Data matching method, device and equipment and computer readable storage medium

Abstract

The invention discloses a data matching method, device and equipment and a computer readable storage medium, and belongs to the technical field of intelligent driving. The method comprises the following steps: acquiring first position data detected by a sensor at a first moment; according to the position data of each target at a second moment, predicting predicted position data of each target at the first moment, the second moment being earlier than the first moment; determining a data set corresponding to the sensor according to the first position data and historical position data detected by the sensor at a historical moment; determining a data set corresponding to each target according to the predicted position data of each target at the first moment and the historical position data of each target at the historical moment; determining a matching degree between the first position data and each target according to the data set corresponding to the sensor and the data set corresponding to each target; and determining the reference target as a target matched with the first position data. According to the method, the accuracy of data matching is higher.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of intelligent driving, and in particular, to a data matching method, device, equipment, and computer-readable storage medium. Background Art

[0002] With the rapid development of intelligent driving technology, the number and types of sensors installed on vehicles are becoming increasingly rich, and the information available for intelligent driving is also increasing. How to determine which target the data detected by the sensor matches has become an extremely important issue.

[0003] In the related art, the position data detected by the sensor at the current moment is obtained; according to the historical data of each target, the predicted position data of each target at the current moment is determined; the distance between the position data and the predicted position data of each target at the current moment is determined, and the target with the smallest distance from the position data is used as the target that matches the position data.

[0004] However, in the above data matching method, only based on the position data detected by the sensor at the current moment and the predicted position data of each target at the current moment, it is determined which target the position data detected by the sensor at the current moment matches, resulting in relatively low accuracy of data matching. Summary of the Invention

[0005] The embodiments of the present application provide a data matching method, device, equipment, and computer-readable storage medium, which can be used to solve the problem of relatively low accuracy of data matching in the related art. The technical solutions are as follows:

[0006] On the one hand, the embodiments of the present application provide a data matching method, which includes:

[0007] Obtain the first position data detected by the sensor at the first moment, where the first position data is the position data relative to the sensor;

[0008] Predict the predicted position data of each target at the first moment according to the position data of each target at the second moment, where the second moment is earlier than the first moment;

[0009] Determine the data set corresponding to the sensor according to the first position data and the historical position data detected by the sensor at the historical moment;

[0010] Determine the data set corresponding to each target according to the predicted position data of each target at the first moment and the historical position data of each target at the historical moment;

[0011] Determine the matching degree between the first location data and each of the targets according to the data set corresponding to the sensor and the data sets corresponding to the respective targets;

[0012] Determine a reference target as the target that matches the first location data, where the reference target is the target among the respective targets whose matching degree with the first location data meets the matching requirement.

[0013] In a possible implementation, the historical location data detected by the sensor at a historical moment is location data relative to a reference location;

[0014] After obtaining the first location data detected by the sensor at the first moment, the method further includes:

[0015] Convert the first location data to obtain second location data, where the second location data is location data relative to the reference location;

[0016] Verify the second location data to obtain a verification result of the second location data;

[0017] The predicting the predicted location data of each target at the first moment according to the location data of each target at the second moment includes:

[0018] When the verification result of the second location data passes, predict the predicted location data of each target at the first moment according to the location data of each target at the second moment.

[0019] In a possible implementation, the determining the data set corresponding to the sensor according to the first location data and the historical location data detected by the sensor at a historical moment includes:

[0020] Determine the data set corresponding to the sensor according to the second location data and the historical location data detected by the sensor at a historical moment, where the data set corresponding to the sensor includes the historical location data detected by the sensor at a historical moment and the second location data.

[0021] In a possible implementation, the verifying the second location data to obtain a verification result of the second location data includes:

[0022] Determine third location data in the historical location data detected by the sensor at a historical moment, where the moment corresponding to the third location data is before the first moment and adjacent to the first moment;

[0023] When the second position data and the third position data are different, and the distance between the position corresponding to the second position data and the position corresponding to the third position data is less than the distance threshold, it is determined that the verification result of the second position data passes;

[0024] When the second position data and the third position data are different, and the distance between the position corresponding to the second position data and the position corresponding to the third position data is not less than the distance threshold, or when the second position data and the third position data are the same, it is determined that the verification result of the second position data fails.

[0025] In a possible implementation manner, the determining the matching degree between the first position data and each target according to the dataset corresponding to the sensor and the datasets corresponding to each target includes:

[0026] For any one of the targets, a first dataset is determined in the dataset corresponding to the sensor, and a second dataset is determined in the dataset corresponding to the any one of the targets. The first dataset includes at least one first reference position data, and the second dataset includes at least one second reference position data. One first reference position data corresponds to one second reference position data, and the moment corresponding to one first reference position data is the same as the moment corresponding to the corresponding second reference position data;

[0027] According to the at least one first reference position data and the at least one second reference position data, determine the distance between the first dataset and the second dataset;

[0028] According to the distance between the first dataset and the second dataset, determine the matching degree between the first position data and the any one of the targets.

[0029] In a possible implementation manner, the determining the distance between the first dataset and the second dataset according to the at least one first reference position data and the at least one second reference position data includes:

[0030] Determine the distance between the position corresponding to each first reference position data and the position corresponding to the second reference position data corresponding to each first reference position data;

[0031] According to the distance between the position corresponding to each first reference position data and the position corresponding to the second reference position data corresponding to each first reference position data, determine the distance between the first dataset and the second dataset.

[0032] In a possible implementation, determining the distance between the first data set and the second data set according to the distances between the positions corresponding to the respective first reference position data and the positions corresponding to the second reference position data corresponding to the respective first reference position data includes:

[0033] Determining the distance between the first data set and the second data set according to the distances between the positions corresponding to the respective first reference position data and the positions corresponding to the second reference position data corresponding to the respective first reference position data, and the weight parameters corresponding to the respective first reference position data, where the weight parameters corresponding to the respective first reference position data are determined based on the positions of the respective first reference position data in the first data set.

[0034] On the other hand, an embodiment of the present application provides a data matching device, and the device includes:

[0035] An acquisition module, configured to acquire first position data detected by a sensor at a first moment, where the first position data is position data relative to the sensor;

[0036] A prediction module, configured to predict the predicted position data of each target at the first moment according to the position data of each target at a second moment, where the second moment is earlier than the first moment;

[0037] A determination module, configured to determine a data set corresponding to the sensor according to the first position data and historical position data detected by the sensor at a historical moment;

[0038] The determination module is further configured to determine a data set corresponding to each target according to the predicted position data of each target at the first moment and the historical position data of each target at the historical moment;

[0039] The determination module is further configured to determine the matching degree between the first position data and each target according to the data set corresponding to the sensor and the data sets corresponding to each target;

[0040] The determination module is further configured to determine a reference target as the target that matches the first position data, where the reference target is the target among each target whose matching degree with the first position data meets the matching requirement.

[0041] In a possible implementation, the historical position data detected by the sensor at the historical moment is position data relative to a reference position;

[0042] The device further includes:

[0043] A conversion module for converting the first position data to obtain second position data, where the second position data is position data relative to the reference position;

[0044] A verification module for verifying the second position data to obtain a verification result of the second position data;

[0045] The prediction module is configured to, when the verification result of the second position data passes, predict the predicted position data of each target at the first moment according to the position data of each target at the second moment.

[0046] In a possible implementation manner, the determination module is configured to determine a dataset corresponding to the sensor according to the second position data and the historical position data detected by the sensor at a historical moment, where the dataset corresponding to the sensor includes the historical position data detected by the sensor at the historical moment and the second position data.

[0047] In a possible implementation manner, the verification module is configured to determine third position data in the historical position data detected by the sensor at a historical moment, where the moment corresponding to the third position data is before the first moment and adjacent to the first moment; when the second position data is different from the third position data and the distance between the position corresponding to the second position data and the position corresponding to the third position data is less than a distance threshold, determine that the verification result of the second position data passes; when the second position data is different from the third position data and the distance between the position corresponding to the second position data and the position corresponding to the third position data is not less than the distance threshold, or when the second position data is the same as the third position data, determine that the verification result of the second position data fails.

[0048] In a possible implementation manner, for any one of the targets, the determination module is configured to determine a first dataset in the dataset corresponding to the sensor and determine a second dataset in the dataset corresponding to the any one of the targets, where the first dataset includes at least one first reference position data, the second dataset includes at least one second reference position data, one first reference position data corresponds to one second reference position data, and the moment corresponding to one first reference position data is the same as the moment corresponding to the corresponding second reference position data; determine the distance between the first dataset and the second dataset according to the at least one first reference position data and the at least one second reference position data; and determine the matching degree between the first position data and the any one of the targets according to the distance between the first dataset and the second dataset.

[0049] In a possible implementation, the determining module is configured to determine the distance between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data; and determine the distance between the first data set and the second data set according to the distance between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data.

[0050] In a possible implementation, the determining module is configured to determine the distance between the first data set and the second data set according to the distance between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data, and the weight parameters corresponding to each first reference position data, where the weight parameters corresponding to each first reference position data are determined based on the positions of each first reference position data in the first data set.

[0051] On the other hand, an embodiment of the present application provides a computer device, which includes a processor and a memory. At least one program code is stored in the memory, and the at least one program code is loaded and executed by the processor to enable the computer device to implement any one of the above data matching methods.

[0052] On the other hand, a computer-readable storage medium is further provided. At least one program code is stored in the computer-readable storage medium, and the at least one program code is loaded and executed by a processor to enable a computer to implement any one of the above data matching methods.

[0053] On the other hand, a computer program or a computer program product is further provided. At least one computer instruction is stored in the computer program or the computer program product, and the at least one computer instruction is loaded and executed by a processor to enable a computer to implement any one of the above data matching methods.

[0054] The technical solution provided by the embodiment of the present application at least brings the following beneficial effects:

[0055] When the technical solution provided by the embodiment of the present application determines which target the first position data detected by the sensor at the first moment belongs to, it not only considers the first position data and the predicted position data of each target at the first moment, but also considers the position data detected by the sensor at historical moments and the position data of each target at historical moments. By using the first position data detected by the sensor at the first moment, the historical position data detected by the sensor at historical moments, the predicted position data of each target at the first moment, and the historical position data of each target at historical moments, it is determined which target among each target the first position data belongs to, making the accuracy of data matching higher. Furthermore, the accuracy of vehicle control can be improved, and the driving safety of the vehicle can be made higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0057] Figure 1 is a schematic diagram of the implementation environment of a data matching method provided by an embodiment of the present application;

[0058] Figure 2 is a flowchart of a data matching method provided by an embodiment of the present application;

[0059] Figure 3 is an architecture diagram of a data matching method provided by an embodiment of the present application;

[0060] Figure 4 is a schematic structural diagram of a data matching device provided by an embodiment of the present application;

[0061] Figure 5 is a schematic structural diagram of a terminal device provided by an embodiment of the present application;

[0062] Figure 6 is a schematic structural diagram of a server provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0064] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that these terms can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0065] Figure 1 FIG. is a schematic diagram of an implementation environment of a data matching method provided by an embodiment of the present application. As Figure 1 shown, the implementation environment includes: a computer device 101. The computer device 101 can be a terminal device or a server, and the embodiments of the present application do not limit this. The data matching method provided by the embodiments of the present application can be executed by the computer device 101.

[0066] Optionally, the computer device 101 is a terminal device, and the terminal device can be any electronic device product that can perform human-computer interaction with the user in one or more ways such as a keyboard, a touchpad, a remote control, voice interaction, or a handwriting device. For example, the terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a PC (Personal Computer), a mobile phone, a PDA (Personal Digital Assistant), a wearable device, a PPC (Pocket PC), a smart car machine, a smart TV, etc.

[0067] The computer device 101 is a server, and the server is a single server, or a server cluster composed of multiple servers, or any one of a cloud computing platform and a virtualization center. The embodiments of the present application do not limit this. The server is directly or indirectly communicatively connected to the terminal device by a wired communication method or a wireless communication method. The server has a data receiving function, a data processing function, and a data sending function. Of course, the server may also have other functions, and the embodiments of the present application do not limit this.

[0068] Those skilled in the art should understand that the above terminal devices and servers are only for illustration. Other existing or future terminal devices or servers that are applicable to the present application should also be included in the protection scope of the present application and are hereby incorporated by reference.

[0069] The embodiments of the present application provide a data matching method, and this method can be applied to the above Figure 1 shown implementation environment toFigure 2 Taking the flowchart of a data matching method provided by an embodiment of the present application shown as an example, this method can be executed by a computer device 101. As Figure 2 shown, this method includes the following steps 201 to 206.

[0070] In step 201, obtain the first position data detected by the sensor at the first moment, where the first position data is the position data relative to the sensor.

[0071] In a possible implementation, the sensor is any sensor installed on the vehicle, and the present application embodiment does not limit the type and installation position of the sensor. Exemplarily, the sensor is a camera. Another example is that the sensor is a radar.

[0072] The sensor and the computer device are communicatively connected through a wired network or a wireless network. After the sensor detects the position data, the sensor sends the detected position data and the detection time of the position data to the computer device together, so that the computer device can obtain the position data detected by the sensor and the detection time of the position data.

[0073] Optionally, after the sensor detects the first position data at the first moment, the sensor sends the first position data detected by the sensor at the first moment to the computer device, so that the computer device can obtain the first position data detected by the sensor at the first moment. Wherein, the first position data is the position data relative to the sensor.

[0074] Optionally, after the sensor detects the first position data at the first moment, the sensor also sends the first moment to the computer device, so that the computer device knows that the first position data is the position data detected at the first moment.

[0075] It should be noted that after the sensor detects the position data, it can immediately send the detected position data to the computer device, or it can stay for a target duration and then send the detected position data to the computer device. The present application embodiment does not limit the sending timing of the position data detected by the sensor. The target duration is set based on experience or adjusted according to the implementation environment, and the present application embodiment does not limit this. Exemplarily, the target duration is 2 seconds.

[0076] In step 202, according to the position data of each target at the second moment, predict the predicted position data of each target at the first moment, where the second moment is earlier than the first moment.

[0077] In a possible implementation, since the historical position data detected by the sensors stored in the computer device is position data relative to a reference position, in order to unify the position data detected by the sensors, it is necessary to convert the first position data to obtain the second position data, where the second position data is position data relative to the reference position. Herein, the reference position is any position of the vehicle, and the embodiments of the present application do not limit this. Exemplarily, the reference position is the center of the rear axle of the vehicle. As another example, the reference position is the center of the front axle of the vehicle.

[0078] Optionally, the process of converting the first position data to obtain the second position data includes: adding a translation vector to the first position data to obtain the second position data. The translation vector is determined based on the position data corresponding to the position of the sensor and the position data corresponding to the reference position. The translation vector is the difference between the position data corresponding to the reference position and the position data corresponding to the position of the sensor.

[0079] Exemplarily, if the position data corresponding to the reference position is (A1, B1, C1) and the position data corresponding to the position of the sensor is (A2, B2, C2), then the translation vector is (A1 - A2, B1 - B2, C1 - C2). If the first position data is (A3, B3, C3), then the second position data is (A3 + A1 - A2, B3 + B1 - B2, C3 + C1 - C2).

[0080] Optionally, after converting the first position data to obtain the second position data, it is also necessary to verify the second position data to obtain the verification result of the second position data. The purpose of verifying the second position data is to determine whether the second position data is duplicate data or abnormal data. When the verification result of the second position data passes, the predicted position data of each target at the first moment is predicted based on the position data of each target at the second moment. When the verification result of the second position data fails, it means that it is not necessary to determine which target the first position data belongs to, and thus it is not necessary to execute the subsequent steps.

[0081] Optionally, the process of verifying the second position data to obtain the verification result of the second position data includes: determining the third position data from the historical position data detected by the sensor at a historical moment, where the moment corresponding to the third position data is before the first moment and adjacent to the first moment; and determining the verification result of the second position data based on the second position data and the third position data.

[0082] Optionally, when the second position data is different from the third position data, and the distance between the position corresponding to the second position data and the position corresponding to the third position data is less than the distance threshold, it is determined that the verification result of the second position data passes. When the second position data is different from the third position data, and the distance between the position corresponding to the second position data and the position corresponding to the third position data is not less than the distance threshold, or when the second position data is the same as the third position data, it is determined that the verification result of the second position data fails.

[0083] Wherein, the distance threshold is set based on experience or adjusted according to the implementation environment, and the embodiments of the present application do not limit this. Exemplarily, the distance threshold is 10 centimeters.

[0084] Optionally, the distance between the position corresponding to the second position data and the position corresponding to the third position data is determined according to the following formula (1).

[0085]

[0086] In the above formula (1), (x2, y2, z2) is the second position data, (x3, y3, z3) is the third position data, and L is the distance between the position corresponding to the second position data and the position corresponding to the third position data.

[0087] In a possible implementation, the target is a specific object whose position change needs to be monitored, tracked, or analyzed. The target can be a movable object or a static object, and the embodiments of the present application do not limit the type of the target. Exemplarily, the target is a certain pedestrian, or the target is a certain building.

[0088] In a possible implementation, the computer device stores the position data of each target at the second moment, and the second moment is earlier than the first moment. The process of predicting the predicted position data of each target at the first moment according to the position data of each target at the second moment includes: for any one of the targets, determining the time difference between the first moment and the second moment; predicting the predicted position data of each target at the first moment according to the position data of any one target at the second moment and the time difference between the first moment and the second moment.

[0089] Optionally, the process of determining the time difference between the first moment and the second moment includes: determining the first timestamp corresponding to the first moment; determining the second timestamp corresponding to the second moment; determining the difference between the first timestamp and the second timestamp as the time difference between the first moment and the second moment.

[0090] Among them, the timestamp refers to the number of seconds calculated starting from 00:00:00 on January 1, 1970, Coordinated Universal Time (UTC).

[0091] Exemplarily, if the first moment is 10:44:30 on March 26, 2025, the first timestamp corresponding to the first moment is 1,743,043,470 seconds. If the second moment is 08:20:50 on March 26, 2025, the second timestamp corresponding to the second moment is 1,743,034,850 seconds. The difference between the first timestamp and the second timestamp is 8,620 seconds. That is, the time difference between the first moment and the second moment is 8,620 seconds.

[0092] In a possible implementation manner, the process of predicting the predicted position data of each target at the first moment according to the position data of any target at the second moment and the time difference between the first moment and the second moment includes: determining the moving distance of any target in the first direction according to the moving speed of any target in the first direction and the time difference between the first moment and the second moment; determining the moving distance of any target in the second direction according to the moving speed of any target in the second direction and the time difference between the first moment and the second moment; determining the moving distance of any target in the third direction according to the moving speed of any target in the third direction and the time difference between the first moment and the second moment; predicting the predicted position data of any target at the first moment according to the position data of any target at the second moment, the moving distance of any target in the first direction, the moving distance of any target in the second direction, and the moving distance of any target in the third direction.

[0093] Among them, the first direction, the second direction, and the third direction are respectively any one of the horizontal direction (X direction), the vertical direction (Y direction), and the depth direction (Z direction), and the first direction, the second direction, and the third direction are all different. Exemplarily, the first direction is the horizontal direction, the second direction is the vertical direction, and the third direction is the depth direction.

[0094] The moving speed of any target in the first direction, the moving speed in the second direction, and the moving speed in the third direction are all set based on experience or adjusted according to the implementation environment, and the embodiments of the present application do not limit this.

[0095] Optionally, the moving speed of any target in the first direction, the moving speed in the second direction, and the moving speed in the third direction can also be determined according to the position data of any target at the third moment, the position data of any target at the second moment, the third moment, and the second moment, where the third moment is earlier than the second moment. Optionally, determine the third timestamp corresponding to the third moment; determine the difference between the second timestamp and the third timestamp; determine the first direction difference between the value in the first direction in the position data of any target at the second moment and the value in the first direction in the position data of any target at the third moment; take the quotient of the first direction difference and the difference between the second timestamp and the third timestamp as the moving speed of any target in the first direction. Determine the second direction difference between the value in the second direction in the position data of any target at the second moment and the value in the second direction in the position data of any target at the third moment; take the quotient of the second direction difference and the difference between the second timestamp and the third timestamp as the moving speed of any target in the second direction. Determine the third direction difference between the value in the third direction in the position data of any target at the second moment and the value in the third direction in the position data of any target at the third moment; take the quotient of the third direction difference and the difference between the second timestamp and the third timestamp as the moving speed of any target in the third direction.

[0096] In a possible implementation, the process of determining the moving distance of any target in the first direction according to the moving speed of any target in the first direction and the time difference between the first moment and the second moment includes: taking the product of the moving speed of any target in the first direction and the time difference between the first moment and the second moment as the moving distance of any target in the first direction. The processes of determining the moving distance of any target in the second direction according to the moving speed of any target in the second direction and the time difference between the first moment and the second moment, and the process of determining the moving distance of any target in the third direction according to the moving speed of any target in the third direction and the time difference between the first moment and the second moment are similar to the above process of determining the moving distance of any target in the first direction according to the moving speed of any target in the first direction and the time difference between the first moment and the second moment, and are not described in detail in this embodiment of the present application.

[0097] Optionally, the process of predicting the predicted position data of any target at the first moment based on the position data of any target at the second moment, the moving distance of any target in the first direction, the moving distance of any target in the second direction, and the moving distance of any target in the third direction includes: taking the sum of the value in the first direction in the position data of any target at the second moment and the moving distance of any target in the first direction as the value in the first direction in the predicted position data of any target at the first moment; taking the sum of the value in the second direction in the position data of any target at the second moment and the moving distance of any target in the second direction as the value in the second direction in the predicted position data of any target at the first moment; taking the sum of the value in the third direction in the position data of any target at the second moment and the moving distance of any target in the third direction as the value in the third direction in the predicted position data of any target at the first moment; determining the predicted position data of any target at the first moment according to the value in the first direction, the value in the second direction, and the value in the third direction in the predicted position data of any target at the first moment.

[0098] In step 203, according to the first position data and the historical position data detected by the sensor at the historical moment, the data set corresponding to the sensor is determined.

[0099] In a possible implementation, since the historical position data detected by the sensor at the historical moment is the position data relative to the reference position, and the first position data is the position data relative to the sensor, it is necessary to convert the first position data to obtain the second position data, where the second position data is the position data relative to the reference position. According to the second position data and the historical position data detected by the sensor at the historical moment, the data set corresponding to the sensor is determined. The data set corresponding to the sensor includes the historical position data detected by the sensor at the historical moment and the second position data.

[0100] Among them, the process of converting the first position data to obtain the second position data has been described in step 202 above and will not be elaborated here.

[0101] Exemplarily, Q n is a data set corresponding to a sensor provided by an embodiment of the present application, Q n =(q1, q2, …, q j , …, q n ). Among them, q1 is the position data detected by the sensor at the first moment, q2 is the position data detected by the sensor at the second moment, q j is the position data detected by the sensor at the jth moment, and q N is the position data detected by the sensor at the nth moment. Both n and j are positive integers, and j is less than n.

[0102] In step 204, according to the predicted position data of each target at the first moment and the historical position data of each target at historical moments, a data set corresponding to each target is determined.

[0103] Wherein, the data set corresponding to any target includes the predicted position data of any target at the first moment and the historical position data of any target at historical moments.

[0104] Exemplarily, P m is a data set corresponding to any target provided by an embodiment of the present application, P m =(p1, p2, …, p i , …, p m ). Wherein, p1 is the position data of any target at the first moment, p2 is the position data of any target at the second moment, p i is the position data of any target at the i-th moment, p m is the position data of any target at the m-th moment. Both m and i are positive integers, and i is less than m.

[0105] In step 205, according to the data set corresponding to the sensor and the data sets corresponding to each target, the matching degree between the first position data and each target is determined.

[0106] In a possible implementation manner, the process of determining the matching degree between the first position data and each target is similar. In the embodiment of the present application, only the example of determining the matching degree between the first position data and any one of each target according to the data set corresponding to the sensor and the data set corresponding to any one of each target is used for illustration. Since the detection frequency of the sensor is inconsistent with the frequency of the position data included in the data set corresponding to any target, the moments corresponding to the position data included in the data set corresponding to the sensor and the position data included in the data set corresponding to any target are not in one-to-one correspondence. Therefore, it is necessary to determine a first data set in the data set corresponding to the sensor and a second data set in the data set corresponding to any target. Wherein, the first data set includes at least one first reference position data, and the second data set includes at least one second reference position data. A first reference position data corresponds to a second reference position data, and the moment corresponding to a first reference position data is the same as the moment corresponding to the corresponding second reference position data; according to at least one first reference position data and at least one second reference position data, the distance between the first data set and the second data set is determined; according to the distance between the first data set and the second data set, the matching degree between the first position data and any target is determined.

[0107] Exemplarily, A r is the first data set, B r is the second data set, A r=(a1,a2,…,a r ), B r =(b1,b2,…,b r ), a1 corresponds to b1, and the moments corresponding to a1 and b1 are the same; a2 and b2, and the moments corresponding to a2 and b2 are the same; a r and b r correspond, and the moments corresponding to a r and b r are the same, and r is a positive integer.

[0108] In a possible implementation manner, the process of determining the distance between the first data set and the second data set according to at least one first reference position data and at least one second reference position data includes: determining the distance between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data; determining the distance between the first data set and the second data set according to the distance between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data.

[0109] Among them, the process of determining the distance between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data is similar to the process of determining the distance between the position corresponding to the second position data and the position corresponding to the third position data in step 202 above, and this application embodiment will not elaborate here.

[0110] This embodiment of the present application does not limit the process of determining the distance between the first data set and the second data set based on the distances between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data. Optionally, the average of the distances between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data is determined as the distance between the first data set and the second data set. Or, the sum of the distances between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data is determined as the distance between the first data set and the second data set. Or, based on the distances between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data, and the weight parameters corresponding to each first reference position data, the distance between the first data set and the second data set is determined, where the weight parameters corresponding to each first reference position data are determined based on the positions of each first reference position data in the first data set. Optionally, the weight parameter corresponding to any first reference position data is the reciprocal of the value with base 2 and exponent being the position of any first reference position data in the first data set. Exemplarily, if the position of any first reference position data in the first data set is the 3rd position, then the weight parameter corresponding to any first reference position data is

[0111] Optionally, based on the distances between the positions corresponding to each first reference position data and the positions corresponding to the second reference position data corresponding to each first reference position data, and the weight parameters corresponding to each first reference position data, the distance between the first data set and the second data set is determined according to the following formula (2).

[0112] dist=dist(a1,b1)*2 -1 +dist(a2,b2)*2 -2 +…+dist(a k ,b k )*2 -k +…+dist(a r ,b r )*2 -r (2)

[0113] In the above formula (2), dist is the distance between the first data set and the second data set, dist(a1,b1) is the distance between the position corresponding to the first first reference position data and the position corresponding to the first second reference position data, and 2 -1 is the weight parameter corresponding to the first reference position data; dist(a2,b2) is the distance between the position corresponding to the second first reference position data and the position corresponding to the second second reference position data, and 2-2 is the weight parameter corresponding to the second first reference position data; dist(a k , b k ) is the distance between the position corresponding to the k-th first reference position data and the position corresponding to the k-th second reference position data, 2 -k is the weight parameter corresponding to the k-th first reference position data; dist(a r , b r ) is the distance between the position corresponding to the r-th first reference position data and the position corresponding to the r-th second reference position data, 2 -r is the weight parameter corresponding to the r-th first reference position data.

[0114] In a possible implementation, after determining the distance between the first data set and the second data set, the process of determining the matching degree between the first position data and any target according to the distance between the first data set and the second data set includes: using the reciprocal of the distance between the first data set and the second data set as the matching degree between the first position data and any target.

[0115] Exemplarily, if the distance between the first data set and the second data set is dist, the matching degree between the first position data and any target is

[0116] In step 206, it is determined that the reference target is the target that matches the first position data, and the reference target is the target among all targets whose matching degree with the first position data meets the matching requirement.

[0117] In a possible implementation, after determining the matching degrees between the first position data and all targets in the above step 205, it is determined that the reference target is the target that matches the first position data, where the reference target is the target among all targets whose matching degree with the first position data meets the matching requirement. Meeting the matching requirement for the matching degree means the highest matching degree.

[0118] After determining that the reference target is the target that matches the first position data, the predicted position data of the reference target at the first moment can also be deleted, and the second position data is used as the position data of the reference target at the first moment. Then, based on the second position data and the position data of the reference target at historical moments, the vehicle is controlled to avoid the reference target.

[0119] When determining which target's position data the first position data detected by the sensor at the first moment belongs to, the above method not only considers the first position data and the predicted position data of each target at the first moment, but also takes into account the position data detected by the sensor at historical moments and the position data of each target at historical moments. By using the first position data detected by the sensor at the first moment, the historical position data detected by the sensor at historical moments, the predicted position data of each target at the first moment, and the historical position data of each target at historical moments, it is determined which target among each target the first position data belongs to, making the accuracy of data matching higher. Furthermore, the accuracy of vehicle control can be improved, making the driving of the vehicle safer.

[0120] Figure 3 is an architecture diagram of a data matching method provided by an embodiment of the present application. As Figure 3 shown, the architecture includes: a data preprocessing module 301, a data synchronization module 302, a matching degree calculation module 303, and a matching module 304. Among them,

[0121] The data preprocessing module 301 is used to obtain the first position data detected by the sensor at the first moment, where the first position data is the position data relative to the sensor; convert the first position data to obtain the second position data, where the second position data is the position data relative to the reference position; and verify the second position data to obtain the verification result of the second position data.

[0122] The data synchronization module 302 is used to, when the verification result of the second position data passes, predict the predicted position data of each target at the first moment according to the position data of each target at the second moment, where the second moment is earlier than the first moment.

[0123] The matching degree calculation module 303 is used to determine the data set corresponding to the sensor according to the second position data and the historical position data detected by the sensor at historical moments; determine the data set corresponding to each target according to the predicted position data of each target at the first moment and the historical position data of each target at historical moments; and determine the matching degree between the first position data and each target according to the data set corresponding to the sensor and the data sets corresponding to each target.

[0124] The matching module 304 is used to determine the reference target as the target that matches the first position data, where the reference target is the target among each target whose matching degree with the first position data meets the matching requirement.

[0125] Figure 4 Shown is a schematic structural diagram of a data matching device provided by an embodiment of the present application. As Figure 4 shown, the device includes:

[0126] An acquisition module 401, configured to acquire first position data detected by a sensor at a first moment, where the first position data is position data relative to the sensor;

[0127] A prediction module 402, configured to predict predicted position data of each target at the first moment according to the position data of each target at a second moment, where the second moment is earlier than the first moment;

[0128] A determination module 403, configured to determine a data set corresponding to the sensor according to the first position data and historical position data detected by the sensor at a historical moment;

[0129] The determination module 403 is further configured to determine a data set corresponding to each target according to the predicted position data of each target at the first moment and the historical position data of each target at a historical moment;

[0130] The determination module 403 is further configured to determine the matching degree between the first position data and each target according to the data set corresponding to the sensor and the data sets corresponding to each target;

[0131] The determination module 403 is further configured to determine a reference target as the target that matches the first position data, where the reference target is the target among each target whose matching degree with the first position data meets the matching requirement.

[0132] In a possible implementation manner, the historical position data detected by the sensor at a historical moment is position data relative to a reference position;

[0133] The apparatus further includes:

[0134] A conversion module, configured to convert the first position data to obtain second position data, where the second position data is position data relative to the reference position;

[0135] A verification module, configured to verify the second position data to obtain a verification result of the second position data;

[0136] The prediction module 402 is configured to, when the verification result of the second position data passes, predict the predicted position data of each target at the first moment according to the position data of each target at the second moment.

[0137] In a possible implementation manner, the determination module 403 is configured to determine a data set corresponding to the sensor according to the second position data and the historical position data detected by the sensor at a historical moment, and the data set corresponding to the sensor includes the historical position data detected by the sensor at a historical moment and the second position data.

[0138] In a possible implementation, the verification module is configured to determine third location data from the historical location data detected by the sensor at historical moments. The moment corresponding to the third location data is before the first moment and adjacent to the first moment. When the second location data is different from the third location data and the distance between the location corresponding to the second location data and the location corresponding to the third location data is less than the distance threshold, it is determined that the verification result of the second location data passes. When the second location data is different from the third location data and the distance between the location corresponding to the second location data and the location corresponding to the third location data is not less than the distance threshold, or when the second location data is the same as the third location data, it is determined that the verification result of the second location data fails.

[0139] In a possible implementation, the determination module 403 is configured to, for any one of the respective targets, determine a first data set in the data set corresponding to the sensor, and determine a second data set in the data set corresponding to any one of the targets. The first data set includes at least one first reference location data, and the second data set includes at least one second reference location data. One first reference location data corresponds to one second reference location data, and the moment corresponding to one first reference location data is the same as the moment corresponding to the corresponding second reference location data. According to the at least one first reference location data and the at least one second reference location data, determine the distance between the first data set and the second data set. According to the distance between the first data set and the second data set, determine the matching degree between the first location data and any one of the targets.

[0140] In a possible implementation, the determination module 403 is configured to determine the distance between the location corresponding to each first reference location data and the location corresponding to the second reference location data corresponding to each first reference location data. According to the distance between the location corresponding to each first reference location data and the location corresponding to the second reference location data corresponding to each first reference location data, determine the distance between the first data set and the second data set.

[0141] In a possible implementation, the determination module 403 is configured to determine the distance between the first data set and the second data set according to the distance between the location corresponding to each first reference location data and the location corresponding to the second reference location data corresponding to each first reference location data, and the weight parameter corresponding to each first reference location data. The weight parameter corresponding to each first reference location data is determined based on the location of each first reference location data in the first data set.

[0142] When determining which target the first position data detected by the sensor at the first moment belongs to, the above-mentioned device not only considers the first position data and the predicted position data of each target at the first moment, but also considers the position data detected by the sensor at historical moments and the position data of each target at historical moments. By using the first position data detected by the sensor at the first moment, the historical position data detected by the sensor at historical moments, the predicted position data of each target at the first moment, and the historical position data of each target at historical moments, it is determined which target among the various targets the first position data belongs to, making the accuracy of data matching higher. Furthermore, the accuracy of vehicle control can be improved, making the driving of the vehicle safer.

[0143] It should be understood that when the above-mentioned provided device realizes its functions, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above-mentioned embodiment and the method embodiment belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be elaborated here.

[0144] Figure 5 The block diagram of the terminal device 500 provided by an exemplary embodiment of the present application is shown. The terminal device 500 can be any electronic device product that can perform human-computer interaction with the user in one or more ways such as a keyboard, a touchpad, a remote control, voice interaction, or a handwriting device. For example, a PC (Personal Computer), a mobile phone, a smart phone, a PDA (Personal Digital Assistant), a wearable device, a PPC (Pocket PC), a tablet computer, a smart car machine, a smart TV, a smart speaker, a smart watch, etc.

[0145] Generally, the terminal device 500 includes a processor 501 and a memory 502.

[0146] The processor 501 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. The processor 501 may be implemented in at least one of the following hardware forms: DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 501 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 501 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 501 may further include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.

[0147] The memory 502 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 502 may further include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage media in the memory 502 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 501 to implement the data matching method provided in the method embodiments of the present application.

[0148] In some embodiments, the terminal device 500 may further optionally include: a peripheral device interface 503 and at least one peripheral device. The processor 501, the memory 502, and the peripheral device interface 503 may be connected by a bus or signal lines. Each peripheral device may be connected to the peripheral device interface 503 through a bus, signal lines, or a circuit board. Specifically, the peripheral devices include at least one of the following: a radio frequency circuit 504, a display screen 505, a camera assembly 506, an audio circuit 507, and a power supply 508.

[0149] The peripheral device interface 503 can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 501 and the memory 502. In some embodiments, the processor 501, the memory 502, and the peripheral device interface 503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 501, the memory 502, and the peripheral device interface 503 can be implemented on separate chips or circuit boards, and this embodiment does not limit this.

[0150] The radio frequency circuit 504 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 504 communicates with a communication network and other communication devices through electromagnetic signals. The radio frequency circuit 504 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so on. The radio frequency circuit 504 can communicate with other terminal devices through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: the World Wide Web, a metropolitan area network, an intranet, each generation of mobile communication networks (2G, 3G, 4G, and 5G), a wireless local area network, and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 504 may further include a circuit related to NFC (Near Field Communication), and this application does not limit this.

[0151] The display screen 505 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 505 is a touch display screen, the display screen 505 also has the ability to collect touch signals on or above the surface of the display screen 505. The touch signals can be input to the processor 501 as control signals for processing. At this time, the display screen 505 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 505, which is provided on the front panel of the terminal device 500; in other embodiments, there may be at least two display screens 505, which are respectively provided on different surfaces of the terminal device 500 or are in a foldable design; in other embodiments, the display screen 505 may be a flexible display screen, which is provided on the curved surface or the folding surface of the terminal device 500. Even, the display screen 505 can also be set to an irregular non-rectangular shape, that is, a special-shaped screen. The display screen 505 can be prepared from materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0152] The camera module 506 is used to capture images or videos. Optionally, the camera module 506 includes a front camera and a rear camera. Generally, the front camera is provided on the front panel of the terminal device 500, and the rear camera is provided on the back of the terminal device 500. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth-of-field camera, a wide-angle camera, and a telephoto camera respectively, to achieve the function of background blurring by fusing the main camera and the depth-of-field camera, panoramic shooting by fusing the main camera and the wide-angle camera, and VR (Virtual Reality) shooting function or other fused shooting functions. In some embodiments, the camera module 506 may also include a flash. The flash can be a single-color-temperature flash or a two-color-temperature flash. The two-color-temperature flash refers to the combination of a warm-light flash and a cold-light flash, which can be used for light compensation under different color temperatures.

[0153] The audio circuit 507 may include a microphone and a speaker. The microphone is used to collect sound waves of the user and the environment, and convert the sound waves into electrical signals for input to the processor 501 for processing, or input to the radio frequency circuit 504 to achieve voice communication. For the purpose of stereo collection or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal device 500. The microphone may also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signal from the processor 501 or the radio frequency circuit 504 into sound waves. The speaker may be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for uses such as ranging. In some embodiments, the audio circuit 507 may further include a headphone jack.

[0154] The power supply 508 is used to supply power to each component in the terminal device 500. The power supply 508 may be alternating current, direct current, a primary battery or a rechargeable battery. When the power supply 508 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0155] In some embodiments, the terminal device 500 further includes one or more sensors 509. The one or more sensors 509 include but are not limited to: an acceleration sensor 510, a gyroscope sensor 511, a pressure sensor 512, an optical sensor 513, and a proximity sensor 514.

[0156] The acceleration sensor 510 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established with the terminal device 500. For example, the acceleration sensor 510 can be used to detect the components of the gravitational acceleration on the three coordinate axes. The processor 501 can control the display screen 505 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 510. The acceleration sensor 510 can also be used for collecting game or user's motion data.

[0157] The gyroscope sensor 511 can detect the body direction and rotation angle of the terminal device 500. The gyroscope sensor 511 can cooperate with the acceleration sensor 510 to collect the 3D actions of the user on the terminal device 500. According to the data collected by the gyroscope sensor 511, the processor 501 can implement the following functions: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0158] The pressure sensor 512 can be disposed on the side frame of the terminal device 500 and / or the lower layer of the display screen 505. When the pressure sensor 512 is disposed on the side frame of the terminal device 500, it can detect the holding signal of the user on the terminal device 500, and the processor 501 can perform left and right hand recognition or quick operation according to the holding signal collected by the pressure sensor 512. When the pressure sensor 512 is disposed on the lower layer of the display screen 505, the processor 501 can control the operable controls on the UI interface according to the pressure operation of the user on the display screen 505. The operable controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0159] The optical sensor 513 is used to collect the ambient light intensity. In one embodiment, the processor 501 can control the display brightness of the display screen 505 according to the ambient light intensity collected by the optical sensor 513. Specifically, when the ambient light intensity is high, the display brightness of the display screen 505 is increased; when the ambient light intensity is low, the display brightness of the display screen 505 is decreased. In another embodiment, the processor 501 can also dynamically adjust the shooting parameters of the camera module 506 according to the ambient light intensity collected by the optical sensor 513.

[0160] The proximity sensor 514, also known as a distance sensor, is usually disposed on the front panel of the terminal device 500. The proximity sensor 514 is used to collect the distance between the user and the front of the terminal device 500. In one embodiment, when the proximity sensor 514 detects that the distance between the user and the front of the terminal device 500 is gradually decreasing, the processor 501 controls the display screen 505 to switch from the lit state to the off state; when the proximity sensor 514 detects that the distance between the user and the front of the terminal device 500 is gradually increasing, the processor 501 controls the display screen 505 to switch from the off state to the lit state.

[0161] Those skilled in the art can understand that Figure 5 the structure shown in does not limit the terminal device 500, and it may include more or fewer components than shown in the figure, or combine some components, or adopt different component arrangements.

[0162] Figure 6A schematic structural diagram of the server provided by the embodiment of the present application. The server 600 may vary greatly due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) 601 and one or more memories 602. Among them, at least one program code is stored in the one or more memories 602, and the at least one program code is loaded and executed by the one or more processors 601 to implement the data matching method provided by each of the above method embodiments. Of course, the server 600 may also have components such as wired or wireless network interfaces, keyboards, and input / output interfaces for input and output. The server 600 may also include other components for implementing device functions, which will not be elaborated here.

[0163] In an exemplary embodiment, a computer-readable storage medium is also provided. At least one program code is stored in the storage medium, and the at least one program code is loaded and executed by a processor to enable a computer to implement any one of the above data matching methods.

[0164] Optionally, the above computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0165] In an exemplary embodiment, a computer program or a computer program product is also provided. At least one computer instruction is stored in the computer program or the computer program product, and the at least one computer instruction is loaded and executed by a processor to enable a computer to implement any one of the above data matching methods.

[0166] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.), and signals involved in the present application are all authorized by users or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards of relevant countries and regions. For example, the location data involved in the present application is obtained under full authorization.

[0167] It should be understood that the term "a plurality" mentioned herein refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0168] The above are only exemplary embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included within the protection scope of the present application.

Claims

1. A data matching method, characterized in that The method includes: Obtaining first position data detected by a sensor at a first moment, where the first position data is position data relative to the sensor; Predicting predicted position data of each target at the first moment according to the position data of each target at a second moment, where the second moment is earlier than the first moment; Determining a data set corresponding to the sensor according to the first position data and historical position data detected by the sensor at a historical moment; Determining a data set corresponding to each target according to the predicted position data of each target at the first moment and the historical position data of each target at the historical moment; Determining a matching degree between the first position data and each target according to the data set corresponding to the sensor and the data sets corresponding to each target; Determining a reference target as the target that matches the first position data, where the reference target is the target among each target whose matching degree with the first position data meets the matching requirement; 2. The method according to claim 1, wherein The historical position data detected by the sensor at the historical moment is position data relative to a reference position; After obtaining the first position data detected by the sensor at the first moment, the method further includes: Converting the first position data to obtain second position data, where the second position data is position data relative to the reference position; Verifying the second position data to obtain a verification result of the second position data; The predicting the predicted position data of each target at the first moment according to the position data of each target at the second moment includes: When the verification result of the second position data passes, predicting the predicted position data of each target at the first moment according to the position data of each target at the second moment.

3. The method according to claim 2, wherein The determining the data set corresponding to the sensor according to the first position data and the historical position data detected by the sensor at the historical moment includes: Determining the data set corresponding to the sensor according to the second position data and the historical position data detected by the sensor at the historical moment, where the data set corresponding to the sensor includes the historical position data detected by the sensor at the historical moment and the second position data.

4. The method according to claim 2, characterized in that, The verifying the second position data to obtain the verification result of the second position data includes: Determining third position data in the historical position data detected by the sensor at the historical moment, where the moment corresponding to the third position data is before the first moment and adjacent to the first moment; When the second position data is different from the third position data and the distance between the position corresponding to the second position data and the position corresponding to the third position data is less than a distance threshold, determining that the verification result of the second position data passes; When the second position data is different from the third position data, and the distance between the position corresponding to the second position data and the position corresponding to the third position data is not less than the distance threshold, or when the second position data is the same as the third position data, determine that the verification result of the second position data fails.

5. The method according to any one of claims 1 to 4, characterized in that The determining the matching degree between the first position data and each target according to the data set corresponding to the sensor and the data sets corresponding to the respective targets includes: For any one of the respective targets, determine a first data set in the data set corresponding to the sensor, and determine a second data set in the data set corresponding to the any one target. The first data set includes at least one first reference position data, and the second data set includes at least one second reference position data. One first reference position data corresponds to one second reference position data, and the moment corresponding to one first reference position data is the same as the moment corresponding to the corresponding second reference position data. Determine the distance between the first data set and the second data set according to the at least one first reference position data and the at least one second reference position data. Determine the matching degree between the first position data and the any one target according to the distance between the first data set and the second data set.

6. The method according to claim 5, characterized in that, The determining the distance between the first data set and the second data set according to the at least one first reference position data and the at least one second reference position data includes: Determine the distance between the position corresponding to each first reference position data and the position corresponding to the second reference position data corresponding to each first reference position data. Determine the distance between the first data set and the second data set according to the distance between the position corresponding to each first reference position data and the position corresponding to the second reference position data corresponding to each first reference position data.

7. The method according to claim 6, wherein The determining the distance between the first data set and the second data set according to the distance between the position corresponding to each first reference position data and the position corresponding to the second reference position data corresponding to each first reference position data includes: Determine the distance between the first data set and the second data set according to the distance between the position corresponding to each first reference position data and the position corresponding to the second reference position data corresponding to each first reference position data, and the weight parameter corresponding to each first reference position data. The weight parameter corresponding to each first reference position data is determined based on the position of each first reference position data in the first data set.

8. A data matching device, characterized in that, The device includes: An acquisition module, configured to acquire first position data detected by a sensor at a first moment, where the first position data is position data relative to the sensor. A prediction module, configured to predict the predicted position data of each target at the first moment according to the position data of each target at a second moment, where the second moment is earlier than the first moment. A determination module, configured to determine a data set corresponding to the sensor according to the first position data and historical position data detected by the sensor at a historical moment; The determination module is further configured to determine a data set corresponding to each target according to the predicted position data of each target at the first moment and the historical position data of each target at the historical moment; The determination module is further configured to determine a matching degree between the first position data and each target according to the data set corresponding to the sensor and the data sets corresponding to each target; The determination module is further configured to determine a reference target as a target that matches the first position data, where the reference target is a target among each target whose matching degree with the first position data meets a matching requirement.

9. A computer device, characterized in that, The computer device includes a processor and a memory, and at least one program code is stored in the memory. The at least one program code is loaded and executed by the processor to enable the computer device to implement the data matching method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, At least one program code is stored in the computer-readable storage medium. The at least one program code is loaded and executed by a processor to enable a computer to implement the data matching method according to any one of claims 1 to 7.

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