Logistics transportation position monitoring method and system assisted by satellite communication

Abstract

The invention relates to the technical field of satellite communication, and discloses a logistics transportation position monitoring method and system assisted by satellite communication. The method comprises the following steps: calculating a satellite-ground distance and a line-of-sight unit vector according to a satellite position coordinate and a terminal estimation position coordinate, and generating a baseband signal based on a three-axis speed component and the line-of-sight unit vector; calculating a target peak value based on the baseband signal, and when the target peak value exceeds a noise threshold, extracting a pseudo-range measurement value and solving a three-dimensional space positioning solution with the satellite position coordinates; calculating a path constraint position from the three-dimensional space positioning solution to the optimal path section; and accumulating a reference mileage from a starting point to the optimal path section, calculating an accumulated driving mileage, converting the path constraint position, packaging the path constraint position and the accumulated driving mileage into a monitoring data packet, and reporting the monitoring data packet to a logistics monitoring platform. According to the invention, high-precision logistics position monitoring in a mobile scene is realized, and the monitoring precision and scheduling efficiency of logistics transportation are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and in particular to a satellite communication-assisted method and system for monitoring the location of logistics transportation. Background Technology

[0002] The demand for high-precision location information for remote logistics transportation safety monitoring is growing, making satellite positioning technology the primary means of logistics location monitoring. However, existing pure satellite positioning methods face severe challenges in mobile logistics transportation scenarios. The high-speed movement of vehicles and ships causes Doppler frequency shift in the received satellite carrier signals. Without compensation, the phase-locked loop (PLL) will be unable to capture the signal, leading to positioning failure.

[0003] Furthermore, pseudorange measurement noise is significant in mobile environments, and the positioning results obtained by conventional least squares solutions have limited accuracy, making it difficult to meet the needs of logistics monitoring. More seriously, even if positioning is successful, the calculated position will drift randomly around the road or route, failing to accurately reflect the true position of the transport vehicle on the preset path. This positioning drift phenomenon leads to errors in mileage calculation and inaccurate arrival time prediction, affecting logistics scheduling efficiency. Summary of the Invention

[0004] The main objective of this invention is to provide a satellite communication-assisted logistics transportation location monitoring method and system. This invention achieves high-precision logistics location monitoring in mobile scenarios, effectively improving the monitoring accuracy and scheduling efficiency of logistics transportation.

[0005] To achieve the above objectives, the present invention provides a satellite communication-assisted logistics transportation location monitoring method, comprising the following steps: The satellite-to-ground distance and line-of-sight unit vector are calculated based on the satellite position coordinates and the terminal's estimated position coordinates, and a baseband signal is generated based on the three-axis velocity components and the line-of-sight unit vector. The target peak value is calculated based on the baseband signal. When the target peak value exceeds the noise threshold, the pseudorange measurement value is extracted and used with the satellite position coordinates to solve the three-dimensional spatial positioning solution. Calculate the path constraint position from the three-dimensional spatial positioning solution to the optimal path segment; The baseline mileage from the starting point to the optimal path segment is accumulated and the cumulative mileage is calculated. The path constraint position transformation and the cumulative mileage are then encapsulated into a monitoring data packet and reported to the logistics monitoring platform.

[0006] Optionally, in a first implementation of the first aspect of the present invention, before calculating the satellite-to-ground distance and line-of-sight unit vector based on the satellite position coordinates and the terminal estimated position coordinates, the satellite communication-assisted logistics transportation location monitoring method further includes: The first eastward component, the first northward component, and the first celestial component are collected by the inertial measurement unit, and the three-axis velocity components are formed by the first eastward component, the first northward component, and the first celestial component. The system parses ephemeris data from navigation messages and calculates the satellite position coordinates in the geocentric-ground-fixed coordinate system based on Kepler orbital parameters. It also obtains the terminal's estimated position coordinates through the calculation results of the previous positioning cycle or base station-assisted positioning. The system receives a sequence of discrete path points, including latitude and longitude coordinates, from the logistics monitoring platform.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the step of calculating the satellite-to-ground distance and line-of-sight unit vector based on the satellite position coordinates and the terminal's estimated position coordinates, and generating a baseband signal based on the three-axis velocity components and the line-of-sight unit vector, includes: The satellite-to-ground distance is obtained by performing a square root operation on the sum of the squares of the differences between the satellite's position coordinates and the terminal's estimated position coordinates for each coordinate component. Divide the difference between each coordinate component by the star-to-ground distance to obtain the line-of-sight unit vector, which includes a second eastward component, a second northward component, and a second northward component. The radial relative velocity is obtained by multiplying the first eastward component of the three-axis velocity components with the second eastward component of the line-of-sight unit vector, multiplying the first northward component with the second eastward component, multiplying the first celestial component with the second celestial component, and summing the results. The predicted Doppler frequency offset is calculated based on the radial relative velocity. The predicted Doppler frequency offset is then superimposed on the intermediate frequency and the down-conversion reference frequency of the numerically controlled oscillator is set for frequency offset compensation to obtain the baseband signal.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the step of calculating the target peak value based on the baseband signal, and extracting pseudorange measurements and solving for a three-dimensional spatial positioning solution with the satellite position coordinates when the target peak value exceeds a noise threshold, includes: The target peak value is obtained by performing a sliding integral operation on the baseband signal and the local pseudo-random code sequence. The target peak value is compared with a noise threshold. When the target peak value exceeds the noise threshold, the code phase corresponding to the target peak value is latched. The signal propagation distance is obtained by multiplying the code phase by the speed of light, and the signal propagation distance is added to the equivalent distance corresponding to the receiver clock error to obtain the pseudorange measurement value. The three-dimensional spatial positioning solution is obtained by iteratively solving the pseudorange measurement value and the satellite position coordinates.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the step of iteratively solving for a three-dimensional spatial positioning solution based on the pseudorange measurement value and the satellite position coordinates includes: Based on the pseudorange measurement value and the satellite position coordinates, establish a set of pseudorange observation equations about the terminal position and the receiver clock error. Use the estimated terminal position coordinates as the initial estimated position and perform Taylor series linearization expansion on the pseudorange observation equations at the initial estimated position. Calculate the partial derivatives of the pseudorange with respect to each component of the position coordinates and the initial estimated pseudorange. Construct an observation matrix based on the partial derivatives. The difference between the pseudorange measurement and the initial estimated pseudorange is calculated to obtain the pseudorange residual vector. The observation matrix is ​​transposed, multiplied by the observation matrix, and inverted to obtain the inverse result. The inverse result is multiplied sequentially by the transpose of the observation matrix and the pseudorange residual vector to obtain the position correction and clock error correction. The position correction is added to the initial estimated position to obtain the updated position. The updated position is then used as the new initial estimated position for iterative solution to obtain the three-dimensional spatial positioning solution.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the step of multiplying the inverse result with the transpose of the observation matrix and the pseudorange residual vector in sequence to obtain the position correction and clock error correction includes: For each visible satellite, the difference between the pseudorange measurement value and the initial estimated pseudorange is calculated and arranged in the order of the satellites to form a column vector, thus obtaining the pseudorange residual vector; The observation matrix is ​​transposed to obtain the transpose of the observation matrix. The transpose of the observation matrix is ​​multiplied by the observation matrix to obtain a square matrix. The square matrix is ​​then inverted to obtain the inverse matrix. The inverse matrix is ​​multiplied by the transpose of the observation matrix to obtain an intermediate matrix. The intermediate matrix is ​​then multiplied by the pseudorange residual vector to obtain the position correction and clock error correction.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, calculating the path constraint position from the three-dimensional spatial positioning solution to the optimal path segment includes: The latitude and longitude coordinates of each path point in the path point sequence are transformed to the Cartesian coordinate system to obtain the three-dimensional coordinates of the path points. For each initial path segment formed by adjacent path points, the vertical distance from the three-dimensional spatial positioning solution to the initial path segment is calculated. All initial path segments are traversed to find the minimum vertical distance and its corresponding optimal path segment. A parameterized position with parameters as independent variables is established on the optimal path segment, and a constrained optimization objective function is constructed, which includes a position deviation term with the square of the Euclidean distance between the parameterized position and the three-dimensional spatial positioning solution, and a velocity continuity term with the square of the difference between the terminal solution velocity and the initial path segment directional velocity. The first derivative of the constraint optimization objective function with respect to the optimal parameters is calculated and set to zero to obtain the optimal parameters. The optimal parameters are then substituted into the parameterized position to calculate the path constraint position located on the optimal path segment.

[0012] Optionally, in the seventh implementation of the first aspect of the present invention, the step of obtaining the optimal parameters by taking the first derivative of the constraint optimization objective function with respect to the optimal parameters and setting the derivative to zero, and then substituting the optimal parameters into the parameterized position to calculate the path constraint position located on the optimal path segment, includes: The first-order derivative expressions are obtained by taking the partial derivatives of the position deviation term and the velocity continuity term in the constrained optimization objective function with respect to the optimal parameters. Set the first derivative expression to zero, establish a univariate linear equation about the optimal parameters, and solve it to obtain the optimal parameters; Substitute the optimal parameters into the parameterized position to calculate the vector sum of the starting coordinates of the optimal path segment and the path segment direction vector scaled according to the optimal parameters, and obtain the path constraint position located on the optimal path segment.

[0013] Optionally, in an eighth implementation of the first aspect of the present invention, the step of accumulating the baseline mileage from the starting point to the optimal path segment and calculating the cumulative mileage, and encapsulating the path constraint position conversion and the cumulative mileage into a monitoring data packet and reporting it to the logistics monitoring platform, includes: For each adjacent path point in the path point sequence from the starting point to the optimal path segment, calculate the path segment length, sum the path segment lengths, and obtain the baseline mileage. Multiply the optimal parameter by the Euclidean length of the optimal path segment to obtain the offset mileage within the segment, and sum the offset mileage within the segment with the base mileage to obtain the cumulative mileage. The path constraint location is converted from Cartesian coordinates to latitude and longitude coordinates and encapsulated with the cumulative mileage, terminal identifier and timestamp into a monitoring data packet, which is then reported to the logistics monitoring platform through a communication channel.

[0014] The present invention also provides a satellite communication-assisted logistics transportation location monitoring system, comprising: The generation module is used to calculate the satellite-to-ground distance and line-of-sight unit vector based on the satellite position coordinates and the terminal's estimated position coordinates, and to generate a baseband signal based on the three-axis velocity components and the line-of-sight unit vector; The solution module is used to calculate the target peak value based on the baseband signal. When the target peak value exceeds the noise threshold, it extracts the pseudorange measurement value and solves the three-dimensional spatial positioning solution with the satellite position coordinates. The calculation module is used to calculate the path constraint position from the three-dimensional spatial positioning solution to the optimal path segment; The reporting module is used to accumulate the baseline mileage from the starting point to the optimal path segment and calculate the cumulative mileage, and encapsulate the path constraint position conversion and the cumulative mileage into a monitoring data packet, which is then reported to the logistics monitoring platform.

[0015] In summary, this invention calculates radial relative velocity based on the triaxial velocity components of the inertial measurement unit and the geometric relationship with the satellite, and actively compensates the predicted Doppler frequency offset value to the down-conversion reference frequency of the numerically controlled oscillator, effectively controlling the residual frequency offset of the baseband signal and ensuring stable tracking of the phase-locked loop under high-speed movement conditions. This solves the problem of positioning failure caused by carrier lock-up in existing technologies. By performing Taylor series linearization expansion of the pseudorange observation equations at the initial estimated position and constructing the observation matrix, the least squares iterative algorithm of matrix transpose inversion is used to accurately solve the three-dimensional spatial positioning solution. Under the synergistic effect of Doppler compensation, the impact of pseudorange measurement noise on positioning accuracy is reduced. The free positioning solution is projected and constrained onto a preset logistics path network. By constructing a constrained optimization objective function containing position deviation and velocity continuity terms and solving the derivative, a constrained position strictly located on the path segment is obtained, eliminating positioning errors perpendicular to the road direction, achieving path-constrained positioning accuracy, and supporting accurate cumulative mileage calculation and estimated arrival time estimation, effectively improving the monitoring accuracy and scheduling efficiency of logistics transportation. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the steps of a satellite communication-assisted logistics transportation location monitoring method in one embodiment of the present invention; Figure 2 This is a structural block diagram of a satellite communication-assisted logistics transportation location monitoring system according to one embodiment of the present invention.

[0017] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] Reference Figure 1This embodiment provides a satellite communication-assisted logistics transportation location monitoring method, including the following steps: S1 calculates the satellite-to-ground distance and line-of-sight unit vector based on the satellite position coordinates and the terminal's estimated position coordinates, and generates a baseband signal based on the three-axis velocity components and the line-of-sight unit vector; Among them, the three-dimensional position coordinates of each visible satellite at the current moment are obtained from the navigation message, which are the absolute spatial coordinates in the geocentric and Earth-fixed coordinate system, denoted as X. sat Y sat and Z sat Simultaneously, the estimated position coordinates X of the terminal are obtained from the calculation results of the previous positioning cycle or from the auxiliary positioning module. est Y est and Z est By taking the difference between each pair of corresponding coordinate components, i.e. (X... sat -X est ), (Y sat -Y est ), (Z sat -Z est Perform a sum of squares operation and take its square root to obtain the geometric distance R between the satellite and the ground, which is between approximately 20,000 and 26,000 kilometers. Divide the difference of each component by the satellite-to-ground distance R to obtain the line-of-sight unit vector from the satellite to the terminal. Its eastward, northward, and celestial components are represented as the second eastward component u. x The second northward component u y With the second day to component u z Simultaneously, the system collects the three-axis velocity components of the terminal at the current moment, which are denoted as the first eastward velocity V. x First northward velocity V y With the first axial velocity V z and V x with u x Multiplication, V y with u y Multiplication, V z with u z Multiply the three values, add them together, and obtain the radial relative velocity V of the terminal relative to the target satellite. r This represents the terminal's motion trend along the satellite's line-of-sight. A positive radial relative velocity indicates the terminal is moving away from the satellite, while a negative value indicates it is approaching. Based on the relativistic Doppler frequency shift principle, the known carrier center frequency f is used. c Substituting the radial relative velocity into the frequency offset calculation formula, along with the speed of light c, yields the predicted Doppler frequency offset value f. d The predicted Doppler frequency offset value is then compared with the set intermediate frequency f. IF The new reference frequency f is obtained by adding them together. LOThe reference frequency value is set in the control register of the numerically controlled oscillator, so that the local oscillator frequency is pre-compensated and adjusted, thereby driving the mixer of the RF front end to down-convert the received high-frequency signal to the ideal baseband frequency range, and outputting a high-stability baseband signal with the residual frequency deviation compressed to a very small range.

[0020] S2, calculate the target peak value based on the baseband signal. When the target peak value exceeds the noise threshold, extract the pseudorange measurement value and solve the three-dimensional spatial positioning solution with the satellite position coordinates; Specifically, the baseband I / Q signal, after frequency offset pre-compensation, is input to the correlator module of the digital signal processing unit. The correlator calls a pseudo-random code sequence pre-stored in the local read-only memory. The pseudo-random code is the GPS system's C / A code, a 1023-byte Gold code with a code rate of 1.023 megacodes per second, and a complete code period of 1 millisecond. Sliding integration is performed on the baseband signal and the local C / A code across the entire code phase search space, with an integration step set to 0.5 chip increments, thus completing the integration of 2046 correlation points and generating a set of target peak sequences. The maximum correlation amplitude in the target peak sequence is detected in real time and identified as the target peak. The noise standard deviation σ is estimated from the signal to determine the threshold value Th = 3σ; When the detected target peak value is greater than the threshold value Th, it indicates that the local pseudocode and the received signal are basically matched in time, the signal is valid and not blocked or severely attenuated, and the code phase τ corresponding to the target peak value is latched. The physical meaning of the code phase is the propagation delay experienced from the satellite signal transmission to the terminal reception. Its unit is chip length, and it has a detection accuracy of up to 0.01 chip. The physical distance of signal propagation between the satellite and the ground is calculated by multiplying the code phase τ and the speed of light c. Simultaneously, considering the clock error δt of the receiver's local oscillator, which introduces an equivalent distance bias, c·δt is added to the propagation distance, resulting in the pseudorange measurement ρ = c·τ + c·δt. This pseudorange value serves as the right-hand side of the satellite positioning observation equation. Combining the pseudorange measurement with the corresponding satellite's three-dimensional position coordinates (Xi, Yi, Zi) in the geocentric-ground coordinate system, at least four sets of nonlinear pseudorange observation equations are constructed. Using the terminal's unknown three-dimensional position (x, y, z) and the receiver clock error as the variables to be solved, the Taylor expansion method is used to linearize this nonlinear equation system at the initial estimation point, forming an approximately linear system. Then, the least squares iterative method is used to solve it in multiple rounds. Each iteration outputs a position increment correction and updates the current estimate until the increment is less than a preset convergence threshold or the number of iterations reaches the upper limit. The solution yields the terminal's three-dimensional spatial free positioning result in the ECEF coordinate system.

[0021] S3, calculate the path constraint position from the three-dimensional spatial positioning solution to the optimal path segment; It should be noted that the geographic latitude and longitude coordinates of each path point in the preset path point sequence are transformed to the geocentric Cartesian coordinate system according to the WGS84 ellipsoid parameters to obtain the absolute position coordinates of the path points in three-dimensional space, represented as P1(X1,Y1,Z1), P2(X2,Y2,Z2)...P n (X n ,Y n Z n Using the vector segment formed by two adjacent path points as the initial path segment, traversing each segment to construct a series of path segment sets, and then targeting the 3D free positioning solution P... calc For each path segment (x, y, z), the shortest distance (vertical distance) to the point is calculated sequentially. First, the displacement vector of the positioning point relative to the starting point of the path segment is constructed, and then projected onto the unit vector of the path segment direction to obtain the projected length. It is then determined whether the projected point falls inside the line segment. If it does, the vertical distance is calculated using the path segment as its base. If the projected point is outside the segment, the Euclidean distance from the positioning point to the endpoint is used as the corresponding distance. After traversing all path segments, the minimum vertical distance and its corresponding path segment index are extracted, and this segment is determined as the optimal path segment for the terminal at the current moment. On the optimal path segment, a linear parameterized path model P(λ) = P is established with the starting point of the path segment as the base point and the path segment direction vector as the direction. k + λ·(P k+1 - P k ), where λ is a dimensionless position parameter defined on the interval [0,1], representing the relative position on the path segment. The parameterized position and the free-position solution P are used. calc The squared Euclidean distance between them constitutes the positional deviation constraint, and is combined with the terminal calculation speed v provided by the IMU. calc directional velocity v of the path segment path The squared difference term between them constructs the velocity continuity constraint term, forming a univariate constrained optimization objective function F(λ) = ||P(λ) - P calc || 2 + α·(v calc - v path ) 2 α is a weighting factor used to balance the importance of position accuracy and velocity continuity. Taking the first derivative of the objective function F(λ) with respect to λ yields the analytical expression for dF / dλ. Setting its derivative to zero creates a univariate equation. Solving this univariate equation yields the optimal parameter λ. opt . λ opt Substituting into the parameterized path expression P(λ), the path constraint position P is obtained. d = P k + λ opt ·(P k+1 - P kThe path constraint position falls on the optimal path segment and has the smallest deviation in spatial position from the free positioning solution. It also maintains consistency with the path direction in terms of velocity trend, effectively eliminating the positioning drift problem caused by poor satellite geometry or signal blockage.

[0022] S4 accumulates the baseline mileage from the starting point to the optimal route segment and calculates the cumulative mileage. It then encapsulates the route constraint location transformation and the cumulative mileage into a monitoring data packet and reports it to the logistics monitoring platform.

[0023] Specifically, for the path point sequence from the starting point P1 to the starting point P of the optimal path segment... k Perform Euclidean distance calculations on each pair of adjacent path points, sequentially calculating the distance from path segment P1 to P2, P2 to P3, and so on. {k-1} To P k The Euclidean length is calculated by taking the square root of the sum of the squares of the differences in the three-dimensional Cartesian coordinates of each path segment. These path segment lengths are continuously accumulated to form the total cumulative distance from the starting point of the path to the starting point of the optimal path segment, denoted as the baseline mileage S2. The baseline mileage represents the length of the main segment that the terminal has traveled along the preset path. In the optimal path segment P... k To P {k+1} Above, using the optimal path parameter λ opt The optimal path parameter represents the relative position ratio of the terminal on that path segment, where λ opt Multiplying the distance by the Euclidean length of the path segment yields the intra-segment offset mileage S1, which reflects the distance the terminal travels within the path segment from the starting point P. k The distance traveled along the starting segment. Add S2 to S1 to obtain the current terminal's total accumulated mileage S3. For the path constraint position P... dA coordinate system transformation is performed, converting the 3D position from the ECEF geocentric Cartesian coordinate system to longitude and latitude in the WGS84 geographic coordinate system. A seven-parameter transformation model is used to complete the coordinate mapping, ensuring that the latitude and longitude conversion accuracy meets the map display requirements of the logistics platform. Simultaneously, the terminal device's unique identifier (ID), current UTC timestamp, and instantaneous speed value calculated by the IMU are read and combined with the cumulative mileage (S3) and current latitude and longitude position to form a fixed-structure data packet. The data packet structure is aligned with a predefined communication protocol format, using a fixed-length 64-byte layout, including terminal ID, timestamp, cumulative mileage, latitude and longitude position, speed value, path segment index, positioning quality parameters (such as HDOP), and verification fields. After encapsulation, the monitoring data packets are periodically reported to the logistics monitoring platform via the BeiDou short message service channel or the TCP / UDP data link of the 4G cellular network, depending on the configured communication method. The upload period is set to 30 seconds. After receiving the data, the platform parses it in real time and displays the terminal's current transportation location and cumulative path on the visualization map interface. At the same time, it judges the deviation based on the cumulative mileage and the planned path. When the deviation distance exceeds the warning threshold or the expected arrival time is delayed, the platform generates an alarm event and pushes it to the operation and maintenance management system.

[0024] In one example, the satellite communication-assisted logistics transportation location monitoring method further includes, before calculating the satellite-to-ground distance and line-of-sight unit vector based on the satellite position coordinates and the terminal's estimated position coordinates: The first eastward component, the first northward component, and the first celestial component are collected by the inertial measurement unit, and the three-axis velocity components are formed by the first eastward component, the first northward component, and the first celestial component. The system parses ephemeris data from navigation messages and calculates the satellite position coordinates in the geocentric-ground-fixed coordinate system based on Kepler orbital parameters. It also obtains the terminal's estimated position coordinates through the calculation results of the previous positioning cycle or base station-assisted positioning. The system receives a sequence of discrete path points, including latitude and longitude coordinates, from the logistics monitoring platform.

[0025] In this example, the inertial measurement unit (IMU) built into the terminal continuously collects the motion state information at the current moment using a high-frequency sampling method. Specifically, this includes dynamic data output by the three-axis accelerometer and the three-axis gyroscope. The integral result of the three-axis accelerometer is converted into eastward, northward, and upward velocity components in the inertial navigation algorithm, which are denoted as the first eastward component V. x First northward component V y and the first directional component V z , with V x V y V zThis forms a three-dimensional velocity vector, representing the three-axis velocity components that reflect the terminal's absolute motion trend in the current reference coordinate system. Simultaneously, the RF front-end receiving module demodulates navigation messages from GNSS satellites and extracts the ephemeris data for each visible satellite. The ephemeris data includes orbital parameters, time bias terms, and broadcast delay information. Based on the Keplerian orbital six elements and time-related correction parameters, combined with the received timestamp and satellite clock bias model, orbital propagation calculations are performed for each visible satellite. Its position is projected onto the Earth-centered Earth-fixed coordinate system to obtain the satellite's three-dimensional spatial coordinates at the current moment, including the X-axis. sat Y sat With Z sat The system consists of three components; simultaneously, it obtains the spatial reference point of the terminal in the current cycle. If a positioning solution exists from the previous cycle, that solution is directly used as the current estimated position X. est Y est Z est Otherwise, it calls the auxiliary location information provided by the base station positioning or coarse positioning module; it receives the transportation route task issued by the logistics monitoring platform through the 4G wireless communication link or Beidou short message, and the transportation route task is expressed in the form of a discrete path point sequence, where each path point includes latitude and longitude coordinates in the WGS84 coordinate system (London). i Lat i ), forming an ordered path set {P1, P2, ..., P n The path point spacing is set between 200 and 2000 meters to ensure path continuity and constraint effectiveness. All path point information is stored in the terminal's local non-volatile memory.

[0026] In one example, the satellite-to-ground distance and line-of-sight unit vector are calculated based on the satellite's position coordinates and the terminal's estimated position coordinates. A baseband signal is then generated based on the three-axis velocity components and the line-of-sight unit vector, including: The satellite-to-ground distance is obtained by performing a square root operation on the sum of squares of the differences between the satellite's position coordinates and the terminal's estimated position coordinates for each coordinate component. Divide the difference of each coordinate component by the star-to-ground distance to obtain the line-of-sight unit vector, which includes the second eastward component, the second northward component, and the second northward component. Multiply the first eastward component of the three-axis velocity components by the second eastward component of the line-of-sight unit vector, multiply the first northward component by the second eastward component, multiply the first axial component by the second axial component, and sum them to obtain the radial relative velocity. The predicted Doppler frequency offset is calculated based on the radial relative velocity. The predicted Doppler frequency offset is then superimposed on the intermediate frequency and the down-conversion reference frequency of the numerically controlled oscillator is set to perform frequency offset compensation, thus obtaining the baseband signal.

[0027] In this example, the three-dimensional position coordinates of each visible satellite in the geocentric-ground-fixed coordinate system are obtained from the navigation message, denoted as X. sat Y sat Z sat Simultaneously, it calls the estimated terminal position coordinates X obtained from the previous positioning cycle. est Y est Z est Calculate the difference between each component of the two sets of coordinates, i.e., ΔX = X sat - X est ΔY = Y sat - Y est ΔZ = Z sat -Z est These differences are squared and summed, and the sum is then squared to calculate the spatial geometric distance R between the terminal and the satellite, which ranges from 20,000 to 26,000 kilometers. To describe the satellite's propagation direction relative to the terminal, ΔX, ΔY, and ΔZ are divided by R to form a normalized direction vector, i.e., the line-of-sight unit vector, which includes the second eastward component u. x = ΔX / R, second northward component u y = ΔY / R, Secondary component u z = ΔZ / R, where the unit vector represents the directional distribution of the satellite signal propagation path in space. The three-axis velocity components at the current moment are extracted from the IMU, namely the first eastward velocity component V. x First northward velocity component V y With the first axial velocity component V z V represents the linear velocity value of the terminal in each direction in the three-dimensional coordinate system; x with u x Multiplication, V y with u y Multiplication, V z with u z Multiply the terms and sum the three results, i.e., V r = V x ·u x + V y ·u y + V z ·u z The radial relative velocity V of the terminal relative to the current target satellite is obtained. r V r The positive and negative values ​​represent the terminal's distance from or approach to the satellite, respectively, with a numerical range not exceeding ±40 meters per second. The frequency offset prediction value f is calculated based on the relativistic Doppler effect model. d f d =-V r ·f c / c, where fc Here, c is the center frequency of the received satellite signal (e.g., 1575.42 MHz for GPS L1 carrier), f is the speed constant of light in a vacuum, and f is the frequency offset. d Distributed within the ±5000 Hz range. The predicted frequency offset is used for dynamic compensation of the RF receiver link, which will be f d Superimposed on the intermediate frequency f IF (e.g., 4.092 MHz) constitutes the preset local oscillator frequency f LO The local oscillator frequency value is written into the control register of the numerically controlled oscillator (NCO), thereby driving the local oscillator signal of the mixer module to be dynamically adjusted. During the down-conversion process, the frequency offset effect caused by the high-speed movement of the terminal is directly eliminated, and a stable baseband signal with residual frequency offset compressed to the range of ±50 Hz is generated.

[0028] The satellite communication-assisted logistics transportation location monitoring method, after obtaining the baseband signal and before performing a sliding integral operation on the baseband signal and the local pseudo-random code sequence, further includes: performing a fast Fourier transform on the baseband signal to obtain the frequency domain amplitude spectrum; searching for the frequency offset corresponding to the maximum amplitude value in the frequency domain amplitude spectrum as the measured residual frequency offset; comparing the absolute value of the measured residual frequency offset with a preset residual frequency offset threshold; when the absolute value of the measured residual frequency offset exceeds the preset residual frequency offset threshold, adding the measured residual frequency offset to the Doppler frequency offset prediction value to obtain the corrected Doppler frequency offset prediction value; superimposing the corrected Doppler frequency offset prediction value onto the intermediate frequency and resetting the down-conversion reference frequency of the numerically controlled oscillator for secondary frequency offset compensation to obtain the corrected baseband signal; performing a fast Fourier transform on the corrected baseband signal again and extracting the residual frequency offset; repeating the frequency offset correction process until the absolute value of the residual frequency offset is less than the preset residual frequency offset threshold or the number of iterations reaches a preset upper limit to obtain the final baseband signal that meets the frequency offset control requirements.

[0029] In one example, the target peak value is calculated based on the baseband signal. When the target peak value exceeds the noise threshold, the pseudorange measurement value is extracted and used to solve for the three-dimensional spatial positioning solution with the satellite position coordinates, including: The target peak value is obtained by performing a sliding integral operation between the baseband signal and the local pseudo-random code sequence. The target peak value is compared with the noise threshold. When the target peak value exceeds the noise threshold, the code phase corresponding to the target peak value is latched. The signal propagation distance is obtained by multiplying the code phase by the speed of light. The signal propagation distance is then added to the equivalent distance corresponding to the receiver clock error to obtain the pseudorange measurement value. The three-dimensional spatial positioning solution is obtained by iteratively solving the pseudorange measurement value and the satellite position coordinates.

[0030] In this example, a stable baseband I / Q signal is input into the correlator array in the digital signal processor, and a pseudo-random code sequence pre-stored in the local ROM is invoked. The pseudo-code used is the C / A code corresponding to the GPS L1 signal. This code is a Gold code with a length of 1023 and a code rate of 1.023 megacodes per second. It is periodically extended with a complete code cycle of 1 millisecond. Point-to-point multiplication and integration operations are performed with the input baseband signal at all code phase positions in a complete code cycle using a sliding correlation method. The integration step size is half a chip, that is, each sliding is equivalent to a time span of 0.5 chips, thus completing 2046 sliding integrations in one code cycle. At each sliding position, the corresponding correlation output amplitude is recorded and a complete correlation output sequence is constructed. The correlation result with the largest amplitude in the correlation output sequence is selected as the target peak value. At the same time, noise statistical analysis is performed on multiple frames of data in the initial stage of baseband signal acquisition, the average noise power of the baseband signal is extracted and its standard deviation σ is calculated, and a threshold threshold Th = 3σ is set as the criterion for judging the effective signal. When the detected target peak value exceeds the noise threshold Th, the current satellite signal is determined to be solvable. The code phase τ corresponding to the target peak value is latched as the basis for pseudorange extraction. The code phase τ is measured in chips, with an accuracy of up to 0.01 chips, corresponding to an equivalent distance accuracy of approximately 3 meters. The acquired code phase τ is multiplied by the speed of light in vacuum c to obtain the physical distance c·τ that the signal travels in vacuum, reflecting the propagation path length between the satellite and the terminal. Due to a slight offset between the local receiver clock and the satellite synchronization, a receiver clock error δt is introduced, which has an impact on the equivalent distance of c·δt. This error is on the order of 30 kilometers. It is added to the propagation distance, i.e., ρ = c·τ + c·δt, to obtain the pseudorange measurement value ρ used for positioning. After acquiring pseudorange measurements from four or more satellites, these measurements are combined with the corresponding satellite's three-dimensional spatial coordinates (Xi, Yi, Zi) in the geocentric-fixed coordinate system to construct a set of pseudorange observation equations. The unknown three-dimensional position of the terminal (x, y, z) and the receiver clock error Δt are used as the parameters to be solved. Each pseudorange equation satisfies ρ i = √[(x - X i ) 2 + (y - Y i ) 2 + (z - Z i )2] + c·Δt, this system of equations is a nonlinear system that cannot be solved directly. By performing Taylor series linearization on it at the coarse initial estimation point (x0, y0, z0), the partial derivatives are extracted to construct the observation matrix H, and combined with the pseudorange residual Δρ, a least-squares linear system of equations Δρ = H·ΔX is constructed. By solving ΔX = (H T ·H) -1 ·H TThe correction value of the current iteration is obtained by Δρ and superimposed on the initial estimated point. The iteration is repeated until the position correction value is less than the preset threshold or the number of iterations reaches the upper limit, and the three-dimensional spatial positioning solution of the terminal in the geocentric coordinate system is obtained.

[0031] In one example, the three-dimensional spatial positioning solution is obtained by iteratively solving based on pseudorange measurements and satellite position coordinates, including: Based on the pseudorange measurement values ​​and satellite position coordinates, a set of pseudorange observation equations is established regarding the terminal position and receiver clock error. The estimated terminal position coordinates are used as the initial estimated position, and the pseudorange observation equations are linearized by Taylor series at the initial estimated position. The partial derivatives of the pseudorange with respect to each component of the position coordinates and the initial estimated pseudorange are calculated. The observation matrix is ​​constructed based on the partial derivatives. The difference between the pseudorange measurement and the initial estimated pseudorange is calculated to obtain the pseudorange residual vector. The observation matrix is ​​transposed, multiplied with the observation matrix, and inverted to obtain the inverse result. The inverse result is multiplied sequentially by the transpose of the observation matrix and the pseudorange residual vector to obtain the position correction and clock error correction. The position correction is added to the initial estimated position to obtain the updated position. The updated position is then used as the new initial estimated position for iterative solution to obtain the three-dimensional spatial positioning solution.

[0032] In this example, pseudorange measurements {ρ1, ρ2, …, ρ} corresponding to four or more satellites are used. n} and its three-dimensional spatial coordinates in the Earth-centered Earth-fixed coordinate system {(X1,Y1,Z1), (X2,Y2,Z2), …, (X n ,Y n Z n Based on this, a set of nonlinear pseudorange observation equations is constructed, where each observation equation is of the form: ρ i = √[(x - Xi) 2 + (y - Yi) 2 +(z - Zi) 2 ] + c·Δt, where x, y, z are the coordinates of the terminal to be determined, Δt is the receiver clock error, and c is the speed of light constant. Since the equation system is a nonlinear system containing square root terms, it cannot be solved analytically directly. Therefore, based on the terminal's estimated position (x0, y0, z0) obtained from the previous cycle and the initially set clock error Δt0 = 0, we use this as the initial estimate. At this point, we perform a first-order Taylor series linearization expansion of each observation equation, expanding the nonlinear terms into their linear approximation at the estimation point, resulting in the linearized pseudorange equation system: Δρ i ≈ ( ρ / x) i ·Δx + ( ρ / y) i ·Δy + ( ρ / z) i ·Δz + c·ΔΔt, where Δρ i Let be the pseudorange residual of the i-th satellite, ( ρ / x) i = -(X i - x0) / ρ 0i , ( ρ / y) i = -(Y i - y0) / ρ 0i , ( ρ / z) i = -(Z i - z0) / ρ 0i , ρ 0i = √[(x0- X i ) 2 + (y0- Y i ) 2 + (z0- Z i ) 2 ] + c·Δt0 is the initial estimated i-th pseudorange value. The above partial derivatives are used to construct the observation matrix H, whose i-th row is [( ρ / x) i , ( ρ / y) i , ( ρ / z) i [1], forming an n×4 dimension observation matrix. Calculate the difference Δρ between each satellite pseudorange measurement and its initial estimated pseudorange value. i = ρ i - ρ oi This forms the pseudorange residual vector Δρ, with dimensions n×1; the observation matrix H is transposed to obtain H. T And calculate H T The result of H is a 4×4 symmetric positive definite matrix, and its inverse matrix (H) is guaranteed. T ·H) -1 It exists, then the inverse matrix is ​​combined with H. T Multiply, and then continue multiplying with the pseudorange residual vector Δρ, i.e., perform in sequence (H). T ·H) -1 ·H T·Δρ, thus obtaining the state correction vector ΔX = [Δx, Δy, Δz, c·ΔΔt] T The first three components of the state correction vector are the terminal position corrections, and the last term is the receiver clock error correction multiplied by the speed of light. Δx, Δy, and Δz are added to the original estimated positions x0, y0, and z0, respectively, to obtain the updated estimated positions x1, y1, and z1. Δt is added to the initial clock error Δt0 to update Δt1, and (x1, y1, z1, Δt1) is used as the new initial estimate input. The above linearization expansion, observation matrix construction, residual calculation, and least squares iteration process are repeated until the norm of the state correction vector ΔX is less than the preset convergence threshold (e.g., 0.3 meters) or the number of iterations reaches the upper limit (e.g., 5 times). The three-dimensional spatial positioning solution of the terminal in the geocentric coordinate system is then output.

[0033] The satellite communication-assisted logistics transportation location monitoring method, after obtaining the pseudorange measurement value and before iteratively solving based on the pseudorange measurement value and satellite position coordinates, further includes: calculating the ratio of the target peak value to the baseband signal noise power for each visible satellite to obtain the signal-to-noise ratio (SNR) index; calculating the ratio of the target peak value to the second largest correlated peak value to obtain the peak sharpness index; calculating the elevation angle corresponding to the satellite position coordinates and obtaining the geometric intensity index by looking up a preset elevation angle-weight mapping table; normalizing the SNR index to a first numerical interval, the peak sharpness index to a second numerical interval, and the geometric intensity index to a third numerical interval, and then normalizing the three normalized values... The uniformity index is weighted and summed according to preset weight coefficients to obtain the pseudorange quality factor of the corresponding satellite. The pseudorange quality factors of each visible satellite are used to form a weight matrix with diagonal elements. In the least squares solution of the pseudorange observation equation system, the transpose of the observation matrix is ​​multiplied by the weight matrix and the observation matrix in turn to obtain the weighted square matrix. The inverse operation of the weighted square matrix is ​​then performed. The inverse result of the weighted square matrix is ​​multiplied by the transpose of the observation matrix, the weight matrix and the pseudorange residual vector in turn to obtain the weighted position correction and weighted clock error correction that take into account the difference in observation quality. The weighted position correction is used to perform iterative solution to obtain the quality-weighted three-dimensional spatial positioning solution.

[0034] In one example, the inverse result is multiplied sequentially by the transpose of the observation matrix and the pseudorange residual vector to obtain the position correction and clock error correction, including: For each visible satellite, the difference between the pseudorange measurement value and the initial estimated pseudorange is calculated and arranged in the order of the satellites to form a column vector, thus obtaining the pseudorange residual vector; Transpose the observation matrix to obtain the transpose of the observation matrix. Multiply the transpose of the observation matrix with the observation matrix to obtain a square matrix. Invert the square matrix to obtain the inverse matrix. Multiply the inverse matrix with the transpose of the observation matrix to obtain the intermediate matrix. Multiply the intermediate matrix with the pseudorange residual vector to obtain the position correction and clock error correction.

[0035] In this example, after obtaining pseudorange measurements from at least four visible satellites, and combining the initial estimated position coordinates (x0, y0, z0) with the initial clock difference Δt0, an initial pseudorange calculation is performed sequentially for each satellite, i.e., the pseudorange estimate ρ is calculated according to the geometric formula. oi = √[(x0- X i ) 2 + (y0- Y i ) 2 + (z0- Z i ) 2 ] + c·Δt0, representing the actual pseudorange measurement value ρ for each satellite i Subtract its corresponding initial estimated pseudo-range ρ oi The pseudorange residual Δρ of the i-th satellite is obtained. i The residual values ​​are arranged in order of satellite number to form an n×1 dimension pseudorange residual column vector Δρ, where n is the number of visible satellites involved in the solution, ranging from 4 to 10. The observation matrix H calculated based on the initial position is transposed to obtain its transpose matrix Hi. T Its dimension is 4×n, and each row of the H matrix records the partial derivatives of a satellite's pseudorange with respect to its position coordinates x, y, z, and clock error. H... T Performing matrix multiplication with H yields a 4×4 square matrix G = H. T ·H, matrix G is a symmetric positive definite matrix, characterized by its unique invertibility. Therefore, performing a matrix inversion operation on matrix G yields its inverse matrix G. -1 = (H T ·H) -1 After completing the above inversion, the inverse matrix G is... -1 With H T Perform matrix multiplication to obtain the intermediate matrix M = G -1 ·H TThe intermediate matrix, with a dimension of 4×n, represents the pseudo-inverse coefficients in the least squares method, used to map the residuals to the state correction space. A matrix multiplication operation is performed between the intermediate matrix M and the pseudorange residual vector Δρ, i.e., ΔX = M·Δρ, resulting in a 4×1 state correction vector ΔX. The first three components are the position corrections of the terminal in the x, y, and z directions, respectively, and the fourth component is the product of the clock error correction and the speed of light, i.e., c·ΔΔt. Δx, Δy, and Δz are added to the original estimated positions x0, y0, and z0, respectively, updating them to x1, y1, and z1. ΔΔt is added to the initial clock error Δt0, updating it to Δt1, completing one iteration of state update. If the Euclidean norm of the state correction is still greater than the preset convergence threshold, the updated new position and clock error are used as the initial estimation input for the next iteration. The above matrix construction, residual calculation, and pseudo-inverse solution process is repeated until the convergence condition is met or the maximum number of iterations is reached. The output three-dimensional coordinates are the high-precision terminal positioning solution.

[0036] In one example, calculating the path constraint position from the 3D spatial localization solution to the optimal path segment includes: Transform the latitude and longitude coordinates of each path point in the path point sequence to the Cartesian coordinate system to obtain the three-dimensional coordinates of the path points. Calculate the vertical distance from the three-dimensional spatial positioning solution to each initial path segment formed by adjacent path points. Traverse all initial path segments to find the minimum vertical distance and its corresponding optimal path segment. Establish a parameterized position with parameters as independent variables on the optimal path segment, and construct a constrained optimization objective function that includes a position deviation term between the parameterized position and the square of the Euclidean distance between the three-dimensional spatial positioning solution and a velocity continuity term that includes the square of the difference between the terminal solution velocity and the initial path segment directional velocity. The optimal parameters are obtained by taking the first derivative of the objective function of the constraint optimization with respect to the optimal parameters and setting the derivative to zero. The optimal parameters are then substituted into the parameterized position calculation to obtain the path constraint position located on the optimal path segment.

[0037] In this example, the path point sequence issued by the logistics monitoring platform is parsed. Each path point contains longitude and latitude information in the WGS84 coordinate system. Based on the Earth ellipsoid parameters (such as the semi-major axis and oblateness of WGS84), the longitude and latitude coordinates of each path point are converted to three-dimensional Cartesian coordinates in the Earth-centered Earth-fixed (ECEF) coordinate system using geodetic coordinate transformation formulas. After the transformation, each path point is represented as a three-dimensional coordinate point P1(X1,Y1,Z1), P2(X2,Y2,Z2), ..., P... n (X n ,Y n Z n After completing the path point coordinate transformation, all adjacent path point pairs are traversed and path segment vectors are constructed. For example, a path segment starts from path point P. kWith P {k+1} The path segment direction vector is defined as V. k = P {k+1} - P k And utilize the current three-dimensional spatial positioning solution P calc (x,y,z) and path segment P k Establish vector difference D from the starting point k = P calc - P k Then calculate D k In V k Projection length proj in the direction k = (D k ·V k ) / ||V k ||, where "·" represents the vector dot product, ||V k || represents the path segment length. The position of the projection point is determined based on the projection length value; if 0 ≤ proj k ≤ ||V k If ||, then the projection point is considered to fall inside the path segment, and the vertical distance d is calculated. k = ||D k - proj k ·(V k / ||V k ||)||;If proj k If the vertical distance is less than 0, then the vertical distance is ||P calc - P k || indicates that the projection falls outside the starting point of the path segment; if proj k >||V k ||, then the vertical distance is ||P calc - P {k+1} The symbol || indicates that the projection falls outside the endpoint of the path segment. This operation is performed on all path segments to select the segment with the minimum vertical distance. opt As the optimal path segment, its starting point P is recorded. opt With the endpoint P opt+1 On the optimal path segment, establish a parameterized path expression P(λ) = P opt + λ·(P opt+1 - P opt ), where λ is a dimensionless parameter defined in the interval [0,1], representing the relative position from the starting point to the ending point on the path segment; based on the parameterized model, a path constraint optimization objective function F(λ) is constructed, which consists of two parts: a position deviation term, defined as ||P(λ) - P calc || 2, used to measure the squared Euclidean distance between parameterized pathpoints and 3D spatial localization solutions; the velocity continuity term is defined as (v calc - v path ) 2 , where v calc v is the scalar value of the terminal's current velocity obtained through IMU calculation. path This is a reference value for the directional velocity of the path segment, used to maintain the continuity between the calculated velocity and the desired velocity of the path. To balance the weights of the two constraints, they are combined into a unified objective function F(λ) = ||P(λ) - P calc || 2 + α·(v calc - v path ) 2 α is an adjustment coefficient. To find the minimum value of the objective function F(λ), we take its first derivative with respect to the parameter λ, dF / dλ, and set the derivative equal to zero, i.e., dF / dλ = 0. This establishes a linear or quadratic equation in one variable, which is then solved analytically or iteratively to obtain the optimal parameter λ. opt Then λ opt Substituting the parameter path expression P(λ), the final path constraint position P is calculated. d = P opt + λ opt ·(P opt+1 - P opt ).

[0038] The satellite communication-assisted logistics transportation location monitoring method, after obtaining the path constraint position on the optimal path segment and before accumulating the baseline mileage from the starting point to the optimal path segment, further includes: comparing the current optimal path segment index with the historical path segment index stored at the previous time; determining a path segment switch when the two indices are inconsistent; extracting the endpoint coordinates of the historical path segment and the starting coordinates of the current optimal path segment; calculating the first distance from the current path constraint position to the endpoint of the historical path segment and the second distance to the starting point of the current optimal path segment; and calculating a first weighting coefficient and a second weighting coefficient based on the sum of the first and second distances, wherein the first weighting coefficient... The first distance is inversely proportional to the second distance, and the second weight coefficient is inversely proportional to the second distance, with the sum of the two weight coefficients being 1. The historical path constraint positions of the most recent consecutive preset number of moments are extracted from the historical buffer, and the average velocity vector of the historical path constraint position sequence is calculated. Based on the average velocity vector and the time interval between the current moment and the previous moment, the predicted position of the current moment is predicted. The historical path segment endpoint is scaled by the first weight coefficient, and the current optimal path segment starting point is scaled by the second weight coefficient, and then summed to obtain the smoothed position of the path segment connection point. The smoothed position of the path segment connection point and the predicted position are weighted and averaged according to a preset smoothing factor to obtain the final smoothed path constraint position.

[0039] In one example, the first derivative of the constraint optimization objective function with respect to the optimal parameters is calculated and set to zero to obtain the optimal parameters. These optimal parameters are then substituted into the parameterized location calculation to obtain the path constraint locations on the optimal path segment, including: By taking the partial derivatives of the position deviation term and velocity continuity term in the constrained optimization objective function with respect to the optimal parameters, we obtain the first-order derivative expressions; By setting the first derivative expression to zero, a univariate linear equation about the optimal parameters is established and solved to obtain the optimal parameters. Substitute the optimal parameters into the parameterized position to calculate the vector sum of the starting coordinates of the optimal path segment and the path segment direction vector scaled according to the optimal parameters, and obtain the path constraint position on the optimal path segment.

[0040] In this example, based on the determined optimal path segment, with its starting coordinates P0 and ending coordinates P1, the path segment direction vector V = P1 - P2 is constructed. Based on this, the parameterized position expression on the path segment is defined as P(λ) = P0 + λ·V, where λ is a dimensionless optimal parameter defined in the interval [0,1], representing the relative position within the path segment. To achieve the optimal projection of the spatial positioning result onto the path, an objective function F(λ) containing two constraint terms is constructed, where the first term is the position deviation term, expressed as ||P(λ) - P calc || 2 Used to measure the relationship between the projected points on the path segment and the current 3D spatial positioning solution P. calc The square of the Euclidean distance between them, the second term is the velocity continuity term, expressed as β·(v calc - v path ) 2 This is used to measure the terminal's current solution speed vcalc and the path segment directional speed v. path The deviation between them, β is the coefficient for adjusting the weight of this term. Considering v path The objective function can be approximated as a constant, and the overall objective function simplifies to F(λ) = ||P0 + λ·V - P calc || 2 + β·(v calc -v path ) 2 Only the first term contains the variable λ. Take the first derivative of F(λ) with respect to λ, and expand ||P(λ) - P calc || 2 We obtain: (P0+λ·V - P) calc )·(P0+ λ·V - P calc Taking its derivative with respect to λ, we get: dF / dλ = 2·V·(P0+ λ·V -P calcSince the second term, velocity continuity, is a constant, its derivative is zero. Therefore, the derivative expression of the objective function is dF / dλ = 2·V·(P0 + λ·V - P). calc Setting the first derivative expression to zero, we get 2·V·(P0+ λ·V - P). calc =0, and after removing the constant term, we get V·(P0+ λ·V - P) calc ) = 0, expanding to V·P0 + λ·V·V - V·P calc = 0, which simplifies to λ·(V·V) = V·(P) calc - P0), thus obtaining a univariate linear equation in λ, solving for the optimal parameter λ. opt = V·(P calc - P0) / (V·V), where V·V is the square of the magnitude of the path segment direction vector, V·(P calc - P0) is the projection of the direction vector and the relative displacement vector of the positioning point. Let λ opt Substituting this into the parameterized path expression P(λ), we can calculate P. d =P0+ λ opt ·V, performs a vector summation operation on the path segment starting point P0 and the path direction vector V after scaling, to obtain the path constraint position on the optimal path segment.

[0041] In one example, the baseline mileage from the starting point to the optimal route segment is accumulated and the cumulative mileage is calculated. The route constraint location transformation and the cumulative mileage are then encapsulated into a monitoring data packet and reported to the logistics monitoring platform, including: For each adjacent path point in the path point sequence from the starting point to the optimal path segment, calculate the path segment length, sum the path segment lengths, and obtain the baseline mileage. Multiply the optimal parameter by the Euclidean length of the optimal path segment to obtain the offset mileage within the segment. Sum the offset mileage within the segment with the base mileage to obtain the cumulative mileage. The path constraint location is converted from Cartesian coordinates to latitude and longitude coordinates and encapsulated with the cumulative mileage, terminal identifier and timestamp into a monitoring data packet. The monitoring data packet is then reported to the logistics monitoring platform through the communication channel.

[0042] In this example, the path is based on the sequence of path points from the starting path point P1 to the starting point P of the optimal path segment. k Calculate the length of all path segments between them, that is, traverse adjacent path pairs P1 to P2, P2 to P3, ... P {k-1} To P kThe Euclidean distance is calculated for each pair of path points in a geocentric-fixed coordinate system (Cartesian coordinate system). The differences in the X, Y, and Z coordinate components of each pair of points are squared and summed. The square root of the sum is then taken to obtain the true three-dimensional spatial length of the path segment. The lengths of the above path segments are successively accumulated to obtain the total driving length from the starting point of the path sequence to the starting point Pk of the optimal path segment, which is defined as the baseline mileage, reflecting the basic length of the path that the terminal has advanced along the path network. The optimal path segment intrinsic parameter λ is then used. opt This parameter indicates the terminal's position within the optimal path segment. k = (P k , P {k+1} The relative position ratio in ) takes values ​​between [0,1]; λ opt With Segment k European length L k Multiplying these together, we get the intra-segment offset mileage S1 = λ. opt ·L k This indicates that the terminal originates from the path segment starting point P. k The distance traveled along the segment. The offset mileage within this segment is summed with the base mileage to obtain the complete cumulative mileage traveled from the path start point P1 to the current path constraint position. For the path constraint position P... d A coordinate system transformation is performed, converting the coordinates from the ECEF 3D Cartesian coordinate system to the latitude and longitude representation in the WGS84 geodetic coordinate system. During the transformation, a seven-parameter inverse coordinate model is used to ensure that the calculation accuracy of longitude and latitude meets sub-meter level visualization requirements. The unique identifier (ID) field of the terminal device, the current UTC timestamp, the current calculation speed, and the aforementioned cumulative mileage are read and organized according to a predetermined data structure, encapsulated into a fixed-format monitoring data packet. The data packet structure includes the device ID (8 bytes), timestamp (8 bytes), latitude and longitude position (4 bytes each), cumulative mileage (4 bytes, in meters), speed value (4 bytes), path segment index k (2 bytes), positioning accuracy indicators such as HDOP (2 bytes), and an integrity check field (2 bytes). The overall length is within 64 bytes to meet communication efficiency requirements. Based on the communication module configuration, a suitable communication channel is selected to report monitoring data packets to the logistics monitoring platform in real time. If the terminal has cellular communication capabilities, data is pushed through the 4G module using the TCP / UDP protocol. If it is in a communication blind spot, data transmission is completed through the Beidou short message channel in an intermittent batch reporting manner. After receiving the data packets, the platform performs unpacking and parsing, trajectory reconstruction and mileage mapping operations, and overlays the current terminal position on the map visualization interface for display. Real-time deviation monitoring is carried out in combination with the planned path and time window.

[0043] Reference Figure 2This embodiment provides a satellite communication-assisted logistics transportation location monitoring system, including: The generation module 1 is used to calculate the satellite-to-ground distance and line-of-sight unit vector based on the satellite position coordinates and the terminal's estimated position coordinates, and to generate a baseband signal based on the three-axis velocity components and the line-of-sight unit vector; Solver Module 2 is used to calculate the target peak value based on the baseband signal. When the target peak value exceeds the noise threshold, it extracts the pseudorange measurement value and solves the three-dimensional spatial positioning solution with the satellite position coordinates. Calculation module 3 is used to calculate the path constraint position from the three-dimensional spatial positioning solution to the optimal path segment; The reporting module 4 is used to accumulate the baseline mileage from the starting point to the optimal route segment and calculate the cumulative driving mileage. It also encapsulates the route constraint position transformation and the cumulative driving mileage into a monitoring data packet and reports it to the logistics monitoring platform.

[0044] In this embodiment, the specific implementation of each unit in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0045] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, system, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, system, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, system, article, or method that includes that element.

[0046] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A satellite communication-assisted method for monitoring the location of logistics transportation, characterized in that, include: The satellite-to-ground distance and line-of-sight unit vector are calculated based on the satellite position coordinates and the terminal's estimated position coordinates, and a baseband signal is generated based on the three-axis velocity components and the line-of-sight unit vector. The target peak value is calculated based on the baseband signal. When the target peak value exceeds the noise threshold, the pseudorange measurement value is extracted and used with the satellite position coordinates to solve the three-dimensional spatial positioning solution. Calculate the path constraint position from the three-dimensional spatial positioning solution to the optimal path segment; The baseline mileage from the starting point to the optimal path segment is accumulated and the cumulative mileage is calculated. The path constraint position transformation and the cumulative mileage are then encapsulated into a monitoring data packet and reported to the logistics monitoring platform.

2. The satellite communication-assisted logistics transportation location monitoring method according to claim 1, characterized in that, Before calculating the satellite-to-ground distance and line-of-sight unit vector based on the satellite position coordinates and the terminal's estimated position coordinates, the satellite communication-assisted logistics transportation location monitoring method further includes: The first eastward component, the first northward component, and the first celestial component are collected by the inertial measurement unit, and the three-axis velocity components are formed by the first eastward component, the first northward component, and the first celestial component. The system parses ephemeris data from navigation messages and calculates the satellite position coordinates in the geocentric-ground-fixed coordinate system based on Kepler orbital parameters. It also obtains the terminal's estimated position coordinates through the calculation results of the previous positioning cycle or base station-assisted positioning. The system receives a sequence of discrete path points, including latitude and longitude coordinates, from the logistics monitoring platform.

3. The satellite communication-assisted logistics transportation location monitoring method according to claim 2, characterized in that, The step of calculating the satellite-to-ground distance and line-of-sight unit vector based on the satellite's position coordinates and the terminal's estimated position coordinates, and generating a baseband signal based on the three-axis velocity components and the line-of-sight unit vector, includes: The satellite-to-ground distance is obtained by performing a square root operation on the sum of the squares of the differences between the satellite's position coordinates and the terminal's estimated position coordinates for each coordinate component. Divide the difference between each coordinate component by the star-to-ground distance to obtain the line-of-sight unit vector, which includes a second eastward component, a second northward component, and a second northward component. The radial relative velocity is obtained by multiplying the first eastward component of the three-axis velocity components with the second eastward component of the line-of-sight unit vector, multiplying the first northward component with the second eastward component, multiplying the first celestial component with the second celestial component, and summing the results. The predicted Doppler frequency offset is calculated based on the radial relative velocity. The predicted Doppler frequency offset is then superimposed on the intermediate frequency and the down-conversion reference frequency of the numerically controlled oscillator is set for frequency offset compensation to obtain the baseband signal.

4. The satellite communication-assisted logistics transportation location monitoring method according to claim 1, characterized in that, The step of calculating the target peak value based on the baseband signal, and extracting pseudorange measurements when the target peak value exceeds a noise threshold, and solving for a three-dimensional spatial positioning solution using the satellite position coordinates, includes: The target peak value is obtained by performing a sliding integral operation on the baseband signal and the local pseudo-random code sequence. The target peak value is compared with a noise threshold. When the target peak value exceeds the noise threshold, the code phase corresponding to the target peak value is latched. The signal propagation distance is obtained by multiplying the code phase by the speed of light, and the signal propagation distance is added to the equivalent distance corresponding to the receiver clock error to obtain the pseudorange measurement value. The three-dimensional spatial positioning solution is obtained by iteratively solving the pseudorange measurement value and the satellite position coordinates.

5. The satellite communication-assisted logistics transportation location monitoring method according to claim 4, characterized in that, The step of iteratively solving for the three-dimensional spatial positioning solution based on the pseudorange measurement value and the satellite position coordinates includes: Based on the pseudorange measurement value and the satellite position coordinates, establish a set of pseudorange observation equations about the terminal position and the receiver clock error. Use the estimated terminal position coordinates as the initial estimated position and perform Taylor series linearization expansion on the pseudorange observation equations at the initial estimated position. Calculate the partial derivatives of the pseudorange with respect to each component of the position coordinates and the initial estimated pseudorange. Construct an observation matrix based on the partial derivatives. The difference between the pseudorange measurement and the initial estimated pseudorange is calculated to obtain the pseudorange residual vector. The observation matrix is ​​transposed, multiplied by the observation matrix, and inverted to obtain the inverse result. The inverse result is multiplied sequentially by the transpose of the observation matrix and the pseudorange residual vector to obtain the position correction and clock error correction. The position correction is added to the initial estimated position to obtain the updated position. The updated position is then used as the new initial estimated position for iterative solution to obtain the three-dimensional spatial positioning solution.

6. The satellite communication-assisted logistics transportation location monitoring method according to claim 5, characterized in that, The step of multiplying the inverse result with the transpose of the observation matrix and the pseudorange residual vector in sequence to obtain the position correction and clock error correction includes: For each visible satellite, the difference between the pseudorange measurement value and the initial estimated pseudorange is calculated and arranged in the order of the satellites to form a column vector, thus obtaining the pseudorange residual vector; The observation matrix is ​​transposed to obtain the transpose of the observation matrix. The transpose of the observation matrix is ​​multiplied by the observation matrix to obtain a square matrix. The square matrix is ​​then inverted to obtain the inverse matrix. The inverse matrix is ​​multiplied by the transpose of the observation matrix to obtain an intermediate matrix. The intermediate matrix is ​​then multiplied by the pseudorange residual vector to obtain the position correction and clock error correction.

7. The satellite communication-assisted logistics transportation location monitoring method according to claim 2, characterized in that, The calculation of the path constraint position from the three-dimensional spatial positioning solution to the optimal path segment includes: The latitude and longitude coordinates of each path point in the path point sequence are transformed to the Cartesian coordinate system to obtain the three-dimensional coordinates of the path points. For each initial path segment formed by adjacent path points, the vertical distance from the three-dimensional spatial positioning solution to the initial path segment is calculated. All initial path segments are traversed to find the minimum vertical distance and its corresponding optimal path segment. A parameterized position with parameters as independent variables is established on the optimal path segment, and a constrained optimization objective function is constructed, which includes a position deviation term with the square of the Euclidean distance between the parameterized position and the three-dimensional spatial positioning solution, and a velocity continuity term with the square of the difference between the terminal solution velocity and the initial path segment directional velocity. The first derivative of the constraint optimization objective function with respect to the optimal parameters is calculated and set to zero to obtain the optimal parameters. The optimal parameters are then substituted into the parameterized position to calculate the path constraint position located on the optimal path segment.

8. The satellite communication-assisted logistics transportation location monitoring method according to claim 7, characterized in that, The optimal parameters are obtained by taking the first derivative of the constraint optimization objective function with respect to the optimal parameters and setting the derivative to zero. These optimal parameters are then substituted into the parameterized position to calculate the path constraint position located on the optimal path segment, including: The first-order derivative expressions are obtained by taking the partial derivatives of the position deviation term and the velocity continuity term in the constrained optimization objective function with respect to the optimal parameters. Set the first derivative expression to zero, establish a univariate linear equation about the optimal parameters, and solve it to obtain the optimal parameters; Substitute the optimal parameters into the parameterized position to calculate the vector sum of the starting coordinates of the optimal path segment and the path segment direction vector scaled according to the optimal parameters, and obtain the path constraint position located on the optimal path segment.

9. The satellite communication-assisted logistics transportation location monitoring method according to claim 8, characterized in that, The process of accumulating the baseline mileage from the starting point to the optimal path segment and calculating the cumulative mileage, and then encapsulating the path constraint position transformation and the cumulative mileage into a monitoring data packet, is reported to the logistics monitoring platform, including: For each adjacent path point in the path point sequence from the starting point to the optimal path segment, calculate the path segment length, sum the path segment lengths, and obtain the baseline mileage. Multiply the optimal parameter by the Euclidean length of the optimal path segment to obtain the offset mileage within the segment, and sum the offset mileage within the segment with the base mileage to obtain the cumulative mileage. The path constraint location is converted from Cartesian coordinates to latitude and longitude coordinates and encapsulated with the cumulative mileage, terminal identifier and timestamp into a monitoring data packet, which is then reported to the logistics monitoring platform through a communication channel.

10. A satellite communication-assisted logistics transportation location monitoring system, characterized in that, The steps for implementing the satellite communication-assisted logistics transportation location monitoring method according to any one of claims 1 to 9 include: The generation module is used to calculate the satellite-to-ground distance and line-of-sight unit vector based on the satellite position coordinates and the terminal's estimated position coordinates, and to generate a baseband signal based on the three-axis velocity components and the line-of-sight unit vector; The solution module is used to calculate the target peak value based on the baseband signal. When the target peak value exceeds the noise threshold, it extracts the pseudorange measurement value and solves the three-dimensional spatial positioning solution with the satellite position coordinates. The calculation module is used to calculate the path constraint position from the three-dimensional spatial positioning solution to the optimal path segment; The reporting module is used to accumulate the baseline mileage from the starting point to the optimal path segment and calculate the cumulative mileage, and encapsulate the path constraint position conversion and the cumulative mileage into a monitoring data packet, which is then reported to the logistics monitoring platform.

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