A zero speed detection method, device, apparatus, storage medium and program product

Abstract

The application provides a zero speed detection method, device, equipment, storage medium and program product, and relates to the technical field of data processing. The method comprises the following steps: acquiring global navigation satellite system (GNSS) data and inertial navigation system (INS) data of a first vehicle in a first time period; calculating M time difference carrier phases (TDCPs) based on the GNSS data and the INS data, wherein each TDCP in the M TDCPs is used to represent the difference between the GNSS data of a different observation satellite at a current observation epoch and a previous observation epoch, and M is a positive integer greater than 1; calculating a target position change amount based on the M TDCPs; determining a first scene type corresponding to the first vehicle based on the M TDCPs and the GNSS data; and determining whether the first vehicle is in a zero speed state in the first time period based on the first scene type and the target position change amount. The application can improve the detection accuracy of the zero speed state of the vehicle.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a zero-speed detection method, apparatus, equipment, storage medium, and program product. Background Technology

[0002] In vehicle navigation systems, the Global Navigation Satellite System (GNSS) is used to locate the vehicle and determine its speed. Specifically, GNSS data is acquired to determine the vehicle's position, which is then used to calculate its speed. Finally, the system checks if the vehicle is at zero speed to correct the speed, resulting in a relatively accurate vehicle speed reading. However, determining whether a vehicle is at zero speed relies primarily on data from the vehicle's Inertial Measurement Unit (IMU). But relying solely on IMU data to determine zero speed can lead to misjudgments under different conditions. For example, IMU data may be identical or similar when the vehicle is moving at a constant speed in a straight line and when it is at zero speed, resulting in incorrect assessments of whether the vehicle is at zero speed.

[0003] It is evident that the relevant technologies suffer from low accuracy in detecting the zero-speed state of vehicles. Summary of the Invention

[0004] This invention provides a zero-speed detection method, apparatus, device, storage medium, and program product to solve the problem of low accuracy in detecting the zero-speed state of vehicles in related technologies.

[0005] To solve the above problems, the present invention is implemented as follows: In a first aspect, embodiments of the present invention provide a zero-velocity detection method, comprising: Acquire the Global Navigation Satellite System (GNSS) data and Inertial Navigation System (INS) data of the first vehicle within the first time period; Based on the GNSS data and the INS data, M time differential carrier phase (TDCP) values ​​are calculated. Each of the M TDCP values ​​is used to characterize the difference between the GNSS data of different observation satellites at the current observation epoch and the previous observation epoch, where M is a positive integer greater than 1. Calculate the target position change based on the M TDCPs; The first scene type corresponding to the first vehicle is determined based on the M TDCPs and the GNSS data; Based on the first scenario type and the change in the target position, determine whether the first vehicle is at zero speed during the first time period.

[0006] Secondly, embodiments of the present invention provide a zero-speed detection device, comprising: The acquisition module is used to acquire the Global Navigation Satellite System (GNSS) data and Inertial Navigation System (INS) data of the first vehicle within a first time period. The first calculation module is used to calculate M time differential carrier phase (TDCP) based on the GNSS data and the INS data. Each of the M TDCPs is used to characterize the difference between the GNSS data of different observation satellites in the current observation epoch and the previous observation epoch, where M is a positive integer greater than 1. The second calculation module is used to calculate the target position change based on the M TDCPs; The first determining module is used to determine the first scene type corresponding to the first vehicle based on the M TDCPs and the GNSS data; The second determining module is used to determine whether the first vehicle is in a zero-speed state during the first time period based on the first scene type and the change in the target position.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, including a transceiver and a processor. The transceiver is used to acquire GNSS data and INS data of the first vehicle within a first time period. The processor is used to calculate M time differential carrier phase (TDCP) based on the GNSS data and the INS data. Each of the M TDCPs is used to characterize the difference between the GNSS data of different observation satellites in the current observation epoch and the previous observation epoch, where M is a positive integer greater than 1. The processor is also used to calculate the target position change based on the M TDCPs; The processor is further configured to determine the first scene type corresponding to the first vehicle based on the M TDCPs and the GNSS data; The processor is further configured to determine whether the first vehicle is in a zero-speed state during the first time period based on the first scene type and the change in the target position.

[0008] Fourthly, embodiments of the present invention provide an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the zero-speed detection method described in the first aspect.

[0009] Fifthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the zero-speed detection method described in the first aspect.

[0010] In a sixth aspect, the present invention also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the zero-speed detection method described in the first aspect.

[0011] In this embodiment of the invention, GNSS data and INS data of a first vehicle within a first time period are acquired. Based on the GNSS data and INS data, M time differential carrier phase (TDCP) values ​​are calculated. Each of the M TDCP values ​​characterizes the difference in GNSS data between the current and previous observation epochs for different observation satellites, where M is a positive integer greater than 1. The target position change is calculated based on the M TDCP values. A first scene type corresponding to the first vehicle is determined based on the M TDCP values ​​and the GNSS data. Whether the first vehicle is in a zero-speed state within the first time period is determined based on the first scene type and the target position change. Thus, by acquiring GNSS and INS data, calculating M TDCP values ​​based on the GNSS and INS data, and calculating the target position change based on the GNSS data, it is possible to determine whether the vehicle is in a zero-speed state based on the first scene type and the target position change. This avoids misjudgments caused by directly determining whether the vehicle is in a zero-speed state based on IMU data, thereby effectively improving the accuracy of vehicle zero-speed state detection. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a zero-speed detection method provided in an embodiment of the present invention; Figure 2 This is an overall flowchart of the process for determining whether a vehicle is in a zero-speed state using GNSS data and INS data, as provided in this embodiment of the invention. Figure 3 This is a flowchart of TDCP calculation provided in an embodiment of the present invention; Figure 4 This is a structural diagram of a zero-speed detection device provided in an embodiment of the present invention; Figure 5This is a structural diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figure 1 , Figure 1 This is a flowchart of a zero-rate detection method provided in an embodiment of the present invention, such as... Figure 1 As shown, it includes the following steps: Step 101: Obtain the Global Navigation Satellite System (GNSS) data and Inertial Navigation System (INS) data of the first vehicle within the first time period.

[0016] The first vehicle mentioned above is the vehicle that needs to be detected at zero speed. By acquiring the GNSS data and INS data of the first vehicle in the first time period, it can be determined whether the first vehicle is in a zero-speed state in the first time period.

[0017] The GNSS data refers to data received by receiving equipment or sensors deployed inside the first vehicle, used to determine the position of the first vehicle. It should be understood that GNSS data can be data from different satellite systems, such as data obtained through the Global Positioning System (GPS) or the BeiDou Navigation Satellite System.

[0018] INS data, on the other hand, is collected by sensors deployed inside the first vehicle and is used to determine information such as the vehicle's position, velocity, and attitude. For example, INS data can be acceleration or angular velocity in different directions.

[0019] The first time period mentioned above is the time period during which it is necessary to determine whether the first vehicle is in a zero-speed state. The specific time period can be set according to the actual situation. For example, if it is necessary to determine whether the first vehicle is in a zero-speed state within 1 hour, then the first time period is 1 hour.

[0020] Step 102: Calculate M Time-Differenced Carrier Phases (TDCPs) based on the GNSS data and the INS data. Each of the M TDCPs is used to characterize the difference between the GNSS data of different observation satellites in the current observation epoch and the previous observation epoch, where M is a positive integer greater than 1.

[0021] The aforementioned M TDCPs refer to the TDCPs of M observation satellites. The M observation satellites are the observation satellites in GNSS. They can be the GNSS data of each of the M observation satellites acquired when the first vehicle acquires GNSS data. The M TDCPs are then calculated based on the GNSS data of each observation satellite, with one TDCP corresponding to one observation satellite.

[0022] In some implementations, M initial TDCPs can be calculated first using GNSS data from each observation satellite, and then the M initial TDCPs can be adjusted using INS data to obtain the final M TDCPs. For example, if a first observation satellite is included among multiple observation satellites, a first TDCP can be calculated using GNSS data from the first observation satellite, and a change can be calculated using INS data. The first TDCP and the change are then weighted to calculate the final TDCP of the first observation satellite.

[0023] In other implementations, N initial TDCPs can be calculated first using GNSS data from each observation satellite, and then the N TDCPs can be filtered using INS data to obtain the final M TDCPs, as detailed in subsequent embodiments.

[0024] Step 103: Calculate the target position change based on the M TDCPs.

[0025] The aforementioned target position change is used to represent the position change of the first vehicle within the first time period. The target position change can be used to determine whether the first vehicle is at zero speed within the first time period.

[0026] In some implementations, the target position change is calculated based on M TDCPs, which can be obtained by weighting the M TDCPs or by averaging the M TDCPs.

[0027] Step 104: Determine the first scene type corresponding to the first vehicle based on the M TDCPs and the GNSS data.

[0028] The first scenario type mentioned above refers to the environment in which the first vehicle is located during the first time period. It should be noted that the specific methods for determining whether the first vehicle is in a zero-speed state differ under different scenario types. For example, in an open, outdoor environment, the first vehicle can accurately receive GNSS data, and the accuracy of the target position change obtained from GNSS and INS data is high. Therefore, the vehicle's zero-speed state can be directly determined based on the target position change. However, in a closed environment, the received GNSS data may be partially missing, resulting in lower accuracy of the target position change obtained from GNSS and INS data. It is necessary to determine whether the target position change needs adjustment before using the adjusted target position change to determine whether the vehicle is in a zero-speed state.

[0029] In some implementations, the first scene type can specifically be an open environment type, a partially blocked signal type, or a completely blocked signal type. Determining the first scene type corresponding to the first vehicle based on the M TDCPs and the GNSS data can involve determining the number of calculated M TDCPs and the number of observations corresponding to the observation satellites included in the GNSS data, and calculating the resolution ratio, which is the ratio of the number of M TDCPs to the number of observations corresponding to the observation satellites. Then, determining the first scene type based on the resolution ratio can be achieved by setting an open environment threshold and a partially blocked signal threshold to determine the first scene type.

[0030] Specifically, if the openness threshold is greater than the partial occlusion threshold, and the calculation ratio is greater than or equal to the openness threshold, the first scene type is determined to be the open environment threshold; if the calculation ratio is greater than or equal to the partial occlusion threshold but less than the openness threshold, the first scene type is determined to be the signal partial occlusion type; and if the calculation ratio is less than the partial occlusion threshold, the first scene type is determined to be the signal complete occlusion type.

[0031] In some implementations, besides directly determining the first scene type through the resolution ratio, the signal-to-noise ratio (SNR) of different satellites can be obtained, and the first scene type can be determined jointly based on the resolution ratio and the satellite SNR. Specifically, multiple mapping relationships can be pre-configured, with different mapping relationships corresponding to different resolution ratios, different satellite SNRs, and different scene types. In this way, the first scene type can be determined through the mapping relationships, the resolution ratio, and the satellite SNR.

[0032] Step 105: Determine whether the first vehicle is at zero speed during the first time period based on the first scenario type and the change in the target position.

[0033] It should be understood that since the first scenario type is the scenario type in which the first vehicle is located in the first time period, the first scenario type can be used to determine whether the first vehicle is in a zero-speed state in the first time period by directly determining the target position change, or by adjusting the target position change to determine whether the first vehicle is in a zero-speed state in the first time period. This can effectively improve the accuracy compared to related technologies that only use IMU data to determine whether the vehicle is in a zero-speed state.

[0034] In some implementations, determining whether the first vehicle is in a zero-speed state during the first time period based on the first scene type and the target position change can be achieved by pre-setting a distance threshold. The target position change, or the target position change adjusted according to the first scene type, is compared with the distance threshold. If the target position change or the target position change adjusted according to the first scene type is less than or equal to the distance threshold, the first vehicle is determined to be in a zero-speed state. If the target position change or the target position change adjusted according to the first scene type is greater than the distance threshold, the first vehicle is determined to be in motion.

[0035] In this embodiment of the invention, GNSS data and INS data of a first vehicle within a first time period are acquired. Based on the GNSS data and INS data, M time differential carrier phase (TDCP) values ​​are calculated. Each of the M TDCP values ​​characterizes the difference in GNSS data between the current and previous observation epochs for different observation satellites, where M is a positive integer greater than 1. The target position change is calculated based on the M TDCP values. A first scene type corresponding to the first vehicle is determined based on the M TDCP values ​​and the GNSS data. Whether the first vehicle is in a zero-speed state within the first time period is determined based on the first scene type and the target position change. Thus, by acquiring GNSS and INS data, calculating M TDCP values ​​based on the GNSS and INS data, and calculating the target position change based on the GNSS data, it is possible to determine whether the vehicle is in a zero-speed state based on the first scene type and the target position change. This avoids misjudgments caused by directly determining whether the vehicle is in a zero-speed state based on IMU data, thereby effectively improving the accuracy of vehicle zero-speed state detection.

[0036] In one embodiment, calculating M time differential carrier phases (TDCPs) based on the GNSS data and the INS data includes: Calculate N TDCPs based on the GNSS data; The observation residuals for each TDCP are calculated based on the INS data; Based on the observation residuals of each TDCP, M TDCPs are selected from the N TDCPs. The difference between the observation residuals of the M TDCPs and the first mean is less than the first product. The first mean is the mean of the N TDCPs. The first product is the product of the first set coefficient and the first standard deviation. The first standard deviation is the standard deviation of the N TDCPs. N is a positive integer greater than 1, and M is less than or equal to N.

[0037] The aforementioned N TDCPs refer to the TDCPs corresponding to the N observation satellites in the GNSS data. It can be understood that the GNSS data from the N observation satellites represents all the GNSS data received by the first vehicle within the first time period. When calculating the TDCP, the TDCP corresponding to each observation satellite is calculated, resulting in N TDCPs.

[0038] In some implementations, GNSS data can be preprocessed to extract data with high angles and low signal-to-noise ratios, resulting in optimized GNSS data.

[0039] In some implementations, GNSS data can be represented by a non-differential carrier phase observation equation, specifically: ; In the formula, The subscript represents the observations from the satellite (i.e., carrier phase observations). Indicates the receiver index, superscript Indicates satellite index; Indicates the carrier wavelength (m); This indicates the geometric distance (satellite-to-ground distance) between the receiver and the satellite. Indicates ionospheric delay; Indicates tropospheric delay; Represents the speed of light; Indicates receiver clock bias; Indicates satellite clock bias; Indicates the ambiguity over an integer period (week); This represents carrier phase observation noise and residual error.

[0040] Given the GNSS data, assuming no cycle slip occurs between two adjacent GNSS observation epochs, subtracting the carrier phase observations from adjacent epochs can eliminate ambiguity parameters, yielding the TDCP observation equations for calculation, specifically: ; In the formula, For TDCP, Indicates a simple difference operator; This represents the residual error of TDCP.

[0041] Furthermore, GNSS data sampling rates are typically 1 Hz or higher. By subtracting observations from adjacent epochs, most common errors, such as ionospheric and tropospheric delay terms, can be eliminated. Therefore, the TDCP observation equation can be simplified to the following: ; The change in geometric distance between the satellite and the vehicle in the above formula It can be written in vector form: ; In the formula, and These represent the times when the satellite transmits the signal. and Position vector in the Earth-centered Earth-fixed system; and These represent the signal reception times. and The position vector of the phase center of the receiver antenna; and These represent the signal reception times. and The receiver antenna phase center points to the unit vector in the direction of the satellite's line of sight.

[0042] set up We can obtain: ; In the formula, This represents the change in receiver position between epochs; the first term on the right-hand side of the equation represents the Doppler effect caused by satellite motion, and the second term represents the change in the direction of the satellite-to-ground line of sight between adjacent epochs. These two terms can be directly calculated from navigation messages and observations, and are denoted as... and Meanwhile, the change in satellite clock bias It can also be calculated using ephemeris with very small error, so the TDCP calculation formula can be expressed as: ; In the formula, This represents the final calculated TDCP after simplification.

[0043] The aforementioned INS data was collected by sensors deployed inside the first vehicle. The position at the current epoch can be determined using the INS data. The position of the previous epoch is used to obtain the change over the previous epoch. Furthermore, the observation residuals of the TDCP can be calculated using the positional changes between epochs. Specifically, the observation residuals of each TDCP are calculated based on the INS data. This can be achieved using the following formula: ; The superscripts 1, 2, ..., n in the formula represent the observed satellites. It should be noted that the relative positional changes in INS recursive accuracy over a short period of time... High precision, while absolute position error affects The impact is very small, therefore it can be considered... The term error is very small, while the receiver clock bias variation in all TDCP observation equations is small. Similarly, if no cycle slip occurs in the carrier phase observations of adjacent epochs, substitute the initial value of the receiver clock bias change. (Usually, the clock error change obtained from the TDCP solution of the previous epoch is taken) then the residual error of all TDCP observations. It should fluctuate slightly around a certain constant. Therefore, in this embodiment, the first mean is calculated. and the first standard deviation This allows for the determination of whether cycle slips are sent in adjacent epochs, and then filtering out TDCPs that have not experienced cycle slips.

[0044] Specifically, based on the observation residuals of each TDCP, M TDCPs are selected from N TDCPs. The difference between the observation residuals of the M TDCPs and the first mean is less than the first product. The first mean is the mean of the N TDCPs. The first product is the product of the first set coefficient and the first standard deviation. The first standard deviation is the standard deviation of the N TDCPs, where N is a positive integer greater than 1, and M is less than or equal to N. The first set coefficient can be 3. Selecting M TDCPs means that for a TDCP, if it satisfies... If the observation value of the carrier phase of satellite j in adjacent epochs has a cycle slip, it is marked as a TDCP gross error and deleted, and is not included in the subsequent calculation, thus obtaining M TDCPs.

[0045] In this embodiment of the invention, N TDCPs are calculated based on the GNSS data; the observation residual of each TDCP is calculated based on the INS data; and M TDCPs are selected from the N TDCPs based on the observation residual of each TDCP. Thus, by selecting M TDCPs from the N TDCPs, TDCPs corresponding to satellites that may have cycle slips can be filtered out, improving the accuracy of the TDCPs.

[0046] In one embodiment, calculating the target location change based on the M TDCPs includes: The M TDCPs are solved using the least squares algorithm and a preset observation weight matrix to obtain the first change and M solution residuals; Construct the first residual matrix based on the M solution residuals; A first matrix is ​​constructed based on the preset observation weight matrix and the first residual matrix; If the first matrix satisfies the chi-square test, the first change is set as the change in the target position.

[0047] It should be noted that, in order to obtain a more accurate target position change, a chi-square test is introduced in this embodiment. The accuracy of the TDCP is determined by whether the solution residuals of the M TDCPs satisfy a chi-square distribution. Specifically, if the M TDCPs are accurate and there are no cycle slips between adjacent epochs, the solution residuals of the M TDCPs satisfy a chi-square distribution; however, if the M TDCPs are inaccurate, or if there are cycle slips between adjacent epochs, the solution residuals of the M TDCPs do not satisfy a chi-square distribution. In this way, the chi-square test ensures the accuracy of the calculated target position change.

[0048] Specifically, the M TDCPs are solved using the least squares algorithm and a preset observation weight matrix to obtain the positional change between epochs, i.e., the first change. The receiver positional change and clock bias change between adjacent epochs are used as parameters to be estimated. By simultaneously solving all the TDCP observation equations, the positional changes between epochs can be obtained using the least squares method. The accuracy of this result is at the centimeter level, effectively ensuring the accuracy of the calculated first change. The specific calculation process can be expressed by the following formula: ; In the formula, The coefficient matrix, The matrix constructed for the observations, The first change and M settlement residuals are calculated using the formula based on the preset observation weight matrix.

[0049] Furthermore, after obtaining the first change, a first residual matrix is ​​constructed based on the M solution residuals. The first matrix is ​​constructed based on the preset observation weight matrix and the first residual matrix. This is used to determine whether the first matrix satisfies the chi-square test. If it does, it means that the M TDCPs are accurate and there are no cycle slips between adjacent epochs. In this case, the calculated first change has a high accuracy and can be directly used as the change in the target position.

[0050] In this embodiment of the invention, the M TDCPs are solved using the least squares algorithm and a preset observation weight matrix to obtain a first change and M solution residuals; a first residual matrix is ​​constructed based on the M solution residuals; a first matrix is ​​constructed based on the preset observation weight matrix and the first residual matrix; if the first matrix satisfies the chi-square test, the first change is set as the target position change. Thus, the accuracy of the first change is determined by whether the first residual matrix passes the chi-square test, thereby obtaining a target position change with higher accuracy.

[0051] In one embodiment, the method further includes: If the first matrix does not satisfy the chi-square test, the first TDCP is determined. The first TDCP is the TDCP with the largest solution residual among the M TDCPs. The M-1 TDCPs are solved using the least squares algorithm and a preset observation weight matrix to obtain the second change and M-1 solution residuals. The M-1 TDCPs are obtained by deleting the first TDCP from the M TDCPs. Construct a second residual matrix based on the M-1 solution residuals; A second matrix is ​​constructed based on the preset observation weight matrix and the second residual matrix; If the second matrix satisfies the chi-square test, the second change is set as the change in the target position.

[0052] It should be understood that if the first matrix does not satisfy the chi-square test, the accuracy of the calculated first change is considered low. In this case, at least one TDCP among the M TDCPs has a large error (i.e., the first TDCP with the largest residual). Therefore, in this embodiment, the first TDCP is deleted, and the second change is calculated based on the remaining M-1 TDCPs. Then, the accuracy of the second change is determined. Specifically, based on the calculated M-1 residuals, a second residual matrix is ​​constructed. A second matrix is ​​constructed based on the preset observation weight matrix and the second residual matrix. The accuracy of the second change is determined by whether the second matrix satisfies the chi-square test.

[0053] If the second matrix satisfies the chi-square test, the second change has a higher accuracy and can be set as the target position change. If the second matrix also does not satisfy the chi-square test, the TDCP with the largest residual is deleted, and a new change and a new matrix are recalculated until the new matrix satisfies the chi-square test.

[0054] Furthermore, if the chi-square test still fails after continuously deleting TDCP, and the TDCP is insufficient to calculate the change, the solution is considered to have failed. In this case, it is impossible to determine whether the vehicle is in a zero-speed state by TDCP. IMU data can be used to determine whether the vehicle is in a zero-speed state.

[0055] Specifically, the overall flowchart for determining whether a vehicle is at zero speed using GNSS and INS data can be shown as follows: Figure 2 and Figure 3 As shown, after obtaining GNSS data, M TDCP data points are obtained by combining INS data. Least squares algorithm and chi-square test are then used to determine whether the TDCP settlement was successful. If the settlement is successful, the change in target position is used to determine if the vehicle is at zero speed. If the settlement fails, IMU data is used to determine if the vehicle is at zero speed. Specifically, if the chi-square test fails, the TDCP data point with the largest residual is deleted, and the least squares algorithm and chi-square test are then performed on it.

[0056] In one embodiment, determining whether the first vehicle is at zero speed during the first time period based on the first scene type and the target position change includes: When the first scenario type is an open environment type, it is determined whether the first vehicle is at zero speed during the first time period based on the target position change and the set change threshold. Wherein, if the change in the target position is less than or equal to a set change threshold, the first vehicle is in a zero-speed state during the first time period. If the change in the target position is greater than the set change threshold, the first vehicle is in motion during the first time period.

[0057] It should be understood that when the first scenario is an open environment, the accuracy of the first vehicle in acquiring the GNSS data is relatively high, and the change in the target position obtained by the calculation can be used to determine whether the first vehicle is at zero speed in the first time period.

[0058] Specifically, a threshold value for the change in target position can be set to determine whether the first vehicle is at zero speed within a first time period. If the change in target position is less than or equal to the threshold value, the first vehicle is at zero speed within the first time period; if the change in target position is greater than the threshold value, the first vehicle is in motion within the first time period.

[0059] Furthermore, such as Figure 3As shown, the first scene type is determined by GNSS data and M TDCP data, and can also be determined by combining signal-to-noise ratio information.

[0060] In one embodiment, the GNSS data includes velocity data and observation time intervals, and the INS data includes prior variations; Determining whether the first vehicle is at zero speed during the first time period based on the M type and the target position change includes: When the first scenario type is a partial signal obstruction type, the position change between epochs is calculated, and the position change between epochs is the product of the velocity measurement data and the observation time interval; If the first difference is greater than or equal to a set test threshold, or if the second difference is less than or equal to a second product, it is determined whether the first vehicle is in a zero-speed state during the first time period based on the target position change and the set change threshold. The first difference is the difference between the position change between the epochs and the prior change, the second difference is the difference between the target position change and the prior change, and the second product is the product of a second set coefficient and the set test threshold.

[0061] It should be noted that in the case of partial signal obstruction in the first scenario, the acquired GNSS data may not be accurate enough. In this case, it is necessary to perform a consistency check on the target position change to determine whether the target position change matches the INS data.

[0062] Specifically, calculate the change in position between epochs. The inter-epoch positional change is the product of the velocity measurement data and the observation time interval, and is then judged by a first difference and a set test threshold, and a second difference and a second product. The first difference is the inter-epoch positional change and the prior change. The difference is the first difference, and the second difference is the change in target position. The difference between the a priori change and the second product is the product of the second set coefficient and the set test threshold. That is, it can be expressed by the formula: ,or, In this case, the system directly determines whether the first vehicle is at zero speed within the first time period by the change in the target position and a set threshold for the change. 3 is the second set coefficient to set the test threshold.

[0063] In some implementations, even after passing the consistency check, some erroneous variations may still not be filtered out. In such cases, the variations that passed the consistency check can be further checked for lateral and vertical variations. Specifically, if the vehicle does not skid or bounce, it satisfies the non-integrity constraint condition, meaning the vehicle's lateral and vertical velocities are 0. This indicates that during approximately linear planar motion, the lateral and vertical displacements calculated by TDCP are also close to 0. Based on this, it is determined whether the vehicle is turning or bouncing. If it satisfies approximately linear planar motion, the target position change in the Earth-centered Earth-fixed system calculated by TDCP is used based on INS attitude information. The transformation yields the changes in the vehicle coordinate system (including changes in forward position). Lateral position change and vertical position change Specifically, it can be expressed as: ; If the lateral position change Less than the set lateral threshold, and the vertical position change is less than [a certain value]. If all values ​​are less than the set vertical threshold, the check is considered passed and the TDCP calculation is considered successful. At this point, the change in target position is used to determine whether the first vehicle is in a zero-speed state. Otherwise, the TDCP calculation is considered to have failed, and subsequent IMU data can be used to determine whether the vehicle is in a zero-speed state.

[0064] In some implementations, based on the change in the target position and setting a threshold for change Determine whether the first vehicle is at zero speed during the first time period. If the change in the target position is less than or equal to a set change threshold, the first vehicle is at zero speed during the first time period; if the change in the target position is greater than the set change threshold, the first vehicle is in motion during the first time period.

[0065] In one embodiment, determining whether the first vehicle is at zero speed during the first time period based on the first scene type and the target position change further includes: In the case where the first scenario type is a complete signal blockage type, or in the case where the first difference is less than the set test threshold and the second difference is greater than the second product, or in the case where TDCP calculation fails, the data of multiple inertial measurement units (IMUs) of the first vehicle in the first time period are obtained. Calculate a first quantity and a second quantity, wherein the first quantity is the number of third differences less than a first threshold among a plurality of third differences, the plurality of third differences being the differences between the plurality of IMU data and a second mean, the second mean being the mean of the plurality of IMU data, the first threshold being the product of a third set coefficient and a second standard deviation, the second standard deviation being the standard deviation of the plurality of IMU data, and the second quantity being the number of IMU data whose absolute value is less than a second threshold, the second threshold being the sum of the absolute value of the second mean and the first threshold; If the speed measurement data is less than a set speed threshold, the first ratio is greater than the first set ratio, and the second ratio is greater than the second set ratio, it is determined that the first vehicle is in a zero-speed state during the first time period. The first ratio is the ratio of the first quantity to the quantity of the plurality of IMU data, and the second ratio is the ratio of the second quantity to the quantity of the plurality of IMU data.

[0066] It should be understood that in the scenario where the signal is completely blocked, the acquired GNSS data may be incomplete or incorrect, leading to TDCP calculation failure. In this case, IMU data is needed to determine if the first vehicle is at zero speed. Furthermore, in the scenario where the signal is partially blocked, if the first difference is less than the set verification threshold and the second difference is greater than the second product, the TDCP calculation is considered to have failed, and IMU data is also needed to determine if the first vehicle is at zero speed. Further, for other TDCP calculation failure scenarios, IMU data is also needed to determine if the first vehicle is at zero speed.

[0067] Specifically, first, acquire multiple IMU data points, perform zero-bias correction on the IMU data, and then calculate their second mean and second standard deviation. ; In the formula, This represents IMU data, where the subscript... This represents a data index for an IMU observation epoch. The data correspond to the x (front), y (right), and z (bottom) axes of the gyroscope and the x, y, and z axes of the accelerometer, respectively, with subscripts. This represents the epoch index within the IMU data window. , This indicates the number of IMU data points within the sliding time window; its maximum value is typically set based on the IMU data sampling rate. This represents the second mean. This represents the second standard deviation.

[0068] Furthermore, stability testing is performed on multiple IMU data points. The IMU data points that pass the stability test are denoted as the first quantity, which is the number of third differences among multiple third differences that are less than a first threshold. The multiple third differences are the differences between the multiple IMU data points and the second mean, where the second mean is the average of the multiple IMU data points. The first threshold is the product of a third set coefficient and the second standard deviation, where the second standard deviation is the standard deviation of the multiple IMU data points. This can be specifically expressed by the following formula: , ; In the formula, This represents the first threshold of IMU data for a certain axis. This indicates that the stability test passed the count (first quantity) and .

[0069] It should be noted that only the gyroscope z-axis and accelerometer x-axis and y-axis information, which best reflect the motion, are used for statistical analysis here. Only values ​​3, 4, and 5 are considered. If the difference between the gyroscope z-axis and accelerometer x and y-axis data of this IMU epoch and their corresponding mean values ​​is less than the first threshold... Then, the IMU stability test passes the count (i.e., the first count). Add one.

[0070] Furthermore, absolute value detection needs to be performed on multiple IMU data points. The IMU data points that pass the absolute value detection are designated as the second data point. The second quantity is the number of IMU data points whose absolute values ​​are less than a second threshold. The second threshold is the sum of the absolute value of the second mean and the first threshold. This can be expressed by the following formula: , ; In the formula, This represents the second threshold of IMU data for a certain axis. This indicates that the stability test is performed by counting (i.e., the second quantity) and .

[0071] It should be noted that only the x-axis observation data from the three-axis gyroscope and accelerometer are taken here, and their absolute values ​​are compared with a set threshold. If the absolute values ​​of both the three-axis gyroscope and accelerometer x-axis observations are less than the second threshold, the comparison is made accordingly. Then, the absolute value detection of the IMU is passed by counting (i.e., the second quantity). Add one.

[0072] In some implementations, the first threshold and the second threshold can be obtained by the following formula: ; The first threshold and the second threshold are calculated using the above formula to determine the first quantity and the second quantity.

[0073] Furthermore, after calculating the first and second quantities, the system uses these quantities to determine whether the vehicle is at zero speed. Specifically, in the speed measurement data... Less than the set speed threshold First proportion Greater than the first set ratio And the second proportion Greater than the second set ratio In the case where the first vehicle is at zero speed during the first time period, the first ratio is the ratio of the first quantity to the quantity of multiple IMU data, and the second ratio is the ratio of the second quantity to the quantity of multiple IMU data; otherwise, the vehicle is in motion.

[0074] In this embodiment of the invention, IMU data is used to determine whether the vehicle is in a zero-speed state, so that even if GNSS data cannot be obtained or TDCP calculation fails, it is still possible to determine whether the vehicle is in a zero-speed state, thereby ensuring that it can continuously determine whether the vehicle is in a zero-speed state.

[0075] Please see Figure 4 , Figure 4 This is a structural diagram of a zero-speed detection device provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the zero-speed detection device 400 includes: The acquisition module 401 is used to acquire the Global Navigation Satellite System (GNSS) data and Inertial Navigation System (INS) data of the first vehicle within a first time period; The first calculation module 402 is used to calculate M time differential carrier phase (TDCP) based on the GNSS data and the INS data. Each of the M TDCPs is used to characterize the difference between the GNSS data of different observation satellites in the current observation epoch and the previous observation epoch, where M is a positive integer greater than 1. The second calculation module 403 is used to calculate the target position change based on the M TDCPs; The first determining module 404 is used to determine the first scene type corresponding to the first vehicle based on the M TDCPs and the GNSS data; The second determining module 405 is used to determine whether the first vehicle is in a zero-speed state during the first time period based on the first scene type and the target position change amount.

[0076] In one embodiment, the first computing module 402 includes: The first calculation unit is used to calculate N TDCPs based on the GNSS data; The second calculation unit is used to calculate the observation residuals of each TDCP based on the INS data; A filtering unit is configured to filter M TDCPs from the N TDCPs based on the observation residuals of each TDCP, wherein the difference between the observation residuals of the M TDCPs and a first mean is less than a first product, the first mean is the mean of the N TDCPs, the first product is the product of a first set coefficient and a first standard deviation, the first standard deviation is the standard deviation of the N TDCPs, N is a positive integer greater than 1, and M is less than or equal to N.

[0077] In one embodiment, the second computing module 403 includes: The third calculation unit is used to solve the M TDCPs based on the least squares algorithm and the preset observation weight matrix to obtain the first change and M solution residuals; The first component unit is used to construct the first residual matrix based on the M solution residuals; The second component unit is used to construct a first matrix based on the preset observation weight matrix and the first residual matrix; The setting unit is used to set the first change amount as the target position change amount when the first matrix satisfies the chi-square test.

[0078] In one embodiment, the zero-velocity detection device 400 further includes: The third determining module is used to determine the first TDCP when the first matrix does not satisfy the chi-square test. The first TDCP is the TDCP with the largest solution residual among the M TDCPs. The third calculation module is used to solve M-1 TDCPs based on the least squares algorithm and a preset observation weight matrix to obtain the second change and M-1 solution residuals. The M-1 TDCPs are obtained by deleting the first TDCP from the M TDCPs. The first construction module is used to construct a second residual matrix based on the M-1 solution residuals; The second component module is used to construct a second matrix based on the preset observation weight matrix and the second residual matrix; The setting module is used to set the second change amount as the target position change amount when the second matrix satisfies the chi-square test.

[0079] In one embodiment, the second determining module 405 includes: The first determining unit is used to determine whether the first vehicle is in a zero-speed state during the first time period based on the target position change amount and a set change amount threshold when the first scene type is an open environment type. Wherein, if the change in the target position is less than or equal to a set change threshold, the first vehicle is in a zero-speed state during the first time period. If the change in the target position is greater than the set change threshold, the first vehicle is in motion during the first time period.

[0080] In one embodiment, the GNSS data includes velocity data and observation time intervals, and the INS data includes prior variations; The second determining module 405 includes: The fourth calculation unit is used to calculate the position change between epochs when the first scene type is a signal partial obstruction type, wherein the position change between epochs is the product of the velocity measurement data and the observation time interval. The second determining unit is used to determine whether the first vehicle is in a zero-speed state during the first time period based on the target position change and the set change threshold, when the first difference is greater than or equal to a set inspection threshold, or when the second difference is less than or equal to a second product. The first difference is the difference between the position change between the epochs and the prior change, the second difference is the difference between the target position change and the prior change, and the second product is the product of the second set coefficient and the set inspection threshold.

[0081] In one embodiment, the second determining module 405 further includes: The acquisition unit is used to acquire multiple inertial measurement unit (IMU) data of the first vehicle in the first time period when the first scenario type is a signal complete blockage type, or when the first difference is less than the set test threshold and the second difference is greater than the second product, or when TDCP calculation fails. The fifth calculation unit is used to calculate a first quantity and a second quantity. The first quantity is the number of third differences among a plurality of third differences that are less than a first threshold. The plurality of third differences are the differences between the plurality of IMU data and a second mean. The second mean is the mean of the plurality of IMU data. The first threshold is the product of a third set coefficient and a second standard deviation. The second standard deviation is the standard deviation of the plurality of IMU data. The second quantity is the number of IMU data whose absolute value is less than a second threshold. The second threshold is the sum of the absolute value of the second mean and the first threshold. The third determining unit is used to determine that the first vehicle is in a zero-speed state during the first time period when the speed measurement data is less than a set speed threshold, the first ratio is greater than the first set ratio, and the second ratio is greater than the second set ratio. The first ratio is the ratio of the first quantity to the quantity of the plurality of IMU data, and the second ratio is the ratio of the second quantity to the quantity of the plurality of IMU data.

[0082] The zero-speed detection device provided in this embodiment of the invention can realize each process of each embodiment of the above-mentioned zero-speed detection method. The technical features are one-to-one and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0083] It should be noted that the zero-speed detection device in the embodiments of the present invention can be a device, or it can be a component, integrated circuit, or chip in an electronic device.

[0084] This invention also provides an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the above-described functionality. Figure 1 The various processes of the zero-speed detection method embodiment shown herein can achieve the same technical effect, and will not be described again here to avoid repetition.

[0085] For details, see Figure 5 As shown, this embodiment of the invention also provides an electronic device, including a bus 501, a transceiver 502, an antenna 503, a bus interface 504, a processor 505, and a memory 506.

[0086] The transceiver 502 is used to acquire the Global Navigation Satellite System (GNSS) data and Inertial Navigation System (INS) data of the first vehicle within a first time period. The processor 505 is used to calculate M time differential carrier phase (TDCP) based on the GNSS data and the INS data. Each of the M TDCPs is used to characterize the difference between the GNSS data of different observation satellites in the current observation epoch and the previous observation epoch, where M is a positive integer greater than 1. The processor 505 is also used to calculate the target position change based on the M TDCPs; The processor 505 is further configured to determine the first scene type corresponding to the first vehicle based on the M TDCPs and the GNSS data; The processor 505 is further configured to determine whether the first vehicle is in a zero-speed state during the first time period based on the first scene type and the target position change.

[0087] In one embodiment, calculating M time differential carrier phases (TDCPs) based on the GNSS data and the INS data includes: Calculate N TDCPs based on the GNSS data; The observation residuals for each TDCP are calculated based on the INS data; Based on the observation residuals of each TDCP, M TDCPs are selected from the N TDCPs. The difference between the observation residuals of the M TDCPs and the first mean is less than the first product. The first mean is the mean of the N TDCPs. The first product is the product of the first set coefficient and the first standard deviation. The first standard deviation is the standard deviation of the N TDCPs. N is a positive integer greater than 1, and M is less than or equal to N.

[0088] In one embodiment, calculating the target location change based on the M TDCPs includes: The M TDCPs are solved using the least squares algorithm and a preset observation weight matrix to obtain the first change and M solution residuals; Construct the first residual matrix based on the M solution residuals; A first matrix is ​​constructed based on the preset observation weight matrix and the first residual matrix; If the first matrix satisfies the chi-square test, the first change is set as the change in the target position.

[0089] In one embodiment, the processor 505 is further configured to determine a first TDCP when the first matrix does not satisfy the chi-square test, wherein the first TDCP is the TDCP with the largest solution residual among the M TDCPs; The processor 505 is further configured to solve M-1 TDCPs based on the least squares algorithm and a preset observation weight matrix to obtain a second change and M-1 solution residuals, wherein the M-1 TDCPs are obtained by deleting the first TDCP from the M TDCPs; The processor 505 is also used to construct a second residual matrix based on the M-1 solution residuals; The processor 505 is further configured to construct a second matrix based on the preset observation weight matrix and the second residual matrix; The processor 505 is further configured to set the second change amount as the target position change amount when the second matrix satisfies the chi-square test.

[0090] In one embodiment, determining whether the first vehicle is at zero speed during the first time period based on the first scene type and the target position change includes: When the first scenario type is an open environment type, it is determined whether the first vehicle is at zero speed during the first time period based on the target position change and the set change threshold. Wherein, if the change in the target position is less than or equal to a set change threshold, the first vehicle is in a zero-speed state during the first time period. If the change in the target position is greater than the set change threshold, the first vehicle is in motion during the first time period.

[0091] In one embodiment, the GNSS data includes velocity data and observation time intervals, and the INS data includes prior variations; Determining whether the first vehicle is at zero speed during the first time period based on the first scenario type and the change in the target position includes: When the first scenario type is a partial signal obstruction type, the position change between epochs is calculated, and the position change between epochs is the product of the velocity measurement data and the observation time interval; If the first difference is greater than or equal to a set test threshold, or if the second difference is less than or equal to a second product, it is determined whether the first vehicle is in a zero-speed state during the first time period based on the target position change and the set change threshold. The first difference is the difference between the position change between the epochs and the prior change, the second difference is the difference between the target position change and the prior change, and the second product is the product of a second set coefficient and the set test threshold.

[0092] In one embodiment, determining whether the first vehicle is at zero speed during the first time period based on the first scene type and the target position change further includes: In the case where the first scenario type is a complete signal blockage type, or in the case where the first difference is less than the set test threshold and the second difference is greater than the second product, or in the case where TDCP calculation fails, the data of multiple inertial measurement units (IMUs) of the first vehicle in the first time period are obtained. Calculate a first quantity and a second quantity, wherein the first quantity is the number of third differences less than a first threshold among a plurality of third differences, the plurality of third differences being the differences between the plurality of IMU data and a second mean, the second mean being the mean of the plurality of IMU data, the first threshold being the product of a third set coefficient and a second standard deviation, the second standard deviation being the standard deviation of the plurality of IMU data, and the second quantity being the number of IMU data whose absolute value is less than a second threshold, the second threshold being the sum of the absolute value of the second mean and the first threshold; If the speed measurement data is less than a set speed threshold, the first ratio is greater than the first set ratio, and the second ratio is greater than the second set ratio, it is determined that the first vehicle is in a zero-speed state during the first time period. The first ratio is the ratio of the first quantity to the quantity of the plurality of IMU data, and the second ratio is the ratio of the second quantity to the quantity of the plurality of IMU data.

[0093] exist Figure 5 In this document, a bus architecture (represented by bus 501) is used. Bus 501 can include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 505 and memory represented by memory 506. Bus 501 can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 504 provides an interface between bus 501 and transceiver 502. Transceiver 502 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 processor 505 is transmitted over a wireless medium via antenna 503, which further receives data and transmits it to processor 505.

[0094] Processor 505 manages bus 501 and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 506 can be used to store data used by processor 505 during operation.

[0095] Optionally, the processor 505 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a graphics processing unit (GPU).

[0096] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the above-described functions. Figure 1 The various processes corresponding to the zero-speed detection method embodiments, and which achieve the same technical effect, will not be described again here to avoid repetition. The computer-readable storage medium mentioned includes, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0097] The present invention also provides a computer program product, including computer instructions that, when executed by a processor, implement the above-described... Figure 1 The various processes corresponding to the zero-speed detection method embodiments, and which can achieve the same technical effect, will not be described again here to avoid repetition.

[0098] In the embodiments of this invention, the terms "first," "second," etc., are used to distinguish similar object parameters and are not necessarily used to describe a specific order or sequence. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices. Additionally, the use of "and / or" in this application indicates at least one of the connected object parameters, such as A and / or B and / or C, representing seven possibilities: A alone, B alone, C alone, both A and B present, both B and C present, both A and C present, and A, B, and C present.

[0099] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0100] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, etc.) to execute the methods of the various embodiments of this application.

[0101] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A zero-speed detection method, characterized in that, include: Acquire the Global Navigation Satellite System (GNSS) data and Inertial Navigation System (INS) data of the first vehicle within the first time period; Based on the GNSS data and the INS data, M time differential carrier phase (TDCP) values ​​are calculated. Each of the M TDCP values ​​is used to characterize the difference between the GNSS data of different observation satellites at the current observation epoch and the previous observation epoch, where M is a positive integer greater than 1. Calculate the target position change based on the M TDCPs; The first scene type corresponding to the first vehicle is determined based on the M TDCPs and the GNSS data; Based on the first scenario type and the change in the target position, determine whether the first vehicle is at zero speed during the first time period.

2. The method as described in claim 1, characterized in that, The calculation of M time differential carrier phases (TDCPs) based on the GNSS data and the INS data includes: Calculate N TDCPs based on the GNSS data; The observation residuals for each TDCP are calculated based on the INS data; Based on the observation residuals of each TDCP, M TDCPs are selected from the N TDCPs. The difference between the observation residuals of the M TDCPs and the first mean is less than the first product. The first mean is the mean of the N TDCPs. The first product is the product of the first set coefficient and the first standard deviation. The first standard deviation is the standard deviation of the N TDCPs. N is a positive integer greater than 1, and M is less than or equal to N.

3. The method as described in claim 2, characterized in that, The calculation of the target position change based on the M TDCPs includes: The M TDCPs are solved using the least squares algorithm and a preset observation weight matrix to obtain the first change and M solution residuals; Construct the first residual matrix based on the M solution residuals; A first matrix is ​​constructed based on the preset observation weight matrix and the first residual matrix; If the first matrix satisfies the chi-square test, the first change is set as the change in the target position.

4. The method as described in claim 3, characterized in that, The method further includes: If the first matrix does not satisfy the chi-square test, the first TDCP is determined. The first TDCP is the TDCP with the largest solution residual among the M TDCPs. The M-1 TDCPs are solved using the least squares algorithm and a preset observation weight matrix to obtain the second change and M-1 solution residuals. The M-1 TDCPs are obtained by deleting the first TDCP from the M TDCPs. Construct a second residual matrix based on the M-1 solution residuals; A second matrix is ​​constructed based on the preset observation weight matrix and the second residual matrix; If the second matrix satisfies the chi-square test, the second change is set as the change in the target position.

5. The method according to any one of claims 1 to 4, characterized in that, Determining whether the first vehicle is at zero speed during the first time period based on the first scenario type and the change in the target position includes: When the first scenario type is an open environment type, it is determined whether the first vehicle is at zero speed during the first time period based on the target position change and the set change threshold. Wherein, if the change in the target position is less than or equal to a set change threshold, the first vehicle is in a zero-speed state during the first time period. If the change in the target position is greater than the set change threshold, the first vehicle is in motion during the first time period.

6. The method according to any one of claims 1 to 4, characterized in that, The GNSS data includes velocity measurement data and observation time intervals, and the INS data includes prior variations. Determining whether the first vehicle is at zero speed during the first time period based on the first scenario type and the change in the target position includes: When the first scenario type is a partial signal obstruction type, the position change between epochs is calculated, and the position change between epochs is the product of the velocity measurement data and the observation time interval; If the first difference is greater than or equal to a set test threshold, or if the second difference is less than or equal to a second product, it is determined whether the first vehicle is in a zero-speed state during the first time period based on the target position change and the set change threshold. The first difference is the difference between the position change between the epochs and the prior change, the second difference is the difference between the target position change and the prior change, and the second product is the product of a second set coefficient and the set test threshold.

7. The method as described in claim 6, characterized in that, The step of determining whether the first vehicle is at zero speed during the first time period based on the first scenario type and the change in the target position further includes: In the case where the first scenario type is a complete signal blockage type, or in the case where the first difference is less than the set test threshold and the second difference is greater than the second product, or in the case where TDCP calculation fails, the data of multiple inertial measurement units (IMUs) of the first vehicle in the first time period are obtained. Calculate a first quantity and a second quantity, wherein the first quantity is the number of third differences less than a first threshold among a plurality of third differences, the plurality of third differences being the differences between the plurality of IMU data and a second mean, the second mean being the mean of the plurality of IMU data, the first threshold being the product of a third set coefficient and a second standard deviation, the second standard deviation being the standard deviation of the plurality of IMU data, and the second quantity being the number of IMU data whose absolute value is less than a second threshold, the second threshold being the sum of the absolute value of the second mean and the first threshold; If the speed measurement data is less than a set speed threshold, the first ratio is greater than the first set ratio, and the second ratio is greater than the second set ratio, it is determined that the first vehicle is in a zero-speed state during the first time period. The first ratio is the ratio of the first quantity to the quantity of the plurality of IMU data, and the second ratio is the ratio of the second quantity to the quantity of the plurality of IMU data.

8. A zero-speed detection device, characterized in that, include: The acquisition module is used to acquire the Global Navigation Satellite System (GNSS) data and Inertial Navigation System (INS) data of the first vehicle within a first time period. The first calculation module is used to calculate M time differential carrier phase (TDCP) based on the GNSS data and the INS data. Each of the M TDCPs is used to characterize the difference between the GNSS data of different observation satellites in the current observation epoch and the previous observation epoch, where M is a positive integer greater than 1. The second calculation module is used to calculate the target position change based on the M TDCPs; The first determining module is used to determine the first scene type corresponding to the first vehicle based on the M TDCPs and the GNSS data; The second determining module is used to determine whether the first vehicle is in a zero-speed state during the first time period based on the first scene type and the change in the target position.

9. An electronic device, characterized in that, Including transceivers and processors, The transceiver is used to acquire GNSS data and INS data of the first vehicle within a first time period. The processor is used to calculate M time differential carrier phase (TDCP) based on the GNSS data and the INS data. Each of the M TDCPs is used to characterize the difference between the GNSS data of different observation satellites in the current observation epoch and the previous observation epoch, where M is a positive integer greater than 1. The processor is also used to calculate the target position change based on the M TDCPs; The processor is further configured to determine the first scene type corresponding to the first vehicle based on the M TDCPs and the GNSS data; The processor is further configured to determine whether the first vehicle is in a zero-speed state during the first time period based on the first scene type and the change in the target position.

10. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the zero-rate detection method as described in any one of claims 1 to 7.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the zero-speed detection method as described in any one of claims 1 to 7.

12. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the zero-speed detection method as described in any one of claims 1 to 7.

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