GNSS drift suppression method, electronic device, storage medium and program product

Abstract

The embodiment of the invention provides a GNSS drift suppression method, an electronic device, a storage medium and a program product, a GNSS data queue is obtained according to a historical GNSS data set, and the GNSS data queue comprises at least two historical GNSS data arranged based on time sequence information within a preset duration; according to the vehicle operation information and the GNSS data queue, obtaining predicted GNSS data, the vehicle operation information representing a motion driving state of the vehicle, and the predicted GNSS data including at least one predicted GNSS value of the vehicle at the target time point; according to the method, the target GNSS data is obtained according to the current GNSS data and the predicted GNSS data, the target GNSS data is the GNSS data obtained by performing drift suppression on the current GNSS data at the target time point, that is, the position of the vehicle at the corresponding time point is determined, and the problem of inaccurate vehicle positioning caused by the scheme of the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a GNSS drift suppression method, electronic equipment, storage medium and program product. Background Art

[0002] The Global Navigation Satellite System (GNSS) is a general term for a type of system that uses navigation satellites to achieve positioning. Currently, GNSS positioning services are used in industries such as vehicle insurance services, large fleet management, logistics management, and rental car management to locate the vehicle's position and then complete the management of the vehicle.

[0003] When positioning is performed using GNSS, factors such as signal refraction may cause the positioning position to drift. In the prior art, the positioning position is usually corrected by filtering GNSS data with signal drift, thereby achieving vehicle positioning.

[0004] However, the solutions of the prior art lead to the problem of inaccurate vehicle positioning. Summary of the invention

[0005] The embodiments of the present application provide a GNSS drift suppression method, an electronic device, a storage medium, and a program product to solve the problem of inaccurate vehicle positioning caused by the solutions of the prior art.

[0006] In a first aspect, an embodiment of the present application provides a GNSS drift suppression method, comprising:

[0007] A GNSS data queue is obtained based on a historical GNSS data set, wherein the GNSS data queue includes at least two historical GNSS data arranged based on timing information within a preset time length; predicted GNSS data is obtained based on vehicle operation information and the GNSS data queue, wherein the vehicle operation information characterizes the motion driving state of the vehicle, and the predicted GNSS data includes at least one predicted GNSS value of the vehicle at a target time point; target GNSS data is obtained based on current GNSS data and the predicted GNSS data, wherein the target GNSS data is GNSS data obtained by drift suppressing the current GNSS data at the target time point.

[0008] In a possible implementation, the vehicle operation information includes vehicle speed information, vehicle acceleration information and inertial navigation information, and the predicted GNSS data is obtained based on the vehicle operation information and the GNSS data queue, including: obtaining vehicle position information based on the vehicle operation information, the vehicle position information representing the probability density distribution of the vehicle traveling to various position points at the target time point; performing cluster analysis on the vehicle position information and the GNSS data queue to obtain the predicted GNSS data.

[0009] In a possible implementation, the method further includes: obtaining a field radius value based on the vehicle speed information, and / or the vehicle acceleration information and / or the inertial navigation information; clustering analysis of the vehicle position information and the GNSS data queue to obtain the predicted GNSS data includes: clustering analysis of the vehicle position information and the GNSS data queue based on the field radius value to obtain the predicted GNSS data.

[0010] In a possible implementation, before obtaining the GNSS data queue based on the historical GNSS data set, the method includes: acquiring vehicle route information, wherein the vehicle route information represents the material environment characteristics of the driving environment in which the vehicle is to travel; obtaining GNSS reception frequency information based on the vehicle route information, wherein the GNSS reception frequency information represents the frequency of the vehicle receiving GNSS data; and obtaining the historical GNSS data set based on the GNSS reception frequency information.

[0011] In a possible implementation, obtaining GNSS receiving frequency information based on the vehicle route information includes: acquiring meteorological environment information, the meteorological environment information representing meteorological environment characteristics at the location to which the vehicle travels when the vehicle is traveling; and obtaining the GNSS receiving frequency information based on the meteorological environment characteristics and the vehicle route information.

[0012] In a possible implementation, obtaining a GNSS data queue according to a historical GNSS data set includes: looping through the following steps: obtaining first historical GNSS data according to the historical GNSS data set, the first historical GNSS data being used to indicate the first historical GNSS data in the GNSS data queue, and the time point corresponding to the first historical GNSS data being the first moment; obtaining second historical GNSS data according to the historical GNSS data set, the second historical GNSS data being used to indicate the last historical GNSS data in the GNSS data queue, and the time point corresponding to the second historical GNSS data being the second moment; if the time difference between the first moment and the second moment is equal to a preset duration, The historical GNSS data between the first historical GNSS data and the second historical GNSS data are arranged in time series information to obtain the GNSS data queue; if the time difference between the first moment and the second moment is greater than a preset duration, the next historical GNSS data of the first historical GNSS data in the GNSS data queue is determined as the first historical GNSS data, and the corresponding next moment is determined as the first moment; if the time difference between the first moment and the second moment is less than a preset duration, the latest historical GNSS data at the latest moment is obtained from the historical GNSS data set, and the latest historical GNSS data is determined as the second historical GNSS data, and the corresponding latest moment is determined as the second moment.

[0013] In a second aspect, an embodiment of the present application provides a GNSS drift suppression device, comprising:

[0014] A first processing module is used to obtain a GNSS data queue according to the historical GNSS data set, wherein the GNSS data queue includes at least two historical GNSS data arranged based on timing information within a preset time period;

[0015] A second processing module is used to obtain predicted GNSS data according to vehicle operation information and the GNSS data queue, wherein the vehicle operation information represents the motion driving state of the vehicle, and the predicted GNSS data includes at least one predicted GNSS value of the vehicle at a target time point;

[0016] The determination module is used to obtain target GNSS data according to the current GNSS data and the predicted GNSS data, wherein the target GNSS data is GNSS data obtained by performing drift suppression on the current GNSS data at the target time point.

[0017] In a possible implementation, the vehicle operation information includes vehicle speed information, vehicle acceleration information and inertial navigation information. When the second processing module obtains the predicted GNSS data based on the vehicle operation information and the GNSS data queue, it is specifically used to: obtain vehicle position information based on the vehicle operation information, the vehicle position information representing the probability density distribution of the vehicle traveling to each position point at the target time point; perform cluster analysis on the vehicle position information and the GNSS data queue to obtain the predicted GNSS data.

[0018] In a possible implementation, the second processing module is further used to: obtain a field radius value based on the vehicle speed information, and / or the vehicle acceleration information and / or the inertial navigation information; the clustering analysis of the vehicle position information and the GNSS data queue to obtain the predicted GNSS data includes: performing cluster analysis on the vehicle position information and the GNSS data queue based on the field radius value to obtain the predicted GNSS data.

[0019] In a possible implementation manner, before obtaining the GNSS data queue based on the historical GNSS data set, the first processing module is specifically used to: obtain vehicle route information, the vehicle route information characterizing the material environment characteristics of the driving environment in which the vehicle is to travel; obtain GNSS reception frequency information based on the vehicle route information, the GNSS reception frequency information characterizing the frequency of the vehicle receiving GNSS data; and obtain the historical GNSS data set based on the GNSS reception frequency information.

[0020] In a possible implementation, when the first processing module obtains the GNSS receiving frequency information based on the vehicle route information, it is specifically used to: obtain meteorological environment information, the meteorological environment information characterizing the meteorological environment characteristics of the location to which the vehicle travels when the vehicle is traveling; and obtain the GNSS receiving frequency information based on the meteorological environment characteristics and the vehicle route information.

[0021] In a possible implementation manner, when the first processing module obtains the GNSS data queue according to the historical GNSS data set, it is specifically used to: loop through the following steps: obtain first historical GNSS data according to the historical GNSS data set, the first historical GNSS data is used to indicate the first historical GNSS data in the GNSS data queue, and the time point corresponding to the first historical GNSS data is the first moment; obtain second historical GNSS data according to the historical GNSS data set, the second historical GNSS data is used to indicate the last historical GNSS data in the GNSS data queue, and the time point corresponding to the second historical GNSS data is the second moment; if the time difference between the first moment and the second moment is equal to a preset duration, the historical GNSS data between the first historical GNSS data and the second historical GNSS data are arranged in time sequence information to obtain the GNSS data queue; if the time difference between the first moment and the second moment is greater than the preset duration, the next historical GNSS data of the first historical GNSS data in the GNSS data queue is determined as the first historical GNSS data, and the corresponding next moment is determined as the first moment; if the time difference between the first moment and the second moment is less than the preset duration, the latest historical GNSS data at the latest moment is obtained in the historical GNSS data set, the latest historical GNSS data is determined as the second historical GNSS data, and the corresponding latest moment is determined as the second moment.

[0022] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0023] The memory stores computer-executable instructions;

[0024] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0025] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0026] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0027] The GNSS drift suppression method, electronic device, storage medium and program product provided in the embodiment of the present application obtain a GNSS data queue according to a historical GNSS data set, wherein the GNSS data queue includes at least two historical GNSS data arranged based on timing information within a preset time length; obtain predicted GNSS data according to vehicle operation information and the GNSS data queue, wherein the vehicle operation information represents the motion driving state of the vehicle, and the predicted GNSS data includes at least one predicted GNSS value of the vehicle at a target time point; obtain target GNSS data according to current GNSS data and the predicted GNSS data, wherein the target GNSS data is the GNSS data obtained by performing drift suppression on the current GNSS data at the target time point. On the basis of obtaining the GNSS data queue based on the historical GNSS data set, at least one predicted GNSS value is obtained by combining the motion driving state of the vehicle, and then by performing error comparison between the received current GNSS data and the predicted GNSS value, the GNSS data obtained by performing drift suppression on the current GNSS data can be obtained, that is, the position of the vehicle at the corresponding time is determined, and the problem of inaccurate vehicle positioning caused by the solution of the prior art is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0029] Figure 1 A schematic diagram of a scenario for the GNSS drift suppression method provided in this application;

[0030] Figure 2 A flowchart of a GNSS drift suppression method provided by one embodiment of the present application;

[0031] Figure 3 A schematic diagram of determining a GNSS data queue based on a historical GNSS data set provided in this application;

[0032] Figure 4 for Figure 2 A schematic diagram of specific implementation steps of step S101 in the illustrated embodiment;

[0033] Figure 5 for Figure 2 A schematic diagram of the steps of determining a historical GNSS data set before step S101 in the illustrated embodiment;

[0034] Figure 6 for Figure 5 A schematic diagram of specific implementation steps of step S1002 in the illustrated embodiment;

[0035] Figure 7 A flowchart of a GNSS drift suppression method provided in another embodiment of the present application;

[0036] Figure 8 for Figure 7 A schematic diagram of specific implementation steps of step S203 in the illustrated embodiment;

[0037] Fig. 9 A schematic diagram of the structure of a GNSS drift suppression device provided in one embodiment of the present application;

[0038] Fig.10 A schematic diagram of the structure of the electronic device provided in this application.

[0039] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0040] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0041] In the technical solution of this application, the user personal information involved and the collection, storage, use, processing, transmission, provision and disclosure of data are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0042] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0043] The application scenarios of the embodiments of the present application are explained below:

[0044] Figure 1 A schematic diagram of a scenario of the GNSS drift suppression method provided in this application, such as Figure 1As shown, the specific application scenario of the present application is a scenario in which a vehicle is positioned through global navigation satellite system (GNSS) data during driving. The executor of the method provided in the embodiment of the present application may be an electronic control unit, a vehicle-mounted device or a vehicle. The electronic control unit is used as the executor for explanation. During vehicle driving, when the vehicle drives into a non-open area (for example, densely packed buildings, mountainous areas), or when the weather changes greatly during driving, the signal will be refracted, thereby causing the GNSS data sent by the GNSS satellite to drift, further causing the vehicle's positioning to be inaccurate. The electronic control unit processes and drift suppresses the GNSS data based on the GNSS drift suppression method provided in the present application, thereby achieving accurate positioning of the vehicle.

[0045] In the prior art, when GNSS data drifts, the drifted GNSS data will be eliminated to correct the vehicle's positioning data. However, when GNSS data drifts continuously, the amount of GNSS data that needs to be eliminated increases, and the amount of missing vehicle positioning data also increases, which leads to inaccurate vehicle positioning.

[0046] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0047] Figure 2 A flowchart of a GNSS drift suppression method provided in one embodiment of the present application is shown in FIG. Figure 2 As shown, the execution subject of the GNSS drift suppression method provided in this embodiment may be an electronic control unit, a vehicle device or a vehicle. By way of example, this embodiment is described by taking the electronic control unit as the execution subject of the method of this embodiment. The GNSS drift suppression method provided in this embodiment includes the following steps:

[0048] Step S101: obtaining a GNSS data queue according to a historical GNSS data set, wherein the GNSS data queue includes at least two historical GNSS data arranged based on timing information within a preset time period.

[0049] Exemplarily, the GNSS data queue includes at least two historical GNSS data arranged based on timing information within a preset time period; during vehicle driving, the electronic control unit inside the vehicle continuously receives GNSS data sent by GNSS satellites, that is, obtains a historical GNSS data set, and then determines the GNSS data queue from the historical GNSS data set according to the preset time period, wherein the GNSS data queue includes at least two historical GNSS data arranged based on timing information, and the preset time period is the difference between the current moment and the historical moment before the current moment. In a possible implementation, Figure 3 A schematic diagram of determining a GNSS data queue based on a historical GNSS data set provided in this application, such as Figure 3 As shown, according to the driving process of the vehicle, there are corresponding time points time_1, time_2, time_3, time_4, time_5, time_6, time_7, time_8 and time_9. From time time_1 to time_9, there is a historical GNSS data set consisting of historical GNSS data. The preset time required for determining the GNSS data queue is time_0. When time_4 is the current time, based on the time difference between time_4 and time_1 being equal to the preset time_0, time time_1 is determined from the historical GNSS data set. The historical GNSS data data_1 to data_4 corresponding to time_4, that is, the GNSS data queue queue_14 is obtained from the historical GNSS data data_1 to data_4; further, when time_8 is the current time, based on the time difference between time_8 and time_6 being equal to the preset duration time_0, the historical GNSS data data_6 to data_8 corresponding to time_6 to time_8 are determined from the historical GNSS data set, that is, the GNSS data queue queue_68 is obtained from the historical GNSS data data_6 to data_8.

[0050] In one possible implementation, Figure 4 for Figure 2 The schematic diagram of the specific implementation steps of step S101 in the embodiment shown is as follows: Figure 4 As shown, the specific implementation steps of step S101 include:

[0051] Step S1011: obtaining first historical GNSS data according to the historical GNSS data set, where the first historical GNSS data is used to indicate the first historical GNSS data in the GNSS data queue, and the time point corresponding to the first historical GNSS data is the first moment.

[0052] Step S1012: obtaining second historical GNSS data according to the historical GNSS data set, where the second historical GNSS data is used to indicate the last historical GNSS data in the GNSS data queue, and the time point corresponding to the second historical GNSS data is the second moment.

[0053] Step S1013, determining the relationship between the time difference between the first moment and the second moment and a preset time length.

[0054] Step S1014: if the time difference between the first moment and the second moment is equal to the preset time length, the historical GNSS data between the first historical GNSS data and the second historical GNSS data are arranged in time sequence information to obtain a GNSS data queue.

[0055] Step S1015, if the time difference between the first moment and the second moment is greater than the preset duration, the next historical GNSS data of the first historical GNSS data in the GNSS data queue is determined as the first historical GNSS data, and the corresponding next moment is determined as the first moment, and the process returns to step S1013.

[0056] Step S1016, if the time difference between the first moment and the second moment is less than the preset time length, obtain the latest historical GNSS data at the latest moment in the historical GNSS data set, determine the latest historical GNSS data as the second historical GNSS data, and determine the corresponding latest moment as the second moment, and return to execute step S1013.

[0057] Exemplarily, after the vehicle is started, the electronic control unit begins to receive and store GNSS data sent by GNSS satellites, and then determines the GNSS data received first as the first historical GNSS data, and determines the first historical GNSS data as the first historical GNSS data in the GNSS data queue, and the corresponding time point is the first moment; determines the historical GNSS data received after the first historical GNSS data as the second historical GNSS data, and determines the second historical GNSS data as the last historical GNSS data in the GNSS data queue, and the corresponding time point is the second moment; and then determines the relationship between the time difference between the first moment and the second moment and the preset time length.

[0058] Combination Figure 3 In the schematic diagram and embodiment shown, if the time difference between the first moment and the second moment is equal to the preset duration, the historical GNSS data between the first historical GNSS data and the second historical GNSS data are arranged in time series information to obtain a GNSS data queue, wherein the first moment corresponds to time_1 and the second moment corresponds to time_4.

[0059] If the time difference between the first moment and the second moment is greater than the preset duration, where the first moment corresponds to time_1 and the second moment corresponds to time_5, then the next historical GNSS data of the first historical GNSS data in the GNSS data queue is determined as the first historical GNSS data, and the corresponding next moment is determined as the first moment, that is, the next moment time_2 of the moment time_1 is determined as the first moment, and the process returns to step S1013 to determine the relationship between the time difference between the first moment time_2 and the second moment time_5 and the preset duration. If the time difference between the first moment time_2 and the second moment time_5 is equal to the preset duration, the corresponding GNSS data queue is obtained. If the time difference between the first moment time_2 and the second moment time_5 is greater than the preset duration, step S1015 is continued.

[0060] If the time difference between the first moment and the second moment is less than the preset duration, where the first moment corresponds to time_1 and the second moment corresponds to time_3, then the latest historical GNSS data of the latest moment is obtained in the historical GNSS data set, and the latest historical GNSS data is determined as the second historical GNSS data, and the corresponding latest moment is determined as the second moment, that is, the latest moment time_4 after moment time_3 is determined as the second moment, and the process returns to step S1013 to determine the relationship between the time difference between the first moment time_1 and the second moment time_4 and the preset duration. Based on the above embodiment, it can be seen that the time difference between the first moment time_1 and the second moment time_4 is equal to the preset duration, and thus the corresponding GNSS data queue is obtained.

[0061] Further, in yet another possible implementation, before obtaining the GNSS data queue according to the historical GNSS data set, the method includes determining a reception frequency of the GNSS data received by the electronic control unit, and then determining the historical GNSS data set based on the reception frequency of the GNSS data; Figure 5 for Figure 2 The schematic diagram of the step of determining the historical GNSS data set before step S101 in the embodiment shown in FIG. Figure 5 As shown, the steps of determining the historical GNSS dataset include:

[0062] Step S1001, obtaining vehicle route information, where the vehicle route information represents the material environment characteristics of the driving environment in which the vehicle is to travel.

[0063] Exemplarily, before the vehicle starts to travel, the vehicle's travel route is generated according to the vehicle's travel departure point and travel destination, and then the material environment characteristics of the travel environment in which the vehicle is to travel are obtained according to the vehicle's travel route, that is, the vehicle route information is obtained. Specifically, for example, the vehicle first travels from A to B, and then from B to C, A is the vehicle's travel departure point, B is the vehicle's first travel destination, and C is the vehicle's second travel destination; the vehicle's travel route from A to B is a wide highway with perfect communication facilities on both sides of the road, so "wide highway, perfect communication facilities on both sides of the road" is the vehicle route information info_ab, and the vehicle's travel route from B to C is a narrow mountain road with imperfect communication facilities on both sides of the mountain road and lush trees, so "narrow mountain road, imperfect communication facilities on both sides of the mountain road and lush trees" is the vehicle route information info_bc.

[0064] Step S1002: obtaining GNSS receiving frequency information according to the vehicle route information. The GNSS receiving frequency information indicates the frequency of the vehicle receiving GNSS data.

[0065] Step S1003: Obtain a historical GNSS data set according to the GNSS reception frequency information.

[0066] Exemplarily, the GNSS receiving frequency information is used to indicate the frequency at which the vehicle (electronic control unit) receives GNSS data; and then the corresponding GNSS receiving frequency is determined based on the vehicle route information; specifically, for example, under the material environment characteristics of the driving environment corresponding to the vehicle route information info_ab, the probability of GNSS data drift is low, then the electronic control unit can receive GNSS data at a low frequency, and then obtain the historical GNSS data set gather_1, and further, the historical GNSS data in the GNSS data queue queue_1 obtained in step S101 is num_1; under the material environment characteristics of the driving environment corresponding to the vehicle route information info_bc, the probability of GNSS data drift is high, then the electronic control unit can receive GNSS data at a high frequency, and then obtain the historical GNSS data set gather_2, and further, the historical GNSS data in the GNSS data queue queue_2 obtained in step S101 is num_2; since the preset time lengths of the GNSS data queues are determined to be the same, num_2 is greater than num_1.

[0067] Furthermore, in another possible implementation, Figure 6 for Figure 5 The schematic diagram of the specific implementation steps of step S1002 in the embodiment shown is as follows: Figure 6 As shown, the specific implementation steps of step S1002 include:

[0068] Step S10021, obtaining meteorological environment information, where the meteorological environment information represents the characteristics of the meteorological environment at the location where the vehicle travels when the vehicle is traveling.

[0069] Step S10022, obtaining GNSS receiving frequency information according to the meteorological environment characteristics and vehicle route information.

[0070] Exemplarily, meteorological environment information is used to indicate the meteorological environment characteristics of the location where the vehicle is traveling. The reasons for the drift of GNSS data also include weather and meteorological factors. Then, by obtaining the meteorological environment characteristics of the location where the vehicle is traveling and further combining it with the vehicle route information, the frequency of receiving GNSS data can be comprehensively obtained. Specifically, for example, the frequencies of receiving GNSS data include freq_1, freq_2, freq_3 and freq_4, freq_4 is greater than freq_3, freq_3 is greater than freq_2, and freq_2 is greater than freq_1; taking the thickness of the cloud layer corresponding to the meteorological environment characteristics as an example, when the cloud layer is thin, the probability of GNSS data drifting is low, and when the cloud layer is thick, the probability of GNSS data drifting is high; therefore, based on the vehicle route information info_ab and the thin cloud layer, the frequency of receiving GNSS data is determined to be freq_1; the vehicle route information info_ab and the thick cloud layer, the frequency of receiving GNSS data is determined to be freq_2; based on the vehicle route information info_bc and the thin cloud layer, the frequency of receiving GNSS data is determined to be freq_3, and the vehicle route information info_bc and the thick cloud layer, the frequency of receiving GNSS data is determined to be freq_4.

[0071] In the steps of this embodiment, the frequency of the vehicle receiving GNSS data is determined by combining the physical environment characteristics and meteorological environment characteristics when the vehicle is driving, so as to ensure that the data volume meets the GNSS data processing method of this application, and update the frequency of the vehicle receiving GNSS data in real time, thereby reducing the power consumption of the vehicle.

[0072] Step S102, obtaining predicted GNSS data according to the vehicle operation information and the GNSS data queue, wherein the vehicle operation information represents the motion driving state of the vehicle, and the predicted GNSS data includes at least one predicted GNSS value of the vehicle at a target time point.

[0073] Exemplarily, the vehicle operation information is used to indicate the motion driving state of the vehicle. The predicted GNSS data includes at least one predicted GNSS value of the vehicle at the target time point. The electronic control unit detects the motion driving state of the vehicle and obtains at least one predicted GNSS value of the vehicle at the target time point in combination with the GNSS data queue. Specifically, for example, the electronic control unit obtains the predicted position point of the vehicle at the target time point based on the vehicle's driving speed and driving direction, and then determines the changing pattern of the historical GNSS data in combination with the historical GNSS data in the GNSS data queue, and then processes and reverses the predicted position point of the vehicle in combination with the changing pattern of the historical GNSS data to obtain the corresponding predicted GNSS value. Since the number of predicted position points of the vehicle is at least one, the predicted GNSS data finally obtained includes at least one predicted GNSS value.

[0074] Step S103, obtaining target GNSS data according to the current GNSS data and the predicted GNSS data, wherein the target GNSS data is GNSS data obtained by performing drift suppression on the current GNSS data at the target time point.

[0075] Exemplarily, after obtaining the predicted GNSS data, and after the electronic control unit receives the current GNSS data sent by the GNSS satellite at the target time point, the predicted GNSS data is compared with the current GNSS data. If the error between the current GNSS data and the predicted GNSS data is within the allowable error range, the current GNSS data and the predicted GNSS data are comprehensively calculated by weighted calculation to obtain the target GNSS data; if the error between the current GNSS data and the predicted GNSS data is not within the allowable error range, the target GNSS data is determined based on the predicted GNSS data.

[0076] In this embodiment, a GNSS data queue is obtained based on a historical GNSS data set, wherein the GNSS data queue includes at least two historical GNSS data arranged based on timing information within a preset time length; predicted GNSS data is obtained based on vehicle operation information and the GNSS data queue, wherein the vehicle operation information represents the motion driving state of the vehicle, and the predicted GNSS data includes at least one predicted GNSS value of the vehicle at a target time point; and target GNSS data is obtained based on current GNSS data and predicted GNSS data, wherein the target GNSS data is GNSS data obtained by performing drift suppression on the current GNSS data at the target time point. On the basis of obtaining a GNSS data queue based on a historical GNSS data set, at least one predicted GNSS value is obtained by combining the motion driving state of the vehicle, and then by performing an error comparison between the received current GNSS data and the predicted GNSS value, GNSS data obtained by performing drift suppression on the current GNSS data can be obtained, that is, the position of the vehicle at the corresponding time is determined, thereby solving the problem of inaccurate vehicle positioning caused by the solution of the prior art.

[0077] Figure 7 A flowchart of a GNSS drift suppression method provided in another embodiment of the present application is shown in FIG. Figure 7 As shown, the GNSS drift suppression method provided in this embodiment is Figure 2 On the basis of the GNSS drift suppression method provided in the illustrated embodiment, step S102 is further refined, and the GNSS drift suppression method provided in this embodiment includes the following steps:

[0078] Step S201: obtaining a GNSS data queue according to a historical GNSS data set, wherein the GNSS data queue includes at least two historical GNSS data arranged based on timing information within a preset time period.

[0079] Step S202, obtaining vehicle position information based on vehicle operation information, the vehicle operation information including vehicle speed information, vehicle acceleration information and inertial navigation information, the vehicle position information represents the probability density distribution of the vehicle traveling to each position point at the target time point.

[0080] Exemplarily, the vehicle operation information includes vehicle speed information, vehicle acceleration information and inertial navigation information, and the vehicle position information represents the probability density distribution of the vehicle traveling to various position points at a target time point; specifically, according to the vehicle speed information, first vehicle position information based on the vehicle speed is obtained, according to the inertial navigation information of the vehicle, second vehicle position information based on the vehicle inertial characteristics is obtained, according to the vehicle acceleration information, third vehicle position information based on the vehicle acceleration is obtained, and then according to the first vehicle position information, the second vehicle position information and the third vehicle position information, the probability density distribution of the vehicle traveling to various position points at the target time point is obtained, which is comprehensively determined based on the vehicle speed, the vehicle inertial characteristics and the vehicle acceleration.

[0081] Step S203: performing cluster analysis on the vehicle location information and the GNSS data queue to obtain predicted GNSS data.

[0082] For example, after obtaining the vehicle location information and the GNSS data queue, the probability density distribution GNSS data is obtained according to each location point corresponding to the vehicle location information, and then the predicted GNSS data can be obtained by performing cluster analysis on the probability density distribution GNSS data and the historical GNSS data of the GNSS data queue at the corresponding time; the process of performing cluster analysis based on the clustering algorithm will not be repeated here.

[0083] Further, Figure 8 for Figure 7 The schematic diagram of the specific implementation steps of step S203 in the embodiment shown is as follows: Figure 8 As shown, the specific implementation steps of step S203 include:

[0084] Step S2031, obtaining a range radius value according to vehicle speed information, and / or vehicle acceleration information and / or inertial navigation information.

[0085] Step S2032, cluster analysis is performed on the vehicle location information and the GNSS data queue according to the domain radius value to obtain predicted GNSS data.

[0086] In one possible implementation, the clustering algorithm used in the method of the present application is a density-based spatial clustering algorithm with noise (Density-Based Spatial Clustering of Applications with Noise, hereinafter referred to as DBSCAN). The DBSCAN clustering algorithm is a density-based spatial clustering algorithm that uses two main parameters: neighborhood radius and minimum number of points. These two parameters together determine how the algorithm identifies core points, boundary points, and noise points, and forms clusters. Based on the corresponding neighborhood radius determined based on the vehicle speed, and / or the vehicle inertial characteristics, and / or the vehicle acceleration, the vehicle position information and the GNSS data queue are subjected to DBSCAN clustering analysis according to the field radius determined based on the vehicle operation information, and the predicted GNSS data can be obtained. Specifically, for example, the inertial navigation information is determined by the vehicle's acceleration and the vehicle's angular velocity, wherein the vehicle's angular velocity can be measured by the vehicle's gyroscope; when the vehicle's speed varies in a large range, and / or the vehicle's acceleration varies in a large range, and / or the vehicle's angular velocity varies in a large range, the various position points corresponding to the vehicle's position information are scattered, so a larger domain radius value can be selected, and a DBSCAN cluster analysis is performed on the vehicle's position information and the GNSS data queue to obtain predicted GNSS data; when the vehicle's speed varies in a small range, and / or the vehicle's acceleration varies in a small range, and / or the vehicle's angular velocity varies in a small range, the various position points corresponding to the vehicle's position information are concentrated, so a smaller domain radius value can be selected, and a DBSCAN cluster analysis is performed on the vehicle's position information and the GNSS data queue to obtain predicted GNSS data. Furthermore, on the basis of using vehicle speed information, and / or vehicle acceleration information and / or inertial navigation information as the basis for determining the neighborhood radius value, the neighborhood radius value can be further determined in combination with the change in two adjacent historical GNSS data in the GNSS data queue. That is, when the change in two adjacent historical GNSS data is large, it indicates that the vehicle is traveling fast, so a larger neighborhood radius value is selected; when the change in two adjacent historical GNSS data is small, it indicates that the vehicle is traveling slow, so a smaller neighborhood radius value is selected, and then cluster analysis is performed.

[0087] In the steps of this embodiment, by performing a qualitative analysis on the vehicle speed, and / or the vehicle inertia characteristics, and / or the vehicle acceleration, the size of the corresponding field radius value is determined, and then the vehicle position information and the GNSS data queue are subjected to DBSCAN clustering analysis, thereby improving the stability of the accuracy of the clustering results, that is, improving the stability of the accuracy of the predicted GNSS data.

[0088] Step S204, obtaining target GNSS data according to the current GNSS data and the predicted GNSS data, wherein the target GNSS data is GNSS data obtained by performing drift suppression on the current GNSS data at the target time point.

[0089] In this embodiment, the implementation method of step S201 and step S204 is the same as that of the present application. Figure 2 The implementation methods of step S101 and step S103 in the illustrated embodiment are the same and will not be described in detail herein.

[0090] Fig. 9 A schematic diagram of the structure of a GNSS drift suppression device provided in one embodiment of the present application is shown in FIG. Fig. 9 As shown, the GNSS drift suppression device 3 provided in this embodiment includes:

[0091] A first processing module 31 is used to obtain a GNSS data queue according to the historical GNSS data set, where the GNSS data queue includes at least two historical GNSS data arranged based on time sequence information within a preset time period;

[0092] A second processing module 32 is used to obtain predicted GNSS data according to the vehicle operation information and the GNSS data queue, wherein the vehicle operation information represents the motion driving state of the vehicle, and the predicted GNSS data includes at least one predicted GNSS value of the vehicle at a target time point;

[0093] The determination module 33 is used to obtain target GNSS data according to the current GNSS data and the predicted GNSS data. The target GNSS data is the GNSS data obtained by performing drift suppression on the current GNSS data at the target time point.

[0094] In a possible implementation, the vehicle operation information includes vehicle speed information, vehicle acceleration information and inertial navigation information. When the second processing module 32 obtains the predicted GNSS data based on the vehicle operation information and the GNSS data queue, it is specifically used to: obtain the vehicle position information based on the vehicle operation information, the vehicle position information representing the probability density distribution of the vehicle traveling to each position point at the target time point; perform cluster analysis on the vehicle position information and the GNSS data queue to obtain the predicted GNSS data.

[0095] In a possible implementation, the second processing module 32 is also used to: obtain a field radius value based on vehicle speed information, and / or vehicle acceleration information and / or inertial navigation information; perform cluster analysis on the vehicle position information and the GNSS data queue to obtain predicted GNSS data, including: perform cluster analysis on the vehicle position information and the GNSS data queue based on the field radius value to obtain predicted GNSS data.

[0096] In a possible implementation, before obtaining the GNSS data queue based on the historical GNSS data set, the first processing module 31 is specifically used to: obtain vehicle route information, the vehicle route information represents the material environment characteristics of the driving environment in which the vehicle is to travel; obtain GNSS reception frequency information based on the vehicle route information, the GNSS reception frequency information represents the frequency of the vehicle receiving GNSS data; and obtain the historical GNSS data set based on the GNSS reception frequency information.

[0097] In a possible implementation, when the first processing module 31 obtains the GNSS receiving frequency information based on the vehicle route information, it is specifically used to: obtain meteorological environment information, the meteorological environment information represents the meteorological environment characteristics of the location where the vehicle travels when the vehicle is traveling; obtain the GNSS receiving frequency information based on the meteorological environment characteristics and the vehicle route information.

[0098] In a possible implementation, when the first processing module 31 obtains the GNSS data queue according to the historical GNSS data set, it is specifically used to: loop through the following steps: obtain first historical GNSS data according to the historical GNSS data set, the first historical GNSS data is used to indicate the first historical GNSS data in the GNSS data queue, and the time point corresponding to the first historical GNSS data is the first moment; obtain second historical GNSS data according to the historical GNSS data set, the second historical GNSS data is used to indicate the last historical GNSS data in the GNSS data queue, and the time point corresponding to the second historical GNSS data is the second moment; if the time difference between the first moment and the second moment is If the time difference between the first moment and the second moment is greater than the preset time length, the next historical GNSS data of the first historical GNSS data in the GNSS data queue is determined as the first historical GNSS data, and the corresponding next moment is determined as the first moment; if the time difference between the first moment and the second moment is less than the preset time length, the latest historical GNSS data of the latest moment is obtained in the historical GNSS data set, and the latest historical GNSS data is determined as the second historical GNSS data, and the corresponding latest moment is determined as the second moment.

[0099] The first processing module 31, the second processing module 32 and the determination module 33 are connected in sequence. The GNSS drift suppression device 3 provided in this embodiment can perform the following steps: Figure 2-Figure 8 The technical solutions of any of the method embodiments shown have similar implementation principles and technical effects, which will not be described in detail here.

[0100] Fig.10This is a schematic diagram of the structure of the electronic device provided in this application. Fig.10 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0101] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0102] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0103] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly implemented as a hardware processor, or can be implemented by a combination of hardware and software modules in the processor.

[0104] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0105] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0106] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0107] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0108] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0109] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0110] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0111] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0113] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0114] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0115] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A GNSS drift suppression method, characterized in that: The method comprises: Obtaining a GNSS data queue according to the historical GNSS data set, wherein the GNSS data queue includes at least two historical GNSS data arranged based on timing information within a preset time period; Obtaining predicted GNSS data according to vehicle operation information and the GNSS data queue, wherein the vehicle operation information represents a motion driving state of the vehicle, and the predicted GNSS data includes at least one predicted GNSS value of the vehicle at a target time point; According to the current GNSS data and the predicted GNSS data, target GNSS data is obtained, where the target GNSS data is GNSS data obtained by performing drift suppression on the current GNSS data at the target time point.

2. The method according to claim 1, characterized in that The vehicle operation information includes vehicle speed information, vehicle acceleration information and inertial navigation information, and the predicted GNSS data is obtained according to the vehicle operation information and the GNSS data queue, including: Obtaining vehicle position information according to the vehicle operation information, wherein the vehicle position information represents the probability density distribution of the vehicle traveling to each position point at the target time point; Cluster analysis is performed on the vehicle position information and the GNSS data queue to obtain the predicted GNSS data.

3. The method according to claim 2, characterized in that The method further comprises: Obtaining a field radius value according to the vehicle speed information, and / or the vehicle acceleration information and / or the inertial navigation information; The performing cluster analysis on the vehicle position information and the GNSS data queue to obtain the predicted GNSS data includes: The vehicle location information and the GNSS data queue are clustered and analyzed according to the domain radius value to obtain the predicted GNSS data.

4. The method according to claim 1, characterized in that: Before obtaining the GNSS data queue according to the historical GNSS data set, the method includes: Acquiring vehicle route information, wherein the vehicle route information represents material environment characteristics of a driving environment in which the vehicle is to travel; According to the vehicle route information, GNSS receiving frequency information is obtained, wherein the GNSS receiving frequency information indicates the frequency of the vehicle receiving GNSS data; The historical GNSS data set is obtained according to the GNSS reception frequency information.

5. The method according to claim 4, characterized in that The obtaining GNSS receiving frequency information according to the vehicle route information includes: Acquiring meteorological environment information, wherein the meteorological environment information represents the characteristics of the meteorological environment at the location where the vehicle travels when the vehicle travels; The GNSS receiving frequency information is obtained according to the meteorological environment characteristics and the vehicle route information.

6. The method according to any one of claims 1 to 5, characterized in that: The step of obtaining a GNSS data queue according to the historical GNSS data set includes: The following steps are executed in a loop: Obtaining first historical GNSS data according to the historical GNSS data set, where the first historical GNSS data is used to indicate the first historical GNSS data in the GNSS data queue, and the time point corresponding to the first historical GNSS data is a first moment; According to the historical GNSS data set, second historical GNSS data is obtained, where the second historical GNSS data is used to indicate the last historical GNSS data in the GNSS data queue, and the time point corresponding to the second historical GNSS data is a second moment; If the time difference between the first moment and the second moment is equal to a preset time length, arranging the time sequence information of the historical GNSS data between the first historical GNSS data and the second historical GNSS data to obtain the GNSS data queue; If the time difference between the first moment and the second moment is greater than a preset time length, the next historical GNSS data of the first historical GNSS data in the GNSS data queue is determined as the first historical GNSS data, and the corresponding next moment is determined as the first moment; If the time difference between the first moment and the second moment is less than a preset duration, the latest historical GNSS data at the latest moment is obtained from the historical GNSS data set, and the latest historical GNSS data is determined as the second historical GNSS data, and the corresponding latest moment is determined as the second moment.

7. A GNSS drift suppression device, characterized in that: include: A first processing module is used to obtain a GNSS data queue according to the historical GNSS data set, wherein the GNSS data queue includes at least two historical GNSS data arranged based on timing information within a preset time period; A second processing module is used to obtain predicted GNSS data according to vehicle operation information and the GNSS data queue, wherein the vehicle operation information represents the motion driving state of the vehicle, and the predicted GNSS data includes at least one predicted GNSS value of the vehicle at a target time point; The determination module is used to obtain target GNSS data according to the current GNSS data and the predicted GNSS data, wherein the target GNSS data is GNSS data obtained by performing drift suppression on the current GNSS data at the target time point.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the GNSS drift suppression method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the GNSS drift suppression method according to any one of claims 1 to 6.

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