Beidou short message transmission timing control method based on multi-source perception and wave posture prediction

Abstract

The application provides a Beidou short message transmission opportunity control method based on multi-source perception and wave posture prediction, and belongs to the technical field of Beidou communication at sea. The method comprises the following steps: collecting state signals, motion data, signal strength indication data and Beidou satellite ephemeris data; determining a six-degree-of-freedom motion state sequence and a heave displacement time-domain waveform of a floating body based on the motion data, and inputting the six-degree-of-freedom motion state sequence and the heave displacement time-domain waveform into a preset floating body motion prediction model to obtain a predicted posture trajectory and a heave displacement extrapolation sequence; discriminating an antenna based on the predicted posture trajectory and the heave displacement extrapolation sequence, and taking a window in which the antenna is completely exposed to air medium as a candidate emergence window; calculating a geometric communication included angle between an antenna normal vector and a target Beidou satellite pointing vector according to the candidate emergence window and the Beidou satellite ephemeris data; generating a transmission enable signal according to the geometric communication included angle and a current time; and performing burst transmission of a short message data packet based on the transmission enable signal that passes the verification.

Description

Technical Field

[0001] This application relates to the field of maritime BeiDou communication technology, and in particular to a method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction. Background Technology

[0002] BeiDou short message communication, with its full coverage and independence from terrestrial networks, has become an important data transmission method for floating platforms such as ocean buoys, drifting marine exploration equipment, and unmanned vessels. However, floating platforms experience continuous six-degree-of-freedom swaying and heaving motions under wave action, causing the BeiDou antenna to be periodically submerged in seawater or covered by waves and water films. Seawater attenuates L-band radio frequency signals. Traditional fixed-period or timed transmission strategies trigger invalid transmissions when the antenna is submerged or blocked by water films. This not only leads to communication failures and data loss but also depletes the platform's limited power reserves due to frequent malfunctions of the radio frequency power amplifier.

[0003] Existing technologies mostly use a single water entry detection sensor for simple transmission blocking, which lacks the ability to predict the future motion attitude of the floating body, and does not consider the impact of the geometric communication angle between the antenna and the satellite on the link quality. They cannot actively predict and select the best launch window, and it is difficult to simultaneously meet the requirements of communication reliability and low power consumption in complex sea conditions. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction.

[0005] A first aspect of this application provides a method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction, comprising: The system collects status signals from water entry detection sensors installed on a floating platform, motion data from MEMS inertial sensors, signal strength indication data from the BeiDou receiving link, and BeiDou satellite ephemeris data. The motion data is subjected to attitude calculation and vertical motion time-frequency analysis to obtain the six-degree-of-freedom motion state sequence of the floating body and the time-domain waveform of the heave displacement. The six-degree-of-freedom motion state sequence of the floating body and the heave displacement time-domain waveform are input into the preset floating body motion prediction model to obtain the predicted attitude trajectory and heave displacement extrapolation sequence of the antenna radiating surface under wave drive. The antenna exposure state is determined based on the predicted attitude trajectory and heave displacement extrapolation sequence, and the window in which the antenna is completely exposed to the air medium is taken as the candidate water exit window; according to the candidate water exit window and the Beidou satellite ephemeris data, the geometric communication angle between the antenna normal vector and the target Beidou satellite pointing vector is calculated. When the geometric communication angle is less than the preset link budget threshold angle and the current time is within the candidate water outlet window, a transmission enable signal is generated; the transmission enable signal is verified based on the signal strength indication data and the status signal, and the transmission enable signal that passes the verification is used as the target transmission enable signal; In response to the target transmit enable signal, the radio frequency power amplifier is woken up to perform short message data packet transmission.

[0006] A second aspect of this application provides a BeiDou short message transmission timing control system based on multi-source sensing and wave attitude prediction, comprising: The multi-source data acquisition module is used to acquire the status signals of the water entry detection sensor installed on the floating platform, the motion data of the MEMS inertial sensor, the signal strength indication data of the Beidou receiving link, and the Beidou satellite ephemeris data. The attitude time-frequency calculation module is used to perform attitude calculation and vertical motion time-frequency analysis on the motion data to obtain the six-degree-of-freedom motion state sequence of the floating body and the heave displacement time-domain waveform. The floating body motion prediction module is used to input the six-degree-of-freedom motion state sequence and heave displacement time-domain waveform of the floating body into the preset floating body motion prediction model to obtain the predicted attitude trajectory and heave displacement extrapolation sequence of the antenna radiating surface under wave drive. The transmission window screening module is used to determine the antenna exposure state based on the predicted attitude trajectory and heave displacement extrapolation sequence, and to select the window in which the antenna is completely exposed to the air medium as a candidate water exit window; and to calculate the geometric communication angle between the antenna normal vector and the target Beidou satellite pointing vector based on the candidate water exit window and the Beidou satellite ephemeris data. The transmit enable verification module is used to generate a transmit enable signal when the geometric communication angle is less than a preset link budget threshold angle and the current time is within the candidate water outlet window; verify the transmit enable signal based on the signal strength indication data and the status signal, and take the transmit enable signal that passes the verification as the target transmit enable signal; The message burst transmission module is used to wake up the radio frequency power amplifier in response to the target transmit enable signal and perform short message data packet transmission.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction.

[0008] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction.

[0009] The beneficial effects of the BeiDou short message transmission timing control method based on multi-source sensing and wave attitude prediction provided in this application are as follows: This application constructs a triple judgment system of hardware sensing, motion prediction, and link verification by simultaneously collecting three types of multi-source data: water entry detection sensor, MEMS inertial sensor, and BeiDou receiving link. First, attitude calculation and time-frequency analysis are performed on the MEMS data to obtain the complete motion state of the floating body; then, the future attitude trajectory and heave displacement are extrapolated by the prediction model to achieve advance prediction of the antenna exit window; based on this, invalid windows that are not valid due to the antenna exiting the water but pointing away from the satellite are further filtered out according to the geometric communication angle constraint; finally, the transmission enable is verified a second time based on the signal strength indication data and status signal to ensure the accuracy and reliability of the transmission timing. In this application, the mutual verification of multi-source data, prediction, and experimental closed-loop verification effectively improve the transmission success rate of BeiDou short messages in wave environments and reduces ineffective power consumption by selectively waking up the power amplifier. Attached Figure Description

[0010] Figure 1 A flowchart illustrating the BeiDou short message transmission timing control method based on multi-source sensing and wave attitude prediction provided in an embodiment of this application. Figure 2 A structural block diagram of a BeiDou short message transmission timing control system with multi-source sensing and wave attitude prediction provided in an embodiment of this application; Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0011] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0012] To make the purpose, technical solution, and advantages of this application clearer, the following will be described in conjunction with the appendix. Figure 1 - Appendix Figure 3 The following is an explanation using specific examples.

[0013] Please refer to Figure 1 , Figure 1A flowchart illustrating a BeiDou short message transmission timing control method based on multi-source sensing and wave attitude prediction, provided in an embodiment of this application, is shown below. The method includes: S101: Collects status signals from water entry detection sensors installed on the floating platform, motion data from MEMS inertial sensors, signal strength indication data from the BeiDou receiving link, and BeiDou satellite ephemeris data.

[0014] In this embodiment, a floating platform refers to various types of unmanned marine equipment carriers that float on the sea surface and move with the waves, used to carry sensors and communication modules, such as marine monitoring buoys, drifting detection floats, and unmanned vessels. A water ingress detection sensor is a sensor installed near the antenna to detect whether the antenna is in contact with or submerged in water. Its status signal is used to indicate the abrupt change in the state of the medium in which the antenna is located. For example, when the sensor electrode or sensitive element of the water ingress detection sensor detects that a part of the antenna housing or the antenna radiator is submerged by a surge, the capacitance value output by the water ingress detection sensor in air and water will change abruptly, resulting in a status signal. A MEMS inertial sensor refers to an inertial measurement unit based on microelectromechanical systems technology, used to collect the motion parameters of the floating body, including a three-axis accelerometer and a three-axis gyroscope. Its motion data is used to represent the linear acceleration and angular velocity of the floating body in three-dimensional space, including six signals from the three-axis accelerometer and the three-axis gyroscope. The BeiDou receiving link refers to the radio frequency channel in a terminal used to receive downlink signals from BeiDou satellites. Its signal strength indicator (RSSI) value indicates the strength of the received signal and thus the current link quality. BeiDou satellite ephemeris data refers to parameter data describing the orbit and position of BeiDou satellites, used to calculate the satellite's position in space at any given time.

[0015] S102: Perform attitude calculation and vertical motion time-frequency analysis on the motion data to obtain the six-degree-of-freedom motion state sequence of the floating body and the time-domain waveform of the heave displacement.

[0016] In this embodiment, attitude calculation refers to the process of converting raw inertial measurement data into the spatial attitude of the floating body, used to solve for the orientation angle of the floating body relative to the geographic coordinate system. Vertical motion refers to the up-and-down undulation motion of the floating body along the vertical direction (gravity direction), which is the most significant motion component of the floating body under wave action. Time-frequency analysis refers to a method of analyzing signals simultaneously in the time and frequency domains, used to reveal the variation law of signal frequency components over time. The six-degree-of-freedom motion state of the floating body refers to the complete motion description of the floating body in three-dimensional space, including translational motion along the X, Y, and Z axes and rotational motion around the three axes, totaling six degrees of freedom. Motion state sequence refers to multiple sets of motion state data arranged in chronological order, constituting a continuous motion trajectory in the time dimension. Heave displacement refers to the displacement of the floating body in the vertical direction relative to the equilibrium position, used to quantify the amplitude of the up-and-down undulation of the floating body. Time-domain waveform refers to the continuous curve of heave displacement changing over time, representing the time history of the floating body's vertical motion.

[0017] Specifically, the motion data is first preprocessed by eliminating inherent sensor errors and random noise through zero-bias calibration, temperature compensation, and noise filtering. Then, an attitude calculation algorithm is used to fuse the three-axis acceleration and three-axis angular velocity data to calculate the three attitude angles of the float: roll, pitch, and yaw. The three-dimensional displacement obtained by acceleration integration constitutes the six-degree-of-freedom motion state. Next, the vertical component is extracted from the six-degree-of-freedom data, and the vertical acceleration is integrated twice to obtain the heave displacement. Then, the heave displacement signal is decomposed by time-frequency analysis, and characteristic parameters such as the main frequency, period, and amplitude of the waves are extracted.

[0018] For example, the preprocessed heave displacement time-domain waveform is first used as input for time-frequency analysis. A sliding time window of appropriate length is selected to segment and window the time-domain signal. The signal within each window is approximated as a stationary signal. A Fast Fourier Transform is performed on the heave displacement data within each window to obtain the spectral distribution corresponding to that time window. The spectra of all time windows are stitched together along the time axis to form a two-dimensional time-frequency energy distribution map, where the horizontal axis represents time, the vertical axis represents frequency, and the color intensity or numerical value represents the signal energy intensity at the corresponding time-frequency point. Subsequently, the frequency corresponding to the energy peak is searched in the spectrum corresponding to each time slice, and this frequency is marked as the wave dominance frequency at the current moment. The reciprocal of the dominance frequency is the wave period at that moment. At the same time, the wave amplitude at the corresponding moment is calculated based on the spectral amplitude at the dominance frequency and the window correction coefficient. The above extraction operation is performed window by window to finally obtain the wave dominance frequency sequence, wave period sequence, and wave amplitude sequence that change continuously with time. The Fast Fourier Transform (FFT) is used to transform a segment of time-domain heave displacement sampling data captured by a sliding time window from the time domain to the frequency domain. The input is the discrete heave displacement sampling points arranged chronologically within the sliding time window, representing the positional changes of the floating body as it rises and falls over time. The output is the complex spectrum corresponding to different frequency components of the signal segment. The modulus of the complex number represents the energy magnitude of the wave component at that frequency, and the phase represents the initial phase of the wave. The modulus value is calculated for each complex number to obtain the amplitude spectrum of each frequency component. The negative frequency portion is then discarded, retaining only the amplitude data in the positive frequency range. Simultaneously, the amplitude is normalized to obtain the spectral distribution with frequency as the horizontal axis and signal amplitude or energy as the vertical axis.

[0019] The wave main frequency sequence, wave period sequence, and wave amplitude sequence are used as wave feature parameters and matched one-to-one with the sampling timestamps of the MEMS inertial sensor. The extracted wave feature parameters at each moment are appended to the data field of the six-degree-of-freedom motion state sequence at the corresponding moment. The roll angle, pitch angle, yaw angle, and X, Y, and Z three-dimensional displacement data output by attitude calculation are kept unchanged. At the same time, the correspondence between the displacement values ​​of each sampling point of the heave displacement time domain waveform and the timestamp is maintained. The heave displacement discrete sampling sequence obtained by attitude calculation and double integration is sorted by sampling time. Finally, the output is a six-degree-of-freedom motion state sequence carrying wave feature parameters and sorted by timestamp, as well as the corresponding heave displacement time domain waveform.

[0020] The attitude calculation algorithm adopts a four-layer progressive hierarchical architecture. The first layer is the data preprocessing layer, which receives motion data output from MEMS inertial sensors, including six raw signals of three-axis acceleration and three-axis angular velocity, and completes basic corrections such as zero-bias calibration, temperature compensation, and noise filtering to eliminate inherent sensor errors and random interference. The second layer is the attitude angle calculation layer, which uses acceleration and angular velocity as inputs and solves the three attitude angles of the floating body through a multi-sensor fusion algorithm: roll angle, pitch angle, and yaw angle. The third layer is the displacement calculation layer, which separates the vertical and horizontal components from the acceleration data and obtains the three-dimensional displacement through a double integration operation. The fourth layer is the six-degree-of-freedom state output layer, which aligns and combines the attitude angles and three-dimensional displacements according to timestamps to output a complete six-degree-of-freedom motion state sequence of the floating body.

[0021] The algorithm employs a complementary framework of accelerometer static observation, gyroscope dynamic integration, and fusion correction. The gyroscope, with its high short-term accuracy and strong anti-interference capabilities, is suitable for short-term attitude dynamic integration; the accelerometer, with its long-term stability and lack of cumulative drift, can provide a gravity direction reference in static or slowly changing scenarios to correct for gyroscope integration drift. The two are combined through a fusion algorithm, utilizing the gyroscope's dynamic response to capture rapid bouncing of the floating body while leveraging the accelerometer's long-term stability to suppress drift.

[0022] S103: Input the six-degree-of-freedom motion state sequence of the floating body and the heave displacement time-domain waveform into the preset floating body motion prediction model to obtain the predicted attitude trajectory and heave displacement extrapolation sequence of the antenna radiating surface under wave drive.

[0023] In this embodiment, the six-degree-of-freedom motion state sequence of the floating body refers to a time-series dataset with a unified timestamp, output by attitude calculation. It represents the continuous motion parameters of the floating platform's translation along three axes and rotation around three axes, including roll, pitch, yaw angles, and X / Y / Z three-dimensional displacement time-series arrays. The heave displacement time-domain waveform refers to the continuous curve data of the floating body's vertical displacement changing over time, used to represent the variation law of the floating body's up-and-down undulation amplitude over time, such as a periodic sinusoidal time-series array. The preset floating body motion prediction model refers to a time-series prediction algorithm model with pre-initialized parameters adapted to the motion law of sea waves, used to infer future changes in floating body motion based on historical motion data. The antenna radiating surface refers to the effective radiation area of ​​the electromagnetic waves emitted by the BeiDou antenna, serving as the carrier for the external radiation of radio frequency signals, and is used to represent the effective radiation geometric plane of the antenna. Wave drive refers to the dynamic relationship of the continuous action of sea surface wave force on the floating body, causing undulation and swaying, and is used to represent the external excitation source of the floating body's motion. Predicted attitude trajectory refers to the continuous time-series data of the antenna's spatial orientation over a future period, used to represent the change curve of the antenna's attitude angle at each future moment. Heave displacement extrapolation sequence refers to the future vertical displacement time-series data extrapolated forward, used to represent the vertical displacement values ​​of the floating body at subsequent moments.

[0024] The pre-defined floating body motion prediction model consists of four processing layers. Specifically, the first layer is the time-series data normalization layer, which receives the six-degree-of-freedom motion state sequence of the floating body and the time-domain waveform of the heave displacement, and performs timestamp alignment, missing point completion, and data normalization. The second layer is the feature extraction layer, which mines the time-series features related to the floating body's attitude, heave displacement, and waves. The third layer is the future motion extrapolation layer, which performs the prediction calculation of the motion state in the future time period based on the time-series features. The fourth layer is the result mapping output layer, which denormalizes the normalized prediction results inside the floating body motion prediction model, maps them to physical quantities, and outputs the predicted attitude trajectory and heave displacement extrapolation sequence.

[0025] This model is constructed to address the non-stationary temporal motion characteristics of a floating body under wave excitation at sea. The basic model employs a lightweight long short-term memory network, integrating historical six-degree-of-freedom motion information with wave time-frequency characteristics of heave motion, using historical observation time series to drive future motion prediction. The model adopts a multi-input parallel temporal prediction architecture, with one input processing the floating body's rotational attitude time series and the other processing the heave displacement time series. Both branches extract temporal features through long short-term memory network units before undergoing feature fusion in a fully connected layer, taking into account the coupling relationship between the floating body's oscillating rotational motion and vertical undulation motion, and extrapolating the attitude angle and heave displacement respectively.

[0026] Key parameter settings for each layer of the model include network structure hyperparameters and training optimization parameters: For the feature extraction layer, the hidden layer dimension of the Long Short-Term Memory (LSTM) network is set, typically 32 or 64 to balance accuracy and computational power. The number of LSM network layers is set to 1 to 2, and the activation function is set to tanh. Simultaneously, the previously extracted wave main frequency, wave period, and wave amplitude are used as auxiliary feature inputs. The initial learning rate is set to 1e. -3 Configure the learning rate decay coefficient to gradually reduce the learning rate with each training round. Set the model iteration calculation step size to be consistent with the sensor sampling period. Select Adam as the optimizer during the training phase and set the mean squared error (MSE) as the loss function to measure the deviation between the predicted motion sequence and the actual observed sequence.

[0027] In one specific embodiment, the method further includes using an online clustering algorithm to summarize the current sea state category based on characteristic parameters such as the dominant frequency, period, and amplitude of the waves, and selecting a matching floating body motion prediction model according to the current sea state category. Specifically, recursive least squares harmonic fitting extrapolation is used under regular sea states, while a long short-term memory network is switched to under complex sea states. This long short-term memory network outputs a predicted sequence of the floating body's full attitude for the next 2-5 seconds, and uses the jump time of the water entry sensor and the rising edge inflection point of the RSSI as online truth feedback. The truth feedback and the predicted attitude trajectory are compared to obtain the error, which is then calibrated using error-driven long short-term memory network parameters.

[0028] Among them, the recursive least squares harmonic fitting extrapolation is the floating body motion prediction algorithm selected under regular sea conditions. This algorithm abstracts the floating body motion into a simple harmonic motion model of multi-order harmonic superposition. It uses the recursive least squares algorithm to iteratively update the amplitude and phase coefficients of the harmonics and relies on the forgetting factor to weaken the influence of long-term historical data. After completing the harmonic parameter fitting, it performs time extrapolation based on the harmonic model and outputs the predicted attitude trajectory and heave displacement extrapolation sequence. When the waves exhibit broken wave distortion or non-stationary motion, the harmonic assumption fails and the prediction is switched to a long short-term memory network.

[0029] For example, the actual floating body attitude and heave displacement obtained by MEMS calculation are used as the true labels and compared with the predicted attitude trajectory and predicted heave displacement. The mean square error (MSE) formula is used to calculate the loss error, which represents the degree of deviation between the prediction result of the floating body motion prediction model and the actual motion. The loss error is propagated back from the fourth layer result mapping output layer, through the third layer future motion inference layer, and then back to the second layer feature extraction layer. Following the reverse path of the network data flow, the total loss is distributed to each LSTM unit of the feature extraction layer and the future motion inference layer, obtaining the contribution of each parameter in each layer to the loss of the final prediction error.

[0030] Based on the loss contribution obtained from backpropagation, the gradients of the weight matrices and bias vectors corresponding to the input gates, forget gates, and output gates of the LSTM network units in the feature extraction layer and future motion inference layer are calculated respectively. The gradients characterize the degree of influence of the direction and magnitude of parameter adjustment on reducing prediction error. Subsequently, the Adam optimizer is called to perform gradient moment estimation on the original gradients, update the two sets of historical statistics of first-order momentum and second-order momentum, and smooth the instantaneous gradients. The learning rate is multiplied by the Adam-smoothed effective gradients to obtain the parameter correction amount corresponding to each set of weight matrices and each bias vector. Then, the original parameters are superimposed with the parameter correction amount to complete the local update of the weight matrices and bias vectors of the input gates, forget gates, and output gates of the LSTM units in the feature extraction layer and future motion inference layer. During the update process, the network structure hyperparameters such as hidden layer dimension, number of network layers, and tanh activation function remain unchanged.

[0031] S104: The antenna exposure state is determined based on the predicted attitude trajectory and heave displacement extrapolation sequence, and the window in which the antenna is completely exposed to the air medium is taken as the candidate water exit window; according to the candidate water exit window and Beidou satellite ephemeris data, the geometric communication angle between the antenna normal vector and the target Beidou satellite pointing vector is calculated.

[0032] In this embodiment, the antenna exposure state refers to the state of the medium in which the BeiDou antenna radiating surface is located. This indicates whether the antenna is submerged in seawater, partially wetted, or completely exposed to air. Examples include three states: submerged, partially exposed, and fully exposed. The air medium refers to the atmospheric environment. The candidate water exit window refers to a continuous predicted time period, representing the time interval during which the antenna is completely out of the seawater and in the air environment, such as a continuous time segment from 10.2s to 12.5s. The antenna normal vector is a unit spatial vector perpendicular to the antenna radiating surface, representing the direction of maximum electromagnetic wave radiation from the antenna, such as a vector composed of the X, Y, and Z axes in the carrier coordinate system. The target BeiDou satellite refers to the BeiDou communication satellite selected by the current terminal for short message uplink communication, used to carry the message data reception task. The satellite pointing vector is a unit spatial vector pointing from the floating antenna position to the target BeiDou satellite, representing the satellite's spatial orientation relative to the floating body. The geometric communication angle is the angle between the antenna normal vector and the satellite pointing vector in three-dimensional space, representing the degree to which the antenna beam is aligned with the satellite.

[0033] Specifically, the process iterates through each timestamped sampling point in the predicted attitude trajectory and heave displacement extrapolation sequence, reads the vertical displacement value of the floating body at the corresponding moment, and calculates the spatial vertical position of the antenna radiating surface based on the inherent installation height of the antenna relative to the floating body carrier. Moments when the antenna radiating surface's vertical position is above the sea surface reference plane are marked as fully exposed, while moments below this reference plane are determined to be submerged. Subsequently, discrete moments of full exposure are merged into continuous intervals to obtain continuous time intervals where the antenna is fully exposed to the air medium throughout its entire duration. These continuous time intervals are used as candidate emergence windows. The inherent installation height is a fixed vertical offset of the BeiDou antenna radiating surface relative to the floating platform carrier's reference origin. This value is determined by the hardware mechanical installation structure and is a constant parameter determined at the factory.

[0034] S105: When the geometric communication angle is less than the preset link budget threshold angle and the current time is within the candidate water outlet window, generate a transmission enable signal; verify the transmission enable signal based on the signal strength indication data and status signal, and use the transmission enable signal that passes the verification as the target transmission enable signal.

[0035] In this embodiment, the link budget threshold angle refers to a pre-calibrated critical angle parameter used to represent the maximum allowable angle for meeting the minimum communication quality of BeiDou short messages. The current time is used to determine whether the current location falls within the antenna's water exit range. The transmit enable signal is a low-level command output by the main control unit, used to indicate that basic transmit conditions are met. Verification refers to a double-condition check of the initially generated transmit enable signal, used to eliminate false transmit commands caused by prediction deviations and wave surges / water films. The target transmit enable signal is a valid hardware control command that has undergone double-layer verification by the water entry sensor and RSSI link, used to indicate that all communication transmit conditions are met and BeiDou uplink transmission is allowed to begin.

[0036] The link budget threshold angle is the maximum geometric communication angle between the antenna normal vector and the satellite pointing vector that allows the BeiDou antenna to meet the minimum received power requirements when conducting short message communication. The radiation gain at different deviation angles of the antenna is obtained through anechoic chamber measurements, and the maximum deviation angle corresponding to the gain covering the link loss and reaching the demodulation threshold is taken as the link budget threshold angle.

[0037] S106: In response to the target transmit enable signal, wake up the radio frequency power amplifier and execute the transmission of short message data packets.

[0038] In this embodiment, the radio frequency (RF) power amplifier is a power amplification device in the RF path of the BeiDou terminal. It refers to a hardware module that amplifies the baseband-modulated RF signal to the power required for satellite reception, thereby increasing the electromagnetic wave radiation power. For example, an L-band RF power amplifier chip. "Wake-up" refers to the operation of switching the RF power amplifier from a low-power sleep state to a normal operating power supply state, used to remove power-saving restrictions such as power-off and bias shutdown. The short message data packet refers to a structured business data frame to be uploaded to the BeiDou satellite. It is a fully encapsulated communication data carrier used to carry business information such as marine monitoring and buoy positioning. "Transmission" refers to the complete uplink transmission process of modulating and amplifying the complete short message data packet and radiating it to the BeiDou communication satellite through the antenna, used to complete data interaction between the terminal and the satellite.

[0039] In one specific embodiment, when prediction residuals are detected in MEMS inertial sensing, water entry detection, and link layer sensing, and these residuals continuously exceed a preset residual threshold, the current multi-source fusion mode is deemed to have failed, triggering a sensor fault degradation mode. In this sensor fault degradation mode, the arbitration logic for candidate water exit windows based on wave attitude prediction is blocked, and instead, only the signal strength indicator data (RSSI) value from the BeiDou receiver link is used as the sole criterion for determining whether the antenna is exposed to the water surface. Once the RSSI value recovers from the deep fading region and exceeds the preset water exit recovery threshold, the RF power amplifier is immediately activated, and burst transmission of short message data packets is executed. The water surface recovery threshold is determined by using the baseline RSSI value of satellite signals received by the antenna in a dry, interference-free environment, and calibrating it based on the measured attenuation of BeiDou frequency band radio frequency signals by seawater film. The water surface recovery threshold is set higher than the deep fading threshold, forming a hysteresis interval between the two to prevent frequent fluctuations in the RSSI value between the water surface recovery threshold and the deep fading threshold, which could cause repeated jumps in the transmit enable. During calibration, the demodulation sensitivity of the BeiDou receiver chip is also considered, ensuring that the link margin is sufficient to support short message uplink demodulation when the RSSI value reaches the water surface recovery threshold. Furthermore, if the RSSI fails to meet the surface exposure condition within the set maximum waiting time, a forced transmission attempt is initiated to avoid communication interruptions caused by extreme environments or sensor malfunctions, further enhancing the robustness of the terminal under complex operating conditions.

[0040] As can be seen from the above, this application constructs a triple judgment system of hardware perception, motion prediction, and link verification by simultaneously collecting multi-source data from three types of data: water entry detection sensors, MEMS inertial sensors, and BeiDou receiving links. First, attitude calculation and time-frequency analysis are performed on the MEMS data to obtain the complete motion state of the floating body. Then, by extrapolating the future attitude trajectory and heave displacement through a prediction model, the antenna exiting the water window is predicted in advance. Based on this, invalid windows where the antenna exits the water but its pointing direction deviates from the satellite are further filtered out according to geometric communication angle constraints. Finally, the transmission enable is verified a second time based on signal strength indication data and status signals, ensuring the accuracy and reliability of the transmission time. The mutual verification of multi-source data and the closed-loop verification of prediction and actual measurement in this application effectively improve the success rate of BeiDou short message transmission in wave environments and reduce ineffective power consumption by selectively waking up the power amplifier.

[0041] In one embodiment of this application, the geometric communication angle between the antenna normal vector and the target BeiDou satellite pointing vector is calculated based on the candidate water outlet window and BeiDou satellite ephemeris data, including: Obtain the start and end times of the candidate water outlet windows, and sample multiple time nodes within the time interval from the start time to the end time according to a preset first time interval; Based on the predicted attitude trajectory, the antenna normal vectors corresponding to multiple time nodes are calculated respectively, and the azimuth and elevation angles of the antenna normal vectors in the geographic coordinate system are used to construct the antenna pointing sequence of each time node. Based on BeiDou satellite ephemeris data, the satellite pointing vectors of the target BeiDou satellites at multiple time points are calculated respectively. Calculate the spatial angle between the antenna pointing vector and the satellite pointing vector at each of the multiple time points to obtain the geometric communication angle corresponding to each time point; The geometric communication angle corresponding to each time node is compared with the preset link budget threshold angle, and the continuous time intervals where the geometric communication angle is less than the link budget threshold angle are marked as valid transmission sub-windows; The geometric communication angles of each time node in the effective transmission sub-window are weighted and scored according to their deviation from the link budget threshold angle, and the time node with the highest score is taken as the optimal transmission time. The optimal launch time is verified to obtain the optimal launch time for the target. The start and end times of the effective transmission sub-window, as well as the target's optimal transmission time, are output as the solution results of the geometric communication angle.

[0042] In this embodiment, the start and end times of candidate water exit windows are obtained, and the total duration of the candidate water exit windows is evaluated. If the window duration is less than the preset minimum effective window threshold, the candidate water exit window is determined to have no transmission value, and subsequent sampling and calculation processes are not executed. If the window duration meets the requirements, multiple time nodes are sampled within the time interval from the start to the end time according to the preset first time interval. The preset minimum effective window threshold is the minimum time length required to determine whether a candidate water exit window has BeiDou short message transmission value. This minimum effective window threshold is determined based on the actual radio frequency continuous working time required for the transmission of a single BeiDou short message; the longer the radio frequency continuous working time, the larger the minimum effective window threshold. The preset first time interval is the sampling step size for sampling and calculating the geometric communication angle at each time node within the time interval of the candidate water exit window, determining the density of sampling points within the window. This time interval is set according to the rate of change of the floating body's wave motion and the computing power overhead of the BeiDou terminal. The faster the floating body's attitude changes due to severe sea conditions, the smaller the time interval is selected to capture rapid attitude changes.

[0043] Based on the predicted attitude trajectory, the antenna normal vectors for each of the multiple time points are calculated. During the calculation, a direction cosine matrix is ​​constructed from the carrier coordinate system to the geographic coordinate system based on the buoy's roll, pitch, and bow angles at each time point. Then, the fixed-installation normal vector of the antenna in the carrier coordinate system is multiplied by the direction cosine matrix to complete the coordinate transformation, obtaining the three-component expression of the antenna normal vector at each time point in the geographic coordinate system. The azimuth and elevation angles of the antenna normal vector in the geographic coordinate system are used to construct the antenna pointing sequence for each time point. The azimuth angle is the angle between the projection of the normal vector onto the horizontal plane and true north, and the elevation angle is the angle between the normal vector and the horizontal plane. These two angles completely describe the antenna pointing in polar coordinates and are arranged sequentially according to the time points to obtain the antenna pointing sequence.

[0044] In this embodiment, based on the orbital elements in the BeiDou satellite ephemeris data, the three-dimensional position coordinates of the satellite at each time node are recursively derived using the Kepler orbital equations in the geocentric geofixed coordinate system. Then, based on the current latitude, longitude and altitude of the floating body, the satellite position is converted to the station-centric geographic coordinate system. The unit vector pointing to the satellite position with the phase center of the floating body antenna as the origin is the satellite pointing vector at that moment. Its three vector components correspond to the eastward, northward and zenithal components, respectively, and are in the same coordinate system as the antenna normal vector.

[0045] The spatial angle between the antenna pointing vector and the satellite pointing vector at each of multiple time points is calculated to obtain the geometric communication angle corresponding to each time point. The formula for the dot product of two unit vectors is used, where the cosine of the angle is equal to the sum of the products of the corresponding components of the two vectors. The geometric communication angle is then obtained through inverse cosine calculation, with the result expressed in degrees, ranging from 0° to 180°. The geometric communication angle corresponding to each time point is compared with a preset link budget threshold angle. Continuous time intervals where the geometric communication angle is less than the link budget threshold angle are marked as valid transmission sub-windows. A candidate water exit window may contain one or more valid transmission sub-windows.

[0046] The geometric communication angle at each time point within the effective transmission sub-window is weighted and scored based on its deviation from the link budget threshold angle. Specifically, a linear weighted scoring function is used for quantification calculation. The scoring formula is: Score = (Link budget threshold angle - Current geometric communication angle) / Link budget threshold angle × 100, where the link budget threshold angle serves as the scoring benchmark. For every 1 degree the geometric communication angle deviates from the threshold angle, the score changes linearly. During calculation, the geometric communication angle values ​​at each time point are first taken and substituted into the formula to calculate a standardized score within the range of 0 to 100 points. A score of 100 points is given when the angle is 0°, a score of 0 points is given when the geometric communication angle is equal to the link budget threshold angle, and a negative score is given when the geometric communication angle is greater than the link budget threshold angle, and the angle is directly marked as invalid. For example, when the link budget threshold angle is set to 60°, if the geometric communication angle at a certain time node is 15°, the score for that node is (60-15) / 60×100=75 points; if the geometric communication angle at another node is 30°, the score is (60-30) / 60×100=50 points. The smaller the geometric communication angle, the higher the score, and the more sufficient the link margin, the higher the priority. The system iterates through the scores of all time nodes within the effective transmission sub-window, selecting the time node with the highest score as the optimal transmission time. If multiple nodes have the same highest score, the node in the middle of the window is selected to reserve more sufficient forward and backward transmission margin. The optimal transmission time is then verified. If the verification passes, the target optimal transmission time is obtained. If the verification fails, the next best score node is selected for re-verification until a time that meets the conditions is found. Finally, the start and end times of the effective transmission sub-window, as well as the target optimal transmission time, are output as the result of the geometric communication angle calculation. The output results, along with corresponding geometric communication angle values ​​and score values ​​at each time point, are stored in a dedicated result cache for subsequent read and call operations such as launch enable generation and message transmission scheduling. Upon completion of the calculation for each candidate water outlet window, the result cache is immediately updated and subsequent processing is triggered to ensure that the end-to-end latency from prediction to execution meets real-time requirements, allowing all preparatory work to be completed before the optimal launch time arrives.

[0047] In this embodiment, multiple time points are sampled within the candidate water exit window, discretizing the continuous time window into a granularity that can be calculated point by point. The antenna normal vector is calculated point by point based on the predicted attitude trajectory, and a pointing sequence is constructed. Simultaneously, the satellite pointing vector is calculated point by point based on BeiDou satellite ephemeris data. By solving for the geometric spatial angle between the antenna normal vector and the target BeiDou satellite pointing vector, the refinement and comprehensiveness of the geometric communication quality assessment are improved. Continuous intervals with geometric spatial angles less than a threshold are marked as valid transmission sub-windows. Weighted scoring is then applied based on the degree of deviation of the geometric spatial angle, and the optimal transmission time with the highest score is selected. This ensures a wide range of possible transmission opportunities and allows for the selection of the moment with the best link conditions to trigger transmission, maximizing the communication margin and reliability of a single transmission.

[0048] In one embodiment of this application, verifying the optimal launch time to obtain the target optimal launch time includes: Obtain the data packet length of the short message data packet to be transmitted and the nominal transmission rate of the BeiDou short message communication link. Calculate the shortest time required to complete the transmission based on the data packet length and the nominal transmission rate. Based on the predicted attitude trajectory, the angular velocity vector of the antenna normal vector at each time node in the effective transmission sub-window is calculated; Calculate the rate of change of the angle between the antenna pointing vector and the satellite pointing vector at each time point based on the angular velocity vector; Time nodes with positive angle change rates and rates greater than a preset rate of change threshold are filtered out to obtain the first candidate launch time set; For each time node in the first candidate launch time set, calculate the remaining time between each time node and the end time of the effective launch sub-window, and filter out time nodes whose remaining time is less than the shortest time required to complete the launch to obtain the second candidate launch time set. Determine if the optimal launch time is within the set of second candidate launch times. If it is, then the optimal launch time is taken as the target optimal launch time.

[0049] In this embodiment, the data packet length of the short message data packet to be transmitted and the nominal transmission rate of the BeiDou short message communication link are first obtained. Based on the data packet length and the nominal transmission rate, the shortest time required to complete the transmission is calculated. For example, the data packet length is counted in bits, including the total number of bits of all fields such as frame header, payload data, checksum, and frame trailer. The nominal transmission rate is the fixed symbol rate of the BeiDou short message uplink. Dividing the two gives the theoretical transmission time. On this basis, additional time is reserved for hardware overhead such as RF power amplifier wake-up and stabilization, modulator startup, and frame synchronization establishment, as well as link fluctuation redundancy time, to finally obtain the shortest time required to complete the transmission. The required shortest time changes dynamically with the data packet length, and different shortest time thresholds correspond to different service types and different data volumes of messages. The additional reserved RF power amplifier refers to the additional hardware waiting time consumed by the RF power amplifier to wake up from the dormant state and stabilize the output power when calculating the shortest time required to complete the transmission. The specific time parameter is determined by historical experience.

[0050] Secondly, the algorithm iterates through all timestamped time nodes within the effective transmission sub-window, reads the quaternions or attitude angle sequences of the float at the corresponding node and adjacent sampling times in the predicted attitude trajectory, and uses a finite difference differential solution to numerically differentiate the relationship between the antenna normal vector and time. Alternatively, it can utilize the attitude angular velocity in the float's carrier coordinate system and transform it to the geographic coordinate system using a coordinate transformation matrix to derive the angular velocity vector of the antenna normal vector at each time node. Simultaneously, it filters outliers from the calculation results, eliminating singular solutions caused by attitude jumps. Finally, it outputs the angular velocity vector of the antenna normal vector at each time node, including all axial components. The coordinate transformation matrix is ​​a direction cosine matrix that enables the mutual conversion of attitude information between the carrier coordinate system and the geographic coordinate system. This direction cosine matrix is ​​calculated from the float's roll, pitch, and bow attitude angles at the corresponding time and is dynamically generated in real time according to the attitude angles at each time node of the predicted attitude trajectory. It can convert and map the attitude angular velocity components in the carrier coordinate system to the geographic coordinate system.

[0051] Subsequently, the angular velocity vector is decomposed within the plane formed by the antenna pointing vector and the satellite pointing vector. The angular velocity component perpendicular to the antenna pointing vector and located within this plane is extracted. This angular velocity component determines the rate of change of the angle between the two vectors. Alternatively, the rate of change of the angle over time can be obtained by performing a difference operation on the geometric communication angles at adjacent time points. A positive sign indicates that the angle is increasing and the alignment effect is deteriorating, while a negative sign indicates that the angle is decreasing and the alignment effect is improving. The unit of the rate of change of the angle is degrees per second, quantitatively describing the changing trend and rate of change in the alignment relationship between the antenna and the satellite.

[0052] In this embodiment, all nodes with a negative or zero angle change rate are retained, indicating that the alignment is improving or remaining stable. Nodes with a positive angle change rate but less than or equal to a threshold are also retained, indicating that the alignment is deteriorating but at an acceptable rate. Only nodes with a positive angle change rate greater than a preset change rate threshold are filtered out, indicating that the alignment is deteriorating uncontrollably and will fall out of the effective angle range during launch. The preset change rate threshold is a critical judgment parameter used to filter launch times, representing the maximum allowable rate of deterioration of the angle between the antenna and the satellite's geometric communication, measured in degrees per second. This preset change rate threshold is determined based on historical sea state attitude sway measurement data, the shortest time required to complete message transmission, the angular margin of the link budget threshold angle, and the experience of engineering experts.

[0053] The remaining time is the difference between the end time of the window and the current node's time, representing the duration the effective transmission sub-window can continue after transmission starts at that time. If the remaining time is less than the minimum transmission time, it means the window will end before the message is completely sent, and the antenna may re-enter the water or the geometric communication angle may exceed the link budget threshold angle, leading to transmission interruption. All nodes in the second candidate transmission time set after two levels of screening simultaneously meet the constraints of stable attitude change and sufficient window duration, thus possessing the time conditions for reliable transmission.

[0054] Finally, it is determined whether the optimal launch time is in the set of second candidate launch times. If so, the optimal launch time is taken as the target optimal launch time. During the determination, timestamp matching is used to check whether the time value of the optimal launch time falls within the node set of the second candidate launch time set. If the match is successful, it means that the optimal time has both the best geometric communication angle and satisfies the dual constraints of transmission duration and attitude change rate. The optimal time is then locked as the target optimal launch time.

[0055] In this embodiment, the optimal launch time is verified based on the dual constraints of the shortest time required to complete the launch and the rate of change of the angle, thus avoiding false positives where the angle is optimal but unusable. First, the shortest launch time is calculated based on the data packet length and nominal rate, ensuring sufficient margin for the selected time to complete the full message transmission. Then, the rate of change of the angle at each node is calculated using the angular velocity vector, filtering out time nodes with positive angle changes exceeding a preset threshold to avoid phase deterioration of the antenna attitude. Finally, a second level of filtering is performed using the remaining time constraint, forming a second set of candidate launch times. This two-level filtering gradually converges, ensuring that the final target optimal launch time possesses both optimal geometric communication conditions and sufficient time window margin and stable attitude trend, fundamentally reducing message truncation and communication failures caused by mid-launch window failures.

[0056] In one embodiment of this application, a BeiDou short message transmission timing control method based on multi-source sensing and wave attitude prediction further includes: Acquire the measured motion data of the MEMS inertial sensor within the nearest preset time period before the optimal launch time. Using the measured motion data as anchor points, correct the predicted attitude trajectory within a preset time window around the optimal launch time to obtain the corrected local predicted attitude trajectory. The corrected local predicted attitude trajectory is used to replace the trajectory data of the corresponding time interval in the predicted attitude trajectory to obtain the updated predicted attitude trajectory. Based on the updated predicted attitude trajectory, the optimal launch time is determined until the optimal launch time enters the second candidate launch time set.

[0057] In this embodiment, measured motion data from the MEMS inertial sensor within the nearest preset time period before the optimal launch time is acquired. Using this measured motion data as anchor points, the predicted attitude trajectory is corrected within a preset time window surrounding the optimal launch time, resulting in a corrected local predicted attitude trajectory. The preset time period is a continuous sampling interval from 0.3 seconds to 1.5 seconds before the optimal launch time, and the interval length is adaptively adjusted according to sea conditions. For example, in calm waves, the interval can be shortened to ensure timeliness, while in violent waves with high randomness, the interval can be widened to capture the complete motion trend. Before use, the measured motion data undergoes outlier removal and sliding filtering preprocessing to eliminate outliers caused by sensor jitter and electromagnetic interference, ensuring the reliability of the anchor point data.

[0058] In this embodiment, the trajectory data for the corresponding time interval in the predicted attitude trajectory is replaced with the corrected local predicted attitude trajectory to obtain the updated predicted attitude trajectory. Before replacement, the continuity of the boundary between the corrected trajectory and the predicted trajectory is checked, and the jump variables of attitude angle and angular velocity at the boundary points are checked to see if they are within a physically reasonable range. If the jump variables are too large, several sampling points are taken on both sides of the boundary to construct a transition interval, and linear weighting or spline interpolation is used to achieve a smooth transition to avoid unnatural abrupt changes in the trajectory. After replacement, the updated predicted attitude trajectory is obtained.

[0059] Based on the updated predicted attitude trajectory, the optimal launch time is determined until it enters the second candidate launch time set. Specifically, the effective launch sub-window division, geometric communication angle weighted scoring, and two-level candidate set screening process are re-executed to calculate the updated optimal launch time based on the updated trajectory. Then, the updated optimal launch time is matched and verified against the second candidate launch time set. If the updated optimal launch time falls within the second candidate launch time set, the iteration converges, and the updated optimal launch time is output as the final target optimal launch time. If it still does not fall within the set, the next round of correction is initiated, and the latest measured motion data is read again to update the anchor point, repeating the closed-loop process of trajectory correction, replacement, and re-solution. A maximum number of iterations is set. If convergence is not achieved after reaching the upper limit, the time with the highest score from the second candidate launch time set is selected as the downgraded output. The maximum number of iterations is determined with reference to the historical iteration convergence statistics of marine engineering measurements.

[0060] In this embodiment, a closed-loop optimization mechanism of prediction-verification-correction-repreneurial is constructed by using measured motion data as anchor points to locally correct the predicted trajectory. When the optimal launch time obtained in the first solution does not meet the constraints of the second candidate launch time set, it is not directly abandoned or downgraded. Instead, the local predicted trajectory is corrected using measured data before the optimal time, the data in the corresponding time interval of the original trajectory is replaced, and the optimal launch time is solved again. This process is iterated repeatedly until the constraints are met. This rolling trajectory correction mechanism effectively compensates for the cumulative error and random deviation of the floating body motion prediction model. Especially in scenarios with non-stationary waves and sudden changes in sea state, it can continuously calibrate the predicted trajectory through measured anchor points, ensuring that the optimal launch time always remains within the usable constraint range, effectively improving the accuracy and robustness of launch timing prediction under complex sea conditions.

[0061] In one embodiment of this application, using measured motion data as anchor points, the predicted attitude trajectory is corrected within a preset time window surrounding the optimal launch time to obtain a corrected local predicted attitude trajectory, including: Obtain the measurement error distribution parameters of the MEMS inertial sensor, and generate multiple anchor point assumptions around the measured motion data based on the measurement error distribution parameters; Using each anchor point assumption as an anchor point, the trajectory of the predicted attitude trajectory is corrected within a preset time window around the optimal launch time, generating multiple corrected local predicted attitude trajectory assumptions, which constitute a family of local predicted attitude trajectory assumptions. For each local predicted attitude trajectory assumption, the antenna exposure state is re-evaluated based on the predicted attitude trajectory and heave displacement extrapolation sequence, and the optimal transmission time under each assumption is determined. The optimal transmission time under each assumption is evaluated to see if it enters the second candidate transmission time set. From the local predicted attitude trajectory assumptions of the second candidate launch time set under each assumption, the assumption with the smallest overall deviation from the predicted attitude trajectory is selected as the corrected local predicted attitude trajectory.

[0062] In this embodiment, the measurement error distribution parameters of the MEMS inertial sensor include statistical characteristics such as zero-bias noise, angle random walk, and acceleration noise. These statistical characteristics are obtained through factory calibration, power-on self-calibration, or online statistics during operation, and are used to characterize the random fluctuation range of the sensor output data. Based on this error distribution, sampling is performed to generate multiple sets of probabilistically meaningful anchor point hypothesis values ​​within the numerical neighborhood of the measured motion data. During the generation process, the anchor point hypothesis values ​​are constrained to not exceed the reasonable range of the sensor's physical output, and abnormal hypotheses that violate the dynamics of the floating body's motion are eliminated. For example, if the generated anchor point hypothesis value shows that the roll angle of the floating body jumps from -10° to +45° in a very short single sampling interval, this angle change is much greater than the maximum angular acceleration that a floating body can reach under wave excitation at sea, which does not conform to the dynamic constraints of the floating body's motion inertia under the action of ocean waves, and is therefore an abnormal hypothesis that violates the dynamics of the floating body's motion.

[0063] Each anchor point hypothesis is independently processed in a local trajectory correction operation. The correction algorithm follows a pre-defined trajectory compensation logic, completing the compensation and correction of attitude amplitude, phase, and rate of change within a pre-defined time window surrounding the optimal launch time. Each round of calculation outputs an independent corrected local predicted attitude trajectory hypothesis. All trajectory hypotheses are collectively set to form a family of local predicted attitude trajectory hypotheses. This family of local predicted attitude trajectory hypotheses includes various possible motion scenarios caused by MEMS measurement errors, including trajectories close to the original measured motion data and deviation scenarios under sensor noise disturbances, providing multiple candidate schemes for subsequent screening. Simultaneously, all trajectory hypotheses are aligned on the time axis. The pre-defined trajectory compensation logic uses the anchor point hypothesis as a truth constraint to locally correct the attitude amplitude, phase, and angular velocity of the predicted attitude trajectory within a pre-defined time window surrounding the optimal launch time, keeping the predicted attitude trajectory outside the window unchanged. Using the time corresponding to the anchor point as a reference, it adjusts the attitude angle change trend of each sampling point within the window, while constraining the range of attitude angle and angular acceleration changes to not exceed the dynamic boundary of the floating body. Finally, it performs smooth connection processing on the beginning and end boundaries of the window, thus outputting the locally corrected trajectory after anchor point constraints. The preset surrounding time window is a continuous time interval that extends forward and backward from the optimal launch time. It defines the local time range in which trajectory correction needs to be carried out. This preset surrounding time window needs to cover the time interval from the MEMS measured anchor point time to the optimal launch time, the shortest time required to complete the message transmission, and is determined comprehensively based on the floating body attitude change rate. The forward time length needs to include the time range corresponding to the measured anchor point used for correction, and the backward time length needs to be at least greater than the total time taken for the complete message transmission.

[0064] In this embodiment, each trajectory hypothesis within the local predicted attitude trajectory hypothesis family is traversed, keeping the heave displacement extrapolation sequence unchanged, and the corresponding attitude trajectory data is replaced to completely reproduce the antenna exposure state discrimination process, dividing the updated candidate water emergence window and effective transmission sub-window. Next, the geometric communication angle is calculated and weighted, solving for the optimal transmission time under this hypothesis. Then, a two-level screening based on the angle change rate and remaining transmission time is performed again to reconstruct the second candidate transmission time set, determining whether the optimal transmission time obtained by this hypothesis meets the set admission conditions. The evaluation results of all hypotheses are marked and archived, distinguishing between feasible and infeasible hypotheses, and filtering out trajectory hypotheses whose optimal transmission time cannot enter the second candidate transmission time set. A feasible hypothesis means that the optimal transmission time obtained by this hypothesis can enter the second candidate transmission time set, satisfying all transmission time constraints; an infeasible hypothesis means that the optimal transmission time obtained by this hypothesis is not in the second candidate transmission time set, and the conditions for direct scheduling of transmission are not met.

[0065] Among all feasible assumptions, the one with the smallest overall deviation from the predicted attitude trajectory is selected as the corrected local predicted attitude trajectory. During the overall deviation calculation, the difference between the candidate trajectory assumption and each attitude component of the predicted attitude trajectory is calculated point-by-point across all sampling points within a preset time window surrounding the optimal launch time. The cumulative deviation over the entire time window is obtained by summing the values ​​using the L2 norm, thus quantifying the degree of deviation between the two trajectories. Among all feasible trajectory assumptions, the one with the smallest cumulative overall deviation is selected. This assumption satisfies all constraints at the launch time while preserving the trend of the predicted attitude trajectory to the greatest extent possible, avoiding over-correction that introduces additional distortion. This trajectory is ultimately determined as the corrected local predicted attitude trajectory.

[0066] In this embodiment, by introducing MEMS sensor measurement error distribution parameters, a multi-hypothesis trajectory correction framework is constructed, solving the problem of single-anchor-point correction being susceptible to measurement noise interference. Based on the error distribution, multiple anchor-point hypothesis values ​​are generated around the measured motion data. Each hypothesis value independently performs a local trajectory correction, forming a family of trajectory hypotheses. Then, for each hypothetical trajectory, antenna exposure discrimination and optimal transmission time are re-executed to evaluate whether it satisfies the constraints of the second candidate transmission time set. Finally, the hypothesis with the smallest overall deviation from the predicted trajectory is selected from all satisfying conditions as the correction result. This probabilistic multi-hypothesis correction strategy fully incorporates the uncertainty of sensor noise, avoids trajectory correction deviation caused by single measurement anomalies, and maximizes the overall consistency of the predicted trajectory while ensuring the effectiveness of the correction, thus improving the anti-interference capability and result reliability of the correction process.

[0067] In one embodiment of this application, the geometric communication angle corresponding to each time node is compared with a preset link budget threshold angle, and the continuous time interval where the geometric communication angle is less than the link budget threshold angle is marked as a valid transmission sub-window, including: The candidate water outlet window is used as the first physical exposure window; The geometric communication angle corresponding to each time node is compared with the preset link budget threshold angle to obtain the time interval where the geometric communication angle is less than the link budget threshold angle. Based on the time interval, the first layer of physical exposure window is filtered to obtain the second layer of geometric effective sub-window. Based on the heave displacement extrapolation sequence and wave period prediction, the interference time period accompanied by wave slapping or water film coverage in the candidate water outlet window is determined. The time interval falling into the interference time period is removed in the second layer of geometrically effective sub-window to obtain the third layer of usable emission sub-window. The third-level available launch sub-window is used as the valid launch sub-window.

[0068] In this embodiment, the candidate water exit window is first used as the first physical exposure window. The first physical exposure window is a continuous time period during which the antenna is completely exposed to air, determined solely by the heave displacement extrapolation sequence. This first physical exposure window only filters for seawater immersion conditions, ensuring that the antenna radiating surface is theoretically removed from the water body, without considering link losses caused by complex sea conditions such as antenna pointing, wave splashing, and surface water film adhesion. Within the same candidate water exit window, there may be various periods unfavorable to communication, such as mixed antenna beam pointing deviating from the satellite, waves hitting the antenna, and residual water film on the antenna surface.

[0069] Secondly, the geometric communication angles corresponding to each time node are compared with the link budget threshold angle to obtain the time intervals where the geometric communication angle is less than the link budget threshold angle. Time intervals that simultaneously satisfy both the time interval and the first-layer physical exposure window are then selected to obtain the second-layer geometrically effective sub-window. The second-layer geometrically effective sub-window, obtained after this layer of selection, simultaneously satisfies the two basic conditions of antenna theoretical water exposure and geometric alignment between the antenna and the target BeiDou satellite. However, it still does not eliminate the signal attenuation issues caused by wave impact and water film coverage on the antenna surface.

[0070] Based on the heave displacement extrapolation sequence and wave period prediction, interference time periods accompanied by wave splashing or water film coverage are identified within the candidate water outlet window. Time intervals falling into these interference time periods are removed from the second-layer geometrically effective sub-window to obtain the third-layer usable emission sub-window. The interference time periods and the second-layer geometrically effective sub-window are then subtracted along the time axis. If any time intervals in the second-layer geometrically effective sub-window overlap with the interference time periods, the overlapping time segments are completely removed. After this removal, the third-layer usable emission sub-window is obtained.

[0071] The third available transmission sub-window is used as the effective transmission sub-window. After three layers of progressive filtering, the effective transmission sub-window simultaneously meets three constraints: the antenna is completely exposed to the air medium, the angle between the antenna normal vector and the satellite's geometric communication meets the link budget requirements, and there are no periods of interference from wave impact or water film coverage. This third available transmission sub-window has completed multi-dimensional constraint filtering based on physical medium, geometric pointing, and surge interference. If no effective transmission sub-window is found after layer-by-layer screening, the transmission scheduling for this window is directly abandoned, and the system waits for the candidate water exit window in the next round of prediction output.

[0072] In this embodiment, a three-layer progressively filtering window mechanism is constructed to gradually converge candidate water-exit windows from physically usable to actually usable, effectively improving the purity of the launch window. The first layer, the physical exposure window, only ensures that the antenna is exposed above the water surface, which is the most basic prerequisite for launch. The second layer introduces geometric communication angle constraints to eliminate time periods when the antenna pointing away from the satellite, ensuring that the link budget meets communication requirements. The third layer further eliminates interference periods caused by wave impact and water film coverage, solving the hidden attenuation problem caused by the antenna being exposed but covered by water. The three layers of windows filter progressively from wide to narrow and from coarse to fine, with each layer eliminating a specific type of interference factor. The final output third-layer usable launch sub-window simultaneously meets the triple conditions of physical exposure, geometric alignment, and absence of surface water film interference, minimizing the risk of signal attenuation during launch and further effectively improving the success rate of a single launch.

[0073] In one embodiment of this application, the time period of interference accompanied by wave pounding or water film coverage within the candidate water outlet window is predicted based on the heave displacement extrapolation sequence and wave period, including: The curvature change sequence of the heave displacement extrapolation sequence is obtained by performing a second-order difference operation on the heave displacement extrapolation sequence. Based on the curvature change sequence, the inflection point where the heave displacement curve in the candidate outlet window changes from concave upward to convex upward is identified, and the inflection point is taken as the starting phase point of surge interference. Based on the wave cycle, the heave displacement extrapolation sequence within the candidate water outlet window is divided into multiple wave cycle subsequences, and the waveform symmetry index of the rising and falling segments on both sides of the wave crest is calculated for each wave cycle subsequence. When the waveform symmetry index shows a continuous deviation from the symmetry state in the wave crest descent segment, the time period in the wave crest descent segment that produces the continuous deviation is marked as the water film coverage interference segment. Taking the starting phase point of surge interference as the starting point and the end point of the water film covering interference segment in each wave cycle as the end point, the time interval between the starting phase point of surge interference and the end point of the water film covering interference segment is taken as the interference time period.

[0074] In this embodiment, a second-order difference operation is first performed on the extrapolation sequence of heave displacement to obtain the curvature change sequence of the heave displacement curve. A second-order difference calculation is then performed on the curvature change sequence; the result of the second-order difference represents the magnitude and direction of curvature of the heave displacement curve. Finally, the curvature change sequence is output, with each sampling point carrying the sign and amplitude information of curvature at the corresponding time.

[0075] Secondly, all curvature change sequence sampling points within the candidate water exit window time range are traversed to determine the curvature sign reversal event in the curvature change sequence. Specifically, a concave upward curve corresponds to a positive curvature value, and a convex upward curve corresponds to a negative curvature value. When the curvature continuously crosses zero from positive to negative, the position of this zero point is determined to be the inflection point where the curve changes from concave to convex upward. This inflection point represents the phase where the wave begins to recede and the spray begins to hit the antenna after the buoy reaches the wave crest; therefore, the inflection point is used as the starting phase point of surge interference.

[0076] In this embodiment, based on the identified wave crest position, the time sequence of the candidate water outlet window is divided into independent wave cycle subsequences according to the wave cycle duration. Each subsequence corresponds to one complete wave undulation motion. Within each wave cycle subsequence, the wave crest moment is located, and the rising and falling segments are separated using the wave crest moment as the dividing point. An interval of equal duration before and after the wave crest is selected as the comparison interval, and waveform symmetry indices are obtained through comparison of displacement amplitude and rate of change. The waveform symmetry index is used to quantify the degree of waveform overlap between the rising and falling segments.

[0077] When the waveform symmetry index shows a sustained deviation from symmetry during the descent phase of the wave crest, the time period during which this sustained deviation occurs is marked as a water film interference period. Specifically, a sustained deviation from symmetry is determined only when the waveform symmetry index of M consecutive sampling points within the descent phase exceeds a preset deviation threshold. A sustained deviation indicates waveform distortion after the wave crest, with the surge spreading along the float and antenna surface, resulting in a thin film of seawater adhering to the antenna surface, which attenuates the BeiDou radio frequency signal. The time interval between the first occurrence of the deviation exceeding the threshold and the moment the deviation returns to within the threshold during the descent phase is marked as a water film interference period. The preset deviation threshold is a critical parameter used to determine whether waveform distortion and water film interference occur during the descent phase. This threshold is obtained based on measured waveform samples of heave displacement in marine scenarios and statistical analysis of waveform distortion when the antenna is affected by water film interference.

[0078] In this embodiment, if multiple wave cycle subsequences are included within the same candidate water outlet window, the starting phase point of the surge interference and the ending point of the water film coverage interference segment are determined for each cycle, and the corresponding interference time period is generated segment by segment.

[0079] In this embodiment, the accuracy of identifying surge interference and water film coverage periods is improved through second-order difference curvature analysis and waveform symmetry detection of the heave displacement extrapolation sequence. First, the second-order difference of the heave displacement sequence is performed to obtain a curvature change sequence. The starting phase of the surge interference is located by identifying the inflection point where the concave direction changes to the convex direction, capturing the starting point of the wave impact on the antenna. Then, the heave displacement extrapolation sequence is segmented according to the wave period, and the waveform symmetry index of each wave crest's rising and falling segments is calculated. When a continuous asymmetric deviation occurs in the falling segment, it is determined to be a water film coverage interference segment. The interval from the start point of the surge interference to the end point of the water film is marked as the interference time period, thus physically reconstructing the interference process of surge impact - water film adhesion - gradual dissipation. This interference identification method based on waveform morphology characteristics provides a reliable basis for the third-layer window screening.

[0080] In one embodiment of this application, the transmit enable signal is verified based on signal strength indication data and status signals, and the transmit enable signal that passes the verification is used as the target transmit enable signal, including: The status signal of the water entry detection sensor is detected. When a medium change signal indicating that the antenna is partially or completely submerged in water is detected, a water entry status flag is generated. Determine the validity of the water entry status flag. When the water entry status flag is valid, forcibly deny the transmission enable signal and prohibit the transmission enable signal from being used as the target transmission enable signal until the water entry status flag changes from valid to invalid. When the water entry status flag is invalid, read the current RSSI value of the Beidou receiving link; The current RSSI value is compared with the preset deep fading threshold. If the current RSSI value is less than the deep fading threshold, it is determined that there is water film covering or broken wave interference on the antenna surface. The transmit enable signal is temporarily suspended as the target transmit enable signal, and the RSSI value is continuously monitored within the current candidate water exit window until the RSSI value rises back to greater than or equal to the deep fading threshold, or the current candidate water exit window ends. If the current RSSI value is greater than or equal to the deep fading threshold, the verification is deemed successful, and the transmit enable signal is used as the target transmit enable signal.

[0081] In this embodiment, the status signal of the water immersion detection sensor is detected. When a medium change signal indicating partial or complete immersion of the antenna in water is detected, a water immersion status flag is generated. The water immersion detection sensor, based on capacitive or conductive principles, is installed near the antenna's radiating surface. Its status signal is output in the form of analog voltage or digital level and is continuously polled and detected by the main control chip at a high sampling frequency. When the antenna moves from air to water, the sensor output value will change; this change is the medium change signal. Once a signal change matching the medium change characteristics is detected, the corresponding water immersion status flag is immediately set. The flag is stored in the control register as a Boolean value to indicate the current immersion state of the antenna.

[0082] In this embodiment, the validity of the water immersion status flag is checked, and this check takes precedence over all other verification conditions. As long as the water immersion status flag is valid, the transmission path is blocked regardless of whether other conditions such as geometric communication angle or RSSI are met. The forced rejection mechanism is implemented through hardware logic or a high-priority interrupt, with response latency controlled at the microsecond level. This ensures that the transmission enable is immediately cut off the moment the antenna enters the water, preventing the RF power supply from continuing to operate while submerged, thus avoiding power waste and device damage. The rejection state is maintained until the water immersion detection sensor detects that the antenna has left the water again and the water immersion status flag changes from valid to invalid. In this case, the rejection state is lifted, allowing subsequent verification processes to continue.

[0083] Specifically, the RSSI value of the BeiDou receiver link is read. The current RSSI value is sampled by the received signal strength detection circuit inside the BeiDou receiver chip, and is expressed in dBm, representing the actual power level of the downlink signal received by the antenna from the BeiDou satellite. If the BeiDou receiver link tracks multiple satellites simultaneously, the RSSI of the satellite with the strongest signal can be selected as the current RSSI value, or the average value of the RSSIs of multiple satellites can be used as the current RSSI value, thus improving the reliability of the judgment.

[0084] The current RSSI value is compared with the deep fading threshold. If the current RSSI value is less than the deep fading threshold, it is determined that there is water film covering the antenna surface or interference from broken waves. The transmit enable signal is temporarily suspended as the target transmit enable signal, and the RSSI value is continuously monitored within the current candidate water exit window until the RSSI value rises to a level greater than or equal to the deep fading threshold, or the current candidate water exit window ends. The deep fading threshold is calibrated based on the normal RSSI level in a dry environment and the attenuation characteristics of seawater, serving as a critical value for judging abnormal signal attenuation. When the RSSI is less than the deep fading threshold, it indicates that although the antenna is fully above water, the surface is still covered by a water film or is being battered by broken waves, and the radio frequency signal will still suffer severe attenuation. In this case, the transmission success rate is low, so the transmit enable signal is temporarily suspended. During the suspension period, RSSI is continuously sampled and monitored, waiting for the water film to evaporate or the broken waves to dissipate. If the RSSI rises to the target level before the current candidate water exit window ends, the verification process resumes; if it does not rise by the end of the window, the transmission opportunity for this window is abandoned. Among them, the deep fading threshold is the critical threshold of RSSI used to determine whether the Beidou antenna is significantly attenuated by water film or broken waves. The unit is dBm. The deep fading threshold is obtained by superimposing the measured attenuation of the Beidou frequency band radio frequency signal by the baseline RSSI value of the antenna receiving satellite signals in a dry and interference-free environment.

[0085] If the current RSSI value is greater than or equal to the deep fading threshold, the verification is considered successful. When both the water entry status flag and the RSSI compliant condition are met simultaneously, both levels of verification pass, and the transmit enable signal is finally confirmed and marked as a valid target transmit enable signal. The target transmit enable signal is immediately latched into the control register after it is generated, and the subsequent RF power amplifier wake-up and message transmission process is triggered simultaneously.

[0086] In one specific embodiment, the method further includes: when the water ingress status flag is determined to be a false alarm and is blocked, determining that the water ingress detection sensor has a persistent fault; in response to the determination of the persistent fault, triggering a sensor fault degradation mode, in which the hard rejection function of the water ingress detection sensor's status signal for the transmission enable signal verification is blocked, and the comparison result between the RSSI value and the deep fading threshold is used as the sole basis for the transmission enable signal verification.

[0087] Specifically, based on the status signal of the water ingress detection sensor, the heave displacement data calculated by the MEMS inertial sensor, and the RSSI value of the BeiDou receiving link, when the water ingress detection sensor outputs a valid water ingress flag, it simultaneously verifies whether the heave displacement data shows that the antenna radiating surface is higher than the sea surface reference plane and whether the RSSI value is maintained within the receiving range in a dry environment. If both auxiliary criteria indicate that the antenna is in the water out state, the water ingress flag is determined to be a false alarm and temporarily blocked; if the number of false alarms continues, the water ingress detection sensor is determined to have a persistent fault. In response to the determination of a persistent fault, the sensor fault degradation mode is triggered. In this mode, the hard rejection function of the water ingress detection sensor's status signal for the transmission enable signal verification is blocked. That is, the water ingress detection sensor's status signal is no longer used as a rejection condition for transmission enable. The hardware interrupt triggering and status reading process of the sensor are turned off. Only the comparison result of the RSSI value and the deep fading threshold is used as the basis for transmission enable signal verification. At the same time, the fault status is written to the equipment operation log and reported to the remote monitoring platform through a subsequently successfully sent Beidou short message, reminding the maintenance personnel that there is a sensor abnormality in the equipment, which will be repaired and replaced during the next maintenance.

[0088] In this embodiment, a final safety gate is constructed for the transmit enable signal through dual hardware verification using a water immersion detection sensor and RSSI signal strength, effectively preventing invalid transmissions caused by prediction misjudgments. The water immersion status flag serves as the highest priority hard veto signal; once antenna immersion is detected, transmission is immediately and forcibly blocked, physically eliminating power waste from underwater transmission. After the antenna emerges from the water, a second level of verification is performed using the RSSI value from the BeiDou receiving link. If the RSSI is less than the deep fading threshold, it is determined that there is still water film or wave interference on the antenna surface, transmission is temporarily suspended, and monitoring continues until the signal recovers. These two levels of verification, one hardware and one software, complement each other. While the water immersion sensor has a fast response speed, it cannot distinguish water films; RSSI can represent the actual link quality but has a lag. The combination of these two ensures both real-time response and improved accuracy, further enhancing the reliability of the final output target transmit enable signal.

[0089] In one embodiment of this application, continuously monitoring the RSSI value within the current candidate effluent window includes: Based on the wave cycle, determine the phase distribution of the interference time period within each wave cycle, and identify the waveform characteristics of the interference intensity changing with the phase within the interference time period. Based on the waveform characteristics, the point where the interference intensity is at its minimum within the interference time period is marked as the preferred phase point for RSSI sampling. Align the RSSI value sampling and monitoring time to the preferred phase point of RSSI sampling for each wave cycle; Continuous monitoring of RSSI values ​​is performed at the aligned sampling time.

[0090] In this embodiment, the wave period is obtained from the heave displacement extrapolation sequence through spectral analysis or zero-crossing detection. Based on this, the current candidate outlet window is divided into several independent wave period units. For each wave period unit, the interference time period is mapped onto the phase axis of the corresponding wave period to obtain the phase distribution of the interference time period, and to determine the starting phase, ending phase, and phase proportion of the interference within a complete wave period. Furthermore, by analyzing the second-order difference results of the heave displacement and the variation law of the waveform symmetry index, the waveform characteristics of the interference intensity caused by wave pounding and water film adhesion during the interference time period are analyzed as a function of phase. These waveform characteristics represent the complete process of the interference gradually increasing, reaching a peak, and then gradually decreasing.

[0091] Based on waveform characteristics, the point of minimum interference intensity within the interference period is marked as the preferred phase point for RSSI sampling. Specifically, the point of minimum interference intensity represents the phase position where the wave impact is weakest and the water film thickness on the antenna surface is thinnest within the interference period. At this position, the BeiDou receiving link is least affected by surge interference, and the RSSI value can more accurately represent the antenna's potential communication capability.

[0092] In this embodiment, during the identification of minimum points, the local minimum location of the interference intensity is searched within the phase interval of the interference time period based on the changing trend of waveform characteristics, and this phase location is marked as the preferred phase point for RSSI sampling. There may be one or more preferred RSSI sampling phase points within a single interference time period.

[0093] The RSSI sampling monitoring time is aligned to the preferred RSSI sampling phase point of each wave cycle. Using the predicted time-phase mapping relationship as a bridge, each preferred RSSI sampling phase point is converted into a corresponding absolute timestamp. Based on the absolute timestamp, an internal timer or hardware sampling trigger unit is configured to adjust the RSSI sampling timing, abandoning the uniform interval sampling mode, so that the sampling trigger time corresponds one-to-one with the converted timestamp of each wave cycle. If the time interval between the preferred RSSI sampling phase points in different wave cycles is not uniform, the sampling trigger time interval is adjusted so that each RSSI acquisition action is aligned with the preferred RSSI sampling phase, thus achieving alignment between sampling time and phase.

[0094] Continuous monitoring of RSSI values ​​is performed at the aligned sampling time. For example, within the time frame of the current candidate water outlet window, the BeiDou receiving link is triggered to read RSSI values ​​only at the aligned sampling time. After each sampling result is acquired, the current RSSI value is compared with a deep fading threshold. If the sampled RSSI value meets the condition, the monitoring process can end; if it is still less than the deep fading threshold, the process continues to wait for the next aligned sampling time. When the end time of the current candidate water outlet window is reached, regardless of the RSSI monitoring result, the continuous monitoring of RSSI values ​​for this round is terminated, and the process no longer waits for subsequent phase points.

[0095] In this embodiment, non-uniform intelligent sampling is achieved by aligning the RSSI sampling time to the phase point where the wave interference intensity is minimized, thus balancing monitoring accuracy and power efficiency. First, the phase distribution of the interference period is determined based on the wave cycle, and the waveform characteristics of the interference intensity changing with the phase are extracted to locate the optimal phase point with the weakest interference in each cycle. Then, the RSSI sampling times are concentrated and aligned to these optimal phase points, resulting in a non-uniform temporal distribution of sampling. This phase-aligned sampling strategy avoids strong interference phases such as wave surges and the thickest water film, reading RSSI at the weakest interference moment. This provides a more accurate representation of the link quality when the antenna is completely dry, avoiding misjudgment of continuous deep fading due to the sampling time coinciding with the interference peak. Simultaneously, non-uniform sampling reduces the total number of samples, further reducing the computational and power consumption overhead of the monitoring process, making it suitable for long-term battery-powered offshore deployment scenarios.

[0096] Corresponding to the BeiDou short message transmission timing control method based on multi-source sensing and wave attitude prediction in the above embodiment, Figure 2 This is a structural block diagram of a BeiDou short message transmission timing control system based on multi-source sensing and wave attitude prediction, provided as an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The BeiDou short message transmission timing control system 20, which integrates multi-source sensing and wave attitude prediction, includes: a multi-source data acquisition module 21, an attitude time-frequency calculation module 22, a floating body motion prediction module 23, a transmission window screening module 24, a transmission enable verification module 25, and a message burst transmission module 26.

[0097] Among them, the multi-source data acquisition module 21 is used to acquire the status signal of the water entry detection sensor installed on the floating platform, the motion data of the MEMS inertial sensor, the signal strength indication data of the Beidou receiving link, and the Beidou satellite ephemeris data. The attitude time-frequency calculation module 22 is used to perform attitude calculation and vertical motion time-frequency analysis on the motion data to obtain the six-degree-of-freedom motion state sequence of the floating body and the heave displacement time-domain waveform. The floating body motion prediction module 23 is used to input the six-degree-of-freedom motion state sequence of the floating body and the heave displacement time-domain waveform into the preset floating body motion prediction model to obtain the predicted attitude trajectory and heave displacement extrapolation sequence of the antenna radiating surface under wave drive. The launch window screening module 24 is used to determine the antenna exposure state based on the predicted attitude trajectory and heave displacement extrapolation sequence, and to take the window in which the antenna is completely exposed to the air medium as the candidate water exit window; according to the candidate water exit window and Beidou satellite ephemeris data, the geometric communication angle between the antenna normal vector and the target Beidou satellite pointing vector is calculated. The transmit enable verification module 25 is used to generate a transmit enable signal when the geometric communication angle is less than the preset link budget threshold angle and the current time is within the candidate water outlet window; the transmit enable signal is verified based on the signal strength indication data and the status signal, and the transmit enable signal that passes the verification is used as the target transmit enable signal; The message burst transmission module 26 is used to wake up the radio frequency power amplifier in response to the target transmission enable signal and perform short message data packet transmission.

[0098] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned device embodiments, for example... Figure 2 The functions of the multi-source data acquisition module 21, attitude time-frequency calculation module 22, floating body motion prediction module 23, transmission window screening module 24, transmission enable verification module 25, and message burst transmission module 26 are shown.

[0099] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit, or it may be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0100] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display, a speaker, etc.

[0101] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0102] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in any embodiment of the BeiDou short message transmission timing control method for multi-source sensing and wave attitude prediction provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0103] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to implement these processes. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0104] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc., provided on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0105] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0108] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0109] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0110] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction, characterized in that, include: The system collects status signals from water entry detection sensors installed on a floating platform, motion data from MEMS inertial sensors, signal strength indication data from the BeiDou receiving link, and BeiDou satellite ephemeris data. The motion data is subjected to attitude calculation and vertical motion time-frequency analysis to obtain the six-degree-of-freedom motion state sequence of the floating body and the time-domain waveform of the heave displacement. The six-degree-of-freedom motion state sequence of the floating body and the heave displacement time-domain waveform are input into the preset floating body motion prediction model to obtain the predicted attitude trajectory and heave displacement extrapolation sequence of the antenna radiating surface under wave drive. The antenna exposure state is determined based on the predicted attitude trajectory and heave displacement extrapolation sequence, and the window in which the antenna is completely exposed to the air medium is taken as the candidate water exit window; according to the candidate water exit window and the Beidou satellite ephemeris data, the geometric communication angle between the antenna normal vector and the target Beidou satellite pointing vector is calculated. When the geometric communication angle is less than the preset link budget threshold angle, and the current time is within the candidate water outlet window, a transmission enable signal is generated; The transmit enable signal is verified based on the signal strength indication data and the status signal, and the transmit enable signal that passes the verification is taken as the target transmit enable signal; In response to the target transmit enable signal, the radio frequency power amplifier is woken up to perform short message data packet transmission.

2. The method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction according to claim 1, characterized in that, The geometric communication angle between the antenna normal vector and the target BeiDou satellite pointing vector, based on the candidate water outlet window and the BeiDou satellite ephemeris data, includes: Obtain the start and end times of the candidate water outlet window, and sample multiple time nodes at a preset first time interval within the time interval from the start time to the end time; Based on the predicted attitude trajectory, the antenna normal vector corresponding to each of the multiple time nodes is calculated respectively, and the azimuth and elevation angles of the antenna normal vectors in the geographic coordinate system are used to construct the antenna pointing sequence of each time node. Based on the BeiDou satellite ephemeris data, the satellite pointing vectors of the target BeiDou satellites at each of the multiple time points are calculated respectively. Calculate the spatial angle between the antenna pointing vector and the satellite pointing vector for each of the multiple time nodes to obtain the geometric communication angle corresponding to each time node; The geometric communication angle corresponding to each time node is compared with the preset link budget threshold angle, and the continuous time intervals in which the geometric communication angle is less than the link budget threshold angle are marked as valid transmission sub-windows; The geometric communication angle of each time node in the effective transmission sub-window is weighted and scored according to the degree of deviation of the link budget threshold angle, and the time node with the highest score is taken as the optimal transmission time. The optimal launch time is verified to obtain the target's optimal launch time; The start and end times of the effective transmission sub-window, as well as the optimal transmission time of the target, are output as the calculation results of the geometric communication angle.

3. The method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction according to claim 2, characterized in that, The step of verifying the optimal launch time to obtain the target optimal launch time includes: Obtain the data packet length of the short message data packet to be transmitted and the nominal transmission rate of the BeiDou short message communication link. Calculate the shortest time required to complete the transmission based on the data packet length and the nominal transmission rate. Based on the predicted attitude trajectory, calculate the angular velocity vector of the antenna normal vector at each time node in the effective transmission sub-window; Based on the angular velocity vector, calculate the rate of change of the angle between the antenna pointing vector and the satellite pointing vector at each time point; By filtering out time nodes where the angle change rate is positive and the change rate is greater than a preset change rate threshold, a first candidate launch time set is obtained; For each time node in the first candidate launch time set, calculate the remaining time between each time node and the end time of the effective launch sub-window, and filter out time nodes whose remaining time is less than the shortest time required to complete the launch, to obtain the second candidate launch time set. Determine whether the optimal launch time is within the second candidate launch time set. If it is, then the optimal launch time is taken as the target optimal launch time.

4. The BeiDou short message transmission timing control method based on multi-source sensing and wave attitude prediction according to claim 3, characterized in that, Also includes: The measured motion data of the MEMS inertial sensor within the nearest preset time period before the optimal launch time is obtained. Using the measured motion data as anchor points, the predicted attitude trajectory is corrected within a preset time window around the optimal launch time to obtain the corrected local predicted attitude trajectory. The corrected local predicted attitude trajectory is used to replace the trajectory data of the corresponding time interval in the original predicted attitude trajectory to obtain the updated predicted attitude trajectory. Based on the updated predicted attitude trajectory, the optimal launch time is determined until the optimal launch time enters the second candidate launch time set.

5. The BeiDou short message transmission timing control method based on multi-source sensing and wave attitude prediction according to claim 4, characterized in that, The step of using the measured motion data as anchor points to correct the predicted attitude trajectory within a preset time window around the optimal launch time, to obtain the corrected local predicted attitude trajectory, includes: The measurement error distribution parameters of the MEMS inertial sensor are obtained, and multiple anchor point assumptions are generated around the measured motion data based on the measurement error distribution parameters. Using each anchor point assumption as an anchor point, the predicted attitude trajectory is corrected within a preset time window around the optimal launch time, generating multiple corrected local predicted attitude trajectory assumptions, which constitute a family of local predicted attitude trajectory assumptions. For each local predicted attitude trajectory assumption, the antenna exposure state is re-executed based on the predicted attitude trajectory and heave displacement extrapolation sequence to determine the optimal transmission time under each assumption, and it is evaluated whether the optimal transmission time under each assumption enters the second candidate transmission time set. From the local predicted attitude trajectory assumptions that make the optimal launch time enter the reconstructed second candidate launch time set, the assumption with the smallest overall deviation from the predicted attitude trajectory is selected as the corrected local predicted attitude trajectory.

6. The method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction according to claim 2, characterized in that, The step of comparing the geometric communication angle corresponding to each time node with a preset link budget threshold angle, and marking the continuous time interval where the geometric communication angle is less than the link budget threshold angle as a valid transmission sub-window, includes: The candidate water outlet window is used as the first physical exposure window; The geometric communication angle corresponding to each time node is compared with the preset link budget threshold angle to obtain the time interval where the geometric communication angle is less than the link budget threshold angle. Based on the time interval, the first layer of physical exposure window is filtered to obtain the second layer of geometric effective sub-window. Based on the heave displacement extrapolation sequence and wave period prediction, the interference time period accompanied by wave slapping or water film coverage in the candidate water outlet window is determined. The time interval falling into the interference time period is removed in the second layer of geometrically effective sub-window to obtain the third layer of usable emission sub-window. The available emitter sub-windows of the third layer are used as the effective emitter sub-windows.

7. The method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction according to claim 6, characterized in that, Based on the heave displacement extrapolation sequence and wave period, the predicted interference time period within the candidate water outlet window, accompanied by wave splashing or water film coverage, includes: The second-order difference operation is performed on the extrapolated heave displacement sequence to obtain the curvature change sequence of the heave displacement curve. Based on the curvature change sequence, the inflection point where the heave displacement curve in the candidate outlet window changes from concave upward to convex upward is identified, and the inflection point is taken as the starting phase point of surge interference. Based on the wave cycle, the heave displacement extrapolation sequence within the candidate outlet window is divided into multiple wave cycle subsequences, and the waveform symmetry index of the rising and falling segments on both sides of the wave crest is calculated for each wave cycle subsequence. When the waveform symmetry index shows a continuous deviation from the symmetry state in the wave crest descent segment, the time period in the wave crest descent segment that produces the continuous deviation is marked as the water film coverage interference segment. Taking the surge interference starting phase point as the starting point and the water film covering interference segment ending point in each wave cycle as the ending point, the time interval between the surge interference starting phase point and the water film covering interference segment ending point is taken as the interference time period.

8. The method for controlling the timing of BeiDou short message transmission based on multi-source sensing and wave attitude prediction according to claim 6, characterized in that, The step of verifying the transmit enable signal based on the signal strength indication data and the status signal, and using the verified transmit enable signal as the target transmit enable signal, includes: The status signal of the water entry detection sensor is detected, and when a medium change signal indicating that the antenna is partially or completely submerged in water is detected, a water entry status flag is generated. Determine the validity of the water entry status flag bit. When the water entry status flag bit is valid, forcibly reject the transmission enable signal and prohibit the transmission enable signal from being used as the target transmission enable signal until the water entry status flag bit changes from valid to invalid. When the water entry status flag is invalid, read the current RSSI value of the Beidou receiving link; The current RSSI value is compared with a preset deep fading threshold. If the current RSSI value is less than the deep fading threshold, it is determined that there is water film covering or broken wave interference on the antenna surface. The transmit enable signal is temporarily suspended as the target transmit enable signal, and the RSSI value is continuously monitored within the current candidate water exit window until the RSSI value rises back to a level greater than or equal to the deep fading threshold, or the current candidate water exit window ends. If the current RSSI value is greater than or equal to the deep fading threshold, the verification is deemed successful, and the transmit enable signal is used as the target transmit enable signal.

9. The BeiDou short message transmission timing control method based on multi-source sensing and wave attitude prediction according to claim 8, characterized in that, The step of continuously monitoring the RSSI value within the current candidate effluent window includes: Based on the wave cycle, determine the phase distribution of the interference time period within each wave cycle, and identify the waveform characteristics of the interference intensity changing with the phase within the interference time period. Based on the waveform characteristics, the point where the interference intensity is at its minimum within the interference time period is marked as the preferred phase point for RSSI sampling. The sampling and monitoring time of the RSSI value is aligned to the preferred phase point of RSSI sampling in each wave cycle, so that each RSSI sampling is non-uniformly distributed in time and concentrated at the preferred phase point; The RSSI value is continuously monitored at the aligned sampling time.

10. A BeiDou short message transmission timing control system based on multi-source sensing and wave attitude prediction, characterized in that, include: The multi-source data acquisition module is used to acquire the status signals of the water entry detection sensor installed on the floating platform, the motion data of the MEMS inertial sensor, the signal strength indication data of the Beidou receiving link, and the Beidou satellite ephemeris data. The attitude time-frequency calculation module is used to perform attitude calculation and vertical motion time-frequency analysis on the motion data to obtain the six-degree-of-freedom motion state sequence of the floating body and the heave displacement time-domain waveform. The floating body motion prediction module is used to input the six-degree-of-freedom motion state sequence and heave displacement time-domain waveform of the floating body into the preset floating body motion prediction model to obtain the predicted attitude trajectory and heave displacement extrapolation sequence of the antenna radiating surface under wave drive. The transmission window screening module is used to determine the antenna exposure state based on the predicted attitude trajectory and heave displacement extrapolation sequence, and to select the window in which the antenna is completely exposed to the air medium as a candidate water exit window; and to calculate the geometric communication angle between the antenna normal vector and the target Beidou satellite pointing vector based on the candidate water exit window and the Beidou satellite ephemeris data. The transmit enable verification module is used to generate a transmit enable signal when the geometric communication angle is less than a preset link budget threshold angle and the current time is within the candidate water outlet window. The transmit enable signal is verified based on the signal strength indication data and the status signal, and the transmit enable signal that passes the verification is taken as the target transmit enable signal; The message burst transmission module is used to wake up the radio frequency power amplifier in response to the target transmit enable signal and perform short message data packet transmission.

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