Communication optimization method and device of Beidou short message handheld terminal, equipment and medium

Abstract

The invention provides a communication optimization method and device of a Beidou short message handheld terminal, equipment and a medium, which are used for improving the sending success rate and efficiency of a Beidou short message in a complex environment. The method comprises the following steps: screening multi-source satellite signals according to environment information to obtain a candidate satellite signal set, and performing motion trail prediction on equipment motion state data to generate motion trail prediction data, performing signal quality change prediction on the candidate satellite signal set according to the motion trail prediction data to generate signal quality prediction data of each candidate satellite, performing multi-attribute decision-making on the candidate satellite signal set according to the signal quality prediction data to screen out a target satellite, and obtaining corresponding satellite parameters; and calculating a communication pointing deviation according to the satellite parameters and the equipment motion state data, and generating a visual guidance instruction according to the grading guidance strategy and the communication pointing deviation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite communication, in particular to a Beidou short message handheld terminal communication optimization method, device, equipment and medium. BACKGROUND

[0002] When the mobile network is interrupted or cannot be accessed, the short message provides a point-to-point rescue channel with low bandwidth, low power consumption and the ability to penetrate complex terrain, which can reliably send positioning information and rescue text to the rescue platform in mountainous areas, at sea or in disaster areas, making up for the blank of ground communication and improving the efficiency of rescue response, thus having irreplaceable value for personal safety, disaster response and remote operation guarantee.

[0003] According to the satellite azimuth, the pose of the handheld terminal is adjusted, which is the basis for successfully implementing short message communication. The existing handheld terminal connection with the satellite is mainly based on real-time observation data / simple sensors, which guides the user to adjust the equipment to improve the satellite signal received by changing the position. For example, a mobile phone with Beidou short message function shows the user the satellite signal at the current position through a visual interface, and the user determines the best communication position by testing the signal in each range.

[0004] The device reads the instantaneous signal strength of each satellite and selects the current strongest or visible satellite; the magnetometer, gyroscope and electronic compass are used to display the satellite azimuth on the interface and prompt the user to face; there are also implementations that estimate visible satellites through ephemeris / almanac and make rough guidance accordingly. Most of the current connection methods are based on instantaneous signals or static star tables, and lack prediction and adaptation to device motion, environmental shielding and multipath effects, which cannot predict signal attenuation or dynamic shielding in a short time, resulting in frequent retries, increased energy consumption and decreased success rate in complex terrain or mobile scenarios. SUMMARY

[0005] The embodiments of the present application provide a Beidou short message handheld terminal communication optimization method, device, equipment and medium, which is used to improve the success rate and efficiency of Beidou short message transmission in complex environments.

[0006] In a first aspect, the present application provides a Beidou short message handheld terminal communication optimization method, which comprises: According to the collected environmental information, the received multi-source satellite signals are subjected to signal-to-noise ratio screening to obtain a candidate satellite signal set, and the real-time collected device motion state data is subjected to motion trajectory prediction to generate motion trajectory prediction data; According to the motion trajectory prediction data, the signals in the candidate satellite signal set are subjected to signal quality change prediction to generate signal quality prediction data of each candidate satellite; screening the candidate satellites corresponding to the candidate satellite signal set according to the signal quality prediction data, to obtain a target satellite and corresponding satellite parameters; calculating a communication pointing deviation according to the satellite parameters and the device motion state data, and generating a visual guidance instruction according to a preset hierarchical guidance strategy and the communication pointing deviation.

[0007] In a possible implementation, the SNR screening of the received multi-source satellite signals according to the collected environment information to obtain the candidate satellite signal set includes: performing environment type identification according to the collected environment information, and obtaining a corresponding basic SNR threshold value according to the identified environment type through a preset environment and threshold mapping rule; compensating and modifying the basic SNR threshold value according to the detected device pose angle, to generate an SNR screening threshold value; performing SNR testing on the received multi-source satellite signals to obtain real-time SNR measurement values of each satellite signal, and screening out satellite signals with real-time SNR measurement values higher than the SNR screening threshold value according to the SNR screening threshold value and the real-time SNR measurement values, to form the candidate satellite signal set.

[0008] In a possible implementation, the device motion state data includes real-time coordinates and a pose angle; and the motion trajectory prediction of the real-time collected device motion state data to generate motion trajectory prediction data includes: calculating a current speed according to a preset collection time interval and a first real-time coordinate and a last real-time coordinate corresponding to the collection time interval, to generate horizontal acceleration and vertical acceleration through acceleration calculation and vector decomposition according to the collection time interval and the current speed, and to calculate an angular velocity according to the collection time interval and the pose angle; performing motion trajectory prediction according to a preset prediction time interval, the horizontal acceleration, the vertical acceleration, and the angular velocity, to generate motion trajectory prediction data.

[0009] In a possible implementation, the signals in the candidate satellite signal set include trajectory parameters of the corresponding candidate satellites; and the signal quality change prediction of each signal in the candidate satellite signal set according to the motion trajectory prediction data to generate signal quality prediction data of each candidate satellite includes: calculating relative distance change data and relative angle change data between the handheld terminal device and each candidate satellite according to the motion trajectory prediction data and the trajectory parameters, and performing associated calculation of position change and signal attenuation on each candidate satellite according to the relative distance change data, to obtain a basic signal attenuation amount; According to the environment type, an attenuation correction value corresponding to the environment type is obtained from a preset environment effect correction value list, and a basis signal attenuation is corrected according to the attenuation correction value to obtain an environment-optimized signal attenuation; According to the relative angle change data, obstacle identification and signal shielding evaluation are performed on the environment information to generate a communication shielding coefficient. According to the environment-optimized signal attenuation and the communication shielding coefficient, quality change calculation is performed on each signal in the candidate satellite signal set to generate signal quality prediction data of each candidate satellite.

[0010] In a possible implementation, the obstacle identification and signal shielding evaluation on the environment information according to the relative angle change data to generate the communication shielding coefficient includes: Obstacle identification and classification processing are performed on the environment information to obtain an obstacle category, an obstacle direction, and a corresponding coverage range, and a predicted direction range of a corresponding candidate satellite is determined according to the device motion state data and the relative angle change data; When the coverage range and the predicted direction range overlap, a communication shielding coefficient is obtained according to the obstacle category through a preset category and attenuation mapping table.

[0011] In a possible implementation, the screening of the candidate satellite corresponding to the candidate satellite signal set according to the signal quality prediction data to obtain a target satellite and corresponding satellite parameters includes: The signal quality prediction data is normalized to obtain an evaluation index value set corresponding to each candidate satellite; The evaluation index value set is weighted and summed according to a preset index value weight coefficient set to generate a comprehensive evaluation score value corresponding to each candidate satellite; The candidate satellites are sorted according to the comprehensive evaluation score value to screen out an optimal target satellite, and a corresponding spatial position parameter is obtained according to the target satellite to form a corresponding satellite parameter.

[0012] In a possible implementation, the satellite parameter includes a spatial position parameter, the communication pointing deviation includes a direction deviation and a device pitch pointing deviation, and the calculation of the communication pointing deviation according to the satellite parameter and the device motion state data and the generation of a visual guidance instruction according to a preset hierarchical guidance strategy and the communication pointing deviation include: The direction deviation and the device pitch pointing deviation are calculated according to the spatial position parameter and the device motion state data; According to a preset hierarchical guidance strategy, the orientation deviation and the device pitch pointing deviation are respectively subjected to hierarchical deviation threshold comparison and deviation threshold adjustment instruction mapping to obtain corresponding adjustment instruction information; According to a preset instruction generation mode and the adjustment instruction information, a visual guidance instruction is generated.

[0013] In a second aspect, the application provides a communication optimization device of a Beidou short message handheld terminal, the device comprising: A signal screening module is configured to perform signal-to-noise ratio screening on the received multi-source satellite signals according to the collected environmental information to obtain a candidate satellite signal set; A motion prediction module is configured to perform motion trajectory prediction on the real-time collected device motion state data to generate motion trajectory prediction data; A quality analysis module is configured to perform signal quality change prediction on each signal in the candidate satellite signal set according to the motion trajectory prediction data to generate signal quality prediction data of each candidate satellite; A satellite screening module is configured to screen the candidate satellites corresponding to the candidate satellite signal set according to the signal quality prediction data to obtain a target satellite and corresponding satellite parameters; A deviation instruction module is configured to calculate a communication pointing deviation according to the satellite parameters and the device motion state data, and generate a visual guidance instruction according to a preset hierarchical guidance strategy and the communication pointing deviation.

[0014] In a third aspect, the application provides a computing device, comprising: at least one processor; and a memory and a communication interface in communication connection with the at least one processor; The memory stores instructions executable by the at least one processor, and the at least one processor implements the communication optimization method of the Beidou short message handheld terminal as described above by executing the instructions stored in the memory.

[0015] In a fourth aspect, the application provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the communication optimization method of the Beidou short message handheld terminal as described above.

[0016] In summary, the application at least includes the following beneficial technical effects: 1. By predicting and selecting the optimal satellite, the situation of retrying after discovering a poor link after packet sending is reduced, the overall time from user initiation to successful sending is shortened, and the use requirements of high timeliness (such as emergency rescue) are met.

[0017] 2. By incorporating environmental factors and the time-varying effects of motion on signals during the signal screening stage, the failure of a single fixed strategy in different scenarios is avoided, thereby improving the environmental adaptability of communication equipment.

[0018] 3. By increasing the short message packet transmission rate and reducing retries, the burden on back-end operators (e.g., emergency rescue platforms) for repeated location and message processing is reduced, while the frequency of unnecessary signaling and manual intervention during communication is also reduced. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a communication optimization method for a Beidou short message handheld terminal provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a communication optimization device for a Beidou short message handheld terminal provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of the computing device provided in the embodiments of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. With the development of technology and the emergence of new scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0021] The terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the description of embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to those processes, methods, products, or apparatuses.

[0022] like Figure 1 The diagram shown is a flowchart illustrating the communication optimization method for a BeiDou short message handheld terminal provided in this embodiment of the application. The communication optimization method for a BeiDou short message handheld terminal provided in this embodiment of the application includes the following steps.

[0023] Step S1, according to the collected environmental information, the received multi-source satellite signal is filtered according to the signal-to-noise ratio to obtain a candidate satellite signal set, and the real-time collected device motion state data is predicted to generate motion trajectory prediction data.

[0024] In the embodiment of the present application, the sequence is continuously collected at a rate of ten frames per second, which is used to dynamically capture the visual features of the scene where the device is located. In step S1, only the latest high-definition photo in the sequence is extracted as the input data for current environmental analysis. It should be understood that the Beidou short message communication is seriously affected by the environment. For example, in a dense forest environment, the tree leaf blockage will cause signal attenuation to be significantly higher than that in an open area. Therefore, it is necessary to distinguish the environment type according to the visual features to match the differentiated threshold, so as to realize the adjustment of the handheld terminal device according to the environment scene. After obtaining the required photo, the embodiment of the present application uses a lightweight convolutional neural network model to identify the environment type. The lightweight convolutional neural network model is a trained model, which can match the preset environment type according to the input image data. In the embodiment of the present application, the preset environment type includes but is not limited to four types of open land, urban street, forest or valley, and the lightweight convolutional neural network model identifies the features of the input photo and matches the preset environment type, thereby outputting one of the above four types and its confidence. At the same time, the lightweight convolutional neural network model also integrates a preset environment and threshold mapping rule, which is stored in the model in the form of a query table. After identifying the environment type, the lightweight convolutional neural network model maps the obtained environment type to the corresponding basic signal-to-noise ratio threshold according to the environment and threshold mapping rule. For example, the forest environment corresponds to 35 dB-Hz, and the open land corresponds to 28 dB-Hz. The environment and threshold mapping rule used in the present application is obtained through statistical analysis of a large amount of measured data, which ensures that the threshold setting conforms to the signal propagation characteristics in a specific scene.

[0025] Then, the pose angle of the device is obtained in real time by the integrated fusion nine-axis inertial measurement unit in the handheld terminal device, wherein the pose angle of the device includes a heading angle of the device relative to the geomagnetic north pole and a pitch angle relative to the horizontal plane. It should be understood that, since the antenna gain of the handheld device has directivity, its signal receiving sensitivity changes with the relative angle between the device orientation and the satellite incident direction. When the device antenna beam lobe direction is aligned with the satellite signal incident direction, the effective gain is maximum; otherwise, gain loss will occur. Therefore, the base signal-to-noise ratio threshold needs to be compensated and corrected according to the pose angle of the device. The embodiment of the present application adopts a simplified empirical model for compensation and correction operation, which takes the pitch angle of the antenna as the main input parameter to calculate a gain compensation value. For example, when the antenna elevation angle increases, the compensation value is positive, and vice versa. The compensation value and the aforementioned base signal-to-noise ratio threshold are added together to generate a final signal-to-noise ratio screening threshold for signal screening. Through compensation and correction of the base signal-to-noise ratio threshold, the signal screening standard can dynamically adapt to the physical orientation change of the device, avoiding the incorrect filtering of actually available satellite signals due to different user holding postures.

[0026] After completing the threshold setting, the system calls the application program interface of the Beidou radio frequency receiving chip to read the real-time carrier-to-noise power density ratio (i.e., real-time signal-to-noise ratio) measurement value of all visible satellite signals demodulated by it, which can directly represent the signal quality of the corresponding satellite. Further, the real-time signal-to-noise ratio measurement value of each satellite signal is compared with the obtained signal-to-noise ratio screening threshold one by one, only the satellite signals with measurement values higher than the threshold are retained, and their satellite identifiers, real-time signal strengths and orbit parameters and other information are packaged to form a candidate satellite signal set.

[0027] While performing satellite signal screening, the handheld terminal device simultaneously performs the device motion trajectory prediction operation as shown below.

[0028] The handheld terminal device cooperatively collects device motion state data through an integrated nine-axis inertial measurement unit and a Beidou positioning module. The device motion state data includes but is not limited to real-time coordinates and pose angles, wherein the real-time coordinates are derived from the longitude, latitude and elevation data output by the Beidou positioning module, and the update frequency is 1 Hz; the pose angles are derived from the Euler angle data output by the nine-axis inertial measurement unit, and the update frequency is 100 Hz. According to the recorded real-time coordinate sequence at two consecutive time stamps (i.e. the collection time interval, for example 1S), and based on the WGS84 earth model, the longitude, latitude and elevation coordinates are converted into three-dimensional coordinates in the Earth-Centered Earth-Fixed coordinate system, and then the three-dimensional velocity vector (i.e. the current velocity) is calculated by numerical difference method. Subsequently, the velocity vector is again subjected to time difference processing to obtain the acceleration vector, and then the acceleration vector is decomposed into two components parallel and perpendicular to the local horizontal plane, i.e. horizontal acceleration and vertical acceleration. In the embodiment of the present application, the angular velocity is directly obtained from the original data of the gyroscope after coordinate transformation, which reflects the angular rate of the device rotating around its own axis, and is used to represent the change of the device pose caused by the user's limb movement. For example, when the user holds the device to position or prepare to send a message, there is inevitable slight or large arm swing, body rotation and device pose adjustment.

[0029] Finally, through a preset set of kinematic equations (essentially integral formulas), the obtained acceleration and angular velocity are subjected to time domain integral operation, so as to realize the prediction of the device motion trajectory on the basis of the current position information. The prediction time interval adopted in the embodiment of the present application includes a step of 0.05s and a time of 8s, and the position and pose of the device within the future 8s are recursively calculated forward according to the calculated horizontal acceleration, vertical acceleration and angular velocity. The generated output is a device state sequence arranged in time sequence, which completely describes the predicted position, predicted velocity and predicted pose of the device at each time point in the future. Because the user's movement will cause the change of satellite link geometry in a short time before signal transmission, accurate motion prediction is a prerequisite for evaluating future signal quality and selecting the optimal satellite. For example, if the prediction shows that the user is quickly walking towards the shadow area of a building, the system should preferentially select a satellite that can maintain stability in this shadow area although it is not the strongest at present, to carry out communication, so as to avoid communication interruption.

[0030] Step S2, predicting the signal quality change of each signal in the candidate satellite signal set according to the motion trajectory prediction data, and generating signal quality prediction data of each candidate satellite.

[0031] It should be understood that the motion trajectory prediction data is a time series containing the predicted positions, velocities and attitudes of the device at multiple future time points; the candidate satellite signal set contains the satellite list and its corresponding orbit ephemeris parameters that pass the preliminary screening, and the trajectory parameters are the broadcast ephemeris data demodulated from the satellite navigation message and used for accurately calculating the spatial position of the satellite at any time.

[0032] First, according to the device predicted positions at each time point in the motion trajectory prediction data, the geometric relationship between the two is recursively calculated in combination with the broadcast ephemeris of each candidate satellite. For each candidate satellite and each future time point, two sets of parallel calculations are performed: one is the relative distance change data, which calculates the instantaneous distance between the device and the satellite through the distance formula between two points in three-dimensional space, and forms a sequence of the distance change over time; the other is the relative angle change data, which calculates the instantaneous azimuth and elevation angle of the satellite relative to the local horizontal coordinate system of the device through spherical trigonometry, and forms a sequence of the change of the two angles over time. After obtaining the relative distance change data, it is used to calculate the basic signal attenuation. The calculation is based on the path loss model of radio wave propagation in free space, and its core physical principle is that the signal power is inversely proportional to the square of the transmission distance, and the increase of distance will inevitably lead to the attenuation of signal strength. Among them, the basic signal attenuation is used to represent the idealized attenuation degree of the future signal relative to the current signal only considering the distance change factor.

[0033] However, the free space model is too ideal and must be corrected by environmental factors to approach the real scene. The environmental information in this step is the surrounding environment photo sequence collected in step S1, and its role is to provide the system with a continuously updated visual context to identify the physical environment on the signal propagation path. Taking the environment type identified in step S1 as an index, a preset environmental effect correction value list is queried. This list stores the empirical values of typical multipath fading and diffraction attenuation of radio signals in different environment categories, for example, the buildings in urban environment will introduce significant multipath effect. The obtained attenuation correction value is added to the basic signal attenuation to obtain the environment-optimized signal attenuation that is more in line with the actual propagation conditions.

[0034] In addition, the obstruction in the environment is also a key factor affecting the signal quality of the Beidou short message communication. It should be understood that the relative angle change data provides the absolute geographic direction (i.e. azimuth and elevation) of the satellite signal at future time. To realize the obstruction judgment, the relative angle change data must be mapped into the image coordinate system of the camera. This conversion relies on the motion state data collected by the device, which defines the pointing of the optical center of the camera. Through coordinate transformation, the absolute geographic direction of the satellite is converted into its corresponding pixel area in the real-time video frame, that is, the predicted azimuth range. Synchronously, real-time image analysis is performed on the current video frame, and a lightweight target detection neural network is used to identify obstacles (such as trees, vehicles, buildings) in the picture and output their categories, positions in the image and pixel coverage ranges (usually represented by bounding boxes). The obstruction judgment logic is to calculate the geometric overlapping relationship of the predicted azimuth range of the satellite and the coverage range of the obstacle in the image; if there is any pixel overlap between the two areas, it is determined that there is an obstruction risk. Once it is determined that there is an obstruction, the pre-set category and attenuation mapping table is queried according to the identified obstacle category. The table defines the typical attenuation values of Beidou L-band signals of different material obstacles, for example, dense trees correspond to a medium attenuation value (such as 10 dB), and reinforced concrete buildings correspond to a high attenuation value (such as 25 dB). The value obtained by querying is the communication obstruction coefficient, which is a conservative empirical estimate value used to compensate for the accurate physical attenuation information that cannot be provided by visual recognition.

[0035] Finally, the environmental optimization signal attenuation amount and the communication obstruction coefficient are added to obtain the total predicted attenuation amount of the signal on the propagation path. The attenuation amount is subtracted from the current measured signal-to-noise ratio of the candidate satellite, and the predicted signal-to-noise ratio value at future time points is obtained. The above operation is recursively performed on all candidate satellites, and the output is a time series set containing the predicted signal-to-noise ratio values of each satellite, that is, the signal quality prediction data of each candidate satellite. This data is the core basis for the subsequent decision module to select the optimal communication satellite, for example, in a mountainous environment, although the current signal of a satellite is strong, it is predicted to be blocked by a mountain, so the system will preferentially select another satellite with a slightly weaker current signal but a predicted stable signal, thereby ensuring the success of the short message transmission.

[0036] Step S3, screening the candidate satellites corresponding to the candidate satellite signal set according to the signal quality prediction data to obtain a target satellite and corresponding satellite parameters.

[0037] The signal quality prediction data is a structured set containing a series of signal-to-noise ratio prediction values for each satellite in the candidate satellite set within a future prediction time period. The role of the signal quality prediction data is to quantitatively evaluate the future performance of each candidate satellite communication link, and its core value lies in extending the instantaneous signal strength judgment to a stability prediction for a period of time, providing data support for selecting the optimal communication satellite.

[0038] First, key evaluation indicators are extracted from the signal quality prediction data. The system calculates three core indicators for each candidate satellite: the first indicator is the minimum signal-to-noise ratio value within the prediction period, which directly reflects the worst quality condition that the communication link may face in the future, and its physical meaning is the minimum threshold to ensure uninterrupted communication; the second indicator is the signal-to-noise ratio stability index, which is obtained by calculating the ratio of the standard deviation to the mean of the prediction value sequence and normalizing it, this index represents the degree of signal quality fluctuation, the smaller the fluctuation, the more reliable the communication; the third indicator is the available time proportion, which is the ratio of the time length when the prediction value is higher than the communication threshold to the total prediction time length, this indicator describes the proportion of time that the satellite can provide effective communication service. These three indicators describe the future behavior of satellite signals from different dimensions: the minimum signal-to-noise ratio focuses on extreme cases, the stability index focuses on fluctuation characteristics, and the available time focuses on continuous ability.

[0039] Because the dimensions and numerical ranges of these three indicators are different, direct comparison or weighting may introduce bias, so normalization processing must be performed. Normalization processing uses the minimum-maximum value scaling method, which is processed separately for each indicator. Taking the minimum signal-to-noise ratio indicator as an example, first find the maximum and minimum values of all candidate satellites in this indicator, then map each satellite's original indicator value to the interval of zero to one: the satellite with the maximum value gets a score of one, the satellite with the minimum value gets a score of zero, and the scores of the remaining satellites are calculated by linear interpolation. This operation converts indicators of different dimensions into dimensionless values that can be compared, forming a set of evaluation indicator values corresponding to each candidate satellite. Normalization ensures the fairness of evaluation, for example, a satellite performs best in the stability indicator, regardless of the original fluctuation amplitude, it will get the highest score after normalization.

[0040] After obtaining the normalized index values, the system performs weighted summation calculation according to a preset index value weight coefficient set. The index value weight coefficient set is a set of empirical values that define the importance of each index in the final decision. Generally, the minimum signal-to-noise ratio has the highest weight (e.g., 0.5), the stability index has the second highest weight (e.g., 0.3), and the available time proportion has the lowest weight (e.g., 0.2). The weight distribution reflects the priority of communication support: first, ensure uninterrupted signal (focus on the worst case), second, ensure stable communication quality (reduce fluctuations), and finally, consider the available time. The weighted calculation multiplies each satellite's three normalized index values by the corresponding weights and sums them up to generate a comprehensive evaluation score value between zero and one. The higher the score, the better the satellite's overall communication potential.

[0041] Based on the comprehensive evaluation score value, the system sorts all candidate satellites in descending order, and the satellite with the highest score is selected as the optimal target satellite. The determination of the target satellite means that the system believes that the satellite can provide the most reliable communication support in the future. For example, in an emergency rescue scenario, selecting the satellite with the most stable signal instead of the satellite with the strongest instantaneous signal can significantly improve the success rate of the first transmission of the rescue information. After determining the target satellite, the system extracts two groups of key parameters from its navigation message data: spatial position parameters including the azimuth and elevation angles of the satellite relative to the device, which are derived from accurate ephemeris calculations; and communication characteristic parameters including the working frequency and signal modulation mode of the satellite. These parameters together form the satellite parameter set, which serves as input data for the next step of user guidance. The role of the satellite parameters is to provide clear device adjustment targets for the user. The azimuth and elevation angle parameters will be directly converted into intuitive operation instructions such as "turn left" or "raise the angle," guiding the user to align the device antenna to the best communication direction, thereby establishing a high-quality Beidou short message communication link.

[0042] Step S4, calculate the communication pointing deviation based on the satellite parameters and the device motion state data, and generate visual guidance instructions based on a preset hierarchical guidance strategy and the communication pointing deviation.

[0043] Among them, the satellite parameter set is the spatial position parameter of the target satellite, specifically including the azimuth and elevation angles required to observe the target satellite from the current position of the device. The spatial position parameter is derived from accurate satellite ephemeris calculation, and its physical meaning is the absolute direction of the target satellite in the global geographic coordinate system.

[0044] First, the absolute direction of the target satellite is compared with the current orientation of the device. The azimuth deviation is calculated as the algebraic difference between the azimuth angle of the target satellite and the real-time heading angle of the device, which reflects the angle that the device needs to rotate horizontally to align with the satellite. The pitch pointing deviation is calculated as the algebraic difference between the elevation angle of the target satellite and the real-time pitch angle of the device, which reflects the angle that the device needs to tilt vertically to align with the satellite. The calculation of these two deviation values is the basis for generating subsequent user guidance, for example, when the azimuth deviation is positive, it indicates that the device needs to rotate to the right, and when it is negative, it indicates that the device needs to rotate to the left; when the pitch deviation is positive, it indicates that the device needs to lift up, and when it is negative, it indicates that the device needs to press down.

[0045] After obtaining the accurate deviation values, the system processes them using a preset hierarchical guidance strategy. The hierarchical guidance strategy is a decision logic table stored in the device's memory, which divides the continuous deviation values into multiple discrete level intervals and maps corresponding adjustment guidance information for each interval. This strategy usually defines three deviation levels: the coarse adjustment level corresponds to the condition that the deviation angle is greater than fifteen degrees, at which directional guidance information is generated, such as displaying an arrow icon pointing left or right and a text prompt "Please turn the device to the left"; the fine adjustment level corresponds to the condition that the deviation angle is between five and fifteen degrees, at which quantitative guidance information is generated, such as displaying the specific numerical instruction "Turn ten degrees to the left"; the fine adjustment level corresponds to the condition that the deviation angle is less than five degrees, at which state maintenance information is generated, such as displaying the prompt "Please maintain the current device orientation". The purpose of hierarchical processing is to adapt to different operation precision requirements, quickly approach the target direction for large deviations and fine-tune for small deviations, ensuring adjustment efficiency and avoiding user oscillation due to excessive adjustment.

[0046] The hierarchical guidance strategy further integrates device motion state perception function, which judges the user's current state of being stationary, walking or running by monitoring real-time three-dimensional acceleration and angular velocity data of the device. For devices in motion, the system automatically increases the threshold range of each level of deviation and prolongs the debounce processing time, making the generated guidance information more inertial and easier to follow; for devices in stationary state, strict threshold standards are adopted and precise fine-tuning guidance is provided. This adaptive mechanism ensures the practicality of the guidance system in different use scenarios, for example, when the user is in motion and seeks help, the system will provide loose and stable direction guidance, rather than frequent and precise instructions.

[0047] Finally, the visual guidance instruction is generated according to the adjustment guidance information. The visual guidance instruction is realized by superimposing virtual instruction elements on the real-time view of the device camera. The instruction generation engine first obtains the real-time video stream of the camera, and then combines the real-time pose data of the device to render the adjustment guidance information into graphical elements: for azimuth adjustment, an arc-shaped arrow is displayed on the edge of the screen to indicate the target direction; for pitch adjustment, a vertical scale bar is displayed in the center of the screen to indicate the lifting or lowering amplitude; at the same time, text prompt information and color coding are generated (for example, red indicates a large deviation, and green indicates that it has been aligned). The generated instruction data is output to the device display screen through the graphics rendering pipeline to form the final user interaction interface. The role of the visual guidance instruction is to convert the abstract spatial orientation deviation into intuitive visual feedback, guiding the user to quickly and accurately align the device antenna with the target satellite, so as to establish the optimal Beidou short message communication link in a complex environment. For example, in a mountain rescue scenario, even if the user lacks professional communication knowledge, he or she can quickly find the best communication direction according to the arrow direction and text prompt in the picture, significantly improving the success rate of sending a distress signal.

[0048] Please refer to Figure 2 , Figure 2 The structure of a communication optimization device of a Beidou short message handheld terminal provided by the embodiment of the present application is shown in FIG. 2. As shown in FIG. 2, the communication optimization device 2 of the Beidou short message handheld terminal includes a signal screening module 21, a motion prediction module 22, a quality analysis module 23, a satellite screening module 24, and a deviation guidance module 25. Figure 2

[0049] The signal screening module 21 is configured to perform signal-to-noise ratio screening on the received multi-source satellite signals according to the collected environmental information to obtain a candidate satellite signal set.

[0050] In an optional embodiment, the signal screening module 21 is specifically configured to: identify the environment type according to the collected environmental information, and obtain a corresponding basic signal-to-noise ratio threshold value according to the identified environment type through a preset environment and threshold mapping rule; compensate and correct the basic signal-to-noise ratio threshold value according to the detected device pose angle to generate a signal-to-noise ratio screening threshold value; test the signal-to-noise ratio of the received multi-source satellite signals to obtain real-time signal-to-noise ratio measurement values of the satellite signals, and screen out satellite signals with real-time signal-to-noise ratio measurement values higher than the signal-to-noise ratio screening threshold value according to the signal-to-noise ratio screening threshold value and the real-time signal-to-noise ratio measurement values to form a candidate satellite signal set.

[0051] The motion prediction module 22 is configured to perform motion trajectory prediction on the real-time collected device motion state data to generate motion trajectory prediction data. ​

[0052] In an optional implementation, the motion prediction module 22 is specifically configured to: calculate a current speed according to a preset collection time interval and a first real-time coordinate and a last real-time coordinate corresponding to the collection time interval, to perform acceleration calculation and vector decomposition to generate a horizontal acceleration and a vertical acceleration according to the collection time interval and the current speed, and to calculate an angular speed according to the collection time interval and the pose angle; perform motion trajectory prediction according to a preset prediction time interval, the horizontal acceleration, the vertical acceleration, and the angular speed, to generate motion trajectory prediction data.

[0053] The quality analysis module 23 is configured to perform signal quality change prediction on each signal in the candidate satellite signal set according to the motion trajectory prediction data, to generate signal quality prediction data of each candidate satellite.

[0054] In an optional implementation, the quality analysis module 23 is specifically configured to: calculate relative distance change data and relative angle change data between the handheld terminal device and each candidate satellite according to the motion trajectory prediction data and the trajectory parameters, and perform associated calculation of position change and signal attenuation on each candidate satellite according to the relative distance change data, to obtain a basic signal attenuation amount; obtain a corresponding attenuation correction value from a preset environmental effect correction value list according to the environmental type, and correct the basic signal attenuation amount according to the attenuation correction value, to obtain an environment-optimized signal attenuation amount; perform obstacle identification and signal shielding evaluation on the environmental information according to the relative angle change data, to generate a communication shielding coefficient; perform quality change calculation on each signal in the candidate satellite signal set according to the environment-optimized signal attenuation amount and the communication shielding coefficient, to generate signal quality prediction data of each candidate satellite.

[0055] In an optional implementation, the quality analysis module 23 is further configured to: perform obstacle identification and classification processing on the environmental information, to obtain an obstacle category, an obstacle direction, and a corresponding coverage range, and determine a predicted direction range of a corresponding candidate satellite according to the device motion state data and the relative angle change data; when the coverage range and the predicted direction range overlap, obtain a communication shielding coefficient according to the obstacle category through a preset category and attenuation mapping table.

[0056] The satellite screening module 24 is configured to screen the candidate satellites corresponding to the candidate satellite signal set according to the signal quality prediction data, and obtain a target satellite and corresponding satellite parameters.

[0057] In an optional implementation, the satellite screening module 24 is specifically configured to: normalize the signal quality prediction data to obtain an evaluation index value set corresponding to each candidate satellite; perform weighted summation calculation on the evaluation index value set according to a preset index value weight coefficient set to generate a comprehensive evaluation score value corresponding to each candidate satellite; sort the candidate satellites according to the comprehensive evaluation score value to screen out an optimal target satellite, and obtain corresponding spatial position parameters according to the target satellite to form the corresponding satellite parameters.

[0058] The deviation guidance module 25 is configured to calculate a communication pointing deviation according to the satellite parameters and the device motion state data, and generate a visual guidance instruction according to a preset hierarchical guidance strategy and the communication pointing deviation.

[0059] In an optional implementation, the deviation guidance module 25 is specifically configured to: calculate an azimuth deviation and a device pitch pointing deviation according to the spatial position parameters and the device motion state data; perform hierarchical deviation threshold comparison and deviation threshold adjustment guidance mapping on the azimuth deviation and the device pitch pointing deviation according to a preset hierarchical guidance strategy to obtain corresponding adjustment guidance information; generate a visual guidance instruction according to a preset instruction generation manner and the adjustment guidance information.

[0060] The signal screening module 21, the motion prediction module 22, the quality analysis module 23, the satellite screening module 24, and the deviation guidance module 25 can be implemented by software or by hardware. For example, the implementation of the signal screening module 21 is described below. Similarly, the implementation of the motion prediction module 22, the quality analysis module 23, the satellite screening module 24, and the deviation guidance module 25 can be referred to the implementation of the signal screening module 21.

[0061] As an example of a software functional unit, the signal screening module 21 can include code running on a computing instance. The computing instance can include at least one of a physical host (computing device), a virtual machine, a container. Further, the computing instance can be one or more. For example, the signal screening module 21 can include code running on multiple hosts / virtual machines / containers. It should be noted that the multiple hosts / virtual machines / containers for running the code can be distributed in the same region, or in different regions. Further, the multiple hosts / virtual machines / containers for running the code can be distributed in the same availability zone (AZ), or in different AZs, each AZ including one data center or multiple data centers in close geographical proximity. Generally, a region can include multiple AZs.

[0062] Similarly, the multiple hosts / virtual machines / containers for running the code can be distributed in the same virtual private cloud (VPC), or in multiple VPCs. Generally, a VPC is set up within a region, and communication between two VPCs in the same region, or between VPCs in different regions, requires a communication gateway in each VPC to achieve interconnection between VPCs.

[0063] As an example of a hardware functional unit, the signal screening module 21 can include at least one computing device, such as a server, etc. Alternatively, the signal screening module 21 can also be a device implemented by an application-specific integrated circuit (ASIC), or a programmable logic device (PLD), etc. The PLD can be implemented by a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0064] The multiple computing devices included in the signal screening module 21 can be distributed in the same region or in different regions. The multiple computing devices included in the signal screening module 21 can be distributed in the same AZ or in different AZs. Similarly, the multiple computing devices included in the signal screening module 21 can be distributed in the same VPC or in multiple VPCs. The multiple computing devices can be any combination of servers, ASICs, PLDs, CPLDs, FPGAs, and GALs.

[0065] Referring to Figure 3 as shown, Figure 3 A structural schematic diagram of a computing device is provided in the present application. The computing device 100 includes a processor 104, a communication interface 108, a bus 102, and a memory 106. The processor 104, the communication interface 108, and the memory 106 communicate with each other through the bus 102, and in actual applications, communication can also be realized through wireless transmission and other means, which is not limited here.

[0066] The computing device 100 can be a server or a terminal device, and it should be understood that the number of processors and memories in the computing device 100 is not limited in the present application.

[0067] The processor 104 can include any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0068] The communication interface 108 uses a transceiver module such as, but not limited to, a network interface card and a transceiver to realize communication between the computing device 100 and other devices or communication networks.

[0069] The bus 102 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus can be divided into an address bus, a data bus, and a control bus. For ease of representation, Figure 3 only one line is used in the figure, but it does not mean that there is only one bus or only one type of bus. The bus 102 can include a path for transmitting information between various components (e.g., the memory 106, the processor 104, and the communication interface 108) of the computing device 100.

[0070] The memory 106 can include volatile memory, such as random access memory (RAM) including a cache area for the temporary storage of data. The memory 106 also can include non-volatile memory, such as read only memory (ROM), floppy disks, compact disks, or hard disks.

[0071] The memory 106 stores executable program code that is executed by the processor 104 to implement the functions of the aforementioned signal screening module 21, motion prediction module 22, quality analysis module 23, satellite screening module 24, and deviation directing module 25, respectively, thereby implementing the communication optimization method for the Beidou short message handheld terminal. That is, the memory 106 stores instructions for implementing the communication optimization method for the Beidou short message handheld terminal.

[0072] The embodiments of the present application also provide a computer readable storage medium. The computer readable storage medium can be any available medium or data storage device that can be used to store instructions that can be executed by a computing device. The computer readable storage medium can be a magnetic-based medium, such as a floppy diskette, a hard disk drive, or a magnetic tape, an optical-based medium, such as a compact disk (CD) or a digital versatile disk (DVD), or a semiconductor-based medium, such as a solid state disk, or the like. The computer readable storage medium includes instructions that are executable by a computing device to implement the communication optimization method for the Beidou short message handheld terminal.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; even though the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the protection scope of the technical solutions of the embodiments of the present application.

Claims

1. A communication optimization method for a Beidou short message handheld terminal, characterized in that, The method comprises: According to the collected environmental information, the received multi-source satellite signals are subjected to signal-to-noise ratio screening to obtain a candidate satellite signal set, and real-time collected device motion state data is subjected to motion trajectory prediction to generate motion trajectory prediction data; According to the motion trajectory prediction data, the signals in the candidate satellite signal set are subjected to signal quality change prediction to generate signal quality prediction data of each candidate satellite; According to the signal quality prediction data, the candidate satellites corresponding to the candidate satellite signal set are screened to obtain a target satellite and corresponding satellite parameters; According to the satellite parameters and the device motion state data, a communication pointing deviation is calculated, and a visual guidance instruction is generated according to a preset hierarchical guidance strategy and the communication pointing deviation.

2. The method of claim 1, wherein the method is performed by the handheld terminal. According to the collected environmental information, the received multi-source satellite signals are subjected to signal-to-noise ratio screening to obtain a candidate satellite signal set, and real-time collected device motion state data is subjected to motion trajectory prediction to generate motion trajectory prediction data; According to the collected environmental information, the received multi-source satellite signals are subjected to signal-to-noise ratio screening to obtain a candidate satellite signal set, and real-time collected device motion state data is subjected to motion trajectory prediction to generate motion trajectory prediction data; The device motion state data comprises real-time coordinates and an attitude angle; the motion trajectory prediction of the real-time collected device motion state data to generate motion trajectory prediction data comprises: According to a preset collection time interval, a first real-time coordinate corresponding to the collection time interval, and a last real-time coordinate, a current speed is calculated, so as to calculate an acceleration and perform vector decomposition to generate a horizontal acceleration and a vertical acceleration according to the collection time interval and the current speed, and an angular velocity is calculated according to the collection time interval and the attitude angle; 3. The method of claim 1, wherein the method comprises: According to a preset prediction time interval, the horizontal acceleration, the vertical acceleration, and the angular velocity, motion trajectory prediction is performed to generate motion trajectory prediction data. The signals in the candidate satellite signal set comprise trajectory parameters of the corresponding candidate satellites; the signal quality change prediction of the signals in the candidate satellite signal set according to the motion trajectory prediction data to generate signal quality prediction data of each candidate satellite comprises: According to the motion trajectory prediction data and the trajectory parameters, relative distance change data and relative angle change data between the handheld terminal device and each candidate satellite are calculated, and according to the relative distance change data, an associated calculation of position change and signal attenuation of each candidate satellite is performed to obtain a basic signal attenuation amount; 4. The method of claim 2, wherein the method is characterized by: According to the environmental type, a corresponding attenuation correction value is obtained from a preset environmental effect correction value list, and the basic signal attenuation amount is corrected according to the attenuation correction value to obtain an environmental optimized signal attenuation amount; ​ ​ The environment information is subjected to obstacle identification and signal shielding evaluation according to the relative angle change data, and a communication shielding coefficient is generated; The signal quality prediction data is used to screen the candidate satellites corresponding to the candidate satellite signal set, and target satellites and corresponding satellite parameters are obtained.

5. The method of claim 4, wherein the method further comprises: The environment information is subjected to obstacle identification and signal shielding evaluation according to the relative angle change data, and a communication shielding coefficient is generated; The environment information is subjected to obstacle identification and classification processing, and the obstacle category, obstacle direction and corresponding coverage range are obtained, and the predicted direction range of the corresponding candidate satellite is determined according to the device motion state data and the relative angle change data; When the coverage range and the predicted direction range overlap, the communication shielding coefficient is obtained according to the obstacle category through a pre-set category and attenuation mapping table.

6. The method of claim 1, wherein the method is performed by the Beidou short message handheld terminal. The signal quality prediction data is used to screen the candidate satellites corresponding to the candidate satellite signal set, and target satellites and corresponding satellite parameters are obtained. The signal quality prediction data is subjected to normalization processing, and an evaluation index value set corresponding to each candidate satellite is obtained; The evaluation index value set is subjected to weighted summation calculation according to a pre-set index value weight coefficient set, and a comprehensive evaluation score value corresponding to each candidate satellite is generated; The candidate satellites are sorted according to the comprehensive evaluation score value to screen out the optimal target satellite, and the corresponding spatial position parameter is obtained according to the target satellite to form the corresponding satellite parameter.

7. The method of claim 6, wherein the method further comprises: The satellite parameter includes a spatial position parameter, and the communication pointing deviation includes a direction deviation and a device pitch pointing deviation; the communication pointing deviation is calculated according to the satellite parameter and the device motion state data, and a visual guidance instruction is generated according to a pre-set hierarchical guidance strategy and the communication pointing deviation, which includes: The direction deviation and the device pitch pointing deviation are calculated according to the spatial position parameter and the device motion state data; The direction deviation and the device pitch pointing deviation are subjected to hierarchical deviation threshold comparison and deviation threshold and adjustment instruction mapping according to a pre-set hierarchical guidance strategy, and corresponding adjustment instruction information is obtained; A visual guidance instruction is generated according to a pre-set instruction generation mode and the adjustment instruction information.

8. A communication optimization device of a Beidou short message handheld terminal, applied to the communication optimization method of the Beidou short message handheld terminal in claim 1, characterized in that, The device includes: A signal screening module is configured to perform signal-to-noise ratio screening on the received multi-source satellite signals according to the collected environment information to obtain a candidate satellite signal set; A motion prediction module is configured to perform motion trajectory prediction on the real-time collected device motion state data to generate motion trajectory prediction data; A quality analysis module is configured to perform signal quality change prediction on each signal in the candidate satellite signal set according to the motion trajectory prediction data to generate signal quality prediction data of each candidate satellite; A satellite screening module is configured to screen the candidate satellites corresponding to the candidate satellite signal set according to the signal quality prediction data to obtain target satellites and corresponding satellite parameters. A deviation guidance module is configured to calculate a communication direction deviation according to the satellite parameter and the device motion state data, and generate a visual guidance instruction according to a preset hierarchical guidance strategy and the communication direction deviation.

9. A computing device, comprising: The computing device comprises: at least one processor; and a memory and a communication interface in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the communication optimization method of the Beidou short message handheld terminal according to any one of claims 1 to 7 by executing the instructions stored in the memory.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the communication optimization method of the Beidou short message handheld terminal according to any one of claims 1 to 7.

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