Data processing method and device, equipment and storage medium

Abstract

The invention provides a data processing method and device, equipment and a storage medium, and the method comprises the steps: obtaining a first speed data set and a second speed data set, and enabling the first speed data set to comprise speed data at a plurality of moments determined based on a first data detection module, the second speed data set comprises speed data at multiple moments determined based on the second data detection module, and the first data detection module and the second data detection module are different data detection modules; the first speed data set and the second speed data set are aligned, target data deviation is obtained, the target data deviation is data deviation between the first data detection module and the second data detection module, and the data deviation comprises time deviation; and according to the target data deviation, performing data deviation compensation on each piece of speed data in the target speed data set. According to the technical scheme, the speed data detected by different detection modes can be kept synchronous in time.

Description

Technical Field

[0001] This application relates to the field of data alignment, and particularly to data processing methods, devices, equipment, and storage media. Background Art

[0002] In the field of navigation, various different types of sensors are generally used for data acquisition, and then a multi-sensor fusion navigation algorithm is used to perform spatial positioning on a moving device, thereby achieving navigation.

[0003] In order to achieve accurate positioning of the device, multiple detection methods are usually used to separately detect the speed of the device, obtain multiple speed data of the speed, and combine the multiple speed data to comprehensively determine the actual speed of the device, thereby determining the position of the device based on the actual speed. For example, for a car, the actual speed of the car is usually comprehensively determined by combining the speed measured by the speed measurement module on the car and the speed measured based on the global positioning system (GPS). However, multiple speed data are usually obtained by data acquisition and calculation by different types of sensors and / or data acquisition modules, and the clocks and transmission delays of different sensors and / or data acquisition modules are different, which makes the speed data detected by multiple detection methods have a time offset. Summary of the Invention

[0004] This application provides a data processing method, device, equipment, and storage medium to solve the technical problem that multiple speed data detected by multiple detection methods have a time offset.

[0005] In a first aspect, a data processing method is provided, including:

[0006] Obtain a first speed data set and a second speed data set, where the first speed data set includes speed data at multiple moments determined based on a first data detection module, and the second speed data set includes the speed data at the multiple moments determined based on a second data detection module, and the first data detection module and the second data detection module are different data detection modules;

[0007] Align the first speed data set with the second speed data set to obtain a target data deviation, where the target data deviation is the data deviation between the first data detection module and the second data detection module, and the data deviation includes a time deviation;

[0008] According to the target data deviation, data deviation compensation is performed on each speed data in the target speed data set, so that each speed data in the target speed data set is synchronized with the speed data determined based on the first data detection module. The speed data in the target speed data set is the speed data determined based on the second data detection module. The target speed data set includes the second speed data set, and the first data detection module is a reference data detection module.

[0009] In this technical solution, by obtaining a first speed data set and a second speed data set, the first speed data set includes speed data at multiple moments determined based on a first data detection module, the second speed data set includes speed data at multiple moments determined based on a second data detection module, and the first data detection module and the second data detection module are different data detection modules; then the first speed data set and the second speed data set are aligned to obtain a target data deviation, where the target data deviation is the data deviation between the first data detection module and the second data detection module, and the data deviation includes a time deviation; finally, according to the target data deviation, data deviation compensation is performed on each speed data in the target speed data set, so that each speed data in the target speed data set is synchronized with the speed data detected by the reference data detection module; data deviation compensation is performed on the speed data detected by different data detection modules according to the data deviation, so that the speed data detected by different data detection modules is synchronized, and the time offset of the speed data detected by different detection methods can be compensated, so that the speed data detected by different detection methods is synchronized in time; by aligning the speed data sets detected based on different data detection modules to determine the data deviation between different data detection modules, the data deviation between different data detection modules can be determined in real time, ensuring the accuracy of the data deviation, and thus ensuring the accuracy of data synchronization.

[0010] Combined with the first aspect, in a possible implementation manner, aligning the first speed data set with the second speed data set to obtain a target data deviation includes: constructing an error calibration model regarding the data deviation, where the error calibration model is used to reflect the relationship between the data deviation and the target error, and the target error is the data error between the speed data determined based on different data detection modules; determining the optimal solution of the error calibration model according to the first speed data set and the second speed data set, where the optimal solution is the data deviation that minimizes the target error; and determining the optimal solution of the error calibration model as the target data deviation. By constructing an error calibration model and determining the optimal solution of the error calibration model to determine the data deviation between different data detection modules, the data deviation between different data detection modules can be accurately determined.

[0011] In combination with the first aspect, in a possible implementation manner, the determining the optimal solution of the error calibration model according to the first speed data set and the second speed data set includes: obtaining a preset data deviation set, where the preset data deviation set includes multiple groups of preset data deviations; in the preset data deviation set, taking a group of preset data deviations that makes the target error determined based on the first speed data set and the second speed data set the smallest as the initial solution of the error calibration model; based on the initial solution, the first speed data set, and the second speed data set, using an optimization method to solve the optimal solution of the error calibration model. By presetting multiple groups of preset data deviations, taking a group of preset data deviations that makes the error determined based on the two data sets the smallest as the initial solution of the error calibration model, and then calculating the optimal solution based on the initial solution of the error calibration model, it is beneficial to quickly determine the optimal solution of the error calibration model, thereby quickly determining the data deviation between different data detection modules.

[0012] In combination with the first aspect, in a possible implementation manner, the taking a group of preset data deviations that makes the target error determined based on the first speed data set and the second speed data set the smallest as the initial solution of the error calibration model in the preset data deviation set includes: according to the second speed data set and the first speed data set, determining a target comparison speed data set, where the target comparison speed data set is the comparison speed data set of the first speed data set when the data deviation is the target preset data deviation, the target preset data deviation is any one of the multiple groups of preset data deviations, the comparison speed data set includes comparison speed data corresponding to each speed data in the first speed data set, the comparison time corresponding to the comparison speed data is obtained based on the time corresponding to the speed data in the first speed data set and the data deviation, and the comparison speed data is obtained based on the speed data in the second speed data set; according to the first speed data set and the target comparison speed data set, determining the data error corresponding to the target preset data deviation, where the data error corresponding to the target preset data deviation is used to reflect the data error between the speed data in the first speed data set and the comparison speed data in the target comparison speed data set; among the data errors corresponding to the multiple groups of preset data deviations, determining the smallest data error, and taking the preset data deviation corresponding to the smallest data error as the initial solution of the error calibration model.

[0013] In combination with the first aspect, in a possible implementation manner, the determining the target comparison speed data set according to the second speed data set and the first speed data set includes: determining a first moment corresponding to the first speed data, where the first speed data is any speed data in the first speed data set, and the first moment is the moment corresponding to the first speed data; determining a first comparison moment according to the first moment and the time deviation in the target preset data deviation, where the first comparison moment is the comparison moment of the first moment; determining second speed data according to the second speed data set, where the second speed data is the speed data at the first comparison moment determined based on the second data detection module; and using the second speed data as the comparison speed data of the first speed data in the first speed data set when the data deviation is the target preset data deviation.

[0014] In combination with the first aspect, in a possible implementation manner, the determining the second data according to the second speed data set includes: determining the speed data at a second moment and the speed data at a third moment in the second speed data set, where the second moment is earlier than the first comparison moment and is the closest to the first comparison moment, and the third moment is later than the first comparison moment and is the closest to the first comparison moment; and performing data interpolation on the speed data at the second moment and the speed data at the third moment to obtain the second speed data. By performing data interpolation on the speed data at the two moments before and after the comparison moment in the speed data set to obtain the speed data at the comparison moment, the implementation manner is simple and accurate.

[0015] In combination with the first aspect, in a possible implementation, the speed data is angular velocity data, the first data detection module is a positioning sensor, and the second data detection module is an inertial sensor; obtaining the first speed data set and the second speed data set includes: obtaining a first detection data set and a second detection data set, where the first detection data set includes detection data detected by the first data detection module at the multiple moments, and the second detection data set includes detection data detected by the second data detection module at the multiple moments; performing quadratic fitting on the first detection data, the second detection data, and the third detection data in the first detection data set to obtain a quadratic fitting equation corresponding to the first detection data, where the first detection data is the detection data at the target moment in the first detection data set, the target moment is any moment, the second detection data is the detection data at the previous moment of the target moment, and the third detection data is the detection data at the next moment of the target moment; determining the derivative value of the quadratic fitting equation as the transformation data corresponding to the first detection data; replacing each detection data in the first detection data set with the transformation data corresponding to each detection data in the first detection data set to obtain the first speed data set; transforming each detection data in the second detection data set into the horizontal coordinate system to obtain the transformation data corresponding to each detection data in the second detection data set; replacing each detection data in the second detection data set with the transformation data corresponding to each detection data in the second detection data set to obtain the second speed data set. When the speed data is angular velocity data and the two data detection modules are a positioning sensor and an inertial sensor respectively, by performing preprocessing replacement on the data determined based on the positioning sensor and the inertial sensor, the time offset of the angular velocity can be aligned.

[0016] In combination with the first aspect, in a possible implementation, after obtaining the first speed data set and the second speed data set corresponding to the target object, it further includes: performing validity detection on the first speed data set and the second speed data set; aligning the first speed data set with the second speed data set to obtain a target data deviation, including: when both the first speed data set and the second speed data set are valid, aligning the first speed data set with the second speed data set to obtain a target data deviation. Detecting the validity of the data sets before aligning the two data sets and then aligning the two data sets when both data sets are valid can ensure the accuracy of the deviation.

[0017] In combination with the first aspect, in a possible implementation, the validity detection of the first speed data set and the second speed data set includes: calculating the standard deviation of the speed data in the third speed data set, where the third speed data set is the first speed data set or the second speed data set; if the standard deviation is greater than a preset standard deviation threshold, determining that the third speed data set is valid.

[0018] In a second aspect, a data processing device is provided, including:

[0019] A data acquisition module, configured to acquire a first speed data set and a second speed data set, where the first speed data set includes speed data at multiple moments determined based on a first data detection module, the second speed data set includes the speed data at the multiple moments determined based on a second data detection module, and the first data detection module and the second data detection module are different data detection modules;

[0020] An alignment module, configured to align the first speed data set with the second speed data set to obtain a target data deviation, where the target data deviation is the data deviation between the first data detection module and the second data detection module, and the data deviation includes a time deviation;

[0021] A deviation compensation module, configured to perform data deviation compensation on each speed data in a target speed data set according to the target data deviation, so that each speed data in the target speed data set is synchronized with the speed data determined based on the first data detection module, where the speed data in the target speed data set is the speed data determined based on the second data detection module, the target speed data set includes the second speed data set, and the first data detection module is a reference data detection module.

[0022] In a third aspect, a computer device is provided, including a memory and one or more processors, the memory is connected to the one or more processors, and the one or more processors are configured to execute one or more computer programs stored in the memory. When the one or more processors execute the one or more computer programs, the computer device implements the data processing method of the first aspect described above.

[0023] In a fourth aspect, a computer-readable storage medium is provided, and the computer-readable storage medium stores a computer program, where the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the data processing method of the first aspect described above.

[0024] The present application can achieve the following technical effects: compensating for the data deviation of the speed data detected by different data detection modules according to the data deviation, so that the speed data detected by different data detection modules are synchronized, compensating for the time offset of the speed data detected by different detection methods, and making the speed data detected by different detection methods synchronized in time; determining the data deviation between different data detection modules by aligning the speed data sets detected by different data detection modules, being able to determine the data deviation between different data detection modules in real time, ensuring the accuracy of the data deviation, and thus ensuring the accuracy of data synchronization. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a schematic flowchart of a data processing method provided by an embodiment of the present application;

[0027] Figure 2 It is a schematic diagram of a data curve corresponding to a speed data set provided by an embodiment of the present application;

[0028] Figure 3 It is a schematic flowchart of a method for roughly searching for data deviation within a data deviation range provided by an embodiment of the present application;

[0029] Figure 4 It is a schematic flowchart of another data processing method provided by an embodiment of the present application;

[0030] Figure 5 It is a schematic flowchart of a process for obtaining a first speed data set and a second speed data set provided by an embodiment of the present application;

[0031] Figure 6 It is a schematic structural diagram of a data processing device provided by an embodiment of the present application;

[0032] Figure 7 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to make the objectives, technical solutions and advantages of this application more clear, the following further elaborates on this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts fall within the protection scope of this application.

[0034] It should be noted that if there is no conflict, the various features in the embodiments of this application can be combined with each other and all fall within the protection scope of this application. In addition, although functional module division is carried out in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the order in the flowchart. Moreover, the terms "first", "second", "third", etc. used in this application do not limit the data and execution order, but only distinguish between identical or similar items with basically the same functions and effects.

[0035] The technical solution of this application is applicable to the navigation scenario. In the navigation scenario, in order to achieve accurate positioning of the device, usually multiple detection methods are used to detect the speed of the device respectively, and multiple speed data of the speed are obtained. The actual speed of the device is comprehensively determined by combining the multiple speed data, and then the position of the device is determined based on the actual speed. However, the multiple speed data are usually obtained by data collection and calculation through different types of sensors and / or data collection modules. The clocks and transmission delays of different sensors and / or data collection modules are different, which makes the speed data detected by multiple detection methods have a time offset. The existence of the time offset makes the speed data detected at the same moment by multiple detection methods vary greatly, affecting the accuracy of the actual speed determined by combining multiple speed data, and thus affecting the accuracy of the position of the device determined based on the actual speed, that is, affecting the accuracy of navigation.

[0036] In view of this, the present application proposes a data processing solution. By obtaining speed data sets determined based on different data detection modules, multiple speed data sets are obtained. Then, the multiple speed data sets are aligned to obtain the data deviation between different data detection modules, and based on the data deviation between the data of different data detection modules, data deviation compensation is performed on the speed data detected by different data detection modules, so that the speed data detected by different data detection modules is synchronized, thereby compensating for the time offset of the speed data detected by different detection methods and making the speed data detected by different detection methods synchronized in time. Since the speed data set can be obtained at any time, the data deviation between multiple data detection modules can be determined in real time, ensuring the accuracy of the data deviation, and thus ensuring the accuracy of speed synchronization. In addition, by aligning multiple speed data sets to obtain the data deviation between different data detection modules, only processing and calculation need to be performed at the software level, which is easy to implement.

[0037] Among them, the technical solution of the present application can be applied to a target computer device with multiple sensors. The target computer device can be, for example, an automobile with multiple sensors, a mobile robot with multiple sensors, and so on. The multiple sensors on the target computer device can include radar sensors, wheel sensors, positioning sensors (such as GPS sensors), inertial sensors (such as gyroscopes and accelerometers), etc., and are not limited to the examples here.

[0038] The technical solution of the present application is specifically introduced below.

[0039] See Figure 1 , Figure 1 which is a schematic flowchart of a data processing method provided by an embodiment of the present application. This method can be applied to the aforementioned target computer device, such as Figure 1 shown. The method includes the following steps:

[0040] S101, obtain a first speed data set and a second speed data set.

[0041] Here, the first speed data set includes speed data at multiple moments determined based on the first data detection module. Taking the speed data as vehicle speed data and the first data detection module as a GPS sensor as an example, the first speed data set includes vehicle speed data at multiple moments determined based on the GPS sensor. Based on the first speed data set, a speed data curve determined based on the first data detection module can be drawn. Exemplarily, the data curve of the vehicle speed drawn based on the first speed data set can be as shown in S1 in Figure 2 , Figure 2 where the abscissa represents time and the ordinate represents vehicle speed data.

[0042] The first speed data set can be expressed as (x1i , y 1i ), i = 0, 1, 2…, n1, x 1i represents the i-th moment in the first speed data set, y 1i represents the i-th speed data in the first speed data set, and the i-th speed data is the speed data at the i-th moment determined based on the first data detection module. n1 is the number of speed data included in the first speed data set.

[0043] The second speed data set includes data at multiple moments determined based on the second data detection module. Taking the speed data as the vehicle speed data and the second data detection module as the vehicle-mounted main control unit as an example, the second speed data set includes vehicle speed data at multiple moments determined based on the vehicle-mounted main control unit. Based on the second speed data set, a speed data curve determined based on the second data detection module can be plotted. Exemplarily, the data curve of the vehicle speed plotted based on the second speed data set can be as shown in Figure 2 S2 in

[0044] The second speed data set can be expressed as (x 2j , y 2j ), j = 0, 1, 2…, n2, x 2j represents the j-th moment in the second speed data set, y 2j represents the j-th data in the second speed data set, and the j-th data is the speed data at the j-th moment determined based on the second data detection module. n2 is the number of speed data included in the second speed data set.

[0045] Among them, the speed data at different moments can be obtained respectively through the data detection module for measuring speed in the target computer device, so as to obtain the first speed data set and the second speed data set.

[0046] S102. Align the first speed data set with the second speed data set to obtain the target data deviation.

[0047] Here, aligning the first speed data set with the second speed data set to obtain the target data deviation means comparing and analyzing the speed data in the first speed data set with the speed data in the second speed data set, determining the data deviation that enables the speed data curve corresponding to the first speed data set to coincide with the speed data curve corresponding to the second speed data set after data deviation transformation, and determining the data deviation that enables the speed data curve corresponding to the first speed data set to coincide with the speed data curve corresponding to the second speed data set after data deviation transformation as the target data deviation.

[0048] Taking the speed data curve corresponding to the first speed data set and the speed data curve corresponding to the second speed data set as shown in Figure 2 S1 and S2 inFigure 2 It can be seen that there are time deviations and vehicle speed amplitude deviations between the speed data curve S1 and the speed data curve S2. Then, the data deviation includes time deviation and amplitude deviation. Aligning the first speed data set and the second speed data set means comparing and analyzing the speed data in the first speed data set with the speed data in the second speed data set to find the time deviation and amplitude deviation that can make the speed data curve S1 coincide with the speed data curve S2 after the time deviation transformation and amplitude deviation transformation, that is, to find the time deviation and amplitude deviation that make the vehicle speed determined based on the GPS sensor (hereinafter referred to as the first vehicle speed) and the vehicle speed determined based on the vehicle-mounted main control unit (hereinafter referred to as the second vehicle speed) satisfy the following formulas (1) and (2):

[0049] v1(t) = v2(t + Δt) (1)

[0050] v2 = s * v1 (2)

[0051] Wherein, t represents the moment, v1 represents the first vehicle speed, v2 represents the second vehicle speed, Δt represents the time deviation, and s represents the amplitude deviation. Aligning the first speed data set with the second speed data set means finding Δt and s that make the speed data curve S1 and the speed data curve S2 satisfy formulas (1) and (2).

[0052] In a feasible implementation manner, the first speed data set and the second speed data set can be aligned through the following steps A1 - A3 to obtain the target data deviation:

[0053] A1. Construct an error calibration model regarding the data deviation.

[0054] Here, the error calibration model is used to reflect the relationship between the data deviation and the target error, and the target error is the data error between the speed data determined based on different data detection modules.

[0055] For example, the speed data is vehicle speed data, the first data detection module is the GPS sensor, the second data detection module is the vehicle-mounted main control unit, and the target error is the data error between the speed data determined based on the GPS sensor and the speed data determined based on the vehicle-mounted main control unit. The error calibration model is used to reflect the relationship between the data deviation and the target error, and from Figure 2 It can be seen that the data deviation includes time deviation and amplitude deviation. The error calibration model regarding time deviation and amplitude deviation (hereinafter referred to as error calibration model 1) can be constructed by combining the foregoing formulas (1) and (2) as follows:

[0056]

[0057] Wherein, W1 represents the target error, and K represents the total number of data.

[0058] It should be understood that since the types of speed data are different, the first data detection module and the second data detection module are different, and the error calibration model will also be different. The error calibration model can be constructed according to the actual situation, and this application does not make any restrictions. For example, if the speed data is angular velocity data, the first data detection module is a GPS sensor, and the second data detection module is an inertial sensor, and the data deviation between the angular velocities measured by the GPS sensor and the inertial sensor only includes a time deviation, then the error calibration model (hereinafter referred to as error calibration model 2) constructed for the data deviation is as follows:

[0059]

[0060] Wherein, W2 represents the target error, ω1 represents the angular velocity measured based on the GPS sensor, and ω2 represents the angular velocity measured based on the inertial sensor.

[0061] A2. Determine the optimal solution of the error calibration model according to the first speed data set and the second speed data set.

[0062] Here, the optimal solution of the error calibration model is the data deviation that minimizes the target error. For example, if the error calibration model is error calibration model 1, the optimal solution of the error calibration model is Δt and s that minimize W1; another example is that if the error calibration model is error calibration model 2, the optimal solution of the error calibration model is Δt that minimizes W2.

[0063] Among them, the initial solution of the error calibration model can be determined, and then based on the initial solution of the error calibration model, the first speed data set, and the second speed data set, an optimization method is used to solve the optimal solution of the error calibration model. The optimization method can be the gradient descent method, the Newton method, or the L-BFGS (large bfgs) algorithm, etc.

[0064] In the process of determining the initial solution of the error calibration model, any set of data deviations within the data deviation range can be used as the initial solution of the error calibration model. For example, if the speed data is angular velocity data, the first data detection module is a GPS sensor, the second data detection module is an inertial sensor, and the data deviation between the GPS sensor and the inertial sensor is a time deviation. Assuming that the time deviation range is -2.0 seconds to 2.0 seconds, then a value can be randomly selected from -2.0 seconds to 2.0 seconds as the initial solution of the error calibration model. Or, the data deviations within the data deviation range can also be roughly searched according to the first speed data set and the second speed data set, and a set of data deviations obtained from the rough search is used as the initial solution of the error calibration. The specific implementation process of the rough search will be specifically introduced in the corresponding Figure 3 embodiments, and will not be described in detail here.

[0065] A3. Determine the optimal solution of the error calibration model as the target data deviation.

[0066] By constructing an error calibration model and determining the optimal solution of the error calibration model to determine the data deviation between different data detection modules, the data deviation between different data detection modules can be accurately determined.

[0067] Optionally, other methods can also be used to align the first speed data set with the second speed data set to obtain the target data deviation, which is not limited in this application.

[0068] S103. According to the target data deviation, perform data deviation compensation on each speed data in the target speed data set so that each speed data in the target speed data set is synchronized with the speed data determined based on the first data detection module.

[0069] Among them, the first data detection module is the reference data detection module, and the time of the reference data detection module is regarded as the reference time.

[0070] The speed data in the target speed data set is the speed data determined based on the second data detection module. The target speed data set includes the second speed data set. In addition to the speed data in the second speed data set, the target speed data set may also include the speed data at multiple moments after the multiple moments corresponding to the second speed data set.

[0071] Performing data deviation compensation on each speed data in the target speed data set according to the target data deviation so that each speed data in the target speed data set is synchronized with the speed data determined based on the first data detection module means that, according to the time deviation in the target data deviation, adjust the moments of each speed data in the target speed data set so that the moments of the adjusted speed data are obtained by subtracting the time deviation from the moments of the speed data before adjustment; or, according to the time deviation and amplitude deviation in the target data deviation, adjust the moments of each speed data in the target speed data set so that the moments of the adjusted speed data are obtained by subtracting the time deviation from the moments of the speed data before adjustment, and adjust the amplitude deviation of each speed data in the target speed data set, adjust the amplitudes of each speed data in the target speed data set so that the amplitudes of the adjusted speed data are the quotient of the amplitudes of the speed data before adjustment and the amplitude deviation.

[0072] For example, the speed data is angular velocity data, the first data detection module is a GPS sensor, and the second data detection module is an inertial sensor. Assuming the target data deviation is Δt1 and the target speed data set is {(X1, Y1), (X2, Y2), (X3, Y3)...}, where X represents time and Y represents speed data, after performing data deviation compensation on each speed data in the target speed data set, the obtained target speed data set is {(X1 - Δt1, Y1), (X2 - Δt1, Y2), (X3 - Δt1, Y3)...}.

[0073] Another example is that the speed data is vehicle speed data, and the second data detection module is a vehicle-mounted main control unit. Assuming the target data deviations are Δt2 and s0, and the target speed data set is {(X1, Y1), (X2, Y2), (X3, Y3)...}, where X represents time and Y represents speed data, after performing data deviation compensation on each speed data in the target speed data set, the obtained target speed data set is {(X1 - Δt2, Y1 / s0), (X2 - Δt2, Y2 / s0), (X3 - Δt2, Y3 / s0)...}.

[0074] In the above Figure 1 corresponding technical solution, by obtaining a first speed data set and a second speed data set, the first speed data set includes speed data at multiple moments determined based on the first data detection module, the second speed data set includes speed data at multiple moments determined based on the second data detection module, and the first data detection module and the second data detection module are different data detection modules; then aligning the first speed data set and the second speed data set to obtain a target data deviation, the target data deviation being the data deviation between the first data detection module and the second data detection module, and the data deviation including a time deviation; finally, according to the target data deviation, performing data deviation compensation on each speed data in the target speed data set to synchronize each speed data in the target speed data set with the speed data detected by the reference data detection module; compensating the speed data detected by different data detection modules according to the data deviation, so that the speed data detected by different data detection modules is synchronized, which can compensate for the time offset of the speed data detected by different detection methods and make the speed data detected by different detection methods synchronized in time; by aligning the speed data sets detected by different data detection modules to determine the data deviation between different data detection modules, the data deviation between different data detection modules can be determined in real time, ensuring the accuracy of the data deviation and thus ensuring the accuracy of data synchronization.

[0075] See Figure 3 , Figure 3 which is a schematic flowchart of a method for coarsely searching for data deviations within a data deviation range provided by an embodiment of the present application. AsFigure 3 As shown in, it includes the following steps:

[0076] S21. Obtain a preset data deviation set.

[0077] Here, the preset data deviation set includes multiple groups of preset data deviations. If there is only one type of data deviation, one group of preset data deviations includes one data deviation; taking the speed data as the angular velocity data, the first data detection module as the GPS sensor, and the second data detection module as the inertial sensor as an example, since there is only a time offset between the angular velocity measured by the GPS sensor and the angular velocity measured by the inertial sensor, one group of preset data deviations is one time deviation, and the preset data deviation set includes multiple time deviations. If there are multiple types of data deviations, one group of preset data deviations includes multiple types of data deviations; taking the speed data as the vehicle speed data, the first data detection module as the GPS sensor, and the second data detection module as the vehicle-mounted main control unit as an example, since the data deviation includes a time deviation and an amplitude deviation, one group of preset data deviations includes a time deviation and an amplitude deviation.

[0078] Among them, the data deviation range between the first data detection module and the second data detection module can be obtained, the data deviation range between the first data detection module and the second data detection module is divided according to a preset division interval, and multiple groups of preset data deviations are determined according to the obtained data deviation endpoint values, so as to obtain a preset data deviation set. If there is only one type of data deviation, the obtained data deviation endpoint values can be determined as multiple groups of preset data deviations. Taking the speed data as angular velocity data, the first data detection module as a GPS sensor, and the second data detection module as an inertial sensor as an example, the data deviation is a time deviation. Assuming that the time deviation range is -2.0 s to 2.0 s and the preset division interval is 0.1 s, then -2.0 s to 2.0 s can be divided by 0.1 s to obtain multiple time endpoint values, which are -2.0 s, -1.9 s, -1.8 s, …, 1.9 s, 2.0 s respectively. Then the preset data deviation set is {-2.0, -1.9, -1.8, …, 1.9, 2.0}. If there are multiple types of data deviations, the obtained data deviation endpoint values of different types can be combined to obtain multiple groups of preset data deviations. Taking the speed data as vehicle speed data, the first data detection module as a GPS sensor, and the second data detection module as a vehicle-mounted main control unit as an example, the data deviation includes a time deviation and an amplitude deviation. Assuming that the time deviation range is -2.0 s to 2.0 s and the preset division interval is 0.1 s, then -2.0 s to 2.0 s can be divided by 0.1 s to obtain multiple time endpoint values, which are -2.0 s, -1.9 s, -1.8 s, …, 1.9 s, 2.0 s respectively. Assuming that the amplitude deviation range is 0.9 to 1.0 and the preset division interval is 0.01, then 0.9 to 1.0 can be divided by 0.01 to obtain multiple amplitude coefficient endpoint values, which are 0.9, 0.91, 0.92, …, 0.99, 1.0 respectively. By combining the time endpoint values and the amplitude coefficient endpoint values, 40 * 10 = 400 combinations can be obtained. The preset data deviation set composed of 400 combinations is {(0.9, -2.0), (0.9, -1.9), (0.9, -1.8), …, (1.0, 2.0)}.

[0079] S22. In the preset data deviation set, a group of preset data deviations that minimize the target error determined based on the first speed data set and the second speed data set is used as the initial solution of the error calibration model.

[0080] Here, a group of preset data deviations that minimize the target error determined based on the first speed data set and the second speed data set can be determined through the following steps B1 - B3:

[0081] B1. Determine a target comparison speed data set according to the second speed data set and the first speed data set.

[0082] Among them, the target control speed dataset is the control speed dataset of the first speed dataset when the data deviation is the target preset data deviation, and the target preset data deviation is any one of the multiple preset data deviations in the preset data deviation set.

[0083] The control speed dataset includes the control speed data corresponding to each speed data in the first speed dataset. The control moment corresponding to the control speed data is obtained based on the moment corresponding to the speed data in the first speed dataset and the data deviation, and the control speed data is obtained based on the speed data in the second speed dataset.

[0084] The target control speed dataset can be expressed as (x 3i , y 3i ), i = 0, 1, 2…, n3, where n3 is the number of control speed data included in the third speed dataset, and n3 = n1, that is, the number of control speed data included in the target control speed dataset is the same as the number of speed data included in the first speed dataset. x 3i represents the i-th control moment in the target control speed dataset, and x 3i is obtained based on the moment corresponding to the speed data in the first speed dataset and the target preset data deviation. y 3i represents the i-th control speed data in the target control speed dataset, and y 3i is obtained based on the speed data in the second speed dataset.

[0085] Among them, the target control speed dataset can be determined through the following steps B11 - B14:

[0086] B11. Determine the first moment corresponding to the first speed data.

[0087] Here, the first speed data is any speed data in the first speed dataset, the first moment is the moment corresponding to the first speed data, the first speed data can be expressed as the aforementioned y 1i , and the first moment can be expressed as the aforementioned x 1i .

[0088] B12. Determine the first control moment according to the first moment and the time deviation in the target preset data deviation.

[0089] Here, the first control moment refers to the moment obtained by delaying the first moment by the time deviation in the target preset data deviation. The first control moment can be expressed as the aforementioned x 3i , x 3i = x 1i + Δt h , where Δt h is the time deviation in the target preset data deviation.

[0090] B13. Determine the second velocity data according to the second velocity dataset.

[0091] Here, the second velocity data is the velocity data at the first reference time determined based on the second data detection module.

[0092] Among them, it is possible to determine whether there is velocity data at the first reference time in the second velocity dataset. If there is velocity data at the first reference time in the second velocity dataset, then the velocity data at the first reference time is determined as the second velocity data; if there is no velocity data at the first reference time in the second velocity dataset, then data interpolation can be performed on the second velocity dataset to obtain the second velocity data.

[0093] In a feasible implementation manner, data interpolation can be performed on the second velocity dataset through the following steps B121 - B122 to obtain the second velocity data:

[0094] B121. In the second velocity dataset, determine the velocity data at the second time and the velocity data at the third time.

[0095] Among them, the second time is earlier than the first reference time and is the closest to the first reference time, and the third time is later than the first reference time and is the closest to the first reference time. That is, the second time and the third time are the two times before and after the first reference time and are the closest to the first reference time. The second time and the third time can be represented as x 2j and x 2(j+1) , x 2j < x 3i < x 2(j+1) , and the velocity data at the second time and the velocity data at the third time can be represented as y 2j and y 2(j+1) .

[0096] B122. Perform data interpolation on the velocity data at the second time in the second velocity dataset and the velocity data at the third time in the second velocity dataset to obtain the second velocity data at the first reference time.

[0097] Among them, data interpolation can be performed on the velocity data at the second time in the second velocity dataset and the velocity data at the third time in the second velocity dataset through the following formula (3) to obtain the second data at the first reference time:

[0098]

[0099] By performing data interpolation on the velocity data at the two times before and after the reference time in the velocity dataset, the velocity data at the reference time is obtained, and the implementation method is simple and accurate.

[0100] In another feasible implementation, it is also possible to perform data fitting on the speed data in the second speed dataset to obtain the speed data curve corresponding to the second speed dataset, thereby realizing data interpolation for the second speed dataset; then, the data at the first comparison moment on the speed data curve corresponding to the second speed dataset is determined as the second speed data. For example, if the speed data curve corresponding to the second speed dataset is as shown by S2 in Figure 2 then the speed data corresponding to the first comparison moment on the speed data curve S2 can be determined as the second speed data. This application does not limit the specific method of data interpolation.

[0101] B14. Use the second speed data as the comparison speed data for the first speed data in the first speed dataset when the data deviation is the target preset data deviation.

[0102] By processing each speed data in the first speed dataset in the manner of steps B11 - B14 above, the comparison speed data for each speed data in the first speed dataset when the data deviation is the target preset data deviation can be obtained, thereby obtaining the target comparison speed dataset.

[0103] B2. Determine the data error corresponding to the target preset data deviation based on the first speed dataset and the target comparison speed dataset.

[0104] Here, the data error corresponding to the target preset data deviation is used to reflect the data error between the speed data in the first speed dataset and the comparison speed data in the target comparison speed dataset.

[0105] Among them, the speed data in the first speed dataset, the comparison speed data in the target comparison speed dataset, and the target preset data deviation can be substituted into the error calibration model to obtain the data error corresponding to the target preset data deviation.

[0106] Taking the error calibration model as the error calibration model 1 in step A1 above as an example, after determining the target comparison speed dataset, the amplitude deviation in the target preset data deviation can be substituted into s in the above error calibration model 1, the speed data y in the first speed dataset 1i is substituted into v1(t k ) in the above error calibration model 1, and the comparison speed data y in the target comparison speed dataset 3i is substituted into v2(t k +Δt) in the above error calibration model 1, and the data error corresponding to the target preset data deviation is calculated.

[0107] By determining the control speed data set corresponding to each preset data deviation in the preset data deviation set according to the above steps B1 - B2, and determining the data error corresponding to each preset data deviation, multiple groups of preset data deviations in the preset data deviation set can each obtain the corresponding data error.

[0108] B3. Among the data errors corresponding to multiple groups of preset data deviations in the preset data deviation set, determine the minimum data error, and determine the preset data deviation corresponding to the minimum data error as the initial solution of the error calibration model.

[0109] In the above Figure 3 corresponding technical solution, by presetting multiple groups of preset data deviations and using the group of preset data deviations that minimizes the error determined based on the two data sets as the initial solution of the error calibration model, it is beneficial to quickly determine the optimal solution of the error calibration model, thereby quickly determining the data deviation between different data detection modules.

[0110] See Figure 4 , Figure 4 which is a schematic flowchart of another data processing method provided by an embodiment of this application. This method can be applied to the aforementioned target computer device, as Figure 4 shown, and this method includes the following steps:

[0111] S301. Obtain the first speed data set and the second speed data set.

[0112] Here, for the specific implementation manner of step S301, reference can be made to the description of step S2O1 above, and details are not elaborated here.

[0113] S302. Perform validity detection on the first speed data set and the second speed data set.

[0114] Here, performing validity detection on the first speed data set and the second speed data set means detecting the fluctuation of the speed data in the first speed data set and the second speed data set to determine whether the speed data in the first speed data set and the second speed data set is dynamically changing. If the speed data in the first speed data set and the second speed data set remains static and forms two straight lines, the data deviation between the data detection modules cannot be determined, and the speed data in the first speed data set and the second speed data set is invalid; if the speed data in the first speed data set and the second speed data set is dynamically changing and forms two data curves, and the data deviation between the data detection modules can be determined, then the speed data in the first speed data set and the second speed data set is valid.

[0115] In a feasible implementation manner, for the first speed data set and the second speed data set, the validity detection can be performed through the following steps C1 - C3:

[0116] C1. Calculate the standard deviation of the speed data in the third speed data set.

[0117] Here, the third speed data set can be the first speed data set or the second speed data set.

[0118] Among them, the standard deviation of the speed data in the third speed data set can be calculated by the following formula (4):

[0119]

[0120] Among them, σ is the standard deviation, n is the number of speed data in the third speed data set, y i represents the i-th data in the third speed data set, represents the mean value of the speed data in the third speed data set.

[0121] C2. If the standard deviation of the speed data in the third speed data set is greater than the preset standard deviation threshold, determine that the third speed data set is valid.

[0122] Here, the standard deviation of the speed data in the third speed data set being greater than the preset standard deviation threshold indicates that the speed data in the third speed data set fluctuates greatly and the speed data in the third speed data set changes dynamically.

[0123] C3. If the standard deviation of the speed data in the third speed data set is less than or equal to the preset standard deviation threshold, determine that the third speed data set is invalid.

[0124] Here, the standard deviation of the speed data in the third speed data set being less than or equal to the preset standard deviation threshold indicates that the speed data in the third speed data set fluctuates little and the change range of the speed data in the third speed data set is not large.

[0125] Optionally, the variance between the first speed data set and the second speed data set can also be calculated to determine the validity of the first speed data set and the second speed data set, and this application does not make any restrictions.

[0126] S303. When both the first speed data set and the second speed data set are valid, align the first speed data set and the second speed data set to obtain the target data deviation.

[0127] Here, for the specific implementation method of aligning the first speed data set and the second speed data set, reference can be made to the description of the foregoing step S102, and details are not repeated here.

[0128] Among them, in the case where any one of the first speed dataset and the second speed dataset is invalid, new first speed dataset and second speed dataset can be re-obtained, and then the validity detection is performed again until valid first speed dataset and second speed dataset are obtained.

[0129] S304. According to the target data deviation, perform data deviation compensation on each speed data in the target speed dataset, so that each speed data in the target speed dataset is synchronized with the speed data determined based on the first data detection module.

[0130] Here, for the specific implementation manner of step S304, reference can be made to the description of the foregoing step S103, which will not be elaborated here.

[0131] In the above Figure 4 corresponding technical solution, after obtaining the first speed dataset and the second speed dataset corresponding to the target object, first perform validity detection on the first speed dataset and the second speed dataset. In the case where both the first speed dataset and the second speed dataset are valid, then align the first speed dataset and the second speed dataset, and perform data deviation compensation, which can ensure the accuracy of the compensation.

[0132] In some possible cases, the data directly detected by the two data detection modules cannot be directly aligned, and the data detected by the two data detection modules needs to be preprocessed and replaced so that the data is under the same data standard and then aligned. For example, the speed data is angular velocity data, the first data detection module is a positioning sensor (such as a GPS sensor), and the second data detection module is an inertial sensor. Since the data directly detected by the positioning sensor is the orientation angle of the device, and the inertial sensor detects the three-dimensional angular velocity of the device in the coordinate system of the inertial sensor, the data detected by the positioning sensor and the data detected by the inertial sensor need to be converted into the same data standard before alignment.

[0133] The following introduces the process of obtaining the foregoing first speed dataset and second speed dataset when the speed data is angular velocity data, the first data detection module is a positioning sensor, and the second data detection module is an inertial sensor.

[0134] See Figure 5 , Figure 5 which is a schematic flow chart for obtaining the first speed dataset and the second speed dataset provided by an embodiment of the present application, including the following steps:

[0135] S1011. Obtain a first detection dataset and a second detection dataset.

[0136] Here, the first detection data set is the data detected by the first data detection module at multiple moments. The first detection data set can be expressed as (x 4i , y 4i ), where i = 0, 1, 2..., n1, x 4i represents the i-th moment in the first detection data set, and y 4i represents the i-th data in the first detection data set. The i-th data is the orientation angle at the i-th moment, and n1 is the number of detection data included in the first detection data set.

[0137] The second detection data set is the data detected by the second data detection module at multiple moments; the second detection data set can be expressed as (x 5j , y 5j ), where j = 0, 1, 2..., n2, x 5j represents the j-th moment in the second detection data set, and y 5j represents the j-th data in the second detection data set. The j-th data is the angular velocity in the sensor coordinate system at the j-th moment, and n2 is the number of detection data included in the second detection data set.

[0138] S1012. Perform quadratic fitting on the first detection data, the second detection data, and the third detection data in the first detection data set to obtain the quadratic fitting equation corresponding to the first detection data.

[0139] The first detection data is the detection data at the target moment in the first detection data set, the target moment is any moment, the second detection data is the detection data at the previous moment of the target moment, and the third detection data is the detection data at the next moment of the target moment. The first detection data, the second detection data, and the third detection data can be expressed as (x 4(i-1) , y 4(i-1) ), (x 4i , y 4i ), (x 4(i+1) , y 4(i+1) ), respectively.

[0140] Performing quadratic fitting on the first detection data, the second detection data, and the third detection data in the first detection data set to obtain the quadratic fitting equation corresponding to the first detection data means solving the data curve passing through the first detection data, the second detection data, and the third detection data, and determining the quadratic equation corresponding to the data curve as the quadratic fitting equation corresponding to the first detection data.

[0141] In a feasible implementation manner, the analytical formula of the quadratic function can be constructed. The analytical formula of the quadratic function is: y = a * x 2+ b * x + c; Then substitute the first detection data, the second detection data, and the third detection data into the analytical formula of the quadratic function to obtain a system of three linear equations about a, b, and c. The system of three linear equations is as follows:

[0142]

[0143] Finally, solve the system of three linear equations to obtain the coefficients a, b, and c of the quadratic function, thereby obtaining the quadratic fitting equation corresponding to the first detection data.

[0144] S1013, Determine the derivative value of the quadratic fitting equation corresponding to the first detection data as the transformation data corresponding to the first detection data.

[0145] Since the quadratic fitting equation corresponding to the first detection data is y = a * x 2 + b * x + c, after taking the derivative of the quadratic fitting equation corresponding to the first detection data, the derivative function of the first detection data y = 2a * x + b can be obtained; then substitute the target time x 4i into the derivative function of the first detection data to obtain the derivative value of the first detection data as y 1i = 2a * x 4i + b. The derivative value y 1i of the first detection data is the angular velocity at the target time. The transformation data corresponding to the first detection data can be expressed as (x 1i , y 1i ), where x 1i = x 4i .

[0146] Process each detection data in the first detection data set according to the above steps S1012 to S1 - 1013 to obtain the transformation data corresponding to each detection data in the first detection data set.

[0147] S1014, Replace each detection data in the first detection data set with the transformation data corresponding to each detection data in the first detection data set to obtain the first velocity data set.

[0148] S1015, Transform each detection data in the second detection data set into the horizontal coordinate system to obtain the transformation data corresponding to each detection data in the second detection data set.

[0149] Here, the horizontal coordinate system refers to the plane coordinate system perpendicular to the direction of gravity. Each detection data in the second detection data set can be transformed into the horizontal coordinate system through the following formula (5) to obtain the transformation data corresponding to each detection data:

[0150] y 2j = (Ry 5j ) z (5)

[0151] where R is the rotation matrix of the coordinate system of the inertial sensor relative to the horizontal coordinate system, and y 5j represents the velocity data in the second velocity data set, and y 2j represents the transformation data corresponding to the detection data in the second detection data set, and y 2j is obtained by transforming y 5j through the rotation matrix R and then taking the z-axis component.

[0152] The calculation formula of the rotation matrix R can be obtained through formulas (6) and (7):

[0153]

[0154]

[0155] where a x is the x-axis component of the acceleration detected by the accelerometer of the inertial sensor.

[0156] S1016. Replace each detection data in the second detection data set with the transformation data corresponding to each detection data in the second detection data set to obtain the second velocity data set.

[0157] where the second velocity data set is represented as (x 2j , y 2j ), j = 0, 1, 2..., n2, and x 2j = x 5j .

[0158] In the above Figure 5 corresponding technical solution, after obtaining the first detection data set and the second detection data set, by performing transformation processing on the first detection data set and the second detection data set, the first detection data set and the second detection data set are transformed to the same data standard to obtain the first velocity data set and the second velocity data set, which can align the angular velocity under the same data standard and obtain the time offset.

[0159] The above introduces the method of the present application. Next, the device of the present application will be introduced.

[0160] See Figure 6 , Figure 6 which is a schematic structural diagram of a data processing device provided by an embodiment of the present application. As Figure 6 shown, the data processing device 40 includes:

[0161] A data acquisition module 401, configured to acquire a first speed data set and a second speed data set. The first speed data set includes speed data at multiple moments determined based on a first data detection module, and the second speed data set includes the speed data at the multiple moments determined based on a second data detection module. The first data detection module and the second data detection module are different data detection modules.

[0162] An alignment module 402, configured to align the first speed data set with the second speed data set to obtain a target data deviation. The target data deviation is the data deviation between the first data detection module and the second data detection module, and the data deviation includes a time deviation.

[0163] A deviation compensation module 403, configured to perform data deviation compensation on each speed data in a target speed data set according to the target data deviation, so that each speed data in the target speed data set is synchronized with the speed data determined based on the first data detection module. The speed data in the target speed data set is the speed data determined based on the second data detection module, and the target speed data set includes the second speed data set. The first data detection module is a reference data detection module.

[0164] In a possible design, the above alignment module 402 is specifically configured to: construct an error calibration model regarding the data deviation, where the error calibration model is used to reflect the relationship between the data deviation and a target error, and the target error is the data error between the speed data determined based on different data detection modules; determine an optimal solution of the error calibration model according to the first speed data set and the second speed data set, where the optimal solution is the data deviation that minimizes the target error; and determine the optimal solution of the error calibration model as the target data deviation.

[0165] In a possible design, the above alignment module 402 is specifically configured to: obtain a preset data deviation set, where the preset data deviation set includes multiple groups of preset data deviations; in the preset data deviation set, use a group of preset data deviations that minimizes the target error determined based on the first speed data set and the second speed data set as an initial solution of the error calibration model; and solve the optimal solution of the error calibration model by using an optimization method based on the initial solution, the first speed data set, and the second speed data set.

[0166] In a possible design, the above alignment module 402 is specifically configured to: determine a target reference speed data set according to the second speed data set and the first speed data set, where the target reference speed data set is a reference speed data set of the first speed data set when the data deviation is a target preset data deviation, the target preset data deviation is any one of the multiple groups of preset data deviations, the reference speed data set includes reference speed data corresponding to each speed data in the first speed data set, the reference time corresponding to the reference speed data is obtained based on the time corresponding to the speed data in the first speed data set and the data deviation, and the reference speed data is obtained based on the speed data in the second speed data set; determine the data error corresponding to the target preset data deviation according to the first speed data set and the target reference speed data set, where the data error corresponding to the target preset data deviation is used to reflect the data error between the speed data in the first speed data set and the reference speed data in the target reference speed data set; determine the minimum data error among the data errors corresponding to the multiple groups of preset data deviations, and determine the preset data deviation corresponding to the minimum data error as the initial solution of the error calibration model.

[0167] In a possible design, the above alignment module 402 is specifically configured to: determine a first time corresponding to a first speed data, where the first speed data is any speed data in the first speed data set, and the first time is the time corresponding to the first speed data; determine a first reference time according to the first time and the time deviation in the target preset data deviation, where the first reference time is the reference time of the first time; determine a second speed data according to the second speed data set, where the second speed data is the speed data at the first reference time determined based on the second data detection module; use the second speed data as the reference speed data of the first speed data in the first speed data set when the data deviation is the target preset data deviation.

[0168] In a possible design, the above alignment module 402 is specifically configured to: determine the data at a second time and the data at a third time in the second speed data set, where the second time is earlier than the first reference time and is the closest to the first reference time, and the third time is later than the first reference time and is the closest to the first reference time; perform data interpolation on the data at the second time and the data at the third time to obtain second data.

[0169] In a possible design, the speed data is angular velocity data, the first data detection module is a positioning sensor, and the second data detection module is an inertial sensor; specifically, the data acquisition module 401 is configured to acquire a first detection data set and a second detection data set, where the first detection data set includes detection data detected by the first data detection module at the multiple moments, and the second detection data set includes detection data detected by the second data detection module at the multiple moments; perform quadratic fitting on the first detection data, the second detection data, and the third detection data in the first detection data set to obtain a quadratic fitting equation corresponding to the first detection data, where the first detection data is the detection data at a target moment in the first detection data set, the target moment is any moment, the second detection data is the detection data at the previous moment of the target moment, and the third detection data is the detection data at the next moment of the target moment; determine the derivative value of the quadratic fitting equation as the transformed data corresponding to the first detection data; replace each detection data in the first detection data set with the transformed data corresponding to each detection data in the first detection data set to obtain the first speed data set; transform each detection data in the second detection data set into a horizontal coordinate system to obtain the transformed data corresponding to each detection data in the second detection data set; replace each detection data in the second detection data set with the transformed data corresponding to each detection data in the second detection data set to obtain the second speed data set.

[0170] In a possible design, the alignment module 402 is further configured to: perform validity detection on the first speed data set and the second speed data set; and align the first speed data set and the second speed data set to obtain a target data deviation when both the first speed data set and the second speed data set are valid.

[0171] In a possible design, the alignment module 402 is specifically configured to: calculate the standard deviation of the speed data in a third speed data set, where the third speed data set is the first speed data set or the second speed data set; and determine that the third speed data set is valid if the standard deviation is greater than a preset standard deviation threshold.

[0172] It should be noted that Figure 6 For the content not mentioned in the corresponding embodiments, reference may be made to the description of the foregoing method embodiments, which will not be elaborated here.

[0173] The above device obtains a first speed data set and a second speed data set. The first speed data set includes speed data at multiple moments determined based on a first data detection module, and the second speed data set includes speed data at multiple moments determined based on a second data detection module. The first data detection module and the second data detection module are different data detection modules. Then, the first speed data set and the second speed data set are aligned to obtain a target data deviation, where the target data deviation is the data deviation between the first data detection module and the second data detection module, and the data deviation includes a time deviation. Finally, based on the target data deviation, data deviation compensation is performed on each speed data in the target speed data set so that each speed data in the target speed data set is synchronized with the speed data detected by a reference data detection module. By performing data deviation compensation on the speed data detected by different data detection modules according to the data deviation, the speed data detected by different data detection modules can be synchronized, compensating for the time offset of the speed data detected by different detection methods and making the speed data detected by different detection methods synchronized in time. By aligning the speed data sets detected based on different data detection modules to determine the data deviation between different data detection modules, the data deviation between different data detection modules can be determined in real time, ensuring the accuracy of the data deviation and thus ensuring the accuracy of data synchronization.

[0174] See Figure 7 , Figure 7 FIG. Figure 7 is a schematic structural diagram of a computer device provided by an embodiment of the present application. The computer device 50 includes a processor 501, a memory 502, and a sensor 503. The memory 502 and the sensor 503 are connected to the processor 501, for example, connected to the processor 501 through a bus.

[0175] The processor 501 is configured to support the computer device 50 in performing the corresponding functions in the methods in the foregoing method embodiments. The processor 501 may be a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof. The foregoing hardware chip may be an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The foregoing PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0176] The memory 502 is used to store program codes, etc. The memory 502 may include a volatile memory (VM), such as a random access memory (RAM); the memory 502 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); the memory 502 may further include a combination of the foregoing types of memories.

[0177] There are various types of sensors 503, and the sensors 503 include, but are not limited to, the following sensors: radar sensors, wheel sensors, positioning sensors (such as GPS sensors), and inertial sensors (such as gyroscopes and accelerometers).

[0178] The processor 501 may call the program code to perform the following operations:

[0179] Obtain a first speed data set and a second speed data set, where the first speed data set includes speed data at multiple moments determined based on a first data detection module, the second speed data set includes the speed data at the multiple moments determined based on a second data detection module, and the first data detection module and the second data detection module are different data detection modules;

[0180] Align the first speed dataset with the second speed dataset to obtain a target data deviation, where the target data deviation is the data deviation between the first data detection module and the second data detection module, and the data deviation includes a time deviation.

[0181] According to the target data deviation, perform data deviation compensation on each speed data in the target speed dataset, so that each speed data in the target speed dataset is synchronized with the speed data determined based on the first data detection module. The speed data in the target speed dataset is the speed data determined based on the second data detection module, and the target speed dataset includes the second speed dataset. The first data detection module is the reference data detection module.

[0182] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the method described in the foregoing embodiment.

[0183] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above embodiments of the methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0184] The above-disclosed are only the preferred embodiments of the present application. Of course, the scope of the rights of the present application cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present application still fall within the scope covered by the present application.

Claims

1. A data processing method, characterized in that, Including: Obtain a first speed data set and a second speed data set. The first speed data set includes speed data at multiple moments determined based on a first data detection module, and the second speed data set includes the speed data at the multiple moments determined based on a second data detection module. The first data detection module and the second data detection module are different data detection modules; Align the first speed data set with the second speed data set to obtain a target data deviation. The target data deviation is the data deviation between the first data detection module and the second data detection module, and the data deviation includes a time deviation; According to the target data deviation, perform data deviation compensation on each speed data in the target speed data set, so that each speed data in the target speed data set is synchronized with the speed data determined based on the first data detection module. The speed data in the target speed data set is the speed data determined based on the second data detection module. The target speed data set includes the second speed data set, and the first data detection module is the reference data detection module.

2. The method according to claim 1, characterized in that The step of aligning the first speed data set with the second speed data set to obtain a target data deviation includes: Construct an error calibration model regarding the data deviation. The error calibration model is used to reflect the relationship between the data deviation and the target error, and the target error is the data error between the speed data determined based on different data detection modules; According to the first speed data set and the second speed data set, determine the optimal solution of the error calibration model. The optimal solution is the data deviation that minimizes the target error; Determine the optimal solution of the error calibration model as the target data deviation.

3. The method according to claim 2, characterized in that, The step of determining the optimal solution of the error calibration model according to the first speed data set and the second speed data set includes: Obtain a preset data deviation set, and the preset data deviation set includes multiple groups of preset data deviations; In the preset data deviation set, use a group of preset data deviations that minimizes the target error determined based on the first speed data set and the second speed data set as the initial solution of the error calibration model; Based on the initial solution, the first speed data set, and the second speed data set, use an optimization method to solve the optimal solution of the error calibration model.

4. The method according to claim 3, characterized in that, The step of using, in the preset data deviation set, a group of preset data deviations that minimizes the target error determined based on the first speed data set and the second speed data set as the initial solution of the error calibration model includes: Determine a target reference speed dataset according to the second speed dataset and the first speed dataset, where the target reference speed dataset is the reference speed dataset of the first speed dataset when the data deviation is a target preset data deviation, the target preset data deviation is any one of the multiple groups of preset data deviations, the reference speed dataset includes reference speed data corresponding to each speed data in the first speed dataset, the reference time corresponding to the reference speed data is obtained based on the time corresponding to the speed data in the first speed dataset and the data deviation, and the reference speed data is obtained based on the speed data in the second speed dataset; Determine the data error corresponding to the target preset data deviation according to the first speed dataset and the target reference speed dataset, where the data error corresponding to the target preset data deviation is used to reflect the data error between the speed data in the first speed dataset and the reference speed data in the target reference speed dataset; Among the data errors corresponding to the multiple groups of preset data deviations, determine the minimum data error, and determine the preset data deviation corresponding to the minimum data error as the initial solution of the error calibration model.

5. The method according to claim 4, characterized in that, The determining of the target reference speed dataset according to the second speed dataset and the first speed dataset includes: Determine a first time corresponding to the first speed data, where the first speed data is any speed data in the first speed dataset, and the first time is the time corresponding to the first speed data; Determine a first reference time according to the first time and the time deviation in the target preset data deviation, where the first reference time is the reference time of the first time; Determine second speed data according to the second speed dataset, where the second speed data is the speed data at the first reference time determined based on the second data detection module; Use the second speed data as the reference speed data of the first speed data in the first speed dataset when the data deviation is the target preset data deviation.

6. The method according to claim 5, wherein The determining of the second speed data according to the second speed dataset includes: In the second speed dataset, determine the speed data at a second time and the speed data at a third time, where the second time is earlier than the first reference time and is the closest to the first reference time, and the third time is later than the first reference time and is the closest to the first reference time; Perform data interpolation on the speed data at the second time and the speed data at the third time to obtain the second speed data.

7. The method according to any one of claims 1-6, characterized in that, The speed data is angular velocity data, the first data detection module is a positioning sensor, and the second data detection module is an inertial sensor; The obtaining of the first speed dataset and the second speed dataset includes: Obtain a first detection dataset and a second detection dataset, where the first detection dataset includes the detection data detected by the first data detection module at the multiple times, and the second detection dataset includes the detection data detected by the second data detection module at the multiple times; Perform quadratic fitting on the first detection data, the second detection data, and the third detection data in the first detection data set to obtain a quadratic fitting equation corresponding to the first detection data. The first detection data is the detection data at the target time in the first detection data set, the target time is any time, the second detection data is the detection data at the previous time of the target time, and the third detection data is the detection data at the next time of the target time; Determine the derivative value of the quadratic fitting equation as the transformation data corresponding to the first detection data; Replace each detection data in the first detection data set with the transformation data corresponding to each detection data in the first detection data set to obtain the first speed data set; Transform each detection data in the second detection data set to the horizontal coordinate system to obtain the transformation data corresponding to each detection data in the second detection data set; Replace each detection data in the second detection data set with the transformation data corresponding to each detection data in the second detection data set to obtain the second speed data set.

8. The method according to any one of claims 1-6, characterized in that After obtaining the first speed data set and the second speed data set corresponding to the target object, it further includes: Perform validity detection on the first speed data set and the second speed data set; The alignment of the first speed data set and the second speed data set to obtain the target data deviation includes: When both the first speed data set and the second speed data set are valid, align the first speed data set and the second speed data set to obtain the target data deviation.

9. The method according to claim 8, characterized in that, The validity detection of the first speed data set and the second speed data set includes: Calculate the standard deviation of the speed data in the third speed data set, where the third speed data set is the first speed data set or the second speed data set; If the standard deviation is greater than the preset standard deviation threshold, determine that the third speed data set is valid.

10. A data processing device, characterized in that, It includes: A data acquisition module for acquiring a first speed data set and a second speed data set. The first speed data set includes speed data at multiple times determined based on a first data detection module, and the second speed data set includes the speed data at the multiple times determined based on a second data detection module. The first data detection module and the second data detection module are different data detection modules; An alignment module for aligning the first speed data set and the second speed data set to obtain a target data deviation, where the target data deviation is the data deviation between the first data detection module and the second data detection module, and the data deviation includes a time deviation; The deviation compensation module is configured to perform data deviation compensation on each speed data in the target speed dataset according to the target data deviation, so that each speed data in the target speed dataset is synchronized with the speed data determined based on the first data detection module. The speed data in the target speed dataset is the speed data determined based on the second data detection module. The target speed dataset includes the second speed dataset, and the first data detection module is the reference data detection module.

11. A computer device, characterized in that, It includes a memory, a processor, and a sensor. The memory and the sensor are connected to the processor. The processor is configured to execute one or more computer programs stored in the memory. When the processor executes the one or more computer programs, the computer device implements the method according to any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by the processor, the processor is caused to execute the method according to any one of claims 1-9.

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