Dynamic attitude calibration methods, devices, electronic equipment, storage media, and products

Abstract

This invention provides a method, apparatus, electronic device, storage medium, and product for dynamic attitude calibration, relating to the field of satellite communication technology. The method includes: acquiring user-perceived data based on distributed devices, and determining terminal attitude data based on the user-perceived data; wherein, the distributed devices refer to devices composed of multiple devices that perceive user data; determining the current relative direction of the satellite interacting with the terminal; determining an attitude prediction result based on the terminal attitude data, the current relative direction, and a policy gradient algorithm; determining a dynamic attitude calibration strategy based on the attitude prediction result, and calibrating the terminal's attitude relative to the satellite based on the dynamic attitude calibration strategy. The technical solution of this invention is applicable to the continuous process of voice communication between a user's handheld terminal and a satellite, dynamically sensing terminal attitude data, and realizing dynamic attitude calibration of the terminal relative to the satellite.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and in particular to a dynamic attitude calibration method, apparatus, electronic device, storage medium, and product. Background Technology

[0002] Satellite communication is irreplaceable in emergency rescue, maritime operations, and outdoor communication scenarios, but signal attenuation caused by terminal mobility remains a core bottleneck restricting user experience. When users move their handheld terminals (such as walking or turning around), even slight changes in the device's spatial orientation can cause the antenna beam to misalign with the satellite, resulting in a significant decrease in signal strength, call stuttering, or even interruption.

[0003] Currently, among existing technologies, satellite communication attitude calibration technology mainly uses a single-device sensing scheme, and can also use a mechanical adjustment scheme. The single-device sensing scheme mainly relies on the terminal's own sensors or external auxiliary equipment, which cannot capture the micro-changes in terminal posture caused by the user's limb movements in real time. The mechanical adjustment scheme mainly uses UAV-portable station collaborative modeling to build a dynamic orbit model, combined with environmental observation data to predict the channel state and drive mechanical components to adjust the antenna pointing, but it relies on external UAV assistance, which is complex to deploy and costly. Summary of the Invention

[0004] This invention provides a dynamic attitude calibration method, apparatus, electronic device, storage medium, and product to address the shortcomings of existing technologies that cannot capture micro-changes in terminal pose caused by user limb movements in real time, and that rely on external UAV assistance, which is complex and costly to deploy. It achieves dynamic attitude calibration based on multi-device collaboration, acquiring terminal attitude data through distributed devices to capture changes in terminal spatial pose, significantly improving positioning accuracy. Then, attitude prediction is performed to obtain the attitude prediction result, and a dynamic attitude calibration strategy is determined based on the result. The attitude calibration strategy is used to calibrate the terminal's attitude relative to the satellite, enabling rapid reconstruction of the antenna beam direction. This is suitable for continuous satellite communication between a user's handheld terminal and the satellite, where the perception dimensions are not singular, thus enabling dynamic perception of terminal attitude changes and real-time calibration of the antenna beam direction, maintaining a stable satellite communication link and reducing costs.

[0005] This invention provides a dynamic attitude calibration method, comprising the following steps.

[0006] User-perceived data is acquired based on distributed devices, and terminal attitude data is determined based on the user-perceived data; where distributed devices refer to devices composed of multiple devices that perform data perception on users; terminal attitude data represents the physical attitude of the terminal in physical space when the user's handheld terminal interacts with the satellite.

[0007] Determine the current relative orientation of the satellite interacting with the terminal.

[0008] The attitude prediction result is determined based on the terminal attitude data, the current relative direction, and the policy gradient algorithm.

[0009] The attitude dynamic calibration strategy is determined based on the attitude prediction results, and the attitude of the terminal relative to the satellite is calibrated based on the attitude dynamic calibration strategy.

[0010] According to a dynamic posture calibration method provided by the present invention, the distributed device includes a limb sensing device, a visual sensing device, and an auditory sensing device. User perception data includes limb sensing data, visual sensing data, and auditory sensing data. The limb sensing data includes a first user position parameter, a first user velocity parameter, and a first user posture parameter, which are obtained by the limb sensing device from the user. The visual sensing data includes a second user position parameter, a second user velocity parameter, and a second user posture parameter, which are obtained by the visual sensing device from the user. The auditory sensing data... The perceived data includes the user's third position parameter, user's third velocity parameter, and user's third posture parameter; these parameters are collected from the user through auditory sensing devices. The terminal posture data is determined based on the perceived data, including: determining the terminal position vector based on the user's first, second, and third position parameters; determining the terminal velocity vector based on the user's first, second, and third velocity parameters; determining the terminal posture vector based on the user's first, second, and third posture parameters; and determining the terminal posture data based on the terminal position vector, terminal velocity vector, and terminal posture vector.

[0011] According to a dynamic attitude calibration method provided by the present invention, the current relative orientation of a satellite interacting with a terminal is determined, comprising: acquiring ephemeris data of the satellite interacting with the terminal and the current spatiotemporal coordinates of the user; and determining the current relative orientation of the satellite based on the ephemeris data and the current spatiotemporal coordinates.

[0012] According to the dynamic attitude calibration method provided by the present invention, after calibrating the attitude of the terminal relative to the satellite based on the dynamic attitude calibration strategy, the method further includes: obtaining the signal-to-noise ratio; optimizing the strategy gradient algorithm based on the signal-to-noise ratio; and continuing to execute the step of determining the attitude prediction result based on the terminal attitude data, the current relative direction, and the strategy gradient algorithm based on the optimized strategy gradient algorithm.

[0013] According to the dynamic attitude calibration method provided by the present invention, after calibrating the attitude of the terminal relative to the satellite based on the dynamic attitude calibration strategy, the method further includes: acquiring the dynamic attitude calibration result; determining the calibration offset result if the dynamic attitude calibration result does not meet the calibration result threshold; determining the user's current behavioral intention based on user perception data; and generating a dynamic prompt instruction based on the calibration offset result and the user's current behavioral intention. The dynamic prompt instruction is used to feed back to the distributed device and continue executing the step of acquiring user perception data based on the distributed device.

[0014] According to a dynamic posture calibration method provided by the present invention, the user's current behavioral intention is determined based on user perception data, including: acquiring the user's historical motion trajectory and historical inertial trend; and determining the current behavioral intention based on the user perception data, the user's historical motion trajectory, and the historical inertial trend.

[0015] The present invention also provides a dynamic attitude calibration device, comprising the following modules.

[0016] The data determination module is used to acquire user-perceived data based on distributed devices and determine terminal attitude data based on the user-perceived data; where distributed devices refer to devices composed of multiple devices that perform data perception on users; terminal attitude data represents the physical attitude of the terminal in physical space when the user's handheld terminal interacts with the satellite.

[0017] The orientation determination module is used to determine the current relative orientation of the satellite interacting with the user.

[0018] The attitude prediction module is used to determine the attitude prediction result based on the terminal attitude data, the current relative direction, and the policy gradient algorithm.

[0019] The attitude calibration module is used to determine the dynamic attitude calibration strategy based on the attitude prediction results, and to perform attitude calibration on the terminal relative to the satellite based on the dynamic attitude calibration strategy.

[0020] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a dynamic calibration method for any of the above-described postures.

[0021] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a dynamic calibration method for any of the above-described attitudes.

[0022] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements a dynamic calibration method for any of the above-described postures.

[0023] This invention provides a dynamic attitude calibration method, apparatus, electronic device, storage medium, and product. It acquires user-perceived data based on distributed devices and determines terminal attitude data based on this data. The distributed devices refer to a system composed of multiple devices that perceive user data. The terminal attitude data represents the physical attitude of the user-held terminal in physical space when interacting with a satellite. The current relative direction of the satellite interacting with the terminal is determined. An attitude prediction result is determined based on the terminal attitude data, the current relative direction, and a policy gradient algorithm. A dynamic attitude calibration strategy is determined based on the attitude prediction result, and the terminal's attitude relative to the satellite is calibrated based on this strategy. The technical solution of this invention addresses the shortcomings of existing technologies that cannot capture micro-changes in terminal posture caused by user limb movements in real time, and that rely on external drone assistance, which is complex and costly. This invention achieves dynamic posture calibration based on multi-device collaboration. It acquires terminal posture data through distributed devices, capturing changes in terminal spatial posture and significantly improving positioning accuracy. Then, posture prediction is performed to obtain the prediction result, and a dynamic posture calibration strategy is determined based on the prediction result. The dynamic posture calibration strategy is used to calibrate the terminal's posture relative to the satellite, which can quickly reconstruct the terminal's antenna beam direction. This is suitable for continuous voice communication transmission between a user's handheld terminal and a satellite, dynamically sensing changes in terminal posture and calibrating the antenna beam direction in real time, thereby maintaining a stable communication transmission link and reducing costs. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating the dynamic attitude calibration method provided by the present invention.

[0026] Figure 2 This is a schematic diagram of the dynamic attitude calibration device provided by the present invention.

[0027] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0029] The following is combined with Figure 1 The present invention describes a dynamic attitude calibration method. This method is applicable to continuous voice communication transmission between a user's handheld terminal and a satellite. It dynamically senses changes in the terminal's attitude and calibrates the antenna beam direction in real time, thereby maintaining the stability of the communication transmission link and reducing costs. The execution subject of this method can be an electronic device or a dynamic attitude calibration device installed in the electronic device. The dynamic attitude calibration device can be implemented by software, hardware, or a combination of both. Figure 1 This is a flowchart illustrating the dynamic attitude calibration method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps 101, 102, 103 and 104.

[0030] Step 101: Obtain user perception data based on distributed devices, and determine terminal posture data based on user perception data.

[0031] In this step, distributed devices and terminals form a distributed multi-device sensing system to acquire user perception data and determine terminal posture data based on the user perception data. The user perception data is acquired through multi-source heterogeneous sensor fusion. For example, it can be perception data collected by a distributed pose perception network constructed by wearable devices such as smartwatches, augmented reality (AR) glasses, and true wireless stereo (TWS) earphones. No additional configuration is required. Based on the obtained user perception data, the terminal posture data is determined, which facilitates subsequent dynamic calibration of the terminal posture and significantly improves positioning accuracy. This embodiment does not limit this.

[0032] Distributed sensing devices may include, for example, smartwatches, AR glasses, TWS earphone modules, etc., but this embodiment does not limit them.

[0033] Smartwatches may include, for example, a nine-axis inertial measurement unit (IMU) and an ultra-wideband (UWB) communication unit, which are responsible for sensing the user's arm movement data in real time and achieving high-precision positioning synchronization with other devices through UWB. This embodiment does not limit this.

[0034] AR glasses, for example, may integrate a red-green-blue depth (RGB-D) camera and a visual-inertial navigation unit (using a module that employs simultaneous localization and mapping (SLAM)) to sense the user's head movements and changes in their spatial position relative to the environment. This embodiment does not limit this.

[0035] TWS earphones may, for example, be equipped with a miniature three-axis accelerometer to supplement the micro-vibration information of the user's head and upper limbs, and further refine the terminal's pose perception resolution. This embodiment does not limit this.

[0036] The sensing frequency for acquiring user perception data based on distributed devices can be, for example, 50 Hz, to ensure continuous tracking of users in dynamic scenarios. This embodiment does not limit this.

[0037] Specifically, terminal attitude data refers to the real-time attitude data of the mobile terminal held by the user. The terminal attitude data represents the physical attitude of the terminal in physical space when the user's handheld terminal interacts with the satellite. It is mainly obtained by fusing user perception data acquired by distributed devices.

[0038] The advantage of this setup is that it enables the construction of a multi-source sensor network through collaboration between smartwatches (nine-axis IMU + UWB), AR glasses (RGB-D camera + SLAM), TWS earphones (three-axis accelerometer), and satellite communication terminals, thereby solving the problem of a single device having a single perception dimension.

[0039] In one specific embodiment, the distributed device includes a limb sensing device, a visual sensing device, and an auditory sensing device. User sensing data includes limb sensing data, visual sensing data, and auditory sensing data. The limb sensing data includes a first user position parameter, a first user velocity parameter, and a first user posture parameter, which are obtained by the limb sensing device from the user. The visual sensing data includes a second user position parameter, a second user velocity parameter, and a second user posture parameter, which are obtained by the visual sensing device from the user. The auditory sensing data packet... This includes user third position parameters, user third velocity parameters, and user third posture parameters; these parameters are obtained by collecting data from the user through auditory sensing devices; the terminal posture data is determined based on the user's perception data, including: determining the terminal position vector based on the user's first, second, and third position parameters; determining the terminal velocity vector based on the user's first, second, and third velocity parameters; determining the terminal posture vector based on the user's first, second, and third posture parameters; and determining the terminal posture data based on the terminal position vector, terminal velocity vector, and terminal posture vector.

[0040] In this step, the distributed devices include limb sensing devices, visual sensing devices, and auditory sensing devices. The limb sensing device may be, for example, a smartwatch, the visual sensing device may be, for example, AR glasses, and the limb sensing device may be, for example, TWS earphones. The limb sensing data may be, for example, collected from the user through the limb sensing device, the visual sensing data may be, for example, collected from the user through the visual sensing device, and the auditory sensing data may be, for example, collected from the user through the auditory sensing device. This embodiment does not limit the scope of the data.

[0041] The first user position parameter refers to the user position collected by the position sensor built into the limb sensing device; the first user velocity parameter refers to the user movement speed collected by the velocity sensor built into the limb sensing device; the first user posture parameter refers to the user posture collected by the posture sensor built into the limb sensing device; the second user position parameter refers to the user position collected by the position sensor built into the visual sensing device; the second user velocity parameter refers to the user movement speed collected by the velocity sensor built into the visual sensing device; the second user posture parameter refers to the user posture collected by the posture sensor built into the visual sensing device; the third user position parameter refers to the user position collected by the position sensor built into the auditory sensing device; the third user velocity parameter refers to the user movement speed collected by the velocity sensor built into the auditory sensing device; and the third user posture parameter refers to the user posture collected by the posture sensor built into the auditory sensing device. This embodiment does not limit these parameters.

[0042] Specifically, the terminal position vector is determined based on the user's first position parameter, second position parameter, and third position parameter. The terminal velocity vector is determined based on the user's first velocity parameter, second velocity parameter, and third velocity parameter. The terminal attitude vector is determined based on the user's first attitude parameters, second attitude parameters, and third attitude parameters. The terminal attitude data is determined based on the terminal position vector, terminal velocity vector, and terminal attitude vector. The terminal attitude data includes... The calculation is shown in formula (1).

[0043] (1)

[0044] In formula (1), Represents the terminal position vector. Represents the terminal velocity vector. This represents the terminal's attitude vector in three-dimensional space.

[0045] In one specific embodiment, to address the issues of data asynchrony and error coupling among multiple devices in different sensor domains (e.g., IMU / UWB / SLAM), a device weight matrix is ​​introduced during attitude estimation. To dynamically adjust the impact of each sensor's observations on the fusion result, the specific algorithm for predicting the terminal's current moment is as follows: ,in, This represents the optimal attitude estimate of the terminal's attitude data at time t-1. This represents the external control vector applied to the terminal at time t-1. The external control vector can represent the change in the terminal's motion attitude. Represents the physical model of the system. This indicates that at time t-1, for The result of prediction based on the terminal attitude data at any given time. This represents the device weight matrix at the current time t. After obtaining... After the terminal attitude data at a given time, based on This determines the final terminal attitude data. Among them, terminal attitude data This refers to the terminal's posture data, obtained by fusing sensory data collected from multiple sensing devices such as limb sensing devices, visual sensing devices, and auditory sensing devices. This represents the total number of sensing devices. The weight of the i-th sensing device is the weight assigned to the i-th sensor, representing the degree of trust in the measurement results of the sensor built into this sensing device. Let represent the observation matrix of the i-th sensing device. This is a matrix that maps the system's real state space to the measurement space of the sensors built into the i-th sensing device. This represents measurement noise, which describes the random errors present in all sensor measurements; this embodiment does not limit this.

[0046] The advantage of this setup is that it introduces a device weight matrix to dynamically adjust the weights of each sensor's data, effectively handling data asynchrony and error coupling, and improving the accuracy of the terminal's attitude data.

[0047] In one specific embodiment, the terminal attitude data can be further processed. To update, i.e., to update the terminal attitude data Add corrections This yields optimized terminal attitude data. Among them, the optimized terminal attitude data This represents the optimal estimate obtained at time t based on all user-perceived data (observations). This represents the Kalman gain matrix, used to adjust the weights of the influence of new observations on the state estimation. This represents the observation matrix, used to map the state space to the observation space.

[0048] Wherein, the Kalman gain matrix The calculation is shown in formula (2).

[0049] (2)

[0050] In formula (2), Represents the Kalman gain matrix; The prediction covariance matrix represents the prediction error covariance obtained at time t based on the user-perceived data from the previous time t-1. Represents the observation matrix Transpose of; The observation noise covariance refers to the adaptive adjustment of observation errors from distributed devices such as limb sensing devices, visual sensing devices, and auditory sensing devices through a sliding window statistical noise covariance. .

[0051] Step 102: Determine the current relative orientation of the satellite interacting with the terminal.

[0052] In this step, the current relative direction refers to the direction information of the satellite relative to the user, and this embodiment does not limit this.

[0053] In one specific embodiment, determining the current relative direction of the satellite interacting with the terminal includes: acquiring the ephemeris data of the satellite interacting with the terminal and the user's current spatiotemporal coordinates; and determining the current relative direction of the satellite based on the ephemeris data and the current spatiotemporal coordinates.

[0054] In this step, ephemeris data refers to a time function that describes the precise position and trajectory of a satellite over time; this embodiment does not limit this.

[0055] The user's current spatiotemporal coordinates refer to the user's precise location, and the current relative direction refers to the relative direction the user is pointing towards the satellite, such as an elevation angle of a certain degree or an azimuth angle of a certain degree. This embodiment does not limit these aspects.

[0056] Specifically, after acquiring the ephemeris data of the satellites interacting with the user and the user's current spatiotemporal coordinates, a spatial geometric model is established, and the current relative orientation of the satellite is obtained based on the ephemeris data, the current spatiotemporal coordinates, and the spatial ensemble model.

[0057] For example, the spatial geometric model determines spatial geometric point A through ephemeris data, spatial geometric point B through current spatiotemporal coordinates, and the current relative direction is a directed line segment from point B to point A. By subtracting the three-dimensional coordinates (ephemeris data - current spatiotemporal coordinates), the current relative direction of the satellite can be obtained. The current relative direction of the satellite includes the direction information of the satellite relative to the user, and this embodiment does not limit this aspect.

[0058] Step 103: Determine the attitude prediction result based on the terminal attitude data, the current relative direction, and the policy gradient algorithm.

[0059] In this step, the policy gradient algorithm can be, for example, the Deep Deterministic Policy Gradient (DDPG) algorithm, which is used for attitude prediction. This embodiment does not limit this algorithm.

[0060] The attitude prediction results may include, for example, the user's running trend and / or the next attitude change direction of the terminal, which is not limited in this embodiment.

[0061] Specifically, after determining the terminal attitude data and the current relative direction of the satellite interacting with the user, attitude prediction is performed based on the terminal attitude data, the current relative direction, and the policy gradient algorithm to obtain the user's running trend and / or the next attitude change direction of the terminal.

[0062] In one specific embodiment, the policy gradient algorithm may be, for example, a reinforcement learning model that incorporates temporal attention mechanisms; however, this embodiment does not limit this to a specific model.

[0063] Step 104: Determine the attitude dynamic calibration strategy based on the attitude prediction results, and perform attitude calibration on the terminal relative to the satellite based on the attitude dynamic calibration strategy.

[0064] In this step, the attitude dynamic calibration strategy is an actual adjustment strategy based on the attitude prediction results, which adjusts the main lobe direction of the beam by adjusting the phase difference of each element of the antenna array that controls the connection between the satellite and the terminal. The adjustment response time of the antenna array can be, for example, 200 milliseconds, to ensure that there is no perceptible delay between the user's handheld terminal and the satellite. This embodiment does not limit this.

[0065] Specifically, after determining the attitude prediction results, an attitude dynamic calibration strategy is determined based on the attitude prediction results. The attitude dynamic calibration strategy is then used to quickly control the phase difference of each element in the antenna array and adjust the direction of the main lobe of the beam, thereby performing attitude dynamic calibration for satellite communication between the user and the satellite and achieving rapid dynamic compensation.

[0066] In one specific embodiment, after calibrating the attitude of the terminal relative to the satellite based on the attitude dynamic calibration strategy, the method further includes: obtaining the signal-to-noise ratio; optimizing the strategy gradient algorithm based on the signal-to-noise ratio; and continuing to execute the step of determining the attitude prediction result based on the terminal attitude data, the current relative direction, and the strategy gradient algorithm based on the optimized strategy gradient algorithm.

[0067] In this step, the signal-to-noise ratio (SNR) is used to reflect the signal quality during the user's interaction with the satellite.

[0068] Specifically, the signal-to-noise ratio (SNR) is obtained; the policy gradient algorithm is optimized based on the SNR; and the optimized policy gradient algorithm is used to continue executing the steps of determining the attitude prediction result based on the terminal attitude data, the current relative direction, and the policy gradient algorithm.

[0069] The advantage of this setup is that it allows for timely optimization of the strategy gradient algorithm based on the signal-to-noise ratio, resulting in more accurate attitude predictions. This, in turn, enables the determination of dynamic attitude calibration strategies based on the attitude predictions to better perform dynamic attitude calibration for user-satellite communications.

[0070] In one specific embodiment, after calibrating the attitude of the terminal relative to the satellite based on the attitude dynamic calibration strategy, the method further includes: acquiring the attitude dynamic calibration result; determining the calibration offset result if the attitude dynamic calibration result does not meet the calibration result threshold; determining the user's current behavioral intention based on user perception data; and generating a dynamic prompt instruction based on the calibration offset result and the user's current behavioral intention. The dynamic prompt instruction is used to feed back to the distributed device and continue executing the step of acquiring user perception data based on the distributed device.

[0071] In this step, the attitude dynamic calibration result is used to reflect the calibration status after performing attitude dynamic calibration on the satellite communication between the user and the satellite based on the attitude dynamic calibration strategy. The calibration result threshold is a preset calibration condition, which can be, for example, a preset difference between the calibration result and the attitude dynamic calibration result within a range of ±3. This embodiment does not limit this.

[0072] The calibration offset result can be, for example, the calibrated attitude offset angle. The dynamic prompt command is determined based on the calibration offset result, the user's current behavioral intent, and the calibration relationship mapping rule. The calibration relationship mapping rule is determined in advance based on historical calibration offset results and the user's historical behavioral intent, and this embodiment does not limit it.

[0073] The user's current behavioral intent is used to predict the user's response delay.

[0074] Specifically, after performing attitude dynamic calibration on the user-satellite communication based on the attitude dynamic calibration strategy, the attitude dynamic calibration result is obtained; if the attitude dynamic calibration result does not meet the calibration result threshold, the calibration offset result is determined. Based on the calibration offset results and the user's current behavioral intent, dynamic prompts are generated. These dynamic prompts are then fed back to the distributed devices, and the process of acquiring user-perceived data based on the distributed devices continues.

[0075] For example, combining calibration offset results Based on the calibration offset results and the user's current behavioral intent, a dynamic prompt instruction is generated. For example, a dynamic prompt instruction could be that turning 10° to the left is beneficial for signal enhancement, but this embodiment does not limit this.

[0076] In one specific embodiment, determining the user's current behavioral intent based on user perception data includes: acquiring the user's historical movement trajectory and historical inertial trend; and determining the intent for the current behavior based on the user perception data, the user's historical movement trajectory, and the historical inertial trend.

[0077] In this step, the user's historical motion trajectory refers to the trajectory of the user's historical running posture, and the historical inertial trend refers to the user's historical posture inertial trend.

[0078] Specifically, it acquires the user's historical movement trajectory and historical inertial trends; and determines the intent for the current behavior based on the user's perception data, historical movement trajectory, and historical inertial trends.

[0079] In one specific embodiment, to generate dynamic prompts and achieve seamless user interaction based on these prompts, a cross-device multimodal feedback strategy selection function is designed. According to the calibration offset results Predicted values ​​of user's current behavioral intent and user response delay Selecting the most suitable feedback strategy: Feedback strategy selection function As shown in formula (3).

[0080] (3)

[0081] In formula (3), the predicted value of user response delay is... ,in, This indicates the average delay of the last 5 responses. Indicates the delayed standard deviation. This represents the adjustment coefficient, which is not limited in this embodiment.

[0082] The advantage of this setup is that by using multimodal feedback methods such as AR visual cues, TWS voice guidance, and watch haptic vibration, combined with the user's reaction delay prediction value, the feedback strategy can be dynamically selected. It can guide the user's natural movements to cooperate with the calibration without the user's active intervention. This solves the problem of the experience fragmentation and operational risks caused by the user having to look at the screen and manually adjust their posture in the existing technology, and improves the convenience and security of satellite communication in emergency rescue and other scenarios.

[0083] In one specific embodiment, it also includes recording the user's historical running trajectory and historical inertial trend at all times, so as to generate dynamic prompts to perform personalized posture calibration for the user, improve response efficiency and user experience smoothness.

[0084] This invention provides a dynamic attitude calibration method, which acquires user-perceived data based on distributed devices and determines terminal attitude data based on the user-perceived data. The distributed devices refer to a system composed of multiple devices that perceive user data. The terminal attitude data represents the physical attitude of the user-held terminal in physical space when interacting with a satellite. The current relative direction of the satellite interacting with the terminal is determined. An attitude prediction result is determined based on the terminal attitude data, the current relative direction, and a policy gradient algorithm. A dynamic attitude calibration strategy is determined based on the attitude prediction result, and the terminal's attitude relative to the satellite is calibrated based on the dynamic attitude calibration strategy. The technical solution of this invention addresses the shortcomings of existing technologies that cannot capture micro-changes in terminal posture caused by user limb movements in real time, and that rely on external drone assistance, which is complex and costly. This invention achieves dynamic posture calibration based on multi-device collaboration. It acquires terminal posture data through distributed devices, capturing changes in terminal spatial posture and significantly improving positioning accuracy. Then, posture prediction is performed to obtain the prediction result, and a dynamic posture calibration strategy is determined based on the prediction result. The dynamic posture calibration strategy is used to calibrate the terminal's posture relative to the satellite, which can quickly reconstruct the terminal's antenna beam direction. This is suitable for continuous voice communication transmission between a user's handheld terminal and a satellite, dynamically sensing changes in terminal posture and calibrating the antenna beam direction in real time, thereby maintaining communication link stability and reducing costs.

[0085] The dynamic attitude calibration device provided by the present invention will be described below. The dynamic attitude calibration device described below can be referred to in correspondence with the dynamic attitude calibration method described above.

[0086] Figure 2 This is a schematic diagram of the dynamic attitude calibration device provided by the present invention, with reference to... Figure 2 As shown, the dynamic attitude calibration device 200 includes: a data determination module 201, a direction determination module 202, an attitude prediction module 203, and an attitude calibration module 204.

[0087] The data determination module 201 is used to acquire user-perceived data based on distributed devices and determine terminal attitude data based on the user-perceived data; wherein, the distributed devices refer to devices composed of multiple devices that perform data perception on the user; the terminal attitude data represents the physical attitude of the terminal in physical space when the user's handheld terminal interacts with the satellite.

[0088] The orientation determination module 202 is used to determine the current relative orientation of the satellite interacting with the terminal.

[0089] The attitude prediction module 203 is used to determine the attitude prediction result based on the terminal attitude data, the current relative direction, and the policy gradient algorithm.

[0090] The attitude calibration module 204 is used to determine the attitude dynamic calibration strategy based on the attitude prediction results, and to perform attitude calibration on the terminal relative to the satellite based on the attitude dynamic calibration strategy.

[0091] In one example embodiment, the distributed device includes a limb sensing device, a visual sensing device, and an auditory sensing device. User perception data includes limb sensing data, visual sensing data, and auditory sensing data. The limb sensing data includes a first user position parameter, a first user velocity parameter, and a first user posture parameter, which are obtained by the limb sensing device from the user. The visual sensing data includes a second user position parameter, a second user velocity parameter, and a second user posture parameter, which are obtained by the visual sensing device from the user. The auditory sensing data includes a third user position parameter, a third user velocity parameter, and a third user posture parameter, which are obtained by the auditory sensing device from the user.

[0092] In one example embodiment, the data determination module 201 is specifically used to: determine a terminal position vector based on a user's first position parameter, a user's second position parameter, and a user's third position parameter; determine a terminal velocity vector based on a user's first velocity parameter, a user's second velocity parameter, and a user's third velocity parameter; determine a terminal attitude vector based on a user's first attitude parameter, a user's second attitude parameter, and a user's third attitude parameter; and determine terminal attitude data based on the terminal position vector, the terminal velocity vector, and the terminal attitude vector.

[0093] In one example embodiment, the direction determination module 202 is specifically used to: acquire ephemeris data of the satellite interacting with the terminal and the user's current spatiotemporal coordinates; and determine the current relative direction of the satellite based on the ephemeris data and the current spatiotemporal coordinates.

[0094] In one example embodiment, the device further includes an algorithm optimization module. The algorithm optimization module is configured to: after calibrating the terminal's attitude relative to the satellite based on an attitude dynamic calibration strategy, obtain the signal-to-noise ratio (SNR); optimize the strategy gradient algorithm based on the SNR; and continue executing the step of determining the attitude prediction result based on the terminal attitude data, the current relative direction, and the strategy gradient algorithm based on the optimized strategy gradient algorithm.

[0095] In one example embodiment, the device further includes an instruction generation module. The instruction generation module is configured to: after performing attitude calibration on the terminal relative to the satellite based on an attitude dynamic calibration strategy, obtain an attitude dynamic calibration result; if the attitude dynamic calibration result does not meet a calibration result threshold, determine a calibration offset result; determine the user's current behavioral intent based on user perception data; and generate a dynamic prompt instruction based on the calibration offset result and the user's current behavioral intent; wherein the dynamic prompt instruction is used to feed back to the distributed device and continue executing the step of obtaining user perception data based on the distributed device.

[0096] In one example embodiment, the instruction generation module determines the user's current behavioral intention based on user perception data, specifically by: acquiring the user's historical movement trajectory and historical inertial trend; and determining the current behavioral intention based on the user perception data, the user's historical movement trajectory, and the historical inertial trend.

[0097] The apparatus of this embodiment can be used to execute the method of any embodiment in the dynamic attitude calibration method side embodiment. Its specific implementation process and technical effects are similar to those in the dynamic attitude calibration method side embodiment. For details, please refer to the detailed description in the dynamic attitude calibration method side embodiment, which will not be repeated here.

[0098] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 3 As shown, the electronic device may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a dynamic attitude calibration method. This method includes: acquiring user-perceived data based on distributed devices, and determining terminal attitude data based on the user-perceived data; wherein, distributed devices refer to devices composed of multiple devices that perceive user data; the terminal attitude data represents the physical attitude of the user-held terminal in physical space when interacting with a satellite; determining the current relative direction of the satellite interacting with the terminal; determining the attitude prediction result based on the terminal attitude data, the current relative direction, and a policy gradient algorithm; determining a dynamic attitude calibration strategy based on the attitude prediction result, and calibrating the terminal's attitude relative to the satellite based on the dynamic attitude calibration strategy.

[0099] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the dynamic attitude calibration method provided by the above methods. The method includes: acquiring user-perceived data based on a distributed device, and determining terminal attitude data based on the user-perceived data; wherein, the distributed device refers to a device composed of multiple devices that perform data perception on the user; the terminal attitude data represents the physical attitude of the terminal in physical space when the user-held terminal interacts with a satellite; determining the current relative direction of the satellite interacting with the terminal; determining the attitude prediction result based on the terminal attitude data, the current relative direction, and a policy gradient algorithm; determining the attitude dynamic calibration strategy based on the attitude prediction result, and performing attitude calibration on the terminal's attitude relative to the satellite based on the attitude dynamic calibration strategy.

[0101] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a dynamic attitude calibration method for performing the above-described methods. The method includes: acquiring user-perceived data based on a distributed device, and determining terminal attitude data based on the user-perceived data; wherein, the distributed device refers to a device composed of multiple devices that perceive user data; the terminal attitude data represents the physical attitude of the terminal in physical space when the user-held terminal interacts with a satellite; determining the current relative direction of the satellite interacting with the terminal; determining an attitude prediction result based on the terminal attitude data, the current relative direction, and a policy gradient algorithm; determining an attitude dynamic calibration strategy based on the attitude prediction result, and performing attitude calibration on the terminal's attitude relative to the satellite based on the attitude dynamic calibration strategy.

[0102] The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of dynamic calibration of attitude, characterized in that, include: User-perceived data is acquired based on distributed devices, and terminal posture data is determined based on the user-perceived data; wherein, the distributed devices refer to devices composed of multiple devices that perform data perception on the user; the terminal posture data represents the physical posture of the terminal in physical space when the user's handheld terminal interacts with the satellite; Determine the current relative orientation of the satellite interacting with the terminal; The attitude prediction result is determined based on the terminal attitude data, the current relative direction, and the policy gradient algorithm. The process further includes: determining a dynamic attitude calibration strategy based on the attitude prediction result, and calibrating the attitude of the terminal relative to the satellite based on the dynamic attitude calibration strategy; after calibrating the attitude of the terminal relative to the satellite based on the dynamic attitude calibration strategy, the process further includes: acquiring a dynamic attitude calibration result; determining a calibration offset result if the dynamic attitude calibration result does not meet a calibration result threshold; determining the user's current behavioral intention based on the user perception data; and generating a dynamic prompt instruction based on the calibration offset result and the user's current behavioral intention; wherein the dynamic prompt instruction is used to feed back to the distributed device, and the step of acquiring user perception data based on the distributed device continues to be executed.

2. The dynamic attitude calibration method according to claim 1, characterized in that, The distributed device includes a limb sensing device, a visual sensing device, and an auditory sensing device. The user perception data includes limb sensing data, visual sensing data, and auditory sensing data. The limb sensing data includes a first user position parameter, a first user velocity parameter, and a first user posture parameter, which are obtained by the limb sensing device from the user. The visual sensing data includes a second user position parameter, a second user velocity parameter, and a second user posture parameter, which are obtained by the visual sensing device from the user. The auditory sensing data includes a third user position parameter, a third user velocity parameter, and a third user posture parameter, which are obtained by the auditory sensing device from the user. Determining the terminal posture data based on the user-perceived data includes: The terminal position vector is determined based on the user's first position parameter, the user's second position parameter, and the user's third position parameter; The terminal speed vector is determined based on the user's first speed parameter, the user's second speed parameter, and the user's third speed parameter; The terminal attitude vector is determined based on the user's first attitude parameters, the user's second attitude parameters, and the user's third attitude parameters; The terminal attitude data is determined based on the terminal position vector, the terminal velocity vector, and the terminal attitude vector.

3. The dynamic attitude calibration method according to claim 1, characterized in that, Determining the current relative direction of the satellite interacting with the terminal includes: Obtain the ephemeris data of the satellite interacting with the terminal and the current spatiotemporal coordinates of the user; The current relative orientation of the satellite is determined based on the ephemeris data and the current spatiotemporal coordinates.

4. The dynamic attitude calibration method according to claim 1, characterized in that, After performing attitude calibration of the terminal relative to the satellite based on the attitude dynamic calibration strategy, the method further includes: Obtain the signal-to-noise ratio; The policy gradient algorithm is optimized based on the signal-to-noise ratio, and the step of determining the attitude prediction result based on the terminal attitude data, the current relative direction, and the policy gradient algorithm is continued based on the optimized policy gradient algorithm.

5. The dynamic attitude calibration method according to claim 1, characterized in that, Determining the user's current behavioral intent based on the user perception data includes: Obtain the user's historical movement trajectory and historical inertial trend; The user's current behavioral intent is determined based on the user's perceived data, the user's historical movement trajectory, and the historical inertial trend.

6. A dynamic attitude calibration device, characterized in that, include: The data determination module is used to acquire user-perceived data based on distributed devices and determine terminal posture data based on the user-perceived data; wherein, the distributed devices refer to devices composed of multiple devices that perform data perception on the user; the terminal posture data represents the physical posture of the terminal in physical space when the user's handheld terminal interacts with the satellite; A direction determination module is used to determine the current relative direction of the satellite interacting with the terminal; The attitude prediction module is used to determine the attitude prediction result based on the terminal attitude data, the current relative direction, and the policy gradient algorithm. An attitude calibration module is used to determine a dynamic attitude calibration strategy based on the attitude prediction result, and to perform attitude calibration on the terminal relative to the satellite based on the dynamic attitude calibration strategy. After performing attitude calibration on the terminal relative to the satellite based on the dynamic attitude calibration strategy, the module further includes: acquiring a dynamic attitude calibration result; determining a calibration offset result if the dynamic attitude calibration result does not meet a calibration result threshold; determining the user's current behavioral intention based on the user perception data; and generating a dynamic prompt instruction based on the calibration offset result and the user's current behavioral intention. The dynamic prompt instruction is used to feed back to the distributed device and continue executing the step of acquiring user perception data based on the distributed device.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the dynamic attitude calibration method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the dynamic attitude calibration method as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the dynamic attitude calibration method as described in any one of claims 1 to 5.

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