Transport case real-time positioning method and device based on multi-sensor fusion

Abstract

The invention discloses a transport case real-time positioning method and device based on multi-sensor fusion, and belongs to the technical field of Internet of Things and intelligent logistics. The method comprises the following steps: acquiring positioning parameters and motion state parameters of a transport case in real time through sensors such as a GPS (Global Positioning System) and an IMU (Inertial Measurement Unit), and carrying out noise filtering, timestamp synchronization and coordinate system unified preprocessing operation on data; an extended Kalman filtering and particle filtering mixed model is adopted, and a weight coefficient is dynamically adjusted according to the GPS confidence coefficient and the IMU confidence coefficient, so that adaptive data fusion is realized; transmitting a positioning result through 4G / 5G and LoRa dual-mode communication, and judging reliability based on a residual error to trigger calibration or switch a positioning mode; according to the method, the problems of insufficient precision, poor robustness and limited real-time performance of a single sensor in a complex environment are solved, centimeter-level positioning precision in the complex environment is realized, the anti-interference performance of positioning is improved, the real-time performance and low power consumption of data transmission are ensured, and a fusion result is prevented from being polluted by abnormal data.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet of Things and intelligent logistics, and specifically discloses a real-time positioning method and device for a transport box based on multi-sensor fusion. Background Art

[0002] Current transport box positioning technologies mainly rely on a single sensor, such as GPS, Bluetooth or RFID, and have the following limitations:

[0003] (1) Insufficient accuracy: A single sensor is vulnerable to interference in complex environments, such as indoors, tunnels, and areas with multiple obstacles, resulting in large positioning errors. For example:

[0004] In indoor, tunnel or high-rise dense areas, GPS satellite signals are blocked or reflected, and the positioning accuracy drops sharply. The horizontal error can reach dozens of meters or even the positioning completely fails.

[0005] Bluetooth / RFID depends on the deployment density of fixed base stations, has a limited coverage range, is easily affected by shielding of metal boxes or goods, and the positioning accuracy is limited by the base station spacing.

[0006] Although inertial navigation sensors can provide continuous positioning through dead reckoning when there is no external signal, the zero-bias errors of their accelerometers and gyroscopes will accumulate over time, resulting in position drift.

[0007] (2) Poor robustness: Existing technologies lack multi-sensor collaboration and redundancy mechanisms, which are specifically manifested as:

[0008] Insufficient environmental adaptability: After GPS fails indoors, the system cannot seamlessly switch to an alternative positioning mode; the cumulative error cannot be suppressed when IMU is used alone.

[0009] Dependence on infrastructure: RFID requires a large number of base stations to be pre-deployed, with high costs and poor flexibility; Bluetooth positioning is limited by the beacon layout and is difficult to adapt to dynamic transportation scenarios.

[0010] Abnormal data interference: Traditional methods do not design a real-time data reliability evaluation mechanism, and abnormal sensor outputs, such as GPS jumps caused by multipath effects, will directly contaminate the positioning results.

[0011] (3) Limited real-time performance: Traditional methods are difficult to balance high-frequency data updates and low-power consumption requirements in dynamic environments.

[0012] In existing technologies, multi-sensor fusion methods are mostly used for vehicle or UAV positioning, but have not been optimized for the lightweight, low-cost and environmental adaptability requirements of transport boxes.

[0013] In view of the above problems, there is an urgent need for a real-time positioning method and device for a transport box based on multi-sensor fusion. Summary of the Invention

[0014] To overcome the above-mentioned defects of the prior art, the present invention provides a real-time positioning method and device for a transport box based on multi-sensor fusion. By collaborating with multiple sensors such as GPS and IMU to collect data, an extended Kalman filter and particle filter hybrid model is adopted, and an adaptive data fusion is achieved by combining a dynamic weight adjustment mechanism. Specifically, it includes the following steps: real-time collecting positioning parameters and motion state parameters; preprocessing the data to eliminate noise and unify the coordinate system; dynamically calculating the weight coefficients of EKF and PF based on the environment and fusing them to generate a high-precision positioning result; real-time transmitting the data through 4G / 5G and LoRa dual-mode communication; the control center judges the reliability of the positioning, triggers sensor calibration or switches to the IMU dead reckoning mode, improves the anti-interference ability in complex environments, reduces the positioning error, and effectively solves the problems mentioned in the background technology.

[0015] To achieve the above object, the present invention provides the following technical solutions: A real-time positioning method for a transport box based on multi-sensor fusion, specifically including the following steps:

[0016] S1. The data acquisition module real-time collects the position data of the transport box, including positioning parameters and motion state parameters;

[0017] S2. The data processing module preprocesses the collected position data to obtain preprocessed data;

[0018] S3. An extended Kalman filter and particle filter hybrid model is adopted, the weights are adjusted according to the environment, and adaptive data fusion is performed on the preprocessed data to obtain a positioning result with high-precision positioning information;

[0019] S4. The communication module real-time transmits the positioning result;

[0020] S5. The control center judges the reliability of the positioning result, triggers sensor calibration or switches the positioning mode, and finally outputs the positioning result.

[0021] Preferably, the positioning parameters specifically include: longitude and latitude, altitude, and timestamp data; the motion state parameters specifically include: three-axis acceleration, three-axis angular velocity, and speed data.

[0022] Preferably, the preprocessing specifically includes: noise filtering, timestamp synchronization, and coordinate system unification.

[0023] Preferably, the calculation method of the EKF weight coefficient is: according to the data obtained in real time, using the formula to calculate, where α GPS represents the GPS confidence level, and α IMU represents the IMU confidence level; the calculation formula of the EKF weight coefficient is: ω PF = 1 - ωEKF ; The calculation formula for the predicted position output by the particle filter is: In the formula, N represents the total number of particles, ω i represents the normalized weight of the i-th particle, ∑ω i = 1, p i represents the position of the i-th particle.

[0024] Preferably, the calculation method of the said α GPS is as follows: In the formula, σ GPS represents the position residual variance, which is calculated according to the actual situation; HDOP represents the GPS horizontal positioning accuracy factor, which is directly obtained through the GPS module; the calculation method of the said α IMU is: α IMU = exp(-||ω|| - ||a||), in the formula, ||ω|| represents the angular velocity modulus, ||a|| represents the acceleration modulus, and the calculation formula of the said ω i is σ represents the standard deviation of the observation noise, which is determined according to the actual scenario and sensor characteristics; z GPS is the direct observation value provided by the GPS.

[0025] Preferably, the method for determining the reliability of the positioning result is: calculate the residual between the GPS observation value and the fused positioning result, and if the residual exceeds the threshold, it is determined to be unreliable.

[0026] Preferably, the working mode of the communication module is as follows: in an open area, the 4G / 5G network is preferentially used to support high-frequency updates; in a weak signal or indoor environment, it switches to the LoRa mode and reduces the transmission frequency to 1Hz to save power consumption.

[0027] Preferably, the method for calculating the positioning result in the standby positioning mode is:

[0028] According to the formulas v k = v k-1 + a IMU ×Δt and p k = p k-1 + v k-1 ×Δt + 0.5×a IMU ×Δt 2 calculate the speed and position of the transport box. In the formula, v k represents the speed at time k, a IMU represents the three-axis acceleration output by the IMU, and the value of Δt is the time interval.

[0029] Preferably, it includes a data acquisition module, a data processing module, a communication module, a control center, and a transport box, and the data acquisition module, the data processing module, and the communication module are installed on the transport box.

[0030] Combining all the above technical solutions, the positive effects of the present invention are as follows:

[0031] (1) By fusing multi-sensor data such as GPS and IMU, combining an extended Kalman filter and a particle filter hybrid model, and dynamically adjusting weights, the error of a single sensor is significantly suppressed, and centimeter-level positioning accuracy in a complex environment is achieved;

[0032] (2) When the GPS signal fails, it automatically switches to the IMU dead reckoning mode and combines particle filter prediction to avoid positioning interruption;

[0033] (3) The dual-mode communication dynamically switches according to the signal strength to ensure the real-time and low-power consumption of data transmission.

[0034] (4) Trigger sensor calibration through residual detection to avoid abnormal data contaminating the fusion result. Description of the Drawings

[0035] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the following drawings without creative efforts.

[0036] Figure 1 It is the flowchart of the method implementation steps of the present invention.

[0037] Figure 2 It is the schematic diagram of the device connection used in the present invention. Detailed Embodiments

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0039] See Figure 2 As shown, the present invention proposes a real-time positioning method and device for a transport box based on multi-sensor fusion, including a data acquisition module, a data processing module, a communication module, a control center, and a transport box, and the data acquisition module, the data processing module, and the communication module are installed on the transport box;

[0040] In a more specific application of the present invention, the data acquisition module is used to collect the position data of the transportation box, specifically including positioning parameters, which specifically include: longitude and latitude, altitude, and timestamp; the motion state parameters specifically include: three-axis acceleration, three-axis angular velocity, and three-axis velocity; specifically, it can be a combination of a GPS module and an inertial measurement unit (IMU).

[0041] The data processing module is composed of an embedded microcontroller; the data processing module is used to perform preprocessing operations on the data collected by the data acquisition module and dynamically adjust the weights according to the environment, and perform adaptive data fusion on the preprocessed data; the preprocessing operations specifically include noise filtering, timestamp synchronization, and coordinate system unification.

[0042] The communication module is used to transmit positioning data; the communication module is a dual-mode communication module supporting 4G / 5G and LoRa, where 4G / 5G is used for high-bandwidth scenarios such as cities and suburbs, and LoRa is adapted to signal-weak areas such as basements and tunnels, ensuring the continuity of data transmission through dynamic switching.

[0043] The control center is used to judge the reliability of the positioning result and control the triggering of sensor calibration or switching of the positioning mode; when the positioning result is determined to be unreliable, the control center will trigger the sensor calibration process, including adjusting the parameters of the sensor, re-initializing the sensor, or performing other necessary calibration operations to ensure the accuracy of subsequent positioning data; in the case of GPS signal failure, the control center will switch to the backup positioning mode; in the embodiment of the present invention, the backup positioning mode mainly relies on IMU dead reckoning; through particle filter prediction, combined with the three-axis acceleration data output by the IMU, the speed and position of the transportation box are calculated; this switching mechanism can ensure the continuity of the positioning function even when the GPS signal is unavailable.

[0044] The transportation box, as the container for goods during the goods transfer process, can specifically be a container.

[0045] See Figure 1 As shown, the specific implementation of the present invention includes the following steps:

[0046] S1. The data acquisition module collects the position data of the transportation box in real time, including positioning parameters and motion state parameters.

[0047] In the above step S1, the positioning parameters include: longitude and latitude, altitude, and timestamp; the motion state parameters include: three-axis acceleration, three-axis angular velocity, and instantaneous velocity; longitude and latitude, altitude, timestamp, and velocity are obtained through GPS, and three-axis acceleration and three-axis angular velocity are obtained through a six-axis IMU.

[0048] S2. The data processing module preprocesses the collected position data to obtain preprocessed data.

[0049] In the above step S2, the preprocessing includes noise filtering of position data, timestamp synchronization, and coordinate system unification;

[0050] It should be further noted that the method for noise filtering of the position data is as follows: For different sensor data characteristics, the data processing module adopts different filtering strategies to eliminate noise interference; for longitude, latitude, altitude, and speed, the Kalman filter is used to dynamically correct position drift and suppress abnormal jitter caused by multipath effects and signal occlusion; for three-axis acceleration and angular velocity, a low-pass filter is used to filter out high-frequency vibration noise, and the low-pass filter coefficient is adjusted through actual data testing to retain the main motion frequency characteristics of the moving box; the method for timestamp synchronization is: using the PPS signal of GPS as the global time reference, for high-frequency data, linear interpolation is used to generate co-frequency sampling points with the GPS timestamp as the reference; for low-frequency data, it is aligned to the GPS time series through nearest neighbor interpolation; the method for coordinate system unification is: unifying all sensor data to the ENU coordinate system;

[0051] Calculation example:

[0052] Kalman filter example:

[0053] Scenario: GPS outputs longitude = 116.4039°, latitude = 39.9155°, speed = 5 m / s, eastward, historical speed standard deviation σ = 0.3 m / s,

[0054] State at the previous moment: X k-1 = [116.4038, 39.9154, 0, 4.9, 0, 0] T ,

[0055] Process noise covariance matrix: Q = diag([0.1 2 , 0.1 2 , 0.5 2 , 0.2 2 , 0.2 2 , 0.2 2 ),

[0056] Observation noise covariance: R = diag([0.05 2 , 0.05 2 , 0.3 2 ),

[0057] Observation value: Z k = [116.4039, 39.9155, 5] T ,

[0058] Kalman gain: K = Q(Q + R) -1 ;

[0059] Calculation process:

[0060] X k预测 = X k-1 + [v E Δt, v N Δt, v U Δt, 0, 0, 0] T

[0061] = [116.4038 + 4.9×1s, 39.9154, 0, 4.9, 0, 0] T

[0062] = [116.4087, 39.9154, 0, 4.9, 0, 0] T ,

[0063] X k = X k预测 + K(Z k - X k预测 ) ≈ [116.40385, 39.91545, 0, 4.95, 0, 0] T ;

[0064] Conclusion: Change the observed value Z k to [116.40385, 39.91545, 0, 4.95, 0, 0] T ;

[0065] Low - pass filtering example:

[0066] Scenario:

[0067] Low - pass filter coefficients:

[0068] y[n] T = 0.0003x[n] T + 0.0006x[n - 1] T + 0.0003x[n - 2] T + 1.984y[n - 1] T -

[0069] 0.9842y[n - 2] T ,

[0070] Output of the previous frame: y[n - 1] = [0.1, 1.2, 9.7] T ,

[0071] Output of the previous two frames: y[n - 2] = [0.0, 1.0, 9.6] T ,

[0072] Input of the previous frame: x[n - 2] = [0.0, 1.0, 9.6]T ,

[0073] The first two frame inputs: x[n - 1] = [0.1, 1.3, 9.7] T ,

[0074] The current input: x[n - 2] = [0.2, 1.5, 9.8] T ;

[0075] Calculation process:

[0076] y[n] T = [(0.0003×0.2 + 0.0006×0.1 + 0.0003×0 + 1.984×0.1 - 0.9842×0),

[0077] (0.0003×1.5 + 0.0006×1.3 + 0.0003×1 + 1.984×1.2 - 0.9842×1),

[0078] (0.0003×9.8 + 0.0006×9.7 + 0.0003×9.6 + 1.984×9.7 - 0.9842×9.6)] T =

[0079] [0.19852,; 1.39713, 9.80872] ≈ [0.2, 1.4, 9.8] T m / s 2 ;

[0080] Conclusion: The filtered three - axis acceleration: The east - direction acceleration is 0.2 m / s 2 . The north - direction acceleration is 1.4 m / s 2 , and the up - direction acceleration is 9.8 m / s 2 ;

[0081] S3. Adopt a hybrid model of extended Kalman filter and particle filter, dynamically adjust the weights according to the environment, and perform adaptive data fusion on the pre - processed data to obtain a highly accurate positioning result for the positioning information;

[0082] The calculation method of the said positioning result is as follows:

[0083] Final position: In the formula, p final represents the final position matrix, ω EKF represents the EKF weight coefficient, ω PF represents the PF weight coefficient, p KEF represents the EKF predicted position, and its calculation method is the same as the calculation method of X k预测 in the above Kalman filter example; represents the estimated position output by the particle filter, and its calculation formula is: In the formula, N represents the total number of particles, and ω i represents the normalized weight of the i-th particle, and ∑ω i = 1, and p i represents the position of the i-th particle; in the formula, ω i The calculation formula of σ represents the standard deviation of the observation noise, which is determined according to the actual scenario and sensor characteristics; z GPS is the direct observation value provided by GPS;

[0084] It should be further noted that in the above formula for calculating the final position: ω PF = 1 - ω EKF ; α GPS represents the GPS confidence level, and α IMU represents the IMU confidence level. The calculation formula of the said α GPS is: In the formula, σ GPS represents the position residual variance; HDOP represents the GPS horizontal positioning dilution of precision; the calculation formula of the said α IMU is: α IMU = exp(-||ω|| - ||a||); in the formula, ||ω|| represents the angular velocity modulus, and ||a|| represents the acceleration modulus;

[0085] It should be further noted that the calculation method of the above σ GPS representing the position residual variance is: statistically calculate the standard deviation of the GPS residuals in real time through the formula: In the formula, z GPS，k represents the k-th GPS observation value, p k represents the position estimate value output by the fusion algorithm, and N represents the number of samples;

[0086] Example of calculating the position residual variance:

[0087] Parameters: Five consecutive GPS observation values z GPS are successively: 100.0, 101.5, 102.0, 100.5, 99.8, and the corresponding estimated values pk are successively: 100.2, 100.8, 101.5, 100.3, 100.0, 101.2,

[0088] Calculation process:

[0089] The residuals of the five consecutive data are successively: -0.2, 0.7, 0.5, 0.2, -0.2,

[0090]

[0091] Example of weight calculation:

[0092] Parameters: GPS horizontal positioning dilution of precision HDOP = 1.2, position residual variance σ GPS = 0.3 m, angular velocity norm ||ω|| = 0.1 rad / s, acceleration norm ||a|| = 1.0 m / s 2

[0093] Calculation process:

[0094] α GPS = 1 / (1.2×0.3) ≈ 2.78,

[0095] α IMU = exp(-0.1 - 1.0) ≈ 0.332,

[0096] ω EKF = 2.78 / (2.78 + 0.332) ≈ 0.893,

[0097] ω PF = 1 - 0.893 = 0.107;

[0098] Conclusion: EKF weight coefficient ω EKF = 0.893, PF weight coefficient ω PF = 0.107;

[0099] PF importance weight calculation example:

[0100] Taking the east direction as an example:

[0101] Parameters: Particle position: p i = 10.5 m, GPS observation value: z GPS = 10.0, observation noise σ = 0.5 m;

[0102] Calculation process:

[0103] Final position calculation example:

[0104] Parameters: ω EKF = 0.754, ω PF = 0.246, p EKF = [100.0, 200.0, 0.0] Tm, E[p PF = [100.2, 199.8, 0.1] T m

[0105]

[0106] S4. The communication module transmits the positioning result in real time;

[0107] In the above step S4, the communication module works as follows: it preferentially uses the 4G / 5G network in open areas to support high-frequency updates; when the signal is weak or in an indoor environment, it switches to the LoRa mode and reduces the transmission frequency to 1Hz to save power consumption;

[0108] S5. The control center determines the reliability of the positioning result, triggers sensor calibration or switches the positioning mode, and finally outputs the positioning result.

[0109] In the above step S5, the method for determining the reliability of the positioning result is: calculate the residual between the GPS observation value and the fused positioning result. If the residual exceeds the threshold, it is determined to be unreliable;

[0110] When GPS fails, switch to the pure PF mode and rely only on IMU dead reckoning;

[0111] The calculation formula is as follows:

[0112] v k =v k-1 +a IMU ×Δt, p k =p k-1 +v k-1 ×Δt+0.5×a IMU ×Δt 2 ,

[0113] In the formula, v k represents the velocity at time k, a IMU represents the three-axis acceleration output by the IMU, and the value of Δt is the time interval;

[0114] Calculation example:

[0115] Taking the calculation of the eastward velocity and position as an example: Parameters: v k-1 =5m / s, acceleration a IMU =-0.01, p k-1 =100m, Δt = 1;

[0116] Calculation process:

[0117] v k =5+(-0.01)×1 = 4.99m / s, p k =100+5×1+0.5×(-0.01)×1 2 =104.995m;

[0118] Conclusion: The eastward velocity at the next moment is 4.99m / s, and the eastward position is 104.995m;

[0119] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of the present technology can make various modifications, supplements, or use similar methods to substitute for the specific embodiments described, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A real-time positioning method for a transport box based on multi-sensor fusion, characterized in that, Specifically, it includes the following steps: S1. The data acquisition module collects the position data of the transport box in real time, including positioning parameters and motion state parameters; S2. The data processing module preprocesses the collected position data to obtain preprocessed data; S3. An extended Kalman filter and particle filter hybrid model is used to adaptively adjust the weights according to the environment, and the preprocessed data is subjected to adaptive data fusion to obtain a positioning result with high-precision positioning information; S4. The communication module transmits the positioning result in real time; S5. The control center judges the reliability of the positioning result, triggers sensor calibration or switches the positioning mode, and finally outputs the positioning result.

2. The real-time positioning method of a transport box based on multi-sensor fusion according to claim 1, characterized in that: The positioning parameters specifically include: longitude and latitude, altitude, and timestamp data; the motion state parameters specifically include: three-axis acceleration, three-axis angular velocity, and speed data.

3. The real-time positioning method of a transport box based on multi-sensor fusion according to claim 1, characterized in that: The preprocessing specifically includes: noise filtering, timestamp synchronization, and coordinate system unification.

4. A real-time positioning method for a transport box based on multi-sensor fusion according to claim 1, characterized in that: The calculation formula for the positioning result is as follows: In the formula, p final represents the final position matrix, ω EKF represents the EKF weight coefficient, ω PF represents the PF weight coefficient, p KEF represents the EKF predicted position, represents the estimated position output by the particle filter.

5. The real-time positioning method of a transport box based on multi-sensor fusion according to claim 4, wherein: The calculation method of the EKF weight coefficient is as follows: According to the data obtained in real time, use the formula to calculate. In the formula, α GPS represents the GPS confidence level, and α IMU represents the IMU confidence level; the calculation formula of the EKF weight coefficient is: ω PF = 1 - ω EKF ; the calculation formula of the estimated position output by the particle filter is: In the formula, N represents the total number of particles, ω i represents the normalized weight of the i-th particle, ∑ω i = 1, and p i represents the position of the i-th particle.

6. The real-time positioning method of a transport box based on multi-sensor fusion according to claim 5, characterized in that: The α GPS is calculated as follows: In the formula, σ GPS represents the position residual variance, which is calculated according to the actual situation; HDOP represents the GPS horizontal positioning dilution of precision, which is directly obtained through the GPS module; the α IMU is calculated as follows: α IMU = exp(-||ω|| - ||a||). In the formula, ||ω|| represents the angular velocity norm, and ||a|| represents the acceleration norm. The formula for i ω is σ represents the standard deviation of the observation noise, which is determined according to the actual scenario and sensor characteristics; z GPS is the direct observation value provided by the GPS.

7. The real-time positioning method of a transport box based on multi-sensor fusion according to claim 1, characterized in that: The method for determining the reliability of the positioning result is: calculating the residual between the GPS observation value and the fused positioning result. If the residual exceeds the threshold, it is determined to be unreliable.

8. The real-time positioning method of a transport box based on multi-sensor fusion according to claim 1, wherein: The working mode of the communication module is as follows: In an open area, the 4G / 5G network is preferentially used to support high-frequency updates; in a weak signal or indoor environment, it switches to the LoRa mode and reduces the transmission frequency to 1Hz to save power consumption.

9. The real-time positioning method of a transport box based on multi-sensor fusion according to claim 1, wherein: The method for calculating the positioning result in the standby positioning mode is: According to the formula v k = v k-1 + a IMU × Δt and p k = p k-1 + v k-1 × Δt + 0.5 × a IMU × Δt 2 Calculate the speed and position of the transport box. In the formula, v k represents the speed at time k, a IMU represents the three-axis acceleration output by the IMU, and the value of Δt is the time interval.

10. The real-time positioning device for a transport box based on multi-sensor fusion according to claim 1, wherein: It includes a data acquisition module, a data processing module, a communication module, a control center, and a transport box. The data acquisition module, the data processing module, and the communication module are installed on the transport box.

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