Buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction method and system

Abstract

This invention discloses a method and system for multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction of buoys. The method includes: compressing and encoding hydrological sensor data and BeiDou positioning data respectively; encapsulating the compressed and encoded data into messages according to a preset encapsulation order, and using the BeiDou positioning data frames obtained by decoding the encapsulated messages as BeiDou observation points; if BeiDou observation points are not received within a preset time, it is determined that the BeiDou signal is interrupted; based on extended Kalman filtering, IMU observation data and BeiDou observation points received again after the BeiDou signal interruption are dynamically fused to predict and correct the buoy trajectory; and the buoy trajectory is judged for curves and corrected for curve deviations based on the IMU observation data and the corrected data of the buoy trajectory. Therefore, this invention can overcome the limitations of restricted channels and achieve efficient backhaul of multi-source data such as positioning and hydrological data; at the same time, it can ensure the continuity of the buoy trajectory and eliminate trajectory breaks and reconstruction distortion caused by signal interruption.

Description

Technical Field

[0001] This invention relates to the field of hydrological drifting buoy technology, and in particular to a method and system for multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction of buoys. Background Technology

[0002] With the increasing demand for inland waterway safety, emergency rescue, and ecological monitoring, buoy-type terminals deployed on the surfaces of rivers, lakes, and reservoirs often integrate multi-source sensors to collect positioning information and heterogeneous data related to the water environment, such as water pressure, water temperature, and water depth. Constrained by on-site power supply conditions and deployment density, these terminals generally use low-power satellite IoT links for backhaul, with BeiDou short message service being widely adopted due to its wide coverage and low power consumption. However, BeiDou short message service is a narrowband, low-speed service with limited single-message payload capacity, uplink latency, and service costs, posing significant challenges to the efficient transmission of multi-source data and the guarantee of trajectory continuity under limited channel conditions.

[0003] Regarding data reporting, multi-source heterogeneous data lacks efficient encoding and compression for limited channels. Typical systems report location data, water pressure, water temperature, and water depth in their own independent formats, making it difficult to simultaneously bundle and report multiple measurements within a single short message. This leads to increased link occupancy, higher probability of air interface collisions and packet loss, and increased energy consumption. Therefore, due to the limited channels of BeiDou short messages, traditional technologies do not compress multi-source heterogeneous data (location, water pressure, water temperature, and water depth), resulting in independent data transmission and the inability to bundle and transmit them within a single message.

[0004] Regarding trajectory continuity and reconstruction, buoys exhibit significant nonlinear motion in river bends, backwater areas, or under the influence of wind and waves. Furthermore, the low-frequency and discontinuous observation characteristics of BeiDou short messages result in sparse, intermittent, and uneven trajectory sampling points. When the trajectory is interrupted, reconstruction becomes distorted. Traditional methods can only record discrete positioning points, and linear interpolation is used to reconstruct the trajectory during the interruption. Systematic offsets occur in bends, incorrectly reconstructing the buoy trajectory to the riverbank, thus leading to reconstruction distortion. Summary of the Invention

[0005] This invention provides a method and system for multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction of buoys. It can overcome the limitations of restricted channels and achieve efficient backhaul of multi-source data such as positioning and hydrological data, solving the problems of data fragmentation and hydrological information loss caused by traditional segmented transmission. At the same time, it can ensure the continuity of buoy trajectory, eliminate trajectory breakage and reconstruction distortion caused by signal interruption, and ensure the spatial reliability of hydrological data.

[0006] Firstly, a method for fusing and transmitting multi-source heterogeneous data from buoys and reconstructing sparse trajectories is provided, including: Acquire hydrological sensor data, BeiDou positioning data, and IMU observation data at the same time from the buoy end of the water area to be measured; The hydrological sensor data and the BeiDou positioning data are compressed and encoded respectively; The compressed and encoded data is encapsulated into messages according to a preset encapsulation order, and the BeiDou positioning data frames obtained by decoding the encapsulated messages are used as BeiDou observation points. If the BeiDou observation point is not received within the preset time, it is determined that the BeiDou signal is interrupted. Based on the extended Kalman filter, the IMU observation data and the BeiDou observation point received again after the BeiDou signal interruption are dynamically fused to predict and correct the buoy trajectory. Based on the IMU observation data and the corrected data of the buoy trajectory, the buoy trajectory is judged for curves and the curve deviation is corrected to obtain the final buoy trajectory.

[0007] Secondly, a buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction system is provided, including: The data acquisition module is used to acquire hydrological sensor data, BeiDou positioning data, and IMU observation data at the same time at the buoy end of the water area to be measured. A data compression module, which is connected to the data acquisition module, is used to compress and encode the hydrological sensor data and the BeiDou positioning data respectively. The encapsulation and decoding module is communicatively connected to the data compression module. It is used to encapsulate the compressed and encoded data into messages according to a preset encapsulation order, and to use the BeiDou positioning data frames obtained by decoding the encapsulated messages as BeiDou observation points. The trajectory prediction and correction module, communicatively connected to the data acquisition module and the encapsulation and decoding module, is used to determine a BeiDou signal interruption when the BeiDou observation point is not received within a preset time, and to predict and correct the buoy trajectory by dynamically fusing the IMU observation data with the BeiDou observation point received again after the signal interruption based on extended Kalman filtering; and, The curve correction module is communicatively connected to the trajectory prediction and correction module. Based on the IMU observation data and the correction data of the buoy trajectory, it performs curve judgment and curve deviation correction on the buoy trajectory to obtain the final buoy trajectory.

[0008] Compared with the prior art, the advantages of the present invention are as follows: 1. Overcoming the limitations of restricted channels, it enables efficient backhaul of multi-source data such as positioning and hydrological data, solving the problems of data fragmentation and loss of hydrological information caused by traditional segmented transmission.

[0009] 2. Ensure the continuity of the buoy trajectory, eliminate trajectory breaks and reconstruction distortion caused by signal interruption, and guarantee the spatial reliability of hydrological data. Attached Figure Description

[0010] Figure 1This is a flowchart illustrating a method for multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction of buoys according to the present invention. Figure 2 This is a schematic diagram of the structure of a buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction system according to the present invention. Detailed Implementation

[0011] Referring now to specific embodiments of the invention, examples of which are illustrated in the accompanying drawings. Although the invention will be described in conjunction with specific embodiments, it will be understood that it is not intended to limit the invention to the described embodiments. Rather, it is intended to cover variations, modifications, and equivalents included within the spirit and scope of the invention as defined by the appended claims. It should be noted that the method steps described herein can be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of both.

[0012] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0013] Note: The examples described below are merely specific examples and are not intended to limit the embodiments of the present invention to the specific steps, values, conditions, data, order, etc. Those skilled in the art can utilize the concept of the present invention to construct more embodiments not mentioned herein by reading this specification.

[0014] Please see Figure 1 The present invention provides a flowchart illustrating a method for fusion transmission and sparse trajectory reconstruction of multi-source heterogeneous data from buoys, comprising: Step S100: Obtain hydrological sensor data, BeiDou positioning data, and IMU observation data at the same time from the buoy end of the water area to be measured; Step S200 involves compressing and encoding the hydrological sensor data and the BeiDou positioning data respectively; including: The hydrological sensor data includes water pressure data and water temperature data; Step S210: Compress and encode the offset of the BeiDou positioning data relative to the preset base station coordinates at the current moment to obtain positioning offset binary data; Preset base station coordinates (e.g., 94.120000°E, 29.650000°N), and calculate the offset of the current latitude and longitude from the reference (e.g., longitude offset 0.003456°, latitude offset 0.004321°).

[0015] Longitude offset 0.003456°: Convert it to 3 bytes of binary data - First, multiply the offset by 1000000 (eliminate decimal places, 0.003456° × 1000000 = 3456), then convert it to hexadecimal "0x3A5B7F", which corresponds to binary "00111010 01011011 01111111".

[0016] Latitude offset 0.004321°: Call the latitude offset - such as 0.004321°, multiply by 1000000 to get 4321, which is converted to hexadecimal "0x01C8D9", corresponding to binary "00000001 11001000 11011001".

[0017] Step S220: Compress and encode the difference between the water pressure data at the current moment and the water pressure data at the previous moment to obtain water pressure differential binary data; Store the water pressure value at the previous moment, such as 101.1 kPa. Calculate the difference between the previous and current values. If the difference is +0.2 kPa, the difference is considered positive. Positive numbers are represented by 0x01-0x7F (decimal 1-127, corresponding to differences of 0.1-12.7 kPa). Multiply the difference by 10 to eliminate decimal places. 0.2 kPa × 10 = 2, which is converted into 1 byte of binary data "00000010", corresponding to hexadecimal 0x02 and decimal 2.

[0018] Step S230: The integer obtained by discretizing the water temperature data at the current time is compressed and encoded to obtain binary water temperature data; Discretize the water temperature data at the current moment to obtain an integer, such as 12.8℃→128, and determine that the value range is between 0 and 255 (1 byte of binary data can represent 0-255). Convert 128 into 1 byte of binary data "10000000", which corresponds to hexadecimal 0x80 and decimal 128.

[0019] Step S240: Compress and encode the current time to obtain the timestamp binary data.

[0020] Call the UTC (Coordinated Universal Time) timestamp of the current data, such as 2025-08-10 14:30:00, and calculate the total number of seconds since the base time (January 1, 2025 00:00:00) - the total number of days from January to July 2025 is 212 days, and from August 1 to 10 is 10 days, for a total of 222 days (222 × 86400 = 19180800 seconds). 14 hours and 30 minutes = 14 × 3600 + 30 × 60 = 52200 seconds, and the total number of seconds = 19180800 + 52200 = 19233000 seconds. The total number of seconds, 19233000, is converted into 4 bytes of binary data (stored in little-endian mode to adapt to the byte order of the BeiDou module and the backend system), corresponding to hexadecimal "0x5DB8A7F0" and binary "01011101 1011100010100111 11110000". In step S300, the compressed and encoded data is encapsulated into a message according to a preset encapsulation order, and the BeiDou positioning data frame obtained by decoding the encapsulated message is used as the BeiDou observation point.

[0021] The step of encapsulating the compressed and encoded data into a message according to a preset encapsulation order includes: The binary data of the buoy header is verified by the CRC16 check function with the binary data of the positioning offset, the binary data of the water pressure difference, the binary data of the water temperature, and the binary data of the timestamp at the same time after compression and encoding, so as to obtain the check code binary data. The buoy header binary data, positioning offset binary data, water pressure difference binary data, water temperature binary data, timestamp binary data, and checksum binary data at the current time are sequentially concatenated to obtain the encapsulated message.

[0022] Specifically, in the embodiments of the present invention, The header field (2 bytes) is used by the backend system to quickly identify the message type (distinguishing hydrological buoy data from other equipment data). The default fixed value is a combination of "equipment identification code + data type code".

[0023] The preset packet header parameters are read and determined to be 2 bytes of binary data. The first byte is the device identification code (fixed 0xAA, corresponding to decimal 170, representing the hydrological drifting buoy of this invention), and the second byte is the data type code (fixed 0x01, corresponding to decimal 1, representing the multi-source heterogeneous fusion data frame). After combination, the binary data of the packet header "10101010 00000001" is obtained.

[0024] Location offset field (6 bytes): The 3 bytes of longitude offset and the 3 bytes of latitude offset are concatenated in the order of "longitude first, latitude last" to obtain the 6-byte location offset binary data "00111010 0101101101111111 00000001 11001000 11011001".

[0025] Water pressure difference field (1 byte): Stores the difference between the water pressure at the current moment and the water pressure at the previous moment, using 1 byte to represent the positive or negative sign and the magnitude of the value.

[0026] Water temperature field (1 byte): Stores the discretized water temperature value, represented by 1 byte.

[0027] Timestamp field (4 bytes): Stores the UTC time of data collection, represented by a 4-byte integer, indicating the number of seconds since the base time.

[0028] Checksum field (2 bytes): Verifies the integrity of data during message transmission, calculated using the CRC16 algorithm.

[0029] The above 14 bytes of data, including "packet header (2 bytes) + positioning offset (6 bytes) + water pressure difference (1 byte) + water temperature (1 byte) + timestamp (4 bytes)," are used as input to calculate a 2-byte checksum (e.g., decimal 8028, corresponding to hexadecimal 0x1F3C), which is then converted into binary "00011111 00111100".

[0030] Finally, the fields are concatenated in a fixed order to form a complete byte stream.

[0031] The splicing rules must strictly follow the order of "header → positioning offset → water pressure differential → water temperature → timestamp → checksum" to ensure that each field can be parsed at a fixed position. Specific steps are as follows: Allocate a 16-byte buffer in RAM memory, and set the starting address for storing each field (bytes 1-2 store the packet header, bytes 3-8 store the location offset, byte 9 store the water pressure difference, byte 10 store the water temperature, bytes 11-14 store the timestamp, and bytes 15-16 store the checksum). Write the prepared binary data for each field into the buffer sequentially according to the above addresses, ultimately forming a complete 16-byte binary byte stream as follows: “10101010 00000001 00111010 01011011 01111111 00000001 1100100011011001 00000010 10000000 01011101 10111000 10100111 11110000 0001111100111100”.

[0032] Meanwhile, the encapsulated message can be sent to the hydrological monitoring ground station, which can parse the message structure as follows: Checksum verification: The CRC16 algorithm is used to verify data integrity and eliminate erroneous messages. Positioning restoration: Base coordinates + offset, 94.120000°E + 0.003456° → 94.123456°E.

[0033] Water pressure restoration: previous value + difference value (101.1kPa + 0.2kPa → 101.3kPa). Water temperature restoration: hexadecimal to integer ÷ 10 (0x80→128÷10→12.8℃).

[0034] Furthermore, it can invert water depth by transmitting only water pressure and temperature: The physical relationship between water temperature and water pressure is as follows: Density formula

[0035] Where t represents water temperature, p represents water pressure, and h represents water depth. Water depth can be retrieved by transmitting water pressure and water temperature, eliminating the need for separate transmission of water depth sensor data. This coupling mode of "measured value + physical model" reduces the amount of sensor data transmission by 1 / 3 at the source (eliminating the need for water depth parameters), while avoiding the spatiotemporal mismatch problem of multi-sensor data (because water depth is calculated from water pressure and water temperature at the same moment, it is naturally synchronized).

[0036] In summary, this invention can overcome the limitations of restricted channels, achieve efficient backhaul of multi-source data such as positioning and hydrological data, and solve the problems of data fragmentation and hydrological information loss caused by traditional segmented transmission.

[0037] When the trajectory of a hydrological buoy is interrupted, reconstruction becomes distorted. Traditional methods can only record discrete positioning points, and linear interpolation is used to reconstruct the trajectory during the interruption. This results in systematic shifts at bends, incorrectly reconstructing the buoy trajectory to the riverbank. This invention encapsulates the compressed and encoded data into messages according to a preset encapsulation order, and then decodes the encapsulated messages to obtain BeiDou positioning data frames, which are used as BeiDou observation points for buoy trajectory prediction. Specifically, [92.123456°E, 29.654321°N] and [h=8.2m, T=12.8℃] are bound to the same timestamp. The BeiDou positioning data frame associated with this timestamp will serve as the base observation point—the BeiDou observation point—for subsequent sparse trajectory reconstruction.

[0038] The 7-dimensional state vector for the buoy's trajectory motion is defined as follows:

[0039] In the formula, Represents the buoy's position along the width of the river channel, in meters (m). Represents the buoy's position along the main channel of the river, in meters (m). The velocity of the buoy along the width of the river channel is expressed in m / s. Represents the speed of the buoy along the main current of the river, in m / s; Represents the angular velocity of the buoy about its vertical axis (perpendicular to the riverbed plane), measured in rad / s; Represents the zero bias of the IMU accelerometer (vector form, including x / y axes), unit m / s²; This represents the zero bias (around the vertical axis) of the IMU gyroscope, measured in m / s².

[0040] The specific trajectory reconstruction steps are as follows: Step S400: If the BeiDou observation point is not received within a preset time, it is determined that the BeiDou signal is interrupted. Based on the extended Kalman filter, the IMU observation data and the BeiDou observation point received again after the BeiDou signal interruption are dynamically fused to predict and correct the buoy trajectory, including: Step S410: If the BeiDou observation point is not received within the preset time, it is determined that the BeiDou signal is interrupted, and at the current moment of BeiDou signal interruption, the buoy trajectory is predicted based on the IMU observation data using extended Kalman filtering. Specifically, in this embodiment of the invention, if the buoy MCU does not receive valid BeiDou data for 60 consecutive seconds (more than 2 positioning cycles), it is determined that the BeiDou signal is interrupted.

[0041] The state vectorX By dynamically fusing IMU predictions and BeiDou observations through the Extended Kalman Filter (EKF), the EKF achieves state estimation of nonlinear systems through a "prediction-update" closed loop. The core is to use IMU data for state prediction (short-term high accuracy) and combine it with BeiDou sparse observations (long-term absolute reference) to correct prediction errors, and finally output the optimal fused state.

[0042] State vector initialization: Taking the buoy deployment time as the initial time (t=0), the initial position (x0, y0) is obtained through the first BeiDou positioning, and the state vector is initialized: (Initial velocity and angular velocity are set to 0, and the initial value of IMU zero bias is set to 0, which will be corrected through iteration later).

[0043] Initial covariance matrix : Reflects the uncertainty of the initial state, and the diagonal elements are the variances of each state component.

[0044]

[0045] Among them, BeiDou positioning error The initial error of velocity / angular velocity is set to a large value (e.g., IMU zero bias initial error .

[0046] Prediction phase: When only IMU observation data is received (without BeiDou observation), the state and uncertainty of the next moment are predicted based on the acceleration and angular velocity observed by the IMU through nonlinear state equations.

[0047] Transverse / longitudinal acceleration (a_x_imu, a_y_imu) and angular velocity measured in real time using an IMU By combining the continuous-time state equation, the state is predicted through numerical integration:

[0048] In the formula, This represents the predicted state at time k. The state equations are nonlinear. =[a_x_imu, a_y_imu, ], IMU measurement input.

[0049] Specifically, for each component (taking position and velocity as examples):

[0050] Calculate the Jacobian matrix of the state equations (Linearizing the nonlinear equations), updating state uncertainties:

[0051] in: .

[0052] (as for) Taking the partial derivative gives ); Process noise covariance matrix (reflects IMU measurement noise and model error, such as accelerometer noise) Gyroscope noise ).

[0053] During the update phase, when BeiDou observation data is received (once every 30 seconds), the absolute position provided by BeiDou is used to correct the deviation of the predicted state (such as the position shift caused by IMU drift).

[0054] Construct the observation equation: BeiDou only directly observes the position, and the observation equation is defined as: ,in BeiDou observation vectors; Observation equation (extracting position components from the state vector); Observation noise (BeiDou positioning error, covariance matrix) ).

[0055] Calculate the Kalman gain: Kalman gain The weights that reflect the correction of the observed state to the predicted state (the more reliable the observation, the greater the weight):

[0056] in The Jacobian matrix (2×8) of the observation equation has only the elements corresponding to the position components set to 1, and the rest set to 0.

[0057] Status correction and update (core fusion steps): By correcting the predicted value using the deviation between the BeiDou observation and the predicted state, the optimal state is obtained.

[0058] in The "observation residual" (the difference between the BeiDou measured position and the IMU predicted position) is calculated using Kalman gain. Inversely correct all components in the state vector (including position, velocity, angular velocity, and IMU bias).

[0059] Therefore, upon receiving each frame of IMU data (10ms / time), step S410 prediction is executed; upon receiving each frame of BeiDou data (30s / time), the aforementioned state correction and update steps are executed, forming a "prediction-update" closed loop. The specific state component updates of the buoy are explained below: In step S420, when a BeiDou observation point is received at any time after the current time, the subsequent BeiDou observation points are screened for anomalies based on the predicted buoy trajectory to obtain valid BeiDou observation points. Specifically, in this embodiment of the invention, the synchronous state output during the EKF prediction phase (aligned with the BeiDou timestamp t) is called. ),in It is the predicted speed of the buoy along the main current of the river channel.

[0060] Positional deviation:

[0061] like Observations >5m are considered abnormal (such as jumps caused by the BeiDou signal being blocked by a canyon), marked as invalid, and discarded.

[0062] Step S430, correcting the predicted buoy trajectory based on the selected effective BeiDou observation points, including: S431, calculate the deviation between the effective BeiDou observation point and the predicted buoy trajectory, calculate the correction amount of the IMU accelerometer zero bias based on the deviation value, and correct the IMU accelerometer zero bias in the predicted buoy trajectory based on the correction amount of the IMU accelerometer zero bias. S432, calculate the correction amount of the IMU gyroscope zero bias based on the heading angle difference between the two effective Beidou observation points, and correct the IMU gyroscope zero bias in the predicted buoy trajectory based on the correction amount of the IMU gyroscope zero bias.

[0063] Calculate the deviation between the effective BeiDou observation point (x, y) and the EKF predicted position to obtain... , .

[0064] IMU accelerometer zero bias calibration: lateral zero bias Correction amount , For calibration coefficients, =30s is the BeiDou cycle, with zero vertical offset. Correction amount .

[0065] Gyroscope zero-bias calibration: First, calculate the actual steering angular velocity using effective BeiDou observation points. , The heading angle difference between two valid BeiDou observation points is then used to calculate the IMU gyroscope zero bias correction as follows: , For calibration coefficients, Angular velocity of the buoy about its vertical axis measured by the IMU Calibration update: The correction is added to the current IMU accelerometer zero bias, i.e.: Similarly, update and .

[0066] Therefore, the output "valid observation points" are the sole basis for calibration—only observations that pass the consistency analysis can ensure that the deviation is correct. Originating from IMU drift rather than observation error, it precisely corrects the zero bias, thus achieving the core objective of "suppressing IMU drift".

[0067] Therefore, the calibrated IMU zero bias ( Substitute the 7-dimensional state vector of the buoy trajectory motion defined above, update the state every 10ms (IMU sampling period), and generate continuous trajectory points.

[0068] S433 uses a velocity attenuation coefficient to correct the buoy's velocity along the main current of the river in the predicted buoy trajectory during BeiDou signal interruption.

[0069] The formula for calculating the velocity attenuation coefficient α is as follows:

[0070] in The actual steering angular velocity calculated using effective BeiDou observation points is located at the curve. When α is large, it approaches 1, reducing excessive velocity decay.

[0071] Therefore, α is used to correct the longitudinal velocity. This can suppress velocity drift predicted by pure IMU.

[0072] Therefore, without calibration, IMU drift can cause the trajectory to deviate by more than 20m within 30 minutes; while after calibration, the drift error can be controlled within 8m, ensuring the feasibility of "relying on the IMU to maintain continuity".

[0073] Step S500: Based on the IMU observation data and the corrected data of the buoy trajectory, perform curve judgment and curve deviation correction on the buoy trajectory to obtain the final buoy trajectory, including: S510, acquire IMU lateral acceleration from IMU observation data. Angular velocity of the buoy about its vertical axis ; S520, the IMU lateral acceleration is corrected based on the corrected IMU accelerometer zero bias to obtain the true lateral acceleration; S530, the angular velocity of the buoy around the vertical axis is corrected based on the corrected IMU gyroscope zero bias to obtain the true angular velocity; right A 5-point moving average filter is used to remove high-frequency noise caused by water flow turbulence (such as interference above 100Hz generated by instantaneous impact).

[0074] With calibration After correcting the data, the actual lateral acceleration is as follows: ; The true angular velocity is: .

[0075] Obtaining clean and accurate key IMU data—lateral acceleration and angular velocity—is crucial for calculating curve curvature. If the data contains noise or zero bias, it will lead to deviations in curvature calculation, which in turn will cause trajectory correction errors.

[0076] S540, if the actual angular velocity corresponding to the buoy trajectory is less than or equal to a preset angular velocity threshold, then it is determined that the buoy is in a straight river channel, and the corrected buoy trajectory is regarded as the final buoy trajectory. Set angular velocity threshold =0.05rad / s (corresponding to a heading angle change of approximately 85° within 30 seconds, indicating entry into a curve).

[0077] like If the buoy is in a straight river channel, then no curvature correction is needed, and the corrected buoy trajectory is directly regarded as the final buoy trajectory.

[0078] S550, if the true angular velocity corresponding to the buoy trajectory is greater than a preset angular velocity threshold, then the buoy is determined to be in a curve, and the buoy trajectory is corrected for curve deviation based on the true lateral acceleration, the true angular velocity, and the predicted buoy velocity along the main current of the river in the buoy trajectory, to obtain the final buoy trajectory, including: S551, calculate the radius of curvature of the bend based on the actual lateral acceleration, the actual angular velocity and the predicted buoy velocity along the main current of the river in the buoy trajectory, and calculate the trajectory offset to the riverbank and the correction coefficient based on the radius of curvature. like The scene was identified as a curve, and curvature calculation was performed.

[0079] When the actual lateral acceleration is not equal to zero, the formula for calculating the radius of curvature of the curve is as follows:

[0080] When the actual lateral acceleration is equal to zero, the formula for calculating the radius of curvature of the curve is as follows:

[0081] In the formula, The predicted buoy trajectory represents the buoy's velocity along the main current of the river channel. This represents the actual lateral acceleration. This is the actual angular velocity.

[0082] Curvature limit: Set a reasonable range for the curve radius. =20m (sharp bend) =500m (gentle curve), when it exceeds the range, take the boundary value (e.g., if the calculated R=15, force it to be set to R=20m to avoid extreme values ​​causing overcorrection).

[0083] The calculated trajectory offset towards the riverbank and the correction factor are as follows: Traditional linear interpolation offset: Without correction, linear interpolation will cause the trajectory to shift towards the riverbank. (L is the straight-line distance between the two BeiDou observation points); Calculate the correction factor based on the radius of curvature R. .

[0084] S552, the curve offset correction is calculated based on the trajectory offset towards the riverbank and the correction coefficient, as shown in the following formula:

[0085] S553, the method for adjusting the buoy's position along the river width direction in the predicted buoy trajectory based on the curve offset correction amount and the actual lateral acceleration, and obtaining the lateral position adjustment value, is shown in the following formula:

[0086] In the formula, For the first k The horizontal position adjustment value at any given time; This is the correction amount for curve offset; This represents the actual lateral acceleration. For the predicted buoy trajectory, the first k The position of the buoy along the width of the river channel at any given time.

[0087] S554, if the deviation between the horizontal position adjustment value and the corresponding BeiDou observation point is not within the preset range, then the correction coefficient is adjusted, and the horizontal position adjustment value is updated according to the adjusted correction coefficient.

[0088] Once the BeiDou signal is restored, compare the predicted position with the corrected trajectory. With "BeiDou measured position" deviation .

[0089] like <2m (meets accuracy requirements), maintain the current correction factor. .

[0090] like >3m (undercorrection or overcorrection), adjust .

[0091] If the deviation is too large, it indicates that the current... If the coefficients do not match the actual river characteristics, feedback adjustment and optimization are required to ensure the accuracy and stability of subsequent curve corrections.

[0092] S554, the actual lateral acceleration The true angular velocity The correction δ for the buoy's angular velocity about its vertical axis is calculated based on the predicted buoy velocity along the main current of the river in its trajectory. ω The calculation formula is as follows:

[0093] In the formula, when the sharp bend R < 50m, k = 0.8; when the gentle bend 50m ≤ R < 200m, k = 0.3; and when the straight section is straight, k = 0.1.

[0094] The formula for calculating the actual turning angular velocity ω of the buoy, based on the correction amount of the buoy's vertical axis angular velocity and the true angular velocity, is as follows:

[0095] Therefore, an adaptive model of curve curvature is constructed using the lateral acceleration (reflecting the centrifugal force of the water flow) measured by the IMU and the angular velocity of the buoy around the vertical axis. By analyzing the ratio of lateral acceleration to angular velocity, the radius of curvature of the trajectory prediction is dynamically adjusted to eliminate the riverbank offset caused by linear interpolation.

[0096] See also Figure 2 As shown, this embodiment of the invention also provides a buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction system, including: The data acquisition module is used to acquire hydrological sensor data, BeiDou positioning data, and IMU observation data at the same time at the buoy end of the water area to be measured. A data compression module, which is connected to the data acquisition module, is used to compress and encode the hydrological sensor data and the BeiDou positioning data respectively. The encapsulation and decoding module is communicatively connected to the data compression module. It is used to encapsulate the compressed and encoded data into messages according to a preset encapsulation order, and to use the BeiDou positioning data frames obtained by decoding the encapsulated messages as BeiDou observation points. The trajectory prediction and correction module, communicatively connected to the data acquisition module and the encapsulation and decoding module, is used to determine a BeiDou signal interruption when the BeiDou observation point is not received within a preset time, and to predict and correct the buoy trajectory by dynamically fusing the IMU observation data with the BeiDou observation point received again after the signal interruption based on extended Kalman filtering; and, The curve correction module is communicatively connected to the trajectory prediction and correction module. Based on the IMU observation data and the correction data of the buoy trajectory, it performs curve judgment and curve deviation correction on the buoy trajectory to obtain the final buoy trajectory.

[0097] In summary, the main advantages of this invention are as follows: 1. Overcoming the limitations of restricted channels, it enables efficient backhaul of multi-source data such as positioning and hydrological data, solving the problems of data fragmentation and loss of hydrological information caused by traditional segmented transmission.

[0098] 2. Ensure the continuity of the buoy trajectory, eliminate trajectory breaks and reconstruction distortion caused by signal interruption, and guarantee the spatial reliability of hydrological data.

[0099] Specifically, this embodiment corresponds one-to-one with the above method embodiments. The functions of each module have been described in detail in the corresponding method embodiments, so they will not be repeated here.

[0100] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements all or part of the method steps of the above method.

[0101] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0102] Based on the same inventive concept, embodiments of this application also provide an electronic device, including a memory and a processor. The memory stores a computer program that runs on the processor. When the processor executes the computer program, it implements all or part of the method steps described above.

[0103] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting all parts of the computer device through various interfaces and lines.

[0104] Memory can be used to store computer programs and / or modules. The processor performs various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital (SD) cards, Flash Cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0105] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0106] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0107] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0109] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for fusion transmission and sparse trajectory reconstruction of multi-source heterogeneous data from buoys, characterized in that, include: Acquire hydrological sensor data, BeiDou positioning data, and IMU observation data at the same time from the buoy end of the water area to be measured; The hydrological sensor data and the BeiDou positioning data are compressed and encoded respectively; The compressed and encoded data is encapsulated into messages according to a preset encapsulation order, and the BeiDou positioning data frames obtained by decoding the encapsulated messages are used as BeiDou observation points. If the BeiDou observation point is not received within the preset time, it is determined that the BeiDou signal is interrupted. Based on the extended Kalman filter, the IMU observation data and the BeiDou observation point received again after the BeiDou signal interruption are dynamically fused to predict and correct the buoy trajectory. Based on the IMU observation data and the correction data of the buoy trajectory, the buoy trajectory is judged for curves and the curve deviation is corrected to obtain the final buoy trajectory.

2. The buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction method as described in claim 1, characterized in that, The compression encoding of the hydrological sensor data and the BeiDou positioning data includes: The hydrological sensor data includes water pressure data and water temperature data; The offset of the BeiDou positioning data at the current moment relative to the preset base station coordinates is compressed and encoded to obtain positioning offset binary data; The difference between the water pressure data at the current moment and the water pressure data at the previous moment is compressed and encoded to obtain water pressure differential binary data; The integer obtained by discretizing the water temperature data at the current moment is compressed and encoded to obtain binary water temperature data; The current moment is compressed and encoded to obtain the timestamp binary data.

3. The buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction method as described in claim 2, characterized in that, The step of encapsulating the compressed and encoded data into a message according to a preset encapsulation order includes: The binary data of the buoy packet is verified by the CRC16 check function with the binary data of the positioning offset, the binary data of the water pressure difference, the binary data of the water temperature, and the binary data of the timestamp at the same time after compression and encoding, so as to obtain the check code binary data. The buoy header binary data, positioning offset binary data, water pressure difference binary data, water temperature binary data, timestamp binary data, and checksum binary data at the current time are sequentially concatenated to obtain the encapsulated message.

4. The buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction method as described in claim 1, characterized in that, If the BeiDou observation point is not received within a preset time, it is determined that the BeiDou signal is interrupted. Based on extended Kalman filtering, the IMU observation data and the BeiDou observation point received again after the signal interruption are dynamically fused to predict and correct the buoy trajectory, including: If the BeiDou observation point is not received within the preset time, it is determined that the BeiDou signal is interrupted, and at the current moment of BeiDou signal interruption, the buoy trajectory is predicted based on the IMU observation data using extended Kalman filtering. When a BeiDou observation point is received at any time after the current time, the subsequent BeiDou observation points are filtered for anomalies based on the predicted buoy trajectory to obtain valid BeiDou observation points. The predicted buoy trajectory is corrected based on the selected effective BeiDou observation points.

5. The buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction method as described in claim 4, characterized in that, The step of correcting the predicted buoy trajectory based on the selected effective BeiDou observation points includes: Calculate the deviation between the effective BeiDou observation point and the predicted buoy trajectory, calculate the correction amount of the IMU accelerometer zero bias based on the deviation value, and correct the IMU accelerometer zero bias in the predicted buoy trajectory based on the correction amount of the IMU accelerometer zero bias. The correction amount of the IMU gyroscope zero bias is calculated based on the heading angle difference between the two effective BeiDou observation points, and the IMU gyroscope zero bias in the predicted buoy trajectory is corrected based on the correction amount of the IMU gyroscope zero bias. During the BeiDou signal interruption, the velocity attenuation coefficient is used to correct the buoy velocity along the main current of the river in the predicted buoy trajectory.

6. The buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction method as described in claim 5, characterized in that, The step of determining curves and correcting curve deviations in the buoy trajectory based on the IMU observation data and the corrected data of the buoy trajectory to obtain the final buoy trajectory includes: Acquire the IMU lateral acceleration and the buoy's angular velocity about the vertical axis from the IMU observation data; The IMU lateral acceleration is corrected based on the corrected IMU accelerometer zero bias to obtain the true lateral acceleration. The angular velocity of the buoy around the vertical axis is corrected based on the corrected IMU gyroscope zero bias to obtain the true angular velocity; If the actual angular velocity corresponding to the buoy trajectory is less than or equal to a preset angular velocity threshold, the buoy is determined to be in a straight river channel, and the corrected buoy trajectory is regarded as the final buoy trajectory. If the actual angular velocity corresponding to the buoy trajectory is greater than the preset angular velocity threshold, it is determined that the buoy is in a curve. The buoy trajectory is then corrected for curve deviation based on the actual lateral acceleration, the actual angular velocity, and the predicted buoy velocity along the main current of the river in the buoy trajectory, to obtain the final buoy trajectory.

7. The buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction method as described in claim 6, characterized in that, The step of correcting the buoy trajectory for curve deviation based on the actual lateral acceleration, the actual angular velocity, and the predicted buoy velocity along the main current of the river channel in the buoy trajectory includes: The radius of curvature of the bend is calculated based on the actual lateral acceleration, the actual angular velocity, and the predicted buoy velocity along the main current of the river in the buoy trajectory. The trajectory offset to the riverbank and the correction coefficient are calculated based on the radius of curvature. Calculate the curve offset correction based on the trajectory offset towards the riverbank and the correction coefficient; The lateral position adjustment value is obtained by adjusting the position of the buoy along the width of the river channel in the predicted buoy trajectory based on the curve offset correction amount and the actual lateral acceleration. If the deviation between the horizontal position adjustment value and the corresponding BeiDou observation point is not within the preset range, the correction coefficient is adjusted, and the horizontal position adjustment value is updated according to the adjusted correction coefficient. The buoy's angular velocity correction around the vertical axis is calculated based on the actual lateral acceleration, the actual angular velocity, and the predicted buoy velocity along the main current of the river in the buoy trajectory. The actual turning angular velocity of the buoy is then obtained based on the buoy's angular velocity correction around the vertical axis and the actual angular velocity.

8. The buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction method as described in claim 7, characterized in that, The calculation of the radius of curvature of the bend based on the actual lateral acceleration, the actual angular velocity, and the predicted buoy velocity along the main current of the river in the buoy trajectory includes: When the actual lateral acceleration is not equal to zero, the formula for calculating the radius of curvature of the curve is as follows: When the actual lateral acceleration is equal to zero, the formula for calculating the radius of curvature of the curve is as follows: In the formula, The predicted buoy trajectory represents the buoy's velocity along the main current of the river channel. This represents the actual lateral acceleration. This is the actual angular velocity.

9. The buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction method as described in claim 7, characterized in that, The method for adjusting the buoy's position along the river channel width in the predicted buoy trajectory based on the curve offset correction amount and the actual lateral acceleration to obtain the lateral position adjustment value is shown in the following formula: In the formula, For the first k The horizontal position adjustment value at any given time; This is the correction amount for curve offset; This represents the actual lateral acceleration. For the predicted buoy trajectory, the first k The position of the buoy along the width of the river channel at any given time.

10. A buoy multi-source heterogeneous data fusion transmission and sparse trajectory reconstruction system, characterized in that, include: The data acquisition module is used to acquire hydrological sensor data, BeiDou positioning data, and IMU observation data at the same time at the buoy end of the water area to be measured. A data compression module, which is connected to the data acquisition module, is used to compress and encode the hydrological sensor data and the BeiDou positioning data respectively. The encapsulation and decoding module is communicatively connected to the data compression module. It is used to encapsulate the compressed and encoded data into messages according to a preset encapsulation order, and to use the BeiDou positioning data frames obtained by decoding the encapsulated messages as BeiDou observation points. The trajectory prediction and correction module, communicatively connected to the data acquisition module and the encapsulation and decoding module, is used to determine a BeiDou signal interruption when the BeiDou observation point is not received within a preset time, and to predict and correct the buoy trajectory by dynamically fusing the IMU observation data with the BeiDou observation point received again after the signal interruption based on extended Kalman filtering; and, The curve correction module is communicatively connected to the trajectory prediction and correction module. Based on the IMU observation data and the correction data of the buoy trajectory, it performs curve judgment and curve deviation correction on the buoy trajectory to obtain the final buoy trajectory.

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