are determined, and the control instructions are generated. The control instructions are transmitted to the beam control module in sequence to adjust the beam emission direction, ensuring that the control logic can smoothly drive the hardware to execute instructions.

[0162] According to the control target data set, the spatial direction parameters of the launch coordinates of each target device relative to the satellite platform are calculated to obtain the direction parameter data set, which specifically includes:

[0163] First, the launch coordinate information of each target device recorded in the control target data set is obtained. The launch coordinate is the three-dimensional geographic spatial position of each target device in the predicted coordinate data set at the current time node. The current spatial position of the satellite platform is used as the origin of the reference coordinate system, and the three-dimensional space vector differential model is used to model the spatial position relationship between each target device and the satellite; by calculating the difference vector between the target device coordinate point and the satellite platform coordinate point, a target pointing vector set is constructed, and converted into directional description parameters such as pitch angle and azimuth angle in the spherical coordinate system to characterize the launch direction of the target device relative to the satellite platform in space. The directional angle is further differentially sampled to improve the directional resolution. In a multi-target scenario, the system uses matrix calculation to batch process the launch vectors of all targets. Finally, the pitch angle and azimuth angle of each target device are recorded as directional parameter data to form a directional parameter data set.

[0164] According to the direction parameter data set, the spatial direction parameter of each target device is converted into a corresponding beam steering angle value to obtain a beam steering angle data set, which specifically includes:

[0165] First, the system determines the minimum acceptable angle step value by consulting the beam steering device's minimum angle adjustment resolution and control increment unit. Second, the elevation and azimuth angles within the directional parameters are quantized and converted into control angle values ​​that match the antenna's actuation mechanism. For example, these are converted into integer angle signals in degrees or specific step-coded signals. During this process, the system can employ interpolation methods to improve direction matching accuracy or implement angle rounding strategies to reduce control latency, depending on actual needs. For multiple target devices sharing a beam direction, the system also determines whether the angle falls within the effective coverage sector of the current steerable beam. If not, it uses a local offset adjustment strategy to correct the direction, ensuring the effectiveness of each control angle. Each set of generated beam steering angle data contains the beam number, elevation control angle, and azimuth control angle. The system records and outputs all control angle values ​​as a beam steering angle dataset.

[0166] The control time synchronization dataset is obtained by determining the time tag based on the beam steering angle dataset and the current time node, specifically including:

[0167] First, the timestamp of the current system operation is obtained, and this time node is used as the synchronization reference time for the control instructions. For each beam control angle data, the system combines its preset execution cycle or control beat parameters to assign a precise time tag to it to identify the triggering moment of the control instruction. In the case of periodic scheduling, the system can adopt a sliding time window strategy to pre-generate multiple future time tags to support batch control. In the case of real-time response or event-triggered tasks, the system directly assigns the current clock value as the control time tag to ensure that the instruction is issued and executed immediately. Then, based on system parameters such as communication delay, beam reconstruction time, and device response time, the time tag is feedforward corrected to form time synchronization data with offset compensation capabilities. Finally, each control instruction will be bound to a unique time tag, and the system will package and output all control data containing time information to form a control time synchronization data set.

[0168] An embodiment of the present invention further provides a distributed multi-target microwave transmission control system, the system comprising:

[0169] The target trajectory module is used to obtain the geographic coordinate information of each target device and associate it with the device identification of each target device to construct the motion trajectory of each target device and obtain motion trajectory data;

[0170] The predicted coordinate module is used to sort the coordinate information of each target device in the order of time nodes based on the motion trajectory data, and accumulate the coordinate differences between adjacent time nodes to obtain a predicted coordinate data set;

[0171] The target merging module is used to extract the coordinate distance values ​​between target devices based on the predicted coordinate data set, group and count the coordinate distance values, and merge the target devices with distance constraints to obtain a position merged data set;

[0172] The beam matching module is used to merge the data sets according to the location, extract the beam direction range information and beam angle interval parameters of the current time period, and match the index of each target device based on them to obtain the beam candidate data set;

[0173] The beam binding module is used to compare the coordinates of each target device based on the beam candidate data set and the predicted coordinate data set to obtain direction offset data, and compare it with the preset offset threshold to obtain beam binding data;

[0174] The control instruction module is used to generate control instructions according to the beam binding data and ensure that the beam adjusts the beam transmission direction according to the control instructions;

[0175] The beam control module is used to adjust the beam transmission direction according to the control instructions.

[0176] It should be noted that this system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0177] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the above-described method. All implementations in the above-described method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0178] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the above-described method. All implementations in the above-described method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0179] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A distributed multi-objective microwave transmission control method, characterized in that: The method comprises: Obtaining geographic coordinate information of each target device and associating it with the device identification of each target device, constructing a motion trajectory of each target device, and obtaining motion trajectory data; According to the motion trajectory data, the coordinate information of each target device is sorted in the order of time nodes, and the coordinate differences between adjacent time nodes are accumulated to obtain the predicted coordinate data set; According to the predicted coordinate data set, the coordinate distance values ​​between each target device are extracted, the coordinate distance values ​​are grouped and counted, and the target devices with distance constraints are merged to obtain a position merged data set; Merge the dataset based on the location, extract the beam direction range information and beam angle interval parameters for the current time period, and use them to match and index each target device to obtain the beam candidate dataset; Based on the beam candidate dataset and the predicted coordinate dataset, coordinates of each target device are compared to obtain direction offset data, which is then compared with a preset offset threshold to obtain beam binding data. Generate control instructions based on beam binding data and ensure that the beam adjusts the beam transmission direction according to the control instructions; Based on the location-based merged dataset, the beam direction range information and beam angle interval parameters for the current time period are extracted. Based on these parameters, matching indexes are performed on each target device to obtain a beam candidate dataset, including: According to the satellite's spatial position information, the center reference direction of the satellite platform is determined, and the left and right extreme transmission directions within its coverage area are extracted to obtain the beam direction range information; Based on the minimum spatial distance between the coordinates of each center of gravity in the position merged data set and the spatial distance from the satellite to the target area, the angle accuracy required for differentiation is calculated to obtain the target resolution angle data; Adjust the unit according to the minimum angle of the satellite beam to determine the control accuracy data; According to the target resolution angle data and the control accuracy data, the larger value of the two is selected as the angle interval parameter to obtain the beam angle interval parameter; According to the beam direction range information and beam angle interval parameters, the satellite beam is discretized into sections to obtain a beam direction sequence data set; According to the beam direction sequence data set, the transmission direction offset value of each center of gravity coordinate in the position merged data set is calculated to obtain the direction offset data set; According to the direction offset dataset, each centroid coordinate is matched to the nearest beam direction, the beam index mapping relationship is determined, and the beam candidate dataset is obtained.

2. A distributed multi-objective microwave transmission control method according to claim 1, characterized in that: Obtain the geographic coordinate information of each target device and associate it with the device identifier of each target device to construct the motion trajectory of each target device and obtain motion trajectory data, including: According to the geographic coordinate information of the target device, the geographic coordinates of each device at different time nodes are extracted to obtain a geographic coordinate data set; According to the geographic coordinate data set, the spatial displacement between the geographic coordinates of each target device is calculated to obtain the displacement change data; By filtering the displacement change data, abnormal data caused by signal noise or environmental interference is removed to obtain denoised displacement change data; According to the denoised displacement change data, the motion trajectory of each target device is fitted, the trajectory curve is constructed, and a smooth trajectory path is obtained; According to the smooth trajectory path, the motion speed and acceleration of each target device are extracted and time-sorted to obtain the motion trajectory data.

3. A distributed multi-objective microwave transmission control method according to claim 2, characterized in that: According to the motion trajectory data, the coordinate information of each target device is sorted in the order of time nodes, and the coordinate differences between adjacent time nodes are accumulated to obtain the predicted coordinate data set, including: According to the time node information of each target device in the motion trajectory data, the geographical coordinates of each target device are sorted in ascending time order to obtain ordered coordinate sequence data; According to the ordered coordinate sequence data, the coordinate values ​​between two adjacent time nodes of the target device are extracted, and their coordinate differences are calculated to obtain a coordinate difference vector set; Normalizing each coordinate difference vector in the coordinate difference vector set according to the time interval between the coordinate difference values ​​to obtain a normalized difference vector data set; According to the normalized difference vector data set, the coordinate difference vectors of each target device are sequentially superimposed to obtain a cumulative displacement vector data set; According to the cumulative displacement vector data set, the real-time coordinates of each target device and the corresponding cumulative displacement vector are vector-superimposed to obtain a predicted coordinate data set.

4. A distributed multi-objective microwave transmission control method according to claim 3, characterized in that: Based on the predicted coordinate data set, the coordinate distance values ​​between each target device are extracted, the coordinate distance values ​​are grouped and counted, and the target devices with distance constraints are merged to obtain a position merged data set, including: According to the coordinate information of each target device in the predicted coordinate data set, a pairwise target device combination is constructed to obtain a target pairing set; According to each pair of target devices in the target pairing set, the coordinate distance value between the coordinates is calculated to obtain a distance value data set; Based on the distance value dataset, all coordinate distance values ​​are classified according to a preset distance threshold, and target device pairs whose distance is not greater than the preset distance threshold are marked as valid merge pairs to obtain a valid merge pair set; According to the valid merged pairing set, multiple target devices with overlapping pairing relationships are connected and aggregated to obtain a merged target group set; Calculate the center of gravity coordinates of each group based on the predicted coordinates of each target device in the merged target group set to obtain a center of gravity coordinate set; The centroid coordinates of each group in the centroid coordinate set are used as representative coordinates to replace the coordinates of each target device in the original group, and a position merged data set is obtained.

5. A distributed multi-objective microwave transmission control method according to claim 4, characterized in that: Based on the beam candidate dataset and the predicted coordinate dataset, coordinates of each target device are compared to obtain direction offset data. This data is then compared with the preset offset threshold to obtain beam binding data, including: According to the coordinates of each beam direction in the beam candidate data set, the target device identifier associated therewith is extracted to obtain the beam target pairing data; According to the target device identifier in the beam target pairing table, the corresponding real-time coordinates in the predicted coordinate data set are extracted to obtain the target real-time coordinate data; According to the real-time coordinates of each beam direction and its associated target device, the space vector between the satellite and the target device is calculated to obtain the target direction vector dataset; According to the angle between each beam direction and the target direction vector, the spatial offset angle value is calculated to obtain the direction offset angle data set; By normalizing the spatial offset angle values ​​in the direction offset angle dataset, a standard offset dataset is obtained; According to the standard offset data set, the offset data of each target device is compared with the preset offset threshold, and the target devices that meet the offset threshold conditions are screened out to obtain the beam binding data.

6. A distributed multi-objective microwave transmission control method according to claim 5, characterized in that: Generate control instructions based on beam binding data and ensure that the beam adjusts the beam transmission direction according to the control instructions, including: According to the target device corresponding to each beam direction in the beam binding data, the transmission coordinates under each beam direction are extracted to obtain the control target data set; According to the control target data set, the spatial direction parameters of the launch coordinates of each target device relative to the satellite platform are calculated to obtain the direction parameter data set; According to the direction parameter data set, the spatial direction parameter of each target device is converted into a corresponding beam steering angle value to obtain a beam steering angle data set; According to the beam steering angle data set, the time tag is determined in combination with the current time node to obtain the control time synchronization data set; According to the control time synchronization data set, the beam number, control angle and time tag are determined, and the control instructions are generated. The control instructions are transmitted to the beam steering module in turn to adjust the beam transmission direction.

7. A distributed multi-target microwave transmission control system, characterized in that: The system is used to perform the method according to any one of claims 1 to 6, and the system comprises: The target trajectory module is used to obtain the geographic coordinate information of each target device and associate it with the device identification of each target device to construct the motion trajectory of each target device and obtain motion trajectory data; The predicted coordinate module is used to sort the coordinate information of each target device in the order of time nodes based on the motion trajectory data, and accumulate the coordinate differences between adjacent time nodes to obtain a predicted coordinate data set; The target merging module is used to extract the coordinate distance values ​​between target devices based on the predicted coordinate data set, group and count the coordinate distance values, and merge the target devices with distance constraints to obtain a position merged data set; The beam matching module is used to merge the data sets according to the location, extract the beam direction range information and beam angle interval parameters of the current time period, and match the index of each target device based on them to obtain the beam candidate data set; The beam binding module is used to compare the coordinates of each target device based on the beam candidate data set and the predicted coordinate data set to obtain direction offset data, and compare it with the preset offset threshold to obtain beam binding data; The control instruction module is used to generate control instructions according to the beam binding data and ensure that the beam adjusts the beam transmission direction according to the control instructions; The beam control module is used to adjust the beam transmission direction according to the control instructions.

8. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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