A positioning method, device, equipment and medium of a heavy diesel vehicle

Abstract

The present application relates to the technical field of vehicle positioning, and discloses a positioning method, device, equipment and medium for a heavy diesel vehicle. The method comprises the following steps: acquiring base station positioning data of the heavy diesel vehicle, AHRS positioning data, GPS / BDS positioning data and navigation positioning data; in the case that the GPS / BDS positioning data is normal, fusing the AHRS positioning data and the GPS / BDS positioning data; in the case that the GPS / BDS positioning data is abnormal and the mobile network of the heavy diesel vehicle is normal, fusing the AHRS positioning data and the base station positioning data; and in the case that both the GPS / BDS positioning data and the mobile network of the heavy diesel vehicle are abnormal, fusing the AHRS positioning data and inertial navigation positioning data to obtain target positioning data of the heavy diesel vehicle, so that the positioning accuracy of the heavy diesel vehicle can be improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle positioning technology, and in particular to a positioning method, device, equipment and medium for heavy-duty diesel vehicles. Background Technology

[0002] Location tracking is increasingly widely used in production and daily life, from self-driving navigation to the defense industry; its omnipresence underscores its importance. In the heavy-duty vehicle business, accurate vehicle location tracking is a crucial technical guarantee for the smooth implementation of core businesses such as environmental data monitoring and auto loans.

[0003] Currently, the common practice for locating heavy-duty diesel vehicles is to use a "GPS / BDS dual-mode positioning module + external antenna". However, this positioning method is heavily dependent on the quality of the GPS / BDS signal that the receiver can receive. When the vehicle is in an area with weak GPS / BDS signal or large obstacles, abnormal signal reception is very likely to occur, leading to positioning failure. Alternatively, if the driver does not want to be located or tracked, they can remove the external antenna, which will prevent the heavy-duty diesel vehicle from being effectively located. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, equipment and medium for positioning heavy-duty diesel vehicles, which can achieve effective positioning of heavy-duty diesel vehicles.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a positioning method for heavy-duty diesel vehicles, comprising the following steps:

[0006] The system acquires base station positioning data obtained by base station positioning, AHRS positioning data obtained by attitude and bearing reference system (AHRS) positioning, GPS / BDS positioning data obtained by GPS / BDS receiver positioning, and inertial navigation positioning data obtained by inertial navigation positioning estimation technology for heavy-duty diesel vehicles.

[0007] When the GPS / BDS positioning data is normal, the first Kalman filter is used to fuse the AHRS positioning data with the GPS / BDS positioning data to obtain the target positioning data of the heavy-duty diesel vehicle.

[0008] When GPS / BDS positioning data is abnormal and the mobile network of heavy-duty diesel vehicles is normal, a second Kalman filter is used to fuse AHRS positioning data with base station positioning data to obtain target positioning data of heavy-duty diesel vehicles.

[0009] When both GPS / BDS positioning data and the mobile network of heavy-duty diesel vehicles are abnormal, a third Kalman filter is used to fuse AHRS positioning data with inertial navigation positioning data to obtain target positioning data of heavy-duty diesel vehicles.

[0010] In some optional embodiments, acquiring the target location data of the heavy-duty diesel vehicle includes:

[0011] Obtain vehicle operation data for heavy-duty diesel vehicles;

[0012] Under normal GPS / BDS positioning data conditions, the first Kalman filter is used to fuse AHRS positioning data, GPS / BDS positioning data and vehicle operation data to obtain target positioning data of heavy-duty diesel vehicles.

[0013] When GPS / BDS positioning data is abnormal and the mobile network of heavy-duty diesel vehicles is normal, a second Kalman filter is used to fuse AHRS positioning data, base station positioning data and vehicle operation data to obtain target positioning data of heavy-duty diesel vehicles.

[0014] When both GPS / BDS positioning data and the mobile network of heavy-duty diesel vehicles are abnormal, a third Kalman filter is used to fuse AHRS positioning data, inertial navigation positioning data and vehicle operation data to obtain target positioning data of heavy-duty diesel vehicles.

[0015] In some optional embodiments, the first Kalman filter, the second Kalman filter, and the third Kalman filter each include two computational loops: a filtering computational loop and a Kalman gain computational loop;

[0016] The filtering calculation loop is used to update GPS / BDS positioning data based on AHRS positioning data, or to update base station positioning data based on AHRS positioning data, or to update inertial navigation positioning data based on AHRS positioning data, in order to obtain target positioning data.

[0017] The Kalman gain calculation loop is used to calculate the gain coefficient so that the filtering calculation loop can update the data based on the gain coefficient.

[0018] In some optional embodiments, after acquiring the target location data of the heavy-duty diesel vehicle, the method further includes:

[0019] The operating trajectory of heavy-duty diesel vehicles can be obtained through map data of heavy-duty diesel vehicles.

[0020] The target positioning data is calibrated by tracking the trajectory of heavy-duty diesel vehicles in order to locate them.

[0021] In some optional embodiments, the AHRS positioning data includes: accelerometer triaxial data, gyroscope triaxial data, and magnetometer triaxial data of the heavy-duty diesel vehicle.

[0022] In some optional embodiments, the AHRS positioning data includes: target speed obtained by fusing speed and accelerometer triaxial data of the heavy-duty diesel vehicle; longitude and latitude data obtained by fusing accelerometer triaxial data and gyroscope triaxial data of the heavy-duty diesel vehicle; and altitude data obtained by fusing gyroscope triaxial data and magnetometer triaxial data of the heavy-duty diesel vehicle.

[0023] In some alternative embodiments, the vehicle operating data includes: vehicle speed, engine speed, and empty / loaded data for heavy-duty diesel vehicles.

[0024] Embodiments of the present invention also provide a positioning device for a heavy-duty diesel vehicle, comprising:

[0025] The data acquisition module is used to acquire base station positioning data obtained by base station positioning, AHRS positioning data obtained by attitude and bearing reference system (AHRS) positioning, GPS / BDS positioning data obtained by GPS / BDS receiver positioning, and inertial navigation positioning data obtained by inertial navigation positioning estimation technology for heavy-duty diesel vehicles.

[0026] The fusion positioning module is used to fuse AHRS positioning data with GPS / BDS positioning data using a first Kalman filter when GPS / BDS positioning data is normal, in order to obtain target positioning data of heavy-duty diesel vehicles.

[0027] When GPS / BDS positioning data is abnormal and the mobile network of heavy-duty diesel vehicles is normal, a second Kalman filter is used to fuse AHRS positioning data with base station positioning data to obtain target positioning data of heavy-duty diesel vehicles.

[0028] When both GPS / BDS positioning data and the mobile network of heavy-duty diesel vehicles are abnormal, a third Kalman filter is used to fuse AHRS positioning data with inertial navigation positioning data to obtain target positioning data of heavy-duty diesel vehicles.

[0029] Embodiments of the present invention also provide a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described positioning method for heavy-duty diesel vehicles.

[0030] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described positioning method for heavy-duty diesel vehicles.

[0031] The positioning method for heavy-duty diesel vehicles provided by this invention has at least the following beneficial effects:

[0032] This invention simultaneously acquires multiple positioning data of heavy-duty diesel vehicles, including base station positioning data obtained through base station positioning, AHRS positioning data obtained through AHRS positioning, GPS / BDS positioning data obtained through GPS / BDS receiver positioning, and inertial navigation positioning data obtained through inertial navigation positioning estimation technology. Furthermore, considering that the actual environment in which the heavy-duty diesel vehicle is located may affect some of the vehicle's positioning data, this invention selects accurate positioning data from the above-mentioned data and fuses it based on the current GPS / BDS positioning data of the heavy-duty diesel vehicle and the anomalies of the mobile network, rather than relying solely on the vehicle's GPS / BDS signal. This effectively improves the positioning accuracy of heavy-duty diesel vehicles. Attached Figure Description

[0033] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0034] Figure 1 This is a flowchart of a positioning method for a heavy-duty diesel vehicle according to an embodiment of the present invention. Figure 1 ;

[0035] Figure 2 This is a flowchart of a positioning method for a heavy-duty diesel vehicle according to an embodiment of the present invention. Figure 2 ;

[0036] Figure 3 This is a schematic diagram of location data fusion according to an embodiment of the present invention;

[0037] Figure 4 This is a schematic diagram of a positioning method for a heavy-duty diesel vehicle according to an embodiment of the present invention;

[0038] Figure 5 This is a schematic diagram of a positioning device for a heavy-duty diesel vehicle according to an embodiment of the present invention;

[0039] Figure 6 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0041] One embodiment of the present invention relates to a positioning method for a heavy-duty diesel vehicle. The implementation details of the positioning method for a heavy-duty diesel vehicle in this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution.

[0042] The specific process of the positioning method for heavy-duty diesel vehicles in this embodiment can be as follows: Figure 1 As shown, it includes:

[0043] Step 101: Obtain base station positioning data obtained by base station positioning, AHRS positioning data obtained by attitude and bearing reference system (AHRS) positioning, GPS / BDS positioning data obtained by GPS / BDS receiver positioning, and inertial navigation positioning data obtained by inertial navigation positioning estimation technology for heavy-duty diesel vehicles.

[0044] Specifically, in this embodiment, when it is necessary to locate a heavy-duty diesel vehicle, the following positioning data of the heavy-duty diesel vehicle are collected simultaneously: base station positioning data of the heavy-duty diesel vehicle collected by a base station located near the heavy-duty diesel vehicle through a mobile network; positioning of the heavy-duty diesel vehicle through an attitude and heading reference system (AHRS) and using the collected positioning data as AHRS positioning data; positioning data of the heavy-duty diesel vehicle collected by a GPS / BDS receiver and used as GPS / BDS positioning data; and positioning of the heavy-duty diesel vehicle through inertial navigation positioning estimation technology and using the collected positioning data as inertial navigation positioning data.

[0045] Step 102: When the GPS / BDS positioning data is normal, a first Kalman filter is used to fuse the AHRS positioning data with the GPS / BDS positioning data to obtain the target positioning data of the heavy-duty diesel vehicle; when the GPS / BDS positioning data is abnormal but the mobile network of the heavy-duty diesel vehicle is normal, a second Kalman filter is used to fuse the AHRS positioning data with the base station positioning data to obtain the target positioning data of the heavy-duty diesel vehicle; when both the GPS / BDS positioning data and the mobile network of the heavy-duty diesel vehicle are abnormal, a third Kalman filter is used to fuse the AHRS positioning data with the inertial navigation positioning data to obtain the target positioning data of the heavy-duty diesel vehicle.

[0046] Specifically, the heavy-duty diesel vehicles are located based on the location data of the aforementioned types of heavy-duty diesel vehicles: such as... Figure 2 As shown, the AHRS system of the heavy-duty diesel vehicle first performs a self-test. After the AHRS system self-test is normal, the positioning data of the heavy-duty diesel vehicle acquired by the AHRS is calibrated. Then, it is determined whether the GPS / BDS positioning data obtained by the GPS / BDS receiver is normal. If the GPS / BDS positioning data is normal, a first Kalman filter is used to fuse the AHRS positioning data with the GPS / BDS positioning data to obtain the target positioning data of the heavy-duty diesel vehicle. If the GPS / BDS positioning data is abnormal, it is determined whether the mobile network of the heavy-duty diesel vehicle is normal. If the GPS / BDS positioning data is abnormal but the mobile network of the heavy-duty diesel vehicle is normal, a second Kalman filter is used to fuse the AHRS positioning data with the base station positioning data to obtain the target positioning data of the heavy-duty diesel vehicle. Alternatively, if both the GPS / BDS positioning data and the mobile network of the heavy-duty diesel vehicle are abnormal, a third Kalman filter is used to fuse the AHRS positioning data with the inertial navigation positioning data to obtain the target positioning data of the heavy-duty diesel vehicle.

[0047] In one example, the AHRS positioning data includes: accelerometer triaxial data, gyroscope triaxial data, and magnetometer triaxial data of the heavy-duty diesel vehicle. Therefore, when calibrating the positioning data of the heavy-duty diesel vehicle collected by AHRS, the accelerometer triaxial data, gyroscope triaxial data, and magnetometer triaxial data need to be calibrated in sequence.

[0048] In another example, such as Figure 3As shown, the AHRS positioning data includes: target speed obtained by fusing speed and accelerometer triaxial data from the heavy-duty diesel vehicle; longitude (θx) and latitude (θy) data obtained by fusing accelerometer and gyroscope triaxial data from the heavy-duty diesel vehicle; and altitude (θz) data obtained by fusing gyroscope and magnetometer triaxial data from the heavy-duty diesel vehicle. When fusing AHRS positioning data with other positioning data from the heavy-duty diesel vehicle, the various data in the AHRS positioning data can be fused first to obtain fuzzy positioning data, and then fused with other positioning data.

[0049] For example, when fusing AHRS positioning data with GPS / BDS positioning data of heavy-duty diesel vehicles, the displacement increment can be obtained by integrating the speed information of the current vehicle (i.e., the heavy-duty diesel vehicle), followed by coordinate transformation to obtain the latitude and longitude increments, and finally the location information. The AHRS estimation period is 10ms. When the received satellite signal is normal, the AHRS estimation data is corrected every 100ms. Under normal GPS / BDS positioning data conditions, the output heavy-duty diesel vehicle positioning data is used to calibrate and compensate the GPS / BDS positioning data, and after updating the real-time positioning information, it is reported to the management platform through the network module. In cases of poor GPS / BDS positioning data quality or deliberate masking, it serves as a supplement to the GPS / BDS positioning data.

[0050] In this embodiment, multiple positioning data of the heavy-duty diesel vehicle are acquired simultaneously, including base station positioning data obtained by base station positioning, AHRS positioning data obtained by AHRS positioning, GPS / BDS positioning data obtained by GPS / BDS receiver positioning, and inertial navigation positioning data obtained by inertial navigation positioning estimation technology. Furthermore, considering that the actual scene in which the heavy-duty diesel vehicle is located may affect some of the vehicle's positioning data, the accurate positioning data is selected from the above positioning data and fused based on the current GPS / BDS positioning data of the heavy-duty diesel vehicle and the anomaly of the mobile network, rather than relying solely on the vehicle's GPS / BDS signal, which can effectively improve the positioning accuracy of the heavy-duty diesel vehicle.

[0051] In some embodiments, when locating a heavy-duty diesel vehicle, it is also necessary to obtain the vehicle's operating data. In this case: when GPS / BDS positioning data is normal, a first Kalman filter is used to fuse AHRS positioning data, GPS / BDS positioning data, and vehicle operating data to obtain target positioning data for the heavy-duty diesel vehicle; when GPS / BDS positioning data is abnormal but the heavy-duty diesel vehicle's mobile network is normal, a second Kalman filter is used to fuse AHRS positioning data, base station positioning data, and vehicle operating data to obtain target positioning data for the heavy-duty diesel vehicle; when both GPS / BDS positioning data and the heavy-duty diesel vehicle's mobile network are abnormal, a third Kalman filter is used to fuse AHRS positioning data, inertial navigation positioning data, and vehicle operating data to obtain target positioning data for the heavy-duty diesel vehicle.

[0052] The vehicle operation data for heavy-duty diesel vehicles includes: vehicle speed, engine speed, and empty / loaded data. When fusing vehicle operation data with other positioning data, a weighted algorithm can be used to assign different weights to different vehicle operation data to further improve the positioning accuracy of heavy-duty diesel vehicles.

[0053] In some embodiments, after obtaining the target positioning data of the heavy-duty diesel vehicle, the running trajectory of the heavy-duty diesel vehicle can also be obtained through the map data of the heavy-duty diesel vehicle, and the target positioning data can be calibrated through the running trajectory of the heavy-duty diesel vehicle to locate the heavy-duty diesel vehicle, thereby further improving the positioning accuracy of the heavy-duty diesel vehicle.

[0054] In the positioning data fusion process described in any of the above embodiments, a Kalman filter is specifically used, such as a Kalman filter, a second Kalman filter, and a third Kalman filter. Each Kalman filter includes two computational loops: a filtering computational loop and a Kalman gain computational loop. The filtering computational loop is used to update GPS / BDS positioning data based on AHRS positioning data, or to update base station positioning data based on AHRS positioning data, or to update inertial navigation positioning data based on AHRS positioning data, to obtain target positioning data. The Kalman gain computational loop is used to calculate the gain coefficient so that the filtering computational loop can complete the data update based on the gain coefficient.

[0055] Filtering calculation loop:

[0056] As an iterative calculation process, the first step is to predict the state variable X:

[0057]

[0058] In the formula, For the calculated value, Φk,k-1 For correction factors, These are predicted values.

[0059] Then, the state variables are corrected and updated based on the observed value Zk and the Kalman gain Kk to obtain the optimal state estimate.

[0060]

[0061] In the formula, Z k For the observed value, K k H is the Kalman gain correction factor. k These are the Kalman correction parameters.

[0062] Kalman gain calculation loop:

[0063] Covariance prediction:

[0064]

[0065] In the formula, P k|k-1 P is the measured value of covariance. k-1 Γ represents the predicted covariance value, Q represents the parameters of the covariance matrix, and Γ represents the adaptive covariance correction parameter.

[0066] Calculate the Kalman gain:

[0067]

[0068] Corrected covariance matrix:

[0069] P k =(IK k H k )P k|k-1

[0070] In the formula, P k I represents the corrected covariance, and I is the covariance constant.

[0071] In one example, to prevent filter divergence, the Kalman filter uses an improved adaptive filtering algorithm, the method of which is as follows:

[0072] Corrected state prediction covariance matrix:

[0073] p(k|k-1)=λ(k)A(k,k-1)P(k-1)A T (k,k-1)+Q(k)

[0074] In the formula, λ(k) is the suboptimal fading factor, and A is the covariance matrix correction constant.

[0075] The residual is:

[0076] V(k)=Z(k)-H(k)X(k|k-1)

[0077] Here, the filter convergence criterion is defined as follows:

[0078] V(k)V T (k)≤H(k)p(k|k-1)H T (k)+R(k)

[0079] If the above conditions are met, the current filter converges; otherwise, it indicates that the current filter is no longer suitable for the current state. R is estimated using the following formula:

[0080] R(k)=d k {[1-H(k)K(k-1)]V(k)V T (k)[1-H(k)K(k-1)] T +H(k)P(k-1)H T (k)}

[0081] +(1-d k R(k-1)

[0082] In the formula, b is the forgetting factor, with a value ranging from 0.95 to 0.99.

[0083] In one embodiment, the positioning method for heavy-duty diesel vehicles of the present invention can be achieved through, as follows: Figure 4 The system logic architecture shown includes an AHRS data acquisition module, a base station positioning data acquisition module, a data filtering unit, a data fusion algorithm unit, a GPS / BDS positioning module, a networking module, a heavy-duty diesel vehicle operation data acquisition module, and a data fusion module, all centrally located within the driving recorder terminal (hereinafter referred to as the terminal) of the heavy-duty diesel vehicle. It also includes a management platform and map data for the heavy-duty diesel vehicle, which interacts with the on-board terminal via the networking module.

[0084] After acquiring base station positioning data and AHRS positioning data of the heavy-duty diesel vehicle through the AHRS data acquisition module and base station positioning data acquisition module, the base station positioning data and AHRS positioning data are fitted to obtain fuzzy positioning data of the heavy-duty diesel vehicle. Then, the vehicle operation data such as vehicle speed, engine speed, and empty / loaded status of the heavy-duty diesel vehicle are acquired through the vehicle operation data acquisition module. The operation data model of the heavy-duty diesel vehicle is obtained through weighted algorithm calculation. The fuzzy positioning data and operation data model of the heavy-duty diesel vehicle are fused and calculated using the algorithm model of the heavy-duty diesel vehicle to output the final auxiliary positioning data (i.e., initial positioning data) of the heavy-duty diesel vehicle. When GPS / BDS positioning data is normal, the output initial positioning data of the heavy-duty diesel vehicle is used to calibrate and compensate for the GPS / BDS positioning data. After updating the real-time positioning information, it is reported to the management platform via the network module. When GPS / BDS positioning data quality is poor or is intentionally masked, the initial positioning data of the heavy-duty diesel vehicle serves as a supplement to the GPS / BDS positioning data, continuously updating the real-time positioning information, writing it to the terminal storage unit, and reporting it to the management platform via the network module after updating the real-time positioning information. After data acquisition by the AHRS data acquisition module, the AHRS positioning data also needs to be filtered by the filtering unit to obtain the final AHRS positioning data.

[0085] Assuming the heavy-duty diesel vehicles are in a platoon, after receiving the location information of all heavy-duty diesel vehicles in the platoon, the management platform completes the arrangement of all vehicles in the platoon based on the location data. By fitting the running trajectory of all vehicles in the platoon using truck map data, the platform performs secondary calibration of the location data of all vehicles in the platoon, obtains accurate location data of all vehicles in the platoon and displays it on the platform, and simultaneously feeds back the calibrated data to the terminals corresponding to all vehicles in the platoon, thus achieving secondary calibration of the vehicle location data.

[0086] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of this invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, without changing the core design of the algorithm and process, are also within the protection scope of this invention.

[0087] Another embodiment of the present invention relates to a positioning device for a heavy-duty diesel vehicle. The implementation details of the positioning device for the heavy-duty diesel vehicle in this embodiment are described below. The following details are provided for ease of understanding and are not essential for implementing this solution. A schematic diagram of the positioning device for the heavy-duty diesel vehicle in this embodiment can be seen as follows: Figure 5As shown, it includes: a data acquisition module 501 and a fusion positioning module 502.

[0088] Specifically, the data acquisition module 501 is used to acquire base station positioning data obtained by base station positioning, AHRS positioning data obtained by attitude and bearing reference system (AHRS) positioning, GPS / BDS positioning data obtained by GPS / BDS receiver positioning, and inertial navigation positioning data obtained by inertial navigation positioning calculation technology for heavy-duty diesel vehicles.

[0089] The fusion positioning module 502 is used to fuse AHRS positioning data with GPS / BDS positioning data using a first Kalman filter when GPS / BDS positioning data is normal, so as to obtain target positioning data of heavy-duty diesel vehicles.

[0090] When GPS / BDS positioning data is abnormal and the mobile network of heavy-duty diesel vehicles is normal, a second Kalman filter is used to fuse AHRS positioning data with base station positioning data to obtain target positioning data of heavy-duty diesel vehicles.

[0091] When both GPS / BDS positioning data and the mobile network of heavy-duty diesel vehicles are abnormal, a third Kalman filter is used to fuse AHRS positioning data with inertial navigation positioning data to obtain target positioning data of heavy-duty diesel vehicles.

[0092] It is not difficult to see that this embodiment is a device embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details and technical effects mentioned in the above embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above embodiments.

[0093] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0094] Another embodiment of the present invention relates to a computer device, such as Figure 6As shown, it includes: at least one processor 601; and a memory 602 communicatively connected to the at least one processor 601; wherein the memory 602 stores instructions executable by the at least one processor 601, the instructions being executed by the at least one processor 601 to enable the at least one processor 601 to perform the positioning method for heavy-duty diesel vehicles in the above embodiments.

[0095] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0096] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0097] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0098] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0099] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A positioning method for a heavy-duty diesel vehicle, characterized in that, The method includes: The system acquires base station positioning data obtained by base station positioning, AHRS positioning data obtained by attitude and bearing reference system (AHRS) positioning, GPS / BDS positioning data obtained by GPS / BDS receiver positioning, and inertial navigation positioning data obtained by inertial navigation positioning estimation technology for heavy-duty diesel vehicles. When the GPS / BDS positioning data is normal, the first Kalman filter is used to fuse the AHRS positioning data with the GPS / BDS positioning data to obtain the target positioning data of the heavy-duty diesel vehicle. When GPS / BDS positioning data is abnormal and the mobile network of heavy-duty diesel vehicles is normal, a second Kalman filter is used to fuse AHRS positioning data with base station positioning data to obtain target positioning data of heavy-duty diesel vehicles. When both GPS / BDS positioning data and the mobile network of heavy-duty diesel vehicles are abnormal, a third Kalman filter is used to fuse AHRS positioning data with inertial navigation positioning data to obtain target positioning data of heavy-duty diesel vehicles.

2. The positioning method for heavy-duty diesel vehicles according to claim 1, characterized in that, The acquisition of target location data for heavy-duty diesel vehicles includes: Obtain vehicle operation data for heavy-duty diesel vehicles; Under normal GPS / BDS positioning data conditions, the first Kalman filter is used to fuse AHRS positioning data, GPS / BDS positioning data and vehicle operation data to obtain target positioning data of heavy-duty diesel vehicles. When GPS / BDS positioning data is abnormal and the mobile network of heavy-duty diesel vehicles is normal, a second Kalman filter is used to fuse AHRS positioning data, base station positioning data and vehicle operation data to obtain target positioning data of heavy-duty diesel vehicles. When both GPS / BDS positioning data and the mobile network of heavy-duty diesel vehicles are abnormal, a third Kalman filter is used to fuse AHRS positioning data, inertial navigation positioning data and vehicle operation data to obtain target positioning data of heavy-duty diesel vehicles.

3. The positioning method for heavy-duty diesel vehicles according to claim 2, characterized in that, The first Kalman filter, the second Kalman filter, and the third Kalman filter all contain two computational loops: a filter computational loop and a Kalman gain computational loop; The filtering calculation loop is used to update GPS / BDS positioning data based on AHRS positioning data, or to update base station positioning data based on AHRS positioning data, or to update inertial navigation positioning data based on AHRS positioning data, in order to obtain target positioning data. The Kalman gain calculation loop is used to calculate the gain coefficient so that the filtering calculation loop can update the data based on the gain coefficient.

4. The positioning method for heavy-duty diesel vehicles according to claim 1, characterized in that, After acquiring the target location data of the heavy-duty diesel vehicle, the following is also included: The operating trajectory of heavy-duty diesel vehicles can be obtained through map data of heavy-duty diesel vehicles. The target positioning data is calibrated by tracking the trajectory of heavy-duty diesel vehicles in order to locate them.

5. The positioning method for heavy-duty diesel vehicles according to any one of claims 1 to 4, characterized in that, The AHRS positioning data includes: accelerometer triaxial data, gyroscope triaxial data, and magnetometer triaxial data for heavy-duty diesel vehicles.

6. The positioning method for a heavy-duty diesel vehicle according to any one of claims 1 to 4, characterized in that, The AHRS positioning data includes: target speed obtained by fusing speed and accelerometer triaxial data of the heavy-duty diesel vehicle; longitude and latitude data obtained by fusing accelerometer triaxial data and gyroscope triaxial data of the heavy-duty diesel vehicle; and altitude data obtained by fusing gyroscope triaxial data and magnetometer triaxial data of the heavy-duty diesel vehicle.

7. The positioning method for a heavy-duty diesel vehicle according to any one of claims 1 to 4, characterized in that, The vehicle operation data includes: vehicle speed, engine speed, and empty / loaded data for heavy-duty diesel vehicles.

8. A positioning device for a heavy-duty diesel vehicle, characterized in that, The device includes: The data acquisition module is used to acquire base station positioning data obtained by base station positioning, AHRS positioning data obtained by attitude and bearing reference system (AHRS) positioning, GPS / BDS positioning data obtained by GPS / BDS receiver positioning, and inertial navigation positioning data obtained by inertial navigation positioning estimation technology for heavy-duty diesel vehicles. The fusion positioning module is used to fuse AHRS positioning data with GPS / BDS positioning data using a first Kalman filter when GPS / BDS positioning data is normal, in order to obtain target positioning data of heavy-duty diesel vehicles. When GPS / BDS positioning data is abnormal and the mobile network of heavy-duty diesel vehicles is normal, a second Kalman filter is used to fuse AHRS positioning data with base station positioning data to obtain target positioning data of heavy-duty diesel vehicles. When both GPS / BDS positioning data and the mobile network of heavy-duty diesel vehicles are abnormal, a third Kalman filter is used to fuse AHRS positioning data with inertial navigation positioning data to obtain target positioning data of heavy-duty diesel vehicles.

9. A computer device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the positioning method for a heavy-duty diesel vehicle as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the positioning method for heavy-duty diesel vehicles as described in any one of claims 1 to 7.

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