A 5G and Beidou-based device cooperative control method and system

Abstract

The application provides a device cooperative control method and system based on 5G and Beidou, the method comprising: firstly acquiring Beidou satellite positioning data and 5G cellular positioning data of a target device, identifying the environment type in which the target device is currently located according to signal quality parameters and map data, when the target device is in a boundary fuzzy area of signal transition, constructing a continuous transition function according to the environment type and historical motion trajectory, and dynamically allocating the contribution weight of the Beidou satellite positioning data and the 5G cellular positioning data in the fusion positioning according to the continuous transition function, performing Kalman filtering fusion on the data after weight adjustment and inertial navigation data to generate a continuous and smooth positioning result, and finally generating a cooperative control instruction according to the positioning result and sending the instruction to the target device through a 5G network or a Beidou short message, so that the control instruction lag or misjudgment caused by positioning jump can be avoided, and the cooperative control precision of an unmanned device in a signal transition area such as a tunnel entrance and exit or a city canyon is improved.

Description

Technical Field

[0001] This application relates to the field of equipment collaboration technology, specifically to a method and system for equipment collaboration control based on 5G and BeiDou. Background Technology

[0002] With the popularization of 5G communication technology and the full completion of the BeiDou Navigation Satellite System, equipment collaborative control methods based on the fusion of 5G and BeiDou have become a key supporting technology in fields such as autonomous driving, drone logistics, and intelligent transportation. Currently, mainstream collaborative positioning schemes typically employ multi-source data fusion technology, fusing BeiDou satellite positioning data, 5G cellular positioning data, and inertial navigation data using Kalman filtering. This leverages BeiDou's high precision in open environments, 5G's dense coverage in urban areas, and inertial navigation's continuous calculation capabilities in signal blind spots. For example, existing technologies obtain parameters such as the number of visible BeiDou satellites, the signal-to-noise ratio, and the reference signal receiving power of 5G base stations. These parameters, combined with a pre-set rule base or classification model, identify the current environment and then select a single dominant positioning source or adopt a fixed-weight fusion mode in specific areas to achieve a certain degree of collaborative positioning and control.

[0003] However, existing methods still have significant shortcomings in areas with overlapping signal coverage and ambiguous boundaries (such as tunnel entrances and exits, urban canyons with tall buildings, and indoor-outdoor boundaries). When a target device moves from one signal coverage environment to another, the contribution weights of BeiDou and 5G data often employ simple threshold switching or fixed weight fusion methods. This either-or switching logic or static weight allocation is difficult to adapt to the continuous and gradual changes in signal quality within the boundary area, resulting in significant jumps and jitters in the fused positioning results at environmental boundaries, making it impossible to generate a continuous and smooth positioning trajectory. The discontinuity of the positioning results not only reduces the reliability of location services but also causes delays or misjudgments in the collaborative control commands generated based on the positioning results, seriously affecting the accuracy and safety of collaborative control of unmanned equipment in complex environments. Summary of the Invention

[0004] This application provides a device collaborative control method and system based on 5G and BeiDou, which can effectively avoid the problem of control command delay or misjudgment caused by positioning jump, and improve the collaborative control accuracy and safety of unmanned equipment in signal transition areas such as tunnel entrances and exits and urban canyons.

[0005] A first aspect of this application provides a device collaborative control method based on 5G and BeiDou, the method comprising: Acquire BeiDou satellite positioning data, 5G cellular positioning data, inertial navigation data of the target device, and high-precision map data of the environment in which the target device is located; Based on the signal quality parameters of the BeiDou satellite positioning data and 5G cellular positioning data, and combined with the high-precision map data, the current environment type of the target device is identified; When the target device is in the boundary ambiguity region of signal transition, a continuous transition function is constructed based on the environment type and the historical motion trajectory of the target device; Based on the continuous transition function, the contribution weights of the BeiDou satellite positioning data and the 5G cellular positioning data in the fused positioning are dynamically allocated; The weighted BeiDou satellite positioning data and the 5G cellular positioning data are fused with the inertial navigation data to generate a continuous and smooth positioning result. Based on the positioning results, a collaborative control command is generated and sent to the target device via a 5G network or BeiDou short message service to achieve collaborative control of the target device.

[0006] In one possible implementation, identifying the current environment type of the target device based on the signal quality parameters of the BeiDou satellite positioning data and 5G cellular positioning data, combined with the high-precision map data, includes: Obtain the signal quality parameters of the BeiDou satellite positioning data and the signal quality parameters of the 5G cellular positioning data; Extract the surrounding environmental elements of the target device's current location from the high-precision map data; The map occlusion index is determined based on the relative positional relationship between the surrounding environmental elements and the target device; The signal quality parameters of the BeiDou satellite, the signal quality parameters of the 5G cell, and the map occlusion index are input into a pre-trained environment type classification model to obtain the current environment type of the target device.

[0007] In one possible implementation, a continuous transition function is constructed based on the environment type and the historical motion trajectory of the target device, including: The type of the boundary ambiguity region is determined based on the direction and rate of change of the environmental type. The normalized transition parameters are determined based on the movement duration or movement distance of the target device after entering the boundary ambiguity region. A continuous transition function is constructed based on the type of the boundary ambiguity region and the normalized transition parameters.

[0008] In one possible implementation, constructing a continuous transition function based on the type of the boundary ambiguity region and the normalized transition parameters includes: Based on the type of the boundary ambiguity region, select the corresponding transition function base class from the preset transition function library; Based on the normalized transition parameters, determine the range of values ​​for the independent variables of the transition function base class, and map the normalized transition parameters to the input of the transition function base class; Based on the degree of change in the boundary ambiguity region, the curve steepness coefficient of the transition function base class is adjusted to construct a continuous transition function that adapts to the current boundary ambiguity region.

[0009] In one possible implementation, the contribution weights of the BeiDou satellite positioning data and the 5G cellular positioning data in the fused positioning are dynamically allocated according to the continuous transition function, including: The first contribution weight of the BeiDou satellite positioning data is calculated based on the continuous transition function. The second contribution weight of the 5G cellular positioning data is determined based on the first contribution weight. Based on the historical motion trajectory, predict the next environmental type that the target device will enter in the next moment; Based on the predicted next environment type, a prediction correction factor is constructed, and the first contribution weight is corrected using the prediction correction factor to obtain the corrected first contribution weight.

[0010] In one possible implementation, predicting the next environment type the target device will enter at the next moment based on the historical motion trajectory includes: Obtain the historical environment type sequence and historical location sequence of the target device over the past N sampling times; The historical environment type sequence and historical location sequence are input into the prediction model, which employs a trajectory fitting algorithm or a Markov chain model. The prediction model is used to calculate the probability that the target device will arrive at each candidate environment type in the next moment. The candidate environment type with the highest probability is used as the next environment type to be predicted, and the corresponding prediction confidence is output. The value of the prediction correction factor is determined based on the prediction confidence level.

[0011] In one possible implementation, the step of fusing the weighted BeiDou satellite positioning data and the 5G cellular positioning data with the inertial navigation data to generate a continuous and smooth positioning result includes: The weighted BeiDou satellite positioning data and the 5G cellular positioning data are input into the Kalman filter as the observation values ​​of the Kalman filter; The inertial navigation data is input into the Kalman filter as the predicted value of the Kalman filter; The Kalman filter outputs a fused localization result based on the observed values ​​and the predicted values.

[0012] This example provides a device collaborative control method based on 5G and BeiDou. First, it acquires BeiDou satellite positioning data, 5G cellular positioning data, inertial navigation data, and high-precision map data of the target device. Based on signal quality parameters and map data, it identifies the current environment type of the target device. When the target device is in a blurred boundary region of signal transition, a continuous transition function is constructed based on the environment type and historical motion trajectory. The contribution weights of BeiDou satellite positioning data and 5G cellular positioning data in the fused positioning are dynamically allocated according to this continuous transition function. The weighted data is then fused with the inertial navigation data using Kalman filtering to generate a continuous and smooth positioning result. Finally, based on the positioning result, a [missing information - likely a system or method] is generated. The collaborative control commands are sent to the target device via 5G network or BeiDou short message service. By constructing a continuous transition function, the data contribution weights within the boundary ambiguity area are smoothly transitioned, avoiding the positioning jumps and jitters caused by traditional threshold switching or fixed weight fusion methods at environmental boundaries. This generates a continuous and smooth positioning trajectory, significantly improving the continuity and reliability of positioning results in complex environments. At the same time, the collaborative control commands generated based on the continuous and smooth positioning results can accurately reflect the real-time status of the target device, effectively avoiding the problem of control command lag or misjudgment caused by positioning jumps. This greatly improves the collaborative control accuracy and safety of unmanned equipment in signal transition areas such as tunnel entrances and exits and urban canyons.

[0013] A second aspect of this application provides a device collaborative control system based on 5G and BeiDou, the system comprising: The first acquisition unit is used to acquire BeiDou satellite positioning data, 5G cellular positioning data, inertial navigation data of the target device, and high-precision map data of the environment in which the target device is located. The first processing unit is used to identify the current environment type of the target device based on the signal quality parameters of the BeiDou satellite positioning data and 5G cellular positioning data, combined with the high-precision map data. The second processing unit is used to construct a continuous transition function based on the environment type and the historical motion trajectory of the target device when the target device is in the boundary ambiguity region of signal transition; The third processing unit is used to dynamically allocate the contribution weights of the BeiDou satellite positioning data and the 5G cellular positioning data in the fused positioning according to the continuous transition function. The fourth processing unit is used to fuse the weighted BeiDou satellite positioning data and the 5G cellular positioning data with the inertial navigation data to generate a continuous and smooth positioning result. The collaborative control unit is used to generate collaborative control commands based on the positioning results and send them to the target device via a 5G network or BeiDou short message service to achieve collaborative control of the target device.

[0014] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the device collaborative control method based on 5G and BeiDou in the first aspect of this application.

[0015] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the 5G and BeiDou-based device collaborative control method of the first aspect of this application.

[0016] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the 5G and BeiDou-based device collaborative control method of the first aspect of this application. The computer program product may be a software installation package. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This application provides a schematic diagram of the overall structure of a device collaborative control method based on 5G and BeiDou. Figure 2 This application provides a schematic diagram of the overall structure of a device collaborative control system based on 5G and BeiDou. Figure 3 This application provides a schematic diagram of the structure of a terminal. Figure label: First acquisition unit-1, first processing unit-2, second processing unit-3, third processing unit-4, fourth processing unit-5, and collaborative control unit-6. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. 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.

[0020] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0021] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0022] The device collaborative control method based on 5G and BeiDou is applied to a device collaborative control system based on 5G and BeiDou. Figure 1 A schematic diagram illustrating the overall process of a device collaborative control method based on 5G and BeiDou is shown. Figure 1 As shown, it includes: S1. Acquire BeiDou satellite positioning data, 5G cellular positioning data, inertial navigation data of the target device, and high-precision map data of the environment in which the target device is located.

[0023] Specifically, the BeiDou positioning module deployed on the target device receives BeiDou satellite signals in real time and analyzes them to obtain BeiDou satellite positioning data. This data includes the latitude and longitude coordinates, elevation information, velocity vector, number of visible satellites, and signal carrier-to-noise ratio of each satellite.

[0024] Specifically, the system interacts with surrounding 5G base stations via the 5G communication module on the target device to obtain 5G cellular positioning data. This data includes the reference signal received power, signal-to-interference-plus-noise ratio, base station identifier, and base station geographical location information of the 5G base station. Inertial navigation data is collected in real time via the inertial measurement unit on the target device. This data includes acceleration values ​​measured by a three-axis accelerometer and angular velocity values ​​measured by a three-axis gyroscope. High-precision map data is retrieved from the target device's local storage or from a cloud server via the 5G network. This data includes geographic information elements such as building outlines, vegetation coverage areas, tunnel entrance and exit locations, viaduct structures, and road boundaries in the area where the target device is located.

[0025] After obtaining the above four types of data, time alignment is performed on each type of data to ensure that data at the same time can be matched. The original data is then subjected to preliminary outlier removal and validity verification. The data that passes the verification is formatted into a unified data frame structure for subsequent steps.

[0026] In this example, by simultaneously acquiring BeiDou satellite positioning data, 5G cellular positioning data, inertial navigation data, and high-precision map data, a complete multi-source data foundation is provided for subsequent environmental type identification, boundary ambiguity area determination, dynamic allocation of contribution weights, and fusion positioning. This ensures that the collaborative control method can obtain sufficient input information support under different signal environments.

[0027] S2. Based on the signal quality parameters of the BeiDou satellite positioning data and 5G cellular positioning data, and combined with the high-precision map data, identify the current environment type of the target device.

[0028] Step S2 includes the following steps: S201. Obtain the signal quality parameters of the BeiDou satellite positioning data and the signal quality parameters of the 5G cellular positioning data.

[0029] The BeiDou satellite positioning data extracts signal quality parameters, including the number of visible satellites and the carrier-to-noise ratio (C / N0) of each visible satellite. The number of visible satellites can be obtained by counting the number of satellites that can successfully receive and decode navigation messages at the current time. The carrier-to-noise ratio is calculated by the ratio of carrier power to noise power of each satellite signal, in dB-Hz. The 5G cellular positioning data extracts 5G cellular signal quality parameters, including the reference signal received power and the signal-to-interference-plus-noise ratio. The reference signal received power is obtained by measuring the received power of the downlink reference signal of the 5G base station, in dBm. The signal-to-interference-plus-noise ratio is calculated by the ratio of the useful signal power to the sum of the interference and noise power, in dB.

[0030] S202. Extract the surrounding environmental elements of the target device's current location from the high-precision map data.

[0031] Based on the target device's current approximate location information (which can be based on the positioning results from the previous moment or the current BeiDou single-point positioning results), geographic information elements within a preset range around the location are retrieved from high-precision map data. The preset range can be set according to the actual application scenario; for example, it can be set to a radius of 200 meters for a drone scenario and a radius of 100 meters for a vehicle scenario. The extracted surrounding environmental elements include building outline data (building height, shape, and geographical location), vegetation cover area data (the range and average height of forests and shrubs), tunnel entrance and exit location data (tunnel axis and entrance / exit coordinates), viaduct structure data (pier location, bridge deck height, and width), and road boundary data.

[0032] After extracting the above elements, the geographic coordinates of each element are uniformly transformed to the same coordinate system as the target device, and a set of surrounding environmental elements containing element type, geometry and spatial location is constructed.

[0033] S203. Determine the map occlusion index based on the relative positional relationship between the surrounding environmental elements and the target device.

[0034] Specifically, based on the set of surrounding environmental elements extracted in step S202, combined with the current location of the target device and the preset BeiDou satellite location and 5G base station location, the degree to which the signal propagation path between the target device and each signal source is blocked by environmental elements is calculated.

[0035] For BeiDou satellite signals, the azimuth and elevation angles of all visible satellites at the current moment are obtained. A ray is generated along the satellite direction, starting from the target device's location. The intersection of this ray with surrounding environmental elements (buildings, vegetation, overpasses, etc.) is determined. If an intersection occurs, the type and depth of the obstruction are recorded. An obstruction marker (0 for no obstruction, 1 for obstruction) is generated for each satellite signal path. For 5G base station signals, the location coordinates of surrounding 5G base stations are obtained. A ray is generated along the base station direction, starting from the target device's location. Similarly, the intersection of this ray with surrounding environmental elements is determined. Based on the above results, a map obstruction index is calculated. , In the formula, The number of BeiDou satellites that were blocked. This represents the total number of visible BeiDou satellites. The number of 5G base stations that are blocked. This represents the total number of detectable 5G base stations. Map occlusion index. The range of values ​​is , The larger the value, the more severe the signal propagation path is blocked by environmental factors.

[0036] In this example, by calculating the map occlusion index, the geographic environmental information in the high-precision map data is quantified into a numerical indicator that can participate in subsequent environmental type identification, providing key input features for the environmental type classification model and significantly improving the accuracy of environmental determination.

[0037] S204. Input the signal quality parameters of the Beidou satellite, the signal quality parameters of the 5G cell, and the map occlusion index into a pre-trained environment type classification model to obtain the current environment type of the target device.

[0038] The process involves constructing a machine learning-based environment type classification model, which can employ one of the following: Support Vector Machine (SVM), Random Forest, or Lightweight Neural Network. During offline training, a large number of BeiDou signal quality parameters (number of visible satellites, signal-to-noise ratio), 5G signal quality parameters (RSRP, SINR), and corresponding map occlusion indices are pre-collected for different scenarios. Each sample is labeled with a true environment type, including at least four categories: open environment, slightly occluded environment, severely occluded environment, and completely occluded environment. After training, the model is deployed to the edge computing unit of the target device or a cloud server. In online applications, the BeiDou signal quality parameters, 5G signal quality parameters, and map occlusion index are combined into a feature vector and input into the environment type classification model. The model outputs probability values ​​for each environment type, and the environment type with the highest probability is taken as the current environment type of the target device.

[0039] Specifically, an open environment corresponds to a map occlusion index below 0.2 and all signal quality parameters above a preset first quality threshold; a slightly occluded environment corresponds to a map occlusion index between 0.2 and 0.5 and fluctuating signal quality parameters; a severely occluded environment corresponds to a map occlusion index between 0.5 and 0.8 and consistently low signal quality parameters; and a completely occluded environment corresponds to a map occlusion index above 0.8 or signal quality parameters below a preset second quality threshold.

[0040] S3. When the target device is in the boundary ambiguity region of signal transition, a continuous transition function is constructed based on the environment type and the historical motion trajectory of the target device.

[0041] Step S3 includes the following steps: S301. Determine the type of the boundary ambiguity region based on the direction and rate of change of the environmental type.

[0042] Specifically, the environmental type sequence output in real time in step S2 is used to monitor the changes in the environmental type of the target device over time. The current moment is recorded. Environment type and the former Environment type sequence at time 1 ,in The preset observation window length (e.g.) The changing trend of environmental types is analyzed based on this sequence: if the environmental type changes progressively from open to slightly shaded, severely shaded, or completely shaded within the observation window, the direction of change is determined to be "from good to bad"; conversely, if the environmental type changes progressively from shaded to more open, the direction of change is determined to be "from bad to good". Simultaneously, the rate of change of environmental type is calculated. The rate of change can be measured by the number of levels of change in environmental type level per unit time. For example, if we define environmental type levels as: open environment = 1, slightly obstructed = 2, severely obstructed = 3, and completely obstructed = 4, then the rate of change... ,in The sampling interval is defined as follows. Based on the combination of the direction and rate of change, the boundary ambiguity region is divided into different types: Type 1 (rapid change from good to bad), Type 2 (slow change from good to bad), Type 3 (rapid change from bad to good), and Type 4 (slow change from bad to good). Different types correspond to different function shapes and parameter adjustment strategies when constructing the transition function.

[0043] S302. Determine the normalized transition parameters based on the movement time or movement distance of the target device after entering the boundary ambiguity region.

[0044] Specifically, when step S2 detects that the target device has entered the boundary ambiguity area for the first time, the entry time is recorded. and the position upon entry Then, the current time is calculated in real time. The duration of motion relative to the entry time Simultaneously, based on inertial navigation data and high-precision map data, the distance the target device moves along its trajectory from the entry point is calculated. ,in This represents the instantaneous velocity vector of the target device. The estimated total duration is based on the boundary ambiguity region. or total length (This can be obtained based on the geometric dimensions and historical motion speed of the blurred boundary areas in high-precision map data), calculate the normalized transition parameters. Its definition is: In the formula, The range of values ​​is This indicates the progress of the target device in completing the transition within the boundary ambiguity region. This indicates that the area has just entered the region with blurred boundaries. This indicates that we are about to leave the boundary ambiguity region. This normalized transition parameter will be used as the independent variable for constructing the continuous transition function later.

[0045] In this example, by monitoring the dynamic changes in the environment type in real time, the type and transition progress of the boundary ambiguity area are accurately determined, providing key input for constructing an adaptive continuous transition function and ensuring that the subsequent weight allocation can reflect the actual trend of environmental changes.

[0046] S303. Construct a continuous transition function based on the type of the boundary ambiguity region and the normalized transition parameters.

[0047] Specifically, step S303 includes the following steps: S3031. Based on the type of the boundary ambiguity region, select the corresponding transition function base class from the preset transition function library.

[0048] Among them, a transition function library is constructed and stored in the local storage of the target device or the database of the cloud server. The function library contains a variety of transition function base classes of different forms, which are adapted to the signal change characteristics of different types of boundary ambiguity regions.

[0049] Furthermore, based on the boundary ambiguity region type (type 1 to type 4) determined in step S301, the corresponding transition function base class is selected from the function library: for type 1 (from good to bad, rapid change), a steep S-shaped function is selected. To quickly respond to a sharp decline in signal quality; for the second type (from good to bad, a slow change), a smooth polynomial function is selected. To achieve a smooth and gradual decrease in weights; for the third type (from poor to good, rapid change), a steep S-shaped function is selected, but the parameter configuration is symmetrical to that of the first type; for the fourth type (from poor to good, slow change), a sinusoidal transition function is selected. Or a smooth polynomial function.

[0050] Furthermore, each transition function base class satisfies , (Used to represent a scenario of transitioning from complete reliance on 5G to complete reliance on BeiDou) or , (Used to represent transitional scenarios in the opposite direction), the specific direction is determined based on the changing direction of the boundary blurring area.

[0051] S3032. Based on the normalized transition parameters, determine the range of values ​​for the independent variables of the transition function base class, and map the normalized transition parameters to the input of the transition function base class.

[0052] Among them, the normalized transition parameters obtained in step S302 This indicates the relative progress of the target device within the boundary ambiguity region. The input arguments of the transition function are directly used as the base class of the transition function to set the arguments of the transition function. For function base classes that require a specific range of independent variables (e.g., sigmoid functions typically require independent variables to cover the entire real number domain), a linear transformation can be used to... Map to the appropriate input range: ,in For a sigmoid function, the preset mapping interval can be taken as follows: To ensure When the function value is close to 0 or 1, The time function value is close to 1 or 0. Once the mapping relationship is determined, the current time value is calculated in real time. The value is then substituted into the base class of the transition function to obtain the corresponding function output value. This output value serves as the benchmark for the contribution weight of BeiDou satellite positioning data in subsequent steps.

[0053] S3033. Based on the degree of change in the boundary ambiguity region, adjust the curve steepness coefficient of the transition function base class to construct a continuous transition function that adapts to the current boundary ambiguity region.

[0054] Among them, the rate of change of environment type obtained from step S301 As an indicator to measure the drastic changes in the boundary ambiguity region. Based on the rate of change. Dynamically adjust the steepness coefficient of the transition function: For sigmoid functions, the steepness coefficient... Directly affects the slope of the function in the transition region, setting , in, As the baseline steepness coefficient, As the adjustment factor, the rate of change The larger The larger the value, the better the function. The steeper the change in the vicinity; for polynomial functions, the curve shape is controlled by adjusting the power or coefficients; for sine functions, the transition rate is controlled by adjusting the frequency parameter. After adjustment, the base class selected in step S3031, the independent variable mapping relationship determined in step S3032, and the steepness coefficients adjusted in step S3033 are combined to form a complete continuous transition function expression: In the formula, The final continuous transition function has the following range: This represents the baseline contribution weight of BeiDou satellite positioning data in fused positioning. This function follows... The continuous change from 0 to 1 enables a smooth transition from one location source dominance to another.

[0055] In this example, by selecting the function base class based on the type of boundary ambiguity area, determining the input mapping based on the normalized transition parameters, and adjusting the curve steepness coefficient based on the degree of change, a continuous transition function adapted to different transition scenarios is constructed. This provides a precise mathematical tool for the subsequent dynamic allocation of contribution weights, ensuring a continuous and smooth transition process from BeiDou-dominated to 5G-dominated or reverse transition.

[0056] S4. Based on the continuous transition function, dynamically allocate the contribution weights of the BeiDou satellite positioning data and the 5G cellular positioning data in the fused positioning.

[0057] Step S4 includes the following steps: S401. Calculate the first contribution weight of the BeiDou satellite positioning data according to the continuous transition function.

[0058] Among them, the continuous transition function constructed from step S3 Read the normalized transition parameters at the current time. Corresponding function value Function value The baseline contribution weight of BeiDou satellite positioning data in fused positioning. The formula is as follows: Among them, according to the properties of continuous transition functions, The range of values ​​is At the entrance of the boundary ambiguity area ( ) Approaching 0 or 1 (depending on the direction of transition), at the exit ( ) The middle region is close to 1 or 0. Continuous change.

[0059] If the target device has not yet entered or has left the boundary ambiguity area at the current moment, the weight calculation in this step will not be performed. Instead, the preset standard weights (e.g., BeiDou weight 0.9 and 5G weight 0.1 in open environment, and BeiDou weight 0.1 and 5G weight 0.9 in completely obscured environment) will be used directly for positioning fusion.

[0060] S402. Determine the second contribution weight of the 5G cellular positioning data based on the first contribution weight.

[0061] In the fusion positioning process, the sum of the contribution weights of BeiDou satellite positioning data and 5G cellular positioning data is 1. Therefore, according to the first contribution weight obtained in step S401... Calculate the second contribution weight of 5G cellular positioning data. The calculation formula is as follows: Among them, when When the value is close to 1, the positioning results are mainly dominated by BeiDou data, which is suitable for open or slightly obstructed environments; when... When the value is close to 1, the positioning result is mainly dominated by 5G data, which is suitable for environments with severe or complete occlusion. In the boundary ambiguity area, the two types of data participate in positioning fusion together according to the weight ratio determined by the continuous transition function to achieve a smooth transition.

[0062] S403. Based on the historical motion trajectory, predict the next environment type that the target device will enter at the next moment.

[0063] Specifically, step S403 includes the following steps: S4031. Obtain the historical environment type sequence and historical location sequence of the target device over the past N sampling times.

[0064] Specifically, from the environment type identification results, the environment type values ​​of N consecutive sampling times prior to the current time are extracted to form a historical environment type sequence. ,in For the current moment, For the preset sequence length (e.g.) ).

[0065] Furthermore, from the BeiDou positioning data or the fused positioning results output in step S5 at the previous moment, the target device's position coordinates at the corresponding moment are extracted to form a historical position sequence. Coordinates of each position These are three-dimensional spatial coordinates.

[0066] The acquired sequence data is validated, abnormal transition points are removed, and missing data is filled in using interpolation methods to ensure the integrity and continuity of the sequence.

[0067] S4032. Input the historical environment type sequence and historical location sequence into the prediction model, wherein the prediction model adopts a trajectory fitting algorithm or a Markov chain model.

[0068] The prediction model can be implemented in two ways: it can use a trajectory fitting algorithm based on historical position sequences. To fit the motion trajectory of the target device, common methods include polynomial fitting or Kalman filter prediction to obtain the motion direction and velocity vector of the target device. Furthermore, a Markov chain model can also be used, based on the sequence of historical environment types. Transition probability matrix between statistical environment types ,in Indicates from the environment type Transfer to environment type The probability. The two methods can be used individually or in combination, depending on the computing resources and accuracy requirements of the actual application scenario.

[0069] In this example, two models can be enabled simultaneously and the results can be weighted and fused to improve the robustness of the prediction.

[0070] S4033. Calculate the probability that the target device will arrive at each candidate environment type in the next moment using the prediction model.

[0071] If a trajectory fitting algorithm is used, the target device's position at the next moment can be predicted based on the fitted motion trajectory. Predicted location By combining the spatial distribution of different environmental types in high-precision map data, calculations are performed. The probability of falling into each type of environment is proportional to the reciprocal of the distance between the location and each type of environment; if a Markov chain model is used, the probability is determined based on the environment type at the current moment. and transition probability matrix Calculate the probability of arriving at each environment type in the next moment. If a fusion approach is used, the probabilities obtained from the two methods are weighted and averaged: in The range of values ​​for the fusion weighting coefficients is as follows: Finally, the candidate environment types were obtained. Corresponding probability .

[0072] S4034. Select the candidate environment type with the highest probability as the next environment type to be predicted, and output the corresponding prediction confidence level.

[0073] Among the probabilities of each candidate environment type, the environment type corresponding to the highest probability is selected as the next environment type to be predicted. : At the same time, this maximum probability value As the confidence level of this prediction , A higher confidence level indicates a more reliable prediction, while a lower confidence level indicates greater uncertainty in the prediction.

[0074] Set confidence threshold (For example ),when When the prediction result is considered reliable, subsequent steps will use this prediction result to adjust the weights; when If the prediction is deemed unreliable, subsequent steps will not use prediction correction or will adopt a conservative correction strategy.

[0075] S4035. Determine the value of the prediction correction factor based on the prediction confidence level.

[0076] Specifically, based on the predicted confidence level output in step S4034 Dynamically determine the prediction correction factor The value of . Correction factor Used to adjust the first contribution weight calculated in step S401 Its value range is This indicates the magnitude of the adjustment to the baseline weight. Correction Factor With confidence level The relationship is defined using linear functions or piecewise functions: In the formula, For adjustment coefficients (e.g.) ), ensuring that when the confidence level reaches the threshold When the confidence level is below the threshold (i.e., no correction). The correction factor acts differently for different types of boundary ambiguity areas: when it is predicted that the environment type will worsen in the next moment (e.g., transitioning from open to occluded), the correction factor is used to reduce the BeiDou weight in advance. When it is predicted that the environmental type will improve in the next moment (e.g., transitioning from obstruction to openness), the correction factor is used to increase the BeiDou weight in advance. ).

[0077] In this step, the weights are adaptively adjusted in advance by predicting the correlation between the direction and the sign of the correction factor.

[0078] S404. Construct a prediction correction factor based on the predicted next environment type, and use the prediction correction factor to correct the first contribution weight to obtain the corrected first contribution weight.

[0079] In this example, the prediction correction factor is determined based on step S403. and the first contribution weight calculated in step S401 Calculate the corrected first contribution weight The calculation formula is as follows: In the formula, This is a limiting function to ensure that the corrected weight value does not exceed [the specified value]. The range. The direction of the correction factor's effect is determined by the predicted direction of change in the environmental type: if it is predicted that the environmental type will worsen (signal quality will deteriorate) in the next moment, then... This results in a lower BeiDou weight than the baseline value, allowing for an earlier shift in positioning dominance to 5G; if it is predicted that the environment will improve (signal quality will increase) in the next moment, then... This makes the BeiDou weight higher than the baseline value, thus shifting the positioning dominance to BeiDou ahead of schedule. After the correction is completed, according to... Re-determining the corrected second contribution weight of 5G cellular positioning data To obtain the final contribution weight This is used for step S5.

[0080] In this example, dynamic adaptive allocation of BeiDou and 5G contribution weights is achieved through baseline weight calculation based on a continuous transition function and weight correction based on historical trajectory prediction. On the one hand, the continuous transition function ensures that the weights change smoothly with the transition progress within the boundary ambiguity region; on the other hand, the prediction correction mechanism introduces forward-looking adjustments, enabling the weight change trend to adapt to upcoming environmental changes in advance, further improving the continuity and smoothness of the positioning results at environmental boundaries. S5. The weighted BeiDou satellite positioning data and the 5G cellular positioning data are fused with the inertial navigation data to generate a continuous and smooth positioning result.

[0081] Step S5 includes the following steps: S501. Input the weighted BeiDou satellite positioning data and the 5G cellular positioning data into the Kalman filter as the observation values ​​of the Kalman filter.

[0082] First, the observation vector of the Kalman filter is constructed. The corrected contribution weights are obtained from step S4. and and the BeiDou satellite positioning data obtained from step S1 at the current moment. and 5G cellular positioning data (These are the three-dimensional position coordinates of the target device). The two types of positioning data are combined into a unified observation vector. : In the formula, , ,therefore It is a 6-dimensional column vector. Simultaneously, an observation noise covariance matrix is ​​constructed based on the contribution weights. This matrix reflects the reliability of each observation. A larger weight should correspond to lower observation noise; therefore, the following settings are used: In the formula, and These are the benchmark observation noise covariance matrices for BeiDou and 5G positioning under ideal conditions (usually set as a diagonal matrix based on the prior accuracy of the sensors). The factor ensures that when the weights are close to 1, the observation noise approaches the baseline value, and when the weights are close to 0, the observation noise tends to infinity, thus automatically reducing the impact of unreliable data during fusion. The constructed observation vectors are then... and observation noise covariance matrix The input Kalman filter serves as the observation update input for the current time step.

[0083] S502. Input the inertial navigation data into the Kalman filter as the predicted value of the Kalman filter.

[0084] The inertial navigation data obtained from step S1 includes acceleration measured by a triaxial accelerometer. Angular velocity measured by a three-axis gyroscope An inertial navigation mechanical orchestration algorithm can be used to update the state estimate from the previous moment to obtain the predicted state value for the current moment. and prediction error covariance matrix The state vector typically contains information such as the target device's position, velocity, and attitude, for example... The time update process follows the standard prediction equation of Kalman filtering: In the formula, This is the state transition matrix (derived from the inertial navigation motion model). To control the input, For the control matrix, Let be the process noise covariance matrix, representing the random error of inertial navigation. Through the above prediction steps, the Kalman filter obtains a predicted value of the current state of the target device based on inertial navigation data. This predicted value does not depend on BeiDou or 5G observations and can maintain continuous output even when the signal is interrupted for a short time.

[0085] S503. The Kalman filter outputs a fused positioning result based on the observed value and the predicted value.

[0086] Specifically, the Kalman filter performs the standard update step, changing the observation vector input in step S501... The state prediction obtained in step S502 The fusion process is then performed. First, the observation residuals are calculated using the formula shown below: in The observation matrix maps the state space to the observation space. Since the observation vector contains positional information, The expression is as follows: That is, the position component in the state is extracted and mapped to BeiDou and 5G observations respectively. Then the Kalman gain is calculated: Finally, update the state estimate and covariance matrix: Updated state estimation This is the fused positioning result, which includes information such as the target device's optimal position, velocity, and attitude at the current moment. The position component is then extracted. This serves as the final positioning coordinate output. Because the Kalman filter combines continuous prediction from inertial navigation with observation corrections from BeiDou / 5G, and the observation noise covariance matrix... By dynamically adjusting the contribution weights, the fusion results can smoothly transition within the boundary ambiguity area, avoiding abrupt changes.

[0087] In this example, by inputting weighted BeiDou and 5G data as observations and inertial navigation data as predictions into a Kalman filter, and dynamically adjusting the observation noise covariance matrix using contribution weights, adaptive fusion of multi-source data is achieved. The recursive estimation characteristic of the Kalman filter ensures the continuity and smoothness of the positioning results, while the adjustment of observation noise by weights automatically favors data sources with higher reliability in the boundary ambiguity region, ultimately generating an accurate and smooth positioning trajectory, providing a reliable position reference for the subsequent generation of cooperative control commands.

[0088] S6. Generate a collaborative control command based on the positioning result and send it to the target device via a 5G network or BeiDou short message to achieve collaborative control of the target device.

[0089] Specifically, based on the continuous and smooth positioning results output in step S5, the precise position coordinates, speed, and direction of movement of the target device at the current moment are obtained. The current positioning results are compared with the preset target trajectory or mission plan to calculate the position deviation, speed deviation, and heading deviation.

[0090] Based on the PID control algorithm, corresponding cooperative control commands can be generated by combining the above-mentioned deviations. These commands include at least the target device's desired speed, desired heading angle, desired altitude (for UAVs) or desired turning angle (for vehicles), as well as mission state switching commands (such as hovering, landing, acceleration, deceleration, obstacle avoidance, etc.).

[0091] Furthermore, after the control command is generated, the transmission channel is selected according to the current communication environment of the target device: if the 5G network signal quality is good (e.g., RSRP is higher than the preset threshold), the control command is sent to the target device in the form of data packets via the 5G network first; if the target device is in a 5G signal blind zone or the 5G network is unavailable, the system switches to the BeiDou short message communication link, encodes the simplified control command into the BeiDou short message format, and sends it to the target device via BeiDou satellite.

[0092] Furthermore, after receiving the control command, the airborne or vehicle-mounted controller parses the command and drives the actuators (motors, control surfaces, brakes, etc.) to complete the corresponding actions, achieving real-time coordinated control of the target equipment. Simultaneously, the system continuously monitors the response status of the target equipment, forming a closed-loop feedback control.

[0093] This example provides a device collaborative control method based on 5G and BeiDou. First, it acquires BeiDou satellite positioning data, 5G cellular positioning data, inertial navigation data, and high-precision map data of the target device. Based on signal quality parameters and map data, it identifies the current environment type of the target device. When the target device is in a blurred boundary region of signal transition, a continuous transition function is constructed based on the environment type and historical motion trajectory. The contribution weights of BeiDou satellite positioning data and 5G cellular positioning data in the fused positioning are dynamically allocated according to this continuous transition function. The weighted data is then fused with the inertial navigation data using Kalman filtering to generate a continuous and smooth positioning result. Finally, based on the positioning result, a [missing information - likely a system or method] is generated. The collaborative control commands are sent to the target device via 5G network or BeiDou short message service. By constructing a continuous transition function, the data contribution weights within the boundary ambiguity area are smoothly transitioned, avoiding the positioning jumps and jitters caused by traditional threshold switching or fixed weight fusion methods at environmental boundaries. This generates a continuous and smooth positioning trajectory, significantly improving the continuity and reliability of positioning results in complex environments. At the same time, the collaborative control commands generated based on the continuous and smooth positioning results can accurately reflect the real-time status of the target device, effectively avoiding the problem of control command lag or misjudgment caused by positioning jumps. This greatly improves the collaborative control accuracy and safety of unmanned equipment in signal transition areas such as tunnel entrances and exits and urban canyons.

[0094] For those consistent with the above, please refer to Figure 2 , Figure 2This application provides a schematic diagram of a device collaborative control system based on 5G and BeiDou. For example... Figure 2 As shown, the system includes: The first acquisition unit 1 is used to acquire BeiDou satellite positioning data, 5G cellular positioning data, inertial navigation data, and high-precision map data of the environment in which the target device is located.

[0095] The first processing unit 2 is used to identify the current environment type of the target device based on the signal quality parameters of the BeiDou satellite positioning data and 5G cellular positioning data, combined with the high-precision map data.

[0096] The second processing unit 3 is used to construct a continuous transition function based on the environment type and the historical motion trajectory of the target device when the target device is in the boundary ambiguity region of signal transition.

[0097] The third processing unit 4 is used to dynamically allocate the contribution weights of the BeiDou satellite positioning data and the 5G cellular positioning data in the fused positioning according to the continuous transition function.

[0098] The fourth processing unit 5 is used to fuse the weighted BeiDou satellite positioning data and the 5G cellular positioning data with the inertial navigation data to generate a continuous and smooth positioning result.

[0099] The collaborative control unit 6 is used to generate collaborative control commands based on the positioning results and send them to the target device via a 5G network or BeiDou short message to achieve collaborative control of the target device.

[0100] For examples consistent with the above embodiments, please refer to... Figure 3 , Figure 3 A schematic diagram of a terminal structure provided in an embodiment of this application is shown in the figure. It includes a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps. Acquire BeiDou satellite positioning data, 5G cellular positioning data, inertial navigation data of the target device, and high-precision map data of the environment in which the target device is located; Based on the signal quality parameters of the BeiDou satellite positioning data and 5G cellular positioning data, and combined with the high-precision map data, the current environment type of the target device is identified; When the target device is in the boundary ambiguity region of signal transition, a continuous transition function is constructed based on the environment type and the historical motion trajectory of the target device; Based on the continuous transition function, the contribution weights of the BeiDou satellite positioning data and the 5G cellular positioning data in the fused positioning are dynamically allocated; The weighted BeiDou satellite positioning data and the 5G cellular positioning data are fused with the inertial navigation data to generate a continuous and smooth positioning result. Based on the positioning results, a collaborative control command is generated and sent to the target device via a 5G network or BeiDou short message service to achieve collaborative control of the target device.

[0101] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0103] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the 5G and BeiDou-based device collaborative control methods described in the above method embodiments.

[0104] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the 5G and BeiDou-based device collaborative control methods described in the above method embodiments.

[0105] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0106] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

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

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

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

[0110] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 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 described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0111] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0112] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A device collaborative control method based on 5G and BeiDou, characterized in that, include: Acquire BeiDou satellite positioning data, 5G cellular positioning data, inertial navigation data of the target device, and high-precision map data of the environment in which the target device is located; Based on the signal quality parameters of the BeiDou satellite positioning data and 5G cellular positioning data, and combined with the high-precision map data, the current environment type of the target device is identified; When the target device is in the boundary ambiguity region of signal transition, a continuous transition function is constructed based on the environment type and the historical motion trajectory of the target device; Based on the continuous transition function, the contribution weights of the BeiDou satellite positioning data and the 5G cellular positioning data in the fused positioning are dynamically allocated; The weighted BeiDou satellite positioning data and the 5G cellular positioning data are fused with the inertial navigation data to generate a continuous and smooth positioning result. Based on the positioning results, a collaborative control command is generated and sent to the target device via a 5G network or BeiDou short message service to achieve collaborative control of the target device. Based on the environment type and the historical motion trajectory of the target device, a continuous transition function is constructed, including: The type of the boundary ambiguity region is determined based on the direction and rate of change of the environmental type. The normalized transition parameters are determined based on the movement duration or movement distance of the target device after entering the boundary ambiguity region. Based on the type of the boundary ambiguity region and the normalized transition parameters, a continuous transition function is constructed; Based on the continuous transition function, the contribution weights of the BeiDou satellite positioning data and the 5G cellular positioning data in the fused positioning are dynamically allocated, including: The first contribution weight of the BeiDou satellite positioning data is calculated based on the continuous transition function. The second contribution weight of the 5G cellular positioning data is determined based on the first contribution weight. Based on the historical motion trajectory, predict the next environmental type that the target device will enter in the next moment; Based on the predicted next environment type, a prediction correction factor is constructed, and the first contribution weight is corrected using the prediction correction factor to obtain the corrected first contribution weight. The step of predicting the next environment type that the target device will enter at the next moment based on the historical motion trajectory includes: Obtain the historical environment type sequence and historical location sequence of the target device over the past N sampling times; The historical environment type sequence and historical location sequence are input into the prediction model, which employs a trajectory fitting algorithm or a Markov chain model. The prediction model is used to calculate the probability that the target device will arrive at each candidate environment type in the next moment. The candidate environment type with the highest probability is used as the next environment type to be predicted, and the corresponding prediction confidence is output. The value of the prediction correction factor is determined based on the prediction confidence level.

2. The device collaborative control method based on 5G and BeiDou according to claim 1, characterized in that, The step of identifying the current environment type of the target device based on the signal quality parameters of the BeiDou satellite positioning data and 5G cellular positioning data, combined with the high-precision map data, includes: Obtain the signal quality parameters of the BeiDou satellite positioning data and the signal quality parameters of the 5G cellular positioning data; Extract the surrounding environmental elements of the target device's current location from the high-precision map data; The map occlusion index is determined based on the relative positional relationship between the surrounding environmental elements and the target device; The signal quality parameters of the BeiDou satellite, the signal quality parameters of the 5G cell, and the map occlusion index are input into a pre-trained environment type classification model to obtain the current environment type of the target device.

3. The device collaborative control method based on 5G and BeiDou according to claim 1, characterized in that, The step of constructing a continuous transition function based on the type of the boundary ambiguity region and the normalized transition parameters includes: Based on the type of the boundary ambiguity region, select the corresponding transition function base class from the preset transition function library; Based on the normalized transition parameters, determine the range of values ​​for the independent variables of the transition function base class, and map the normalized transition parameters to the input of the transition function base class; Based on the degree of change in the boundary ambiguity region, the curve steepness coefficient of the transition function base class is adjusted to construct a continuous transition function that adapts to the current boundary ambiguity region.

4. The device collaborative control method based on 5G and BeiDou according to claim 1, characterized in that, The process of fusing the weighted BeiDou satellite positioning data and the 5G cellular positioning data with the inertial navigation data to generate a continuous and smooth positioning result includes: The weighted BeiDou satellite positioning data and the 5G cellular positioning data are input into the Kalman filter as the observation values ​​of the Kalman filter; The inertial navigation data is input into the Kalman filter as the predicted value of the Kalman filter; The Kalman filter outputs a fused localization result based on the observed values ​​and the predicted values.

5. A device collaborative control system based on 5G and BeiDou, characterized in that, include: The first acquisition unit is used to acquire BeiDou satellite positioning data, 5G cellular positioning data, inertial navigation data of the target device, and high-precision map data of the environment in which the target device is located. The first processing unit is used to identify the current environment type of the target device based on the signal quality parameters of the BeiDou satellite positioning data and 5G cellular positioning data, combined with the high-precision map data. The second processing unit is used to construct a continuous transition function based on the environment type and the historical motion trajectory of the target device when the target device is in the boundary ambiguity region of signal transition; The third processing unit is used to dynamically allocate the contribution weights of the BeiDou satellite positioning data and the 5G cellular positioning data in the fused positioning according to the continuous transition function. The fourth processing unit is used to fuse the weighted BeiDou satellite positioning data and the 5G cellular positioning data with the inertial navigation data to generate a continuous and smooth positioning result. The collaborative control unit is used to generate collaborative control commands based on the positioning results and send them to the target device via a 5G network or BeiDou short message service to achieve collaborative control of the target device. Based on the environment type and the historical motion trajectory of the target device, a continuous transition function is constructed, including: The type of the boundary ambiguity region is determined based on the direction and rate of change of the environmental type. The normalized transition parameters are determined based on the movement duration or movement distance of the target device after entering the boundary ambiguity region. Based on the type of the boundary ambiguity region and the normalized transition parameters, a continuous transition function is constructed; based on the continuous transition function, the contribution weights of the BeiDou satellite positioning data and the 5G cellular positioning data in the fused positioning are dynamically allocated, including: The first contribution weight of the BeiDou satellite positioning data is calculated based on the continuous transition function. The second contribution weight of the 5G cellular positioning data is determined based on the first contribution weight. Based on the historical motion trajectory, predict the next environmental type that the target device will enter in the next moment; Based on the predicted next environment type, a prediction correction factor is constructed, and the first contribution weight is corrected using the prediction correction factor to obtain the corrected first contribution weight. The step of predicting the next environment type that the target device will enter at the next moment based on the historical motion trajectory includes: Obtain the historical environment type sequence and historical location sequence of the target device over the past N sampling times; The historical environment type sequence and historical location sequence are input into the prediction model, which employs a trajectory fitting algorithm or a Markov chain model. The prediction model is used to calculate the probability that the target device will arrive at each candidate environment type in the next moment. The candidate environment type with the highest probability is used as the next environment type to be predicted, and the corresponding prediction confidence is output. The value of the prediction correction factor is determined based on the prediction confidence level.

6. A terminal, characterized in that, The device includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the device collaborative control method based on 5G and BeiDou as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the device collaborative control method based on 5G and BeiDou as described in any one of claims 1-4.

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