Data processing method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a data processing method and device, equipment and a storage medium. The method comprises the following steps: acquiring a first data set of a target motion track; calculating a trajectory distance between the first data set and a second data set of a preset standard trajectory; and determining the accuracy of the target motion track according to the track distance. Through the embodiment of the invention, the accuracy of the motion trail can be evaluated, and convenience is provided for performance evaluation of the mobile terminal with the motion trail function.

Description

Technical Field

[0001] The present disclosure relates to the field of information processing, and in particular to a data processing method, device, equipment and storage medium. Background Art

[0002] As the functions of smart electronic devices become increasingly powerful, various functions that assist in recording users' lives and sports are widely used in various smart electronic devices. For example, navigation functions, sports route functions, etc. These applications that need to record the actual location and movement trajectory of users need to achieve accurate positioning and trajectory recording. Usually, these functions require satellite positioning using satellite signals. However, when the satellite signal is weak or other problems occur, trajectory drift and other issues remain technical challenges in this field.

[0003] Therefore, seeking a method that can accurately and quantitatively evaluate the accuracy of motion trajectories is an important research direction in this field. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present disclosure provides a data processing method, device, equipment and storage medium to overcome the problem that the existing motion trajectory drift is difficult to accurately evaluate.

[0005] According to a first aspect of an embodiment of the present disclosure, there is provided a data processing method, including:

[0006] Acquire a first data set of target motion trajectory;

[0007] Calculating a trajectory distance between the first data set and a second data set of a preset standard trajectory;

[0008] The accuracy of the target motion trajectory is determined according to the trajectory distance.

[0009] In some embodiments, the first data set includes n first data; the second data set includes m second data; both m and n are positive integers; and the calculation of the trajectory distance between the first data set and the second data set of the preset standard trajectory includes:

[0010] Determine a first distance between an i-th first data in the first data set and a j-th second data in the second data set; wherein i is a positive integer less than or equal to n; and j is a positive integer less than or equal to m;

[0011] Determine, according to the sum of the first distance and the minimum distance, a second distance corresponding to the i-th first data and the j-th second data; wherein the minimum distance includes: a minimum value of the second distance corresponding to the i-1-th first data and the j-th second data, the second distance corresponding to the i-th first data and the j-1-th second data, and the second distance corresponding to the i-1-th first data and the j-1-th second data;

[0012] A second distance corresponding to the nth first data and the mth second data is determined as the track distance.

[0013] In some embodiments, determining a first distance between an i-th first data in the first data set and a j-th second data in the second data set includes:

[0014] Determine the earth coordinates of the first data and the second data;

[0015] The first distance is determined according to the earth coordinates corresponding to the i-th first data, the earth coordinates corresponding to the j-th second data, and the radius of the earth.

[0016] In some embodiments, the earth coordinates include: a longitude value and a latitude value;

[0017] The first distance is the distance between a first position indicated by the earth coordinates corresponding to the first data and a second position indicated by the earth coordinates corresponding to the second data.

[0018] In some embodiments, the method further comprises:

[0019] Normalizing the trajectory distance to obtain a normalized trajectory distance;

[0020] Determining the accuracy of the target motion trajectory according to the trajectory distance includes:

[0021] The accuracy of the target motion trajectory is determined according to the normalized trajectory distance.

[0022] In some embodiments, the normalizing the trajectory distance to obtain the normalized trajectory distance includes:

[0023] The ratio of the trajectory distance to the path length of the target motion trajectory is calculated to obtain the normalized trajectory distance.

[0024] In some embodiments, the method further comprises:

[0025] Determining a score value of the target motion trajectory according to the normalized distance;

[0026] The step of determining the accuracy of the target motion trajectory according to the normalized trajectory distance comprises:

[0027] The accuracy of the target motion trajectory is determined according to the score value.

[0028] According to a second aspect of an embodiment of the present disclosure, there is provided a data processing device, including:

[0029] An acquisition module configured to acquire a first data set of a target motion trajectory;

[0030] A calculation module, configured to calculate a trajectory distance between the first data set and a second data set of a preset standard trajectory;

[0031] The determination module is configured to determine the accuracy of the target motion trajectory according to the trajectory distance.

[0032] In some embodiments, the first data set includes n first data; the second data set includes m second data; both m and n are positive integers;

[0033] The computing module comprises:

[0034] A first determination submodule is configured to determine a first distance between an i-th first data in the first data set and a j-th second data in the second data set; wherein i is a positive integer less than or equal to n; and j is a positive integer less than or equal to m;

[0035] A second determination submodule is configured to determine a second distance corresponding to the i-th first data and the j-th second data according to the sum of the first distance and the minimum distance; wherein the minimum distance includes: a minimum value of the second distance corresponding to the i-1-th first data and the j-th second data, the second distance corresponding to the i-th first data and the j-1-th second data, and the second distance corresponding to the i-1-th first data and the j-1-th second data;

[0036] The third determining submodule is configured to determine that the second distance corresponding to the nth first data and the mth second data is the trajectory distance.

[0037] In some embodiments, the first determining submodule is specifically configured as follows:

[0038] Determine the earth coordinates of the first data and the second data;

[0039] The first distance is determined according to the earth coordinates corresponding to the i-th first data, the earth coordinates corresponding to the j-th second data, and the radius of the earth.

[0040] In some embodiments, the earth coordinates include: a longitude value and a latitude value;

[0041] The first distance is the distance between a first position indicated by the earth coordinates corresponding to the first data and a second position indicated by the earth coordinates corresponding to the second data.

[0042] In some embodiments, the apparatus further comprises:

[0043] A normalization module, configured to perform normalization processing on the track distance to obtain a normalized track distance;

[0044] The determination module is specifically configured as follows:

[0045] The accuracy of the target motion trajectory is determined according to the normalized trajectory distance.

[0046] In some embodiments, the normalization module is specifically configured as follows:

[0047] The ratio of the trajectory distance to the path length of the target motion trajectory is calculated to obtain the normalized trajectory distance.

[0048] In some embodiments, the apparatus further comprises:

[0049] A scoring module, configured to determine a score value of the target motion trajectory according to the normalized distance;

[0050] The determination module is specifically configured as follows:

[0051] The accuracy of the target motion trajectory is determined according to the score value.

[0052] According to a third aspect of an embodiment of the present disclosure, there is provided a data processing device, comprising: a processor; and a memory for storing processor executable instructions; wherein the processor is configured to: implement the steps in any one of the above-mentioned data processing methods when executed.

[0053] According to a fourth aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided, and when instructions in the storage medium are executed by a processor of an information processing device, the device is enabled to perform the steps in any one of the above-mentioned data processing methods.

[0054] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0055] The disclosed embodiment provides a data processing method, through which a first data set of a target motion trajectory and a second data set of a standard trajectory are processed to obtain a trajectory distance, and then the accuracy of the target motion trajectory is determined by using the trajectory distance. In this way, it is convenient and accurate to determine whether the target motion trajectory has trajectory drift, thereby reducing errors.

[0056] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0058] Figure 1 The data processing method according to an exemplary embodiment is shown in FIG. Figure 1 ;

[0059] Figure 2 is a schematic diagram of the principle of processing trajectories using a dynamic time warping (DTW) algorithm according to an exemplary embodiment;

[0060] Figure 3 The data processing method according to an exemplary embodiment is shown in FIG. Figure 2 ;

[0061] Figure 4 The data processing method according to an exemplary embodiment is shown in FIG. Figure 3 ;

[0062] Figure 5 is a structural block diagram of a data processing device according to an exemplary embodiment;

[0063] Figure 6 A hardware structure frame of a data processing device according to an exemplary embodiment is shown Figure 1 ;

[0064] Figure 7 A hardware structure frame of a data processing device according to an exemplary embodiment is shown Figure 2 . DETAILED DESCRIPTION

[0065] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0066] The following is a detailed description:

[0067] Figure 1 is a flow chart of a data processing method according to an exemplary embodiment. Figure 1 As shown, the method includes:

[0068] In step S101, a first data set of target motion trajectory is obtained;

[0069] In step S102, a trajectory distance between the first data set and a second data set of a preset standard trajectory is calculated;

[0070] In step S103, the accuracy of the target motion trajectory is determined according to the trajectory distance.

[0071] In the embodiments of the present disclosure, the above-mentioned target motion trajectory can be sampled and acquired by a mobile terminal during the motion process. The mobile terminal here can be a mobile phone, a smart watch, a smart bracelet, and other terminals with positioning, navigation, motion trajectory and other functions.

[0072] By sampling the positioning of the mobile terminal during the movement, the position information is obtained, and a first data set consisting of multiple position information can be obtained. Therefore, the first data contained in the first data set is: discrete data with position information. It can be understood that the above-mentioned target motion trajectory is obtained by connecting these positions in sequence.

[0073] The above-mentioned location information may be coordinate information, for example, XY coordinates corresponding to a plane map, or longitude and latitude coordinates or coordinates customized by the corresponding terminal.

[0074] The standard trajectory may be a preset trajectory corresponding to the same route as the target motion trajectory, which may be acquired by a verified standard device during motion, or may be directly acquired using a map route, that is, by drawing a route on a map to obtain the standard trajectory. In the disclosed embodiment, the standard trajectory may be considered to be an accurate trajectory without trajectory drift, and therefore, by comparing the target motion trajectory with the standard trajectory, it may be determined whether the target motion trajectory is accurate and whether trajectory drift occurs.

[0075] In the disclosed embodiment, the second data set of the standard trajectory may also be composed of discrete data with location information. It should be noted that the data in the first data set and the second data set need to have the same dimension and belong to the same data type. For example, the data of both may be XY coordinates of a plane map, or may be longitude and latitude coordinates, etc.

[0076] Exemplarily, the disclosed embodiment may use the DTW algorithm to calculate the trajectory distance between the first data set and the second data set of the preset standard trajectory. Based on the characteristics of the DTW algorithm, the number of data contained in the first data set and the second data set may be the same or different; and the first data in the data contained in the first data set and the second data set may be in different positions, and the last data in the two data sets does not need to be in the same position, that is, there is no need to align the two routes.

[0077] The DTW algorithm is described in detail below:

[0078] The DTW algorithm is a dynamic programming algorithm that calculates the similarity between two time series. In particular, the DTW algorithm can process two time series of different lengths. The DTW algorithm is mainly used in the field of speech recognition. In the embodiments of the present disclosure, considering that the characteristics of the algorithm have unique advantages for trajectory evaluation, the DTW algorithm can be improved to be applied to the evaluation of motion trajectories.

[0079] The principle of the DTW algorithm is as follows:

[0080] Given two sequences A1~An and B1~Bm, when the lengths of the two sequences are equal, that is, m=n, the two sequences can be directly matched one by one, and the sum of the distances between the points in the two sequences can be calculated to represent the "distance" between the two sequences, and the "distance" can be used to represent the similarity. When m is not equal to n, the two trajectories need to be matched, and the DTW algorithm can solve this problem. Since the two sequences are not in a one-to-one relationship at this time, some of the nodes can be made into a one-to-many relationship, such as Figure 2 As shown in the figure, the target motion trajectory L1 contains node a i The node b included in the second trajectory L2 j The number of nodes of the target motion trajectory L1 and the nodes of the second trajectory L2 may be one-to-many or many-to-one. The ultimate goal is to minimize the sum of the distances generated by all corresponding relationships after the correspondence.

[0081] The above DTW algorithm is applied to the trajectory evaluation in the embodiment of the present disclosure, which can be realized by the following logic: the above A sequence and B sequence can represent the first data set of the above target motion trajectory and the second data set of the standard trajectory respectively, let dtwdis[i][j] represent the minimum distance obtained after matching the first i elements of the A sequence with the first j elements of the B sequence, and dis[i][j] represents the distance between Ai and Bj. Obviously, Ai and Bj match at this time. Then the following DTW algorithm recursive relationship (Formula 1) can be obtained (considering boundary conditions):

[0082]

[0083] The final dtwdis[n][m] is the desired trajectory distance.

[0084] In some embodiments, the first data set includes n first data; the second data set includes m second data; both m and n are positive integers; Figure 3 As shown, in the above step 102, calculating the trajectory distance between the first data set and the second data set of the preset standard trajectory includes:

[0085] Step 201, determining a first distance between an i-th first data in a first data set and a j-th second data in a second data set; wherein i is a positive integer less than or equal to n; and j is a positive integer less than or equal to m;

[0086] Step 202: determining a second distance between the i-th first data and the j-th second data according to the sum of the first distance and the minimum distance; wherein the minimum distance includes: a minimum value of the second distance between the i-1-th first data and the j-th second data, the second distance between the i-1-th first data and the j-1-th second data, and the second distance between the i-1-th first data and the j-1-th second data;

[0087] Step 203: determine the second distance corresponding to the nth first data and the mth second data as the track distance.

[0088] Here, the first data is the data in the A sequence in the above example, and the second data is the data in the B sequence in the above example. The first distance is the distance dis[i][j] between Ai and Bj, and the second distance is the minimum value of the trajectory distance calculated before the current two nodes Ai and Bj, that is, the minimum value of the trajectory distance corresponding to Ai-1 and Bj, the trajectory distance corresponding to Ai-1 and Bj-1, and the trajectory distance corresponding to Ai and Bj-1. In this way, the trajectory distance corresponding to each node can be obtained by iterative calculation in sequence until the trajectory distance corresponding to the nth first data An and the mth second data Bm is calculated.

[0089] In some embodiments, determining a first distance between an i-th first data in the first data set and a j-th second data in the second data set includes:

[0090] Determine the earth coordinates of the first data and the second data;

[0091] The first distance is determined according to the earth coordinates corresponding to the i-th first data, the earth coordinates corresponding to the j-th second data, and the radius of the earth.

[0092] In some embodiments, the earth coordinates include: a longitude value and a latitude value;

[0093] The first distance is the distance between a first position indicated by the earth coordinates corresponding to the first data and a second position indicated by the earth coordinates corresponding to the second data.

[0094] The above DTW algorithm principle introduces the Euclidean distance between corresponding two points in a one-dimensional sequence. The one-dimensional Euclidean distance is the distance between two points on the coordinate axis X. When evaluating the trajectory, the trajectory sequence T(x, y) can be represented by two-dimensional coordinates of longitude and latitude. In the trajectory of mountain climbing or the trajectory of altitude change design, we can represent the trajectory sequence T(x, y, z) by three-dimensional coordinates of longitude and latitude and altitude. The embodiment of this disclosure takes two-dimensional coordinates of longitude and latitude as an example.

[0095] If we calculate the Euclidean distance between two trajectory sequences T1(x,y) and T2(x,y), we can continue to use the DTW algorithm above. However, the DTW distance calculated at this time does not have any physical meaning, and the DTW distance cannot be used to guide the evaluation of the error between the two trajectories. It can only give a relative distance to compare the similarity between the two different trajectories and the standard trajectory, but it cannot quantitatively describe the accuracy of the two trajectories.

[0096] To solve this problem, the distance between two points described in the embodiment of the present disclosure is changed to the "track distance" between two points. The track distance algorithm is as follows (Formula 2):

[0097] dis=R*arcos[cos(Y1)*cos(Y2)*cos(X1-X2)+sin(Y1)*sin(Y2)] (Formula 2)

[0098] The longitude and latitude coordinates of the two points are (X1, Y1) and (X2, Y2), where X1 and X2 are longitudes, and Y1 and Y2 are latitudes; the radius of the earth is R = 6371 km. Dis[i][j], i.e. the first distance mentioned above, is calculated from this, and the above DTW algorithm recursive relationship (Formula 1) is continued to obtain the final DTW distance between the final two trajectory sequences, i.e. the trajectory distance. This trajectory distance has the same meaning as the distance at the actual geographical location. Therefore, the DTW algorithm in this case can not only compare the accuracy between the two target trajectories and the standard trajectory, but also obtain the quantitative analysis results of the error between a single trajectory sequence and the standard trajectory.

[0099] The optimized DTW algorithm mentioned above can already compare the accuracy between the target motion trajectory and the standard trajectory, which is convenient for quantitative error analysis. The accuracy of multiple trajectories on the same path can also be compared. However, through the above method, it is impossible to compare all trajectories horizontally to measure the performance of the terminal in positioning and trajectory recording. For example, a movement of 400m and a movement of 40,000m, even if the trajectory accuracy is very high in the movement of 40,000m, due to the long distance, the DTW algorithm has been accumulating errors, and may eventually result in a larger error value, but in fact the trajectory accuracy of this movement is higher than the 400m error. In order to solve this problem, the embodiment of the present disclosure normalizes the output distance of the DTW algorithm, specifically:

[0100] In some embodiments, Figure 4 As shown, the method also includes:

[0101] Step 301, normalizing the track distance to obtain a normalized track distance;

[0102] In the above step 103, determining the accuracy of the target motion trajectory according to the trajectory distance includes:

[0103] Step 302: Determine the accuracy of the target motion trajectory according to the normalized trajectory distance.

[0104] In this way, no matter how long the trajectory is, as long as it is the same path, a horizontal comparison can be made.

[0105] In some embodiments, the normalizing the trajectory distance to obtain the normalized trajectory distance includes: calculating the ratio between the trajectory distance and the path length of the target motion trajectory to obtain the normalized trajectory distance, as follows (Formula 3):

[0106] dtw_mean=dtwdis[n][m] / lenth(path) (Formula 3)

[0107] Among them, dtw_mean is the normalized trajectory distance, and lenth(path) is to find the path length traversed by the shortest dtwdis[n][m], that is, the total number of times each point in the two trajectories is compared. The reason for choosing to normalize the path instead of the distance or the total length of the data is that when the DTW algorithm is performing path planning, there will be a one-to-many or many-to-one situation. Each comparison will result in error accumulation. The data length or total distance cannot describe the amount of error accumulation. Normalizing them cannot achieve a unified normalization standard for all data, so the horizontal comparison function is also lost.

[0108] In some embodiments, the method further comprises:

[0109] Determining a score value of the target motion trajectory according to the normalized distance;

[0110] The step of determining the accuracy of the target motion trajectory according to the normalized trajectory distance comprises:

[0111] The accuracy of the target motion trajectory is determined according to the score value.

[0112] In order to compare different trajectories, the obtained trajectory distance can be converted into a score by the above method. By normalizing the trajectory distance, each normalized trajectory distance has the same dimension, so it can be easily converted into a score. For example, the score is calculated using the following (Formula 4):

[0113] score=100-dtw_mean (Formula 4)

[0114] That is, the score of the target motion trajectory is obtained by subtracting the normalized trajectory distance from 100. Different target motion trajectories can be compared intuitively through the scores, and the accuracy difference between trajectories of different paths can also be measured through the scores.

[0115] Figure 5 is a structural block diagram of a data processing device according to an exemplary embodiment. Figure 5 As shown, the data processing device 100 includes:

[0116] An acquisition module 110 is configured to acquire a first data set of a target motion trajectory;

[0117] A calculation module 120, configured to calculate a trajectory distance between the first data set and a second data set of a preset standard trajectory;

[0118] The determination module 130 is configured to determine the accuracy of the target motion trajectory according to the trajectory distance.

[0119] In some embodiments, the first data set includes n first data; the second data set includes m second data; both m and n are positive integers;

[0120] The computing module comprises:

[0121] A first determination submodule is configured to determine a first distance between an i-th first data in the first data set and a j-th second data in the second data set; wherein i is a positive integer less than or equal to n; and j is a positive integer less than or equal to m;

[0122] A second determination submodule is configured to determine a second distance corresponding to the i-th first data and the j-th second data according to the sum of the first distance and the minimum distance; wherein the minimum distance includes: a minimum value of the second distance corresponding to the i-1-th first data and the j-th second data, the second distance corresponding to the i-th first data and the j-1-th second data, and the second distance corresponding to the i-1-th first data and the j-1-th second data;

[0123] The third determining submodule is configured to determine that the second distance corresponding to the nth first data and the mth second data is the trajectory distance.

[0124] In some embodiments, the first determining submodule is specifically configured as follows:

[0125] Determine the earth coordinates of the first data and the second data;

[0126] The first distance is determined according to the earth coordinates corresponding to the i-th first data, the earth coordinates corresponding to the j-th second data, and the radius of the earth.

[0127] In some embodiments, the earth coordinates include: a longitude value and a latitude value;

[0128] The first distance is the distance between a first position indicated by the earth coordinates corresponding to the first data and a second position indicated by the earth coordinates corresponding to the second data.

[0129] In some embodiments, the apparatus further comprises:

[0130] A normalization module, configured to perform normalization processing on the track distance to obtain a normalized track distance;

[0131] The determination module is specifically configured as follows:

[0132] The accuracy of the target motion trajectory is determined according to the normalized trajectory distance.

[0133] In some embodiments, the normalization module is specifically configured as follows:

[0134] The ratio of the trajectory distance to the path length of the target motion trajectory is calculated to obtain the normalized trajectory distance.

[0135] In some embodiments, the apparatus further comprises:

[0136] A scoring module, configured to determine a score value of the target motion trajectory according to the normalized distance;

[0137] The determination module is specifically configured as follows:

[0138] The accuracy of the target motion trajectory is determined according to the score value.

[0139] Figure 6 is a block diagram of a data processing device 900 according to an exemplary embodiment. For example, the device 900 may be a mobile phone, a mobile computer, etc.

[0140] Reference Figure 6 , the device 900 may include one or more of the following components: a processing component 902 , a memory 904 , a power component 906 , a multimedia component 908 , an audio component 910 , an input / output (I / O) interface 912 , a sensor component 914 , and a communication component 916 .

[0141] The processing component 902 generally controls the overall operation of the device 900, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 902 may include one or more processors 920 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 902 may include one or more modules to facilitate interaction between the processing component 902 and other components. For example, the processing component 902 may include a multimedia module to facilitate interaction between the multimedia component 908 and the processing component 902.

[0142] The memory 904 is configured to store various types of data to support operations on the device 900. Examples of such data include instructions for any application or method operating on the device 900, contact data, phone book data, messages, pictures, videos, etc. The memory 904 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0143] The power supply component 906 provides power to the various components of the device 900. The power supply component 906 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 900.

[0144] The multimedia component 908 includes a screen that provides an output interface between the device 900 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 908 includes a front camera and / or a rear camera. When the device 900 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0145] The audio component 910 is configured to output and / or input audio signals. For example, the audio component 910 includes a microphone (MIC), and when the device 900 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 904 or sent via the communication component 916. In some embodiments, the audio component 910 also includes a speaker for outputting audio signals.

[0146] I / O interface 912 provides an interface between processing component 902 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0147] The sensor assembly 914 includes one or more sensors for providing various aspects of status assessment for the device 900. For example, the sensor assembly 914 can detect the open / closed state of the device 900, the relative positioning of components, such as the display and keypad of the device 900, and the sensor assembly 914 can also detect the position change of the device 900 or a component of the device 900, the presence or absence of user contact with the device 900, the orientation or acceleration / deceleration of the device 900, and the temperature change of the device 900. The sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 914 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 914 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0148] The communication component 916 is configured to facilitate wired or wireless communication between the device 900 and other devices. The device 900 can access a wireless network based on a communication standard, such as Wi-Fi, 4G or 5G, or a combination thereof. In an exemplary embodiment, the communication component 916 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 916 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0149] In an exemplary embodiment, the apparatus 900 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0150] In an exemplary embodiment, a terminal is also provided, which may include the modules or components in the above-mentioned information processing device, the terminal also includes a first security chip, and the terminal may also include a second security chip. Exemplarily, the terminal may be a smart phone, a tablet computer, a computer, a smart wearable device, and a vehicle-mounted device.

[0151] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, and the instructions can be executed by the processor 920 of the device 900 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0152] A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of an information processing device, enables the data processing device to execute the data processing method in any of the above embodiments.

[0153] Figure 7 1900 is a hardware structure block diagram of an information processing device according to an exemplary embodiment. For example, the device 1900 may be provided as a server. Figure 7, the device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the data processing method in any of the above embodiments.

[0154] The device 1900 may also include a power supply component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input / output (I / O) interface 1958. The device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, MacOS X™, Unix™, Linux™, FreeBSD™, or the like.

[0155] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present disclosure. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present disclosure, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure. The serial numbers of the embodiments of the present disclosure are for description only and do not represent the advantages and disadvantages of the embodiments.

[0156] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0157] In the several embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0158] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0159] In addition, all functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may be separately configured as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0160] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0161] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data processing method, characterized in that: include: Acquire a first data set of target motion trajectory; Calculating a trajectory distance between the first data set and a second data set of a preset standard trajectory; The accuracy of the target motion trajectory is determined according to the trajectory distance.

2. The method according to claim 1, characterized in that The first data set includes n first data; the second data set includes m second data; both m and n are positive integers; The calculating of the trajectory distance between the first data set and a second data set of a preset standard trajectory includes: Determine a first distance between an i-th first data in the first data set and a j-th second data in the second data set; wherein i is a positive integer less than or equal to n; and j is a positive integer less than or equal to m; Determine, according to the sum of the first distance and the minimum distance, a second distance corresponding to the i-th first data and the j-th second data; wherein the minimum distance includes: a minimum value of the second distance corresponding to the i-1-th first data and the j-th second data, the second distance corresponding to the i-th first data and the j-1-th second data, and the second distance corresponding to the i-1-th first data and the j-1-th second data; A second distance corresponding to the nth first data and the mth second data is determined as the track distance.

3. The method according to claim 2, characterized in that The determining a first distance between the i-th first data in the first data set and the j-th second data in the second data set includes: Determine the earth coordinates of the first data and the second data; The first distance is determined according to the earth coordinates corresponding to the i-th first data, the earth coordinates corresponding to the j-th second data, and the radius of the earth.

4. The method according to claim 3, characterized in that The earth coordinates include: longitude and latitude; The first distance is the distance between a first position indicated by the earth coordinates corresponding to the first data and a second position indicated by the earth coordinates corresponding to the second data.

5. The method according to any one of claims 2 to 4, characterized in that: The method further comprises: Normalizing the trajectory distance to obtain a normalized trajectory distance; Determining the accuracy of the target motion trajectory according to the trajectory distance includes: The accuracy of the target motion trajectory is determined according to the normalized trajectory distance.

6. The method according to claim 5, characterized in that The normalizing the trajectory distance to obtain the normalized trajectory distance includes: The ratio of the trajectory distance to the path length of the target motion trajectory is calculated to obtain the normalized trajectory distance.

7. The method according to claim 5, characterized in that The method further comprises: Determining a score value of the target motion trajectory according to the normalized distance; The step of determining the accuracy of the target motion trajectory according to the normalized trajectory distance comprises: The accuracy of the target motion trajectory is determined according to the score value.

8. A data processing device, characterized in that: include: An acquisition module configured to acquire a first data set of a target motion trajectory; A calculation module, configured to calculate a trajectory distance between the first data set and a second data set of a preset standard trajectory; The determination module is configured to determine the accuracy of the target motion trajectory according to the trajectory distance.

9. A data processing device, characterized in that: include: processor; A memory for storing processor executable instructions; wherein the processor is configured to: implement the steps in any one of the data processing methods of claims 1 to 7 when executed.

10. A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by a processor of an information processing device, the device is enabled to perform the steps in any one of the data processing methods of claims 1 to 7.

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